Evaluating The Effects of Cannabis Dependence and Abstinence on Cortical Inhibition and Working Memory
in Schizophrenia
by
Michelle Samantha Goodman
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Sciences University of Toronto
© Copyright by Michelle Samantha Goodman 2015
II
The Effects The Effects of Cannabis Abstinence on Cortical
Inhibition and Working Memory in Schizophrenia
Michelle Samantha Goodman Master of Science
Institute of Medical Sciences University of Toronto
2015
Background: Cannabis is the most commonly used illicit substance among schizophrenia
patients. GABA is inhibited by cannabis and dysfunctional GABAergic neurotransmission
underlies cognitive and cortical inhibitory deficits in schizophrenia patients. This study
investigated the relationship between cannabis dependence, schizophrenia, cortical inhibition
and cognition.
Methodology: Using transcranial magnetic stimulation and the N-back task with
electroencephalography, we assessed cortical inhibition and working memory at baseline and
following 28-days of abstinence in 12 cannabis dependent patients versus 14 cannabis
dependent non-psychiatric controls.
Results: Enhanced GABAB activity, through a prolonged cortical silent period, was found in
patients compared to controls at baseline, with no differences following abstinence. No
differences were found on working memory accuracy or gamma activity.
Conclusions: While these findings suggest that cannabis dependence may have more
impairing effects on GABAB mediated inhibition in controls compared to patients, it is clear
that continued research is needed to better understand this common co-morbidity.
III
Acknowledgments
I would like to express my sincerest gratitude to Dr. Tony George, my supervisor. Thank you for taking a chance on me two years ago and for allowing me to be a part of your amazing team. Your encouragement and confidence in my academic abilities have motivated me to continue on with a career in research.
This study would not have been possible without the guidance and support of Dr. Mera Barr. Thank you for your mentorship, your friendship, and your constant support. I am so grateful that I have the opportunity to continue to work with you and learn from you.
To Alanna, my partner in crime, these past two years would not have been the same without you. I knew we were going to be lifelong friends the day we met (even though you spent most of the day talking about the leafs). Thank you for always keeping me organized and on track, for helping me navigate the confusing halls of med sci, and for always knowing how to make me laugh.
I’d like to give a special thank you to Rachel Rabin, my cannabis teammate throughout these past two years. To Karolina Kozak and Kristen Mackowick, my office mates, thank you for listening to me vent and always laughing at my bad jokes. To all of the wonderful lab mates that I have worked with throughout my time in the BACDRL lab: Emily Simpkin, Maryam Sharif-Razi, Marya Morozova, Matt Tracey, Anushka Talpur, thank you!
To my family members, both given and chosen, I am indebted to you for your unwavering support, guidance and encouragement. To my parents and sisters, thank you for your unconditional love and for supporting me throughout this journey.
And finally, I would like to thank the amazing participants who were integral to this study. I have learned so much from you all and this work truly would not have been possible without your contributions.
IV
Contributions
The thesis was a subset of a larger study entitled “Effects of Cannabis Abstinence on Neurocognition in Schizophrenia”. This study was developed by Dr. Tony George (PI), Dr. Konstantine Zakzanis, Rachel Rabin, Dr. Jeff Daskalakis, and Dr. Mera Barr. The neurophysiologic subset of the study was funded by Dr. Mera Barr’s young investigator NARSAD award and by Dr. Tony George’s CIHR grant.
The MSc. candidate, Michelle S. Goodman, was responsible for recruitment, retention, and conduct of all study sessions. Additionally, Michelle conducted all data analysis, interpretation of research data, and drafting of the thesis, with assistance from Dr. Tony George and Dr. Mera Barr. Dr. Mera Barr trained the candidate on all neurophysiological and neurocognitive measures, including working memory and paired associative stimulation. Rachel Rabin was responsible for conducting the structured clinical interviews and physical examinations performed during the screening session.
The Program Advisory Committee (PAC) members, Dr. Daniel Blumberger and Dr. Martin Zack, assisted with the interpretation of the data and provided feedback on the thesis.
V
Table of Contents
Pages
Abstract………………………………………………………..………………………
Acknowledgments……………………………………………..………………………
Contributions…………………………………………………..………………………
Table of Contents………………………………………………...................................
List of Abbreviations…………………………………………………………….……
List of Tables………………………………………………………..…………………
List of Figures……………………………………………………...…………………..
List of Appendices……………………………………………………………………..
II
III
IV
V
IX
XI
XII
XV
Chapter 1: Literature Review…………………………………….…………
1.1 Schizophrenia…………………………………………….…….…………...... 1
1.1.1 Phenomenology, Epidemiology, and Etiology…………………..…….……… 1
1.1.2 Pathophysiology of Schizophrenia…………………………….……….……... 2
1.1.2.1
1.1.2.2
1.1.2.3
Dopamine……………………………………………………………..........
GABA…………………………………………………………...................
Glutamate………………………………………………….……….............
Cannabis.………………….………………………………………….……….
Pharmacokinetics of Cannabis………………………………………....………
Acute Psychoactive Effects of Cannabis…………………….……..…..………
3
3
5
6
7
9
1.2
1.2.1
1.2.2
1.2.3 Somatic and Health Effects of Cannabis…………………….……………....… 10
1.2.4 Cognitive Effects of Cannabis…………………….…………......……..……… 11
1.2.5
1.3
Treatment of Cannabis Dependence…………………..…….………………….
The Endocannabinoid System…..……………………………………………
12
13
1.4 Cannabis Use in Schizophrenia…..…….…………………………….……… 16
VI
1.4.1
1.4.2
Effects on Prognosis and Course…………….…………………………………
Effects on Cognition………………………………..………………………….
16
17
1.4.3
1.5
Underlying Neurobiological Connection………………………………………
Cortical Inhibition………………………………………………………...….
18
19
1.5.1 Indexing Cortical Inhibition using Transcranial Magnetic Stimulation……..... 19
1.5.2 Cortical Inhibitory Deficits in Schizophrenia……..…………..………………. 22
1.5.3 Cortical Inhibitory Deficits with Cannabis Use……………..……………..…. 26
1.5.4 The Combined Effects of Cannabis Use and Schizophrenia on Cortical Inhibition.………………………...…………………………………………….
26
1.6 Working Memory……………………………………………………………. 27
1.6.1 The Neurophysiology of Working Memory.......................….…………..…… 27
1.6.2 Indexing Working Memory……….………….………………………...…….. 29
1.6.2.1 Neurocognitive Assessments………………………………………..……. 29
1.6.2.2 Neurophysiological Assessments…………………………………..……... 30
1.6.3 Working Memory Deficits in Schizophrenia………….…………..……......... 31
1.6.4 Cannabis’ Effects on Working Memory……………………………..………. 33
1.6.5 The Combined Effects of Cannabis and Schizophrenia on Working Memory. 36
1.7 Overall Of Current Study Objective and Hypotheses………………….... 37
Chapter 2: Methods…………………………………………………..……..
2.1 Study Design…………………………………………………………..…….. 40
2.2 Participants………………………………………………………….……… 42
2.2.1
2.2.2
Power Analysis……………………………………………………..…….......
Total Sample………………………………………………………..………...
42
42
2.3 Measures…………………………………………………………..………… 44
2.3.1 Clinical Interview Measures………………………………………..………... 44
2.3.2 Premorbid IQ……………………………………………………..…………… 45
VII
2.3.3 Extrapyramidal Side Effects.………………………………………………..… 46
2.3.4 Substance Abuse Measures…………………………………………………… 47
2.3.5
2.3.6
Cortical Inhibition………………………………………………………..……
Working Memory……………………………………………………..……….
48
52
2.3.7
2.4
Cannabis Abstinence Paradigm……………………………………………..…
Study Procedure…………………………………………………………….….
55
56
2.5 Data Analysis…………………………………...……………………………. 57
2.5.1 Study Sample…………….….………………………………………………… 57
2.5.2 Cortical Inhibition …………….……...…………………………………...….. 58
2.5.3 Working Memory…………………...……………………………………...…. 58
Chapter 3: Results……………………………….…………………………....
3.1 Baseline Results……………………..…………..……………………………. 59
3.1.1 Demographics……………………………………………...………………….. 59
3.1.2 Baseline TMS Assessments………………...…………………………………. 62
3.1.2.1
3.1.2.2
3.1.3
3.1.3.1
3.1.3.2
Baseline Inhibitory Assessments: SICI, LICI, and CSP……………………
Baseline Excitatory Assessments: RMT and ICF…………………………..
Baseline Working Memory Assessments………………………………………
Baseline Working Memory Performance………………………………….
Baseline Gamma Power…………………………………………………...
62
66
68
68
69
3.2 Exploratory Longitudinal Assessments……...……………………………... 71
3.2.1 Longitudinal TMS Assessments..……………………………...……………… 71
3.2.1.1
3.2.1.2
3.2.2
3.2.2.1
3.2.2.2
Longitudinal Inhibitory Assessments: SICI, LICI, and CSP………….….
Longitudinal Excitatory Assessments: ICF……….....................................
Longitudinal Working Memory Assessments…………...................................
Longitudinal Working Memory Performance…………………………….
Longitudinal Gamma Power……………………………………………...
72
75
77
78
79
VIII
Chapter 4: Discussion……………………………………………………...
4.1 General Discussion………………………………………………………… 80
4.1.1 Study Sample……………………………………………………………….. 81
4.1.2 TMS Findings……………………………………………………………….. 82
4.1.3 Working Memory Findings……………….………………………………… 87
4.1.4
4.1.5
Exploratory Findings…………………………………………………………
Potential Mechanisms...…………………………..…………………………..
91
92
4.2 Limitations………………………………………..………………………… 97
4.3 Conclusions………………………………………..………………………… 100
4.4 Future Directions…………………………………..……………………….. 101
Literature Cited…………………………………..………………………… 106
IX
List of Abbreviations
1 millivolt (1 mV)
2-Arachidonoylglycerol (2-AG)
Abnormal Involuntary Movement Scale (AIMS)
Analysis of Variance (ANOVA)
Barnes Akathisia Rating Scale (BARS)
Biobehavioural Addictions and Concurrent Disorders Research Laboratory (BACDRL)
Carbon Monoxide (CO)
Cannabinoid receptor gene (CNR1)
Cannabinoid Type 1 Receptor (CB1)
Cannabinoid Type 2 Receptor (CB2)
Centre for Addiction and Mental Health (CAMH)
Chlorpromazine (CPZ)
Cigarettes per day (CPD)
Cortical silent period (CSP)
Diagnostic and Statistical Manual of Mental Disorders, 4th. Edition (DSM-IV)
Dorsolateral prefrontal cortex (DLPFC)
Electroencephalography (EEG)
Endocannabinoid (eCB)
Gamma-aminobutyric acid (GABA)
Grams per day (GPD)
Healthy Controls (HC)
X
Hopkins Verbal Learning Test (HVLT)
Intelligence Quotient (IQ)
Intercortical facilitation (ICF)
Megnetoencephaolography (MEG)
Long interval cortical inhibition (LICI)
Positron emission tomography (PET)
Positive and Negative Syndrome Scale (PANSS)
Prefrontal cortex (PFC)
Research Ethics Board (REB)
Resting motor threshold (RMT)
Schizophrenia (SZ)
Short interval cortical inhibition (SICI)
Simpson Angus Rating Scale for Extrapyramidal Symptoms (SARS)
Standard Deviation (SD)
Statistical Package for the Social Sciences (SPSS)
Structured Clinical Interview for the DSM-IV (SCID-IV)
Transcranial Magnetic Stimulation (TMS)
Wechsler Test of Adult Reading (WTAR)
Working Memory (WM)
XI
List of Tables
Pages Table 3.1. Baseline TMS demographics, clinical means, and standard deviations by diagnosis………………………………………………………………..… 62 Table 3.2. Cohen’s D values for TMS Measures…………………………………….. 67
Table 3.3. Baseline N-Back demographics, clinical means, and standard deviations by diagnosis…………………………………………………………….......... 68 Table 3.4 Creatinine-normalized THCCOOH levels in patient and control abstainers and relapsers……………………………………………………………….… 71
Table 3.5. Post TMS demographics, clinical means, and standard deviations of the sample by diagnosis…………………………………………….…………………. 72 Table 3.6. Post WM demographics, clinical means, and standard deviations by diagnosis………………………………………………………………….…………….. 77
XII
List of Figures
Figure 2.1: Outline of overall study design…………………………………………………
Figure 2.2. Electromyography recordings.…………………………………………………
Figure 2.3. N-Back verbal working memory task …..…………………………………….
Figure 3.1. Correlation between IQ and joint years.………………………………………
Figure 3.2. Consort Diagram for patients with schizophrenia and healthy controls….
Figure 3.3. Baseline short interval cortical inhibition (SICI)……………………...…….
Figure 3.4. Baseline long interval cortical inhibition (LICI) …………………….……… Figure 3.5. Baseline cortical silent period (CSP) ……..…………………………….……. Figure 3.6. Baseline cortical facilitation (ICF)……………………………………………. Figure 3.7. Baseline N-back performance between patients and healthy controls…… Figure 3.8. Mean gamma (30-50 Hz) oscillatory power from target correct (TC) responses elicited during the N-back task ………………………………………………….
Figure 3.9. Longitudinal data for short interval cortical inhibition (SICI)……….……
Figure 3.10. Longitudinal data for long interval cortical inhibition (LICI)……………
Figure 3.11. Longitudinal data for cortical silent period (CSP) ……………..…………
Figure 3.12. Longitudinal data for intracortical faciliatation ICF) ………………..…..
Figure 3.13. Longitudinal N-back performance between patients and healthy controls ………………………………………………………………………….……………….
Figure 3.14. Topographical illustration of mean absolute γ power elicited during 1- and 3-back in patients and healthy control ……………………………………………………..
Pages
41
51
53
60
61
63
64
65
66
69
70
73
74
75
76
78
79
XIII
List of Appendices
Pages
A) Telephone Screen.…….…….…………………………………………............ 123
B) Study Consent………….……………………………………………............… 125
C) Case Report Form…………………………………………………………... 134
1
Chapter 1 Literature Review
1.1 Schizophrenia
1.1.1 Phenomenology and Etiology
Schizophrenia is one of the most severe and debilitating brain disorders. While its
prevalence is relatively low compared to other pervasive illnesses, at approximately 1%,
schizophrenia remains to be one of the world’s leading causes of disability (Lewis &
Lieberman, 2000; Whiteford et al., 2013). The early age of onset, high rates of
unemployment, and frequent hospitalizations, highlight the immense personal and societal
burden of this disorder (Rossler, Salize, van Os, & Riecher-Rossler, 2005).
The symptoms associated with schizophrenia are commonly characterized into three
domains, including positive symptoms (e.g., delusions, hallucinations, thought
disorganization), negative symptoms (e.g., blunted affect, alogia, anhedonia, social
dysfunction, amotivation) and cognitive impairment (APA, 2000). Contrary to initial beliefs,
current research supports the notion that functional outcome is better predicted by cognitive
deficits rather than positive symptoms (Green, 2006). Importantly, these cognitive deficits
appear prior to the onset of psychosis and generally persist following stable remission from
positive symptoms (Bora, Yucel, & Pantelis, 2010). Unfortunately, these impairments along
with negative symptoms are not sufficiently treated by current antipsychotic interventions.
The high degree of heterogeneity known to this disorder complicates both diagnostic
and treatment approaches for those with schizophrenia (Andreasen, Arndt, Alliger, Miller, &
Flaum, 1995; Davidson & McGlashan, 1997). This phenotypic heterogeneity is commonly
observed in the age of onset and constellation of symptoms, response to treatment, as well as
2
the overall prognosis. Towards addressing this variability, diagnostic subtyping (paranoid,
catatonic, disorganized, undifferentiated, and residual) was introduced in the DSM-IV TR;
however, the efforts to validate these subtypes were deemed unsuccessful. In response, the
DSM-V targeted clinical symptomatology as markers of the disease (American Psychiatric
Association, 2013).
In addition to the complex presentation of this disorder, the etiology of schizophrenia
is poorly understood. Current theories suggest that environmental and/or psychological
stressors interact with specific genetic vulnerabilities contributing to the illnesses
development. A range of environmental factors implicated in this disorder include, viral
infections (Mednick, Machon, Huttunen, & Bonett, 1988), substance abuse (Moore et al.,
2007) (refer to section 1.4.3) and urbanization (Krabbendam & van Os, 2005). Regarding the
genetic predisposition, high heritability contributes to about 80% of the disorder’s liability
(Gottesman, 1991). However, no one gene appears to be either sufficient or necessary for the
development of schizophrenia (Tandon, Keshavan, & Nasrallah, 2008). Of note,
neurobiological evidence suggests that these genetic alterations may be linked to
abnormalities within neurotransmitter systems including dopamine, γ-aminobutyric acid
(GABA) and glutamate (Harrison & Weinberger, 2005).
1.1.2 Pathophysiology of Schizophrenia
1.1.2.1 Dopamine
Dopamine is one of the most pervasive neurotransmitters in the central nervous
system (CNS). The dopaminergic system has been implicated in numerous behavioural
domains including information processing, memory, pleasure and addiction (Jones,
3
Kilpatrick, & Phillipson, 1986). Importantly, aberrant dopaminergic functioning is thought to
underlie the pathophysiology of several psychiatric disorders including schizophrenia. More
specifically, the dopamine hypothesis of schizophrenia proposes that excessive dopaminergic
transmission is involved in the onset of psychosis (Carlsson and Lindqvist 1963). This theory
was initially developed resulting from the observation that dopamine-enhancing drugs,
primarily amphetamines, induced psychosis-like symptoms in otherwise healthy individuals
(Seeman and Lee, 1975). In support of this finding, later research revealed that the psychosis
reducing properties of atypical antipsychotic medications were largely due to their D2
receptor blocking capabilities (Lieberman, Kane, and Alvir, 1987). While the first
formulation of this hypothesis accounted for positive symptoms, it could not properly explain
the negative symptoms and cognitive deficits inherent to this disorder. Subsequent
neuroimaging, post-mortem and animal studies provided the basis for a reformulated
hypothesis. This hypothesis incorporated hypodopaminergic activity in the prefrontal cortex
(PFC), better accounting for such cognitive deficits and negative symptoms, along with the
initial observation of hyperdopamiergic activity in other subcortical regions (reviewed in
Davis, Kahn, Ko, & Davidson, 1991).
1.1.2.2 GABA
Dysfunctional GABAergic neurotransmission has also been implicated in the
pathophysiology of schizophrenia (Tse, Piantadosi, & Floresco, 2014). As the primary
inhibitory neurotransmitter in the brain, GABA supresses cortical activity by hyperpolarizing
the cell and reducing the neuronal firing of several excitatory neurotransmitters including
dopamine, noradrenaline, and serotonin. GABA is primarily synthesized from glutamate, and
is comprised of two general classes of receptors: ionotropic GABAA receptors and
4
metabotropic GABAB receptors. GABA acts at inhibitory synapses in the brain, binding to
both presynaptic receptors and postsynaptic receptors. This binding ultimately leads to
hyperpolarization due to the influx of negatively charged chloride ions into the cell or
positively charged potassium ions out of the cell.
Following the pivotal discovery of GABA’s role in inhibitory control, the GABA
hypothesis of schizophrenia was developed (Roberts & Frankel, 1950). In support of this,
post-mortem studies revealed numerous deviations in GABAergic functioning. For example,
cortical reductions in the messenger RNA and protein for glutamic acid decarboxylase-67
(GAD 67), an enzyme necessary for the synthesis of GABA, have been found in the DLPFC
(Akbarian & Huang, 2006) and other regions (Hashimoto et al., 2008) in individuals with
schizophrenia. Furthermore, advances in neurophysiological methodologies have allowed
researchers to investigate potential GABAergic neurotransmitter dysfunctions in vivo.
Studies utilizing non-invasive brain stimulation techniques have provided insight into
specific GABAA and GABAB receptor deficits in individuals with schizophrenia (Daskalakis,
Christensen, Chen, et al., 2002; Farzan et al., 2010a; Takahashi et al., 2013). Similarly,
position emission tomography (PET) studies have revealed that aberrant GABA functioning,
specifically in the PFC and hippocampus, may underlie negative symptoms (Asai et al.,
2008) and cognitive deficits in those with schizophrenia (Lewis, Volk, & Hashimoto, 2004).
Clinical findings and pharmacological studies have provided further support for the
involvement of GABA in schizophrenia. For example, benzodiazepines have been shown to
reduce the likelihood of experiencing a psychotic episode during relapse (Carpenter,
Buchanan, Kirkpatrick, & Breier, 1999; Hashimoto et al., 2008). Given GABAs capacity to
reduce synaptic dopamine levels, GABA was originally targeted as a potential treatment for
5
schizophrenia; however this approach proved to be somewhat unsuccessful. Instead of
focusing on the GABA’s involvement in positive symptoms, researchers have directed their
efforts towards better understanding the underlying association between aberrant GABA
neurotransmission and cognitive dysfunction.
1.1.2.3 Glutamate
Over the past several decades, evidence supporting the involvement of glutamatergic
neurotransmission in the pathophysiology of schizophrenia has grown substantially.
Glutamate is not only a key precursor of GABA, it is also the most abundant excitatory
neurotransmitter in the brain. Glutamatergic neurotransmission acts through metabotropic
and ionotropic receptors, subdivided into N-methyl-D-aspartate (NMDA) receptors, α-
amino-3-hydroxy-5-methyl-4-isoazolepropionic acid (AMPA) receptors, and kainite. Given
its prevalence in the CNS, glutamate is thought to play a key role in several cognitive and
behavioural domains including synaptic plasticity, learning and memory, and executive
function (Johnson, 1972).
Early observations noted that phencyclidine (PCP) and ketamine, commonly abused
NMDA antagonists, induced schizophrenia-like symptoms in otherwise healthy individuals
(Javitt, 2007; Lahti, Holcomb, Gao, & Tamminga, 1999; Lahti et al., 1997) and significantly
exacerbated positive symptoms in those with schizophrenia (Lahti, Weiler, Tamara
Michaelidis, Parwani, & Tamminga, 2001). Thus, it was originally hypothesized that
hypofunctional glutamatergic activity contributed to the development of schizophrenia. Post-
mortem studies have provided support for this hypothesis, demonstrating decreased NMDA
receptor densities in individuals with schizophrenia, specifically within the superior frontal
(Sokolov, 1998) and temporal cortices (Humphries, Mortimer, Hirsch, & de Belleroche,
6
1996). Furthermore, recent neuroimaging studies have revealed that along with psychotic-
like symptoms, ketamine administration in healthy individuals led to abnormal cortical
functional connectivity and activation (Driesen, McCarthy, Bhagwagar, Bloch, Calhoun,
D'Souza, Gueorguieva, He, Ramachandran, et al., 2013; Driesen, McCarthy, Bhagwagar,
Bloch, Calhoun, D'Souza, Gueorguieva, He, Leung, et al., 2013).
Taken together, this extensive clinical and preclinical research suggests that
widespread neurotransmitter dysfunction underlies the pathophysiology of schizophrenia.
These diverse set of findings highlight the fact that no one neurotransmitter alone can
account for this complex brain disorder; as such, symptoms of schizophrenia likely arise
from the dysfunctions within each of these neurotransmitter systems. It is clear however that
many unknowns still exist regarding the pathophysiology and etiology of this disorder and
continued research is necessary.
1.2 Cannabis
Cannabis, derived from the Cannabis sativa, Cannabis indica, or Cannabis ruderalis
plant, has been used for centuries for its psychoactive and medicinal properties. Cannabis is
most often used in the form of marijuana, a mixture of cut, dried, and ground flowers, leaves,
and stems. Less common preparations include kief, a powder sifted from the leaves and
flowers of the cannabis plant, and hashish, which is a concentrated resin formed from pressed
kief. The primary psychoactive component in cannabis is delta-9-tetrahydrocannabinol
(THC); however herbal cannabis contains over 400 additional chemical components and 60
cannabinoids including cannabidiol (CBD), cannabigerol and cannabinol (Iversen, 2008).
These cannabinoid concentrations vary significantly among the different strains of marijuana.
For example, cannabis sativa has been shown to contain high concentrations of THC, thus is
7
known for its psychoactive side effects, whereas cannabis indica is prepared from high CBD
concentrations and thus is used for its sedative effects (Joy, 1999). Furthermore, cannabis
potency (i.e., THC concentrations) varies substantially between its preparations. Cannabis or
marijuana typically contains 5% THC, whereas resin and cannabis oils can contain THC
concentrations as high as 20 and 60% respectively (United Nations Office on Drugs and
Crime, 2009). In contrast, there is currently no consistent or reliable information concerning
the concentrations of other cannabinoids including CBD, among the various cannabis strains.
Next to tobacco and alcohol, cannabis is the third most commonly used recreational
drug (Iversen, 2003) and the single most commonly used illicit drug with approximately 150
million users worldwide (Cohen, Solowij, & Carr, 2008). It has been reported that 3.3 to
4.4% of the world’s population between the ages of 15-64 have used cannabis at least once in
their lifetime (United Nations Office on Drugs and Crime, 2009). Among 20,000 individuals
surveyed in North America, over 4% displayed signs of cannabis abuse and nearly 60% of
these cases met criteria for cannabis dependence (Hall, Solowij, & Lemon, 1994). More
specifically, in Canada, lifetime use has increased significantly from 23.2% in 1989 to 44.5%
in 2004, with approximately 3% of Canadians reporting daily use (Adlaf, 2005).
1.2.1 Pharmacokinetics of Cannabis
The most common and most efficient method of cannabis administration is smoking.
Cannabis is typically smoked as a joint or blunt, which differ in terms of the papers used to
wrap the cannabis. Joints typically contain anywhere from 0.25 to 1 gram of cannabis and
may be supplemented with tobacco, which aids in its burning. Additional methods of
smoking include pipes or bongs as well as vaporizers. Vaporizers have become more popular
8
in recent years, as these devices are able to extract THC and other cannabinoids at
temperatures lower than burning, thus eliminating the harmful secondary carcinogenic tars
and carbon monoxide associated with smoking. Finally, cannabis may be consumed orally
through “edibles”, which are typically used medicinally. Given that cannabis is hydrophobic,
THC must be extracted using lipids or alcohol in order to preserve its psychoactive properties
(Wolff & Johnston, 2014).
The pharmacokinetics of cannabis vary with respect to the route of administration.
Regardless of the method of consumption, the high lipid-solubility of cannabis aids in its
ability to quickly cross the blood-brain barrier. Cannabinoids accumulate in fatty tissue,
yielding a long elimination half-life and persisting in the body for extended periods of time.
THC is rapidly absorbed through the lungs following inhalation and is detectable in blood
plasma within seconds. The psychotropic outcomes reach their maximum effect within 10
minutes, and can last up to 2-3 hours (Huestis, Henningfield, & Cone, 1992). The
bioavailability varies in regards to depth of inhibition, breathhold, and puff duration.
Additionally, THC may be lost in the butt of the joint, through sidestream smoke or through
incomplete lung absorption (Grotenhermen, 2003). Furthermore, oral administration
introduces more variability as well as delayed or diminished effects by up to 30%, due to the
slower rate of absorption of THC from the digestive tract (Perez-Reyes et al., 1991).
THC metabolism takes place mainly in the liver, and to a lesser extent in the lungs
and heart (Harvey, 1976). Initial hydroxylation results in a short-lived, potent psychoactive
THC metabolite. Further oxidation in the liver converts 11-OH-THC to several inactive
metabolites. To date, close to 100 metabolites have been discovered for THC (Harvey &
Brown, 1991), including THC-COOH, the most abundant non-psychoactive metabolite in
9
plasma and urine (Adams & Martin, 1996; Hawksworth & McArdle, 2004). The exact
elimination half-life of THC measured from plasma has proven to be difficult to calculate,
due to variability in the dose, route of administration, period of observation and experience of
the user. THC plasma elimination half-lives have been reported to range anywhere from 20
hours (Hunt & Jones, 1980) to up to 12 days (Johansson, Halldin, Agurell, Hollister, &
Gillespie, 1989). A plausible explanation for this divergence may lie within the ability to
distinguish THC from its other, longer lasting metabolites (Grotenhermen, 2003). Complete
elimination has been shown to take as long as 12.9 days (ranging from 3-29 days) in light
users to 31.5 days in chronic, heavy users (Ellis, Mann, Judson, Schramm, & Tashchian,
1985). Given the long elimination half-life, repeated cannabis use among chronic users leads
to the accumulation of cannabinoids in the body and brain (Ashton, 1999), and has been
suggested to lead to a form of “reverse tolerance” (Julien, 2001). However, there is no clear
and consistent relationship between the plasma THC levels and behavioural effects (Agurell
et al., 1986).
1.2.2 Acute Psychoactive Effects of Cannabis
The use of cannabis for its psychoactive and medicinal qualities dates back centuries
(Zuardi, 2006). The psychoactive effects of cannabis are subjective and vary based on the
experience of the individual, method of administration, dose and strain of cannabis. Cannabis
use produces a variety of psychological and physiological effects; unlike other psychoactive
substances, cannabis exhibits properties of hallucinogens, stimulants and depressants.
Generally, mild cannabis intoxication acutely leads to euphoria, lethargy, perceptual and time
distortions, and the overall augmentation of ordinary sensory experiences (Tart, 1970). At
higher doses, side effects include short-term memory impairment, intense mood alterations,
10
and impaired motor coordination (Jaffe, 1985). While many individuals report anxiolytic
effects following cannabis consumption, at higher doses, increased anxiety and panic
reactions including paranoia and acute toxic psychosis are commonly reported (Ashton,
1999; Iversen, 2008).
1.2.3 Somatic and Health Effects of Cannabis
In addition to the psychoactive effects, cannabis produces a variety of acute somatic
effects. While these side effects may only last for a short period of time, research has begun
to uncover the long-term health outcomes associated with chronic cannabis use. For example,
cannabis has been shown to lead to tachycardia, which has been suggested to accelerate the
development of heart problems in at risk individuals. In support of this, studies have shown
that the risk of myocardial infarction during the first hour after cannabis consumption
increased 4.8-fold as compared to non-users (Hartung, Kauferstein, Ritz-Timme, & Daldrup,
2014; Mittleman, Lewis, Maclure, Sherwood, & Muller, 2001). Furthermore, research
regarding the bronchopulmonary side effects of cannabis use have been reliably
demonstrated. These effects include chronic inflammatory changes in the respiratory tract,
which can manifest as chronic cough, wheezing, and phlegm (Van Hoozen & Cross, 1997).
Additional adverse health effects include cancer, bronchitis and emphysema (Ashton, 1999).
Finally, acute somatic effects of cannabis use include ocular redness, dry mouth and
increased appetite commonly referred to as "the munchies" (Sansone & Sansone, 2014). It
should be noted that the acute toxicity of cannabis is extremely low, as there have been no
reported cases of human deaths directly from cannabis poisoning (Hall et al., 1994).
11
1.2.4 Cognitive Effects of Cannabis
There is a growing body of research investigating the impairing effects associated
with cannabis use on cognition. Interestingly, comparisons have been drawn between
impairments in perceptual, emotional, and cognitive domains accompanying cannabis use,
and those seen in patients with schizophrenia (Radhakrishnan, Wilkinson, & D'Souza, 2014;
Solowij & Michie, 2007). For example, studies have found that acute cannabis use impairs
features of decision-making and planning including response times and accuracy (Ramaekers
et al., 2006; Vadhan et al., 2007). Moreover, cannabis intoxication is associated with
increased risk-taking through tasks assessing impulsivity and inhibition (McDonald,
Schleifer, Richards, & de Wit, 2003). However, evidence regarding the impairing effects on
attention and concentration is mixed, whereby the impairing effects seem to be stronger in
less frequent users. In contrast, research suggests that attention in the chronic users may be
disrupted more so by acute abstinence than intoxication (Crean, Crane, & Mason, 2011).
These disparate findings may be due in part to the variability in study methodology,
specifically associated with frequency and recency of cannabis use.
The long-term effects of cannabis use have received significant attention in recent
years. Yet similarly to the findings regarding the acute effects, inconsistencies in the
literature exist. Several studies have shown that cannabis appears to have continued
impairing effects on executive function following a prolonged abstinence period. The most
enduring deficits seem to lie within the domains of decision-making, concept formation, and
planning (Verdejo-Garcia, Rivas-Perez, Lopez-Torrecillas, & Perez-Garcia, 2006). However,
contrasting research has shown that cognitive deficits seen in long-term cannabis users may
be eliminated following 28-day abstinence (Pope, Gruber, Hudson, Huestis, & Yurgelun-
12
Todd, 2001). The reversibility of these deficits indicates that chronic cannabis use may not
permanently alter cognitive function in all domains.
1.2.5 Treatment of Cannabis Dependence
Treatment approaches targeting cannabis dependence have been adapted in a large
part from alcohol use disorder interventions. However, compared to these better-established
interventions, evidence-base research regarding the management of cannabis dependence is
still somewhat lacking. In terms of behavioural interventions, motivational enhancement
therapy (MET) and cognitive-behavioural therapy (CBT) have proven to be the most reliable
and successful treatment approaches. MET focuses on resolving the users ambivalence about
engaging in treatment and aims to enhance their motivation to change. In contrast, CBT
addresses the skills necessary to quit or reduce ones substance use and change unhelpful
thinking and behaviours that may impact treatment outcomes. Randomized control trials
validating MET and CBT suggest that multicomponent therapy combining these treatments is
most effective (Dennis et al., 2004; Stephens, Roffman, & Curtin, 2000).
A promising addition to these behavioural interventions is contingency management
(Budney, Moore, Rocha, & Higgins, 2006; Kadden, Litt, Kabela-Cormier, & Petry, 2007).
Current literature has provided support for the efficacy of contingency management in
treating cannabis dependence in otherwise healthy individuals (Budney, Higgins,
Radonovich, & Novy, 2000) and those with severe mental illness (Sigmon, Steingard,
Badger, Anthony, & Higgins, 2000). Contingency management promotes behavioural
changes through the use of positive or negative contingencies in an organized manner. In
regards to substance use interventions, contingency management encourages behaviours
13
associated with treatment attendance and retention as well as decreasing drug use behaviours.
Importantly, this method has proven to be effective in populations with low motivation for
changing drug use behaviours (Sinha, Easton, Renee-Aubin, & Carroll, 2003).
Finally, there is a growing body of support for the use of pharmacotherapy targeting
withdrawal symptoms associated with cannabis abstinence. However, this work is still in its
early stages, as no government-approved medications currently exist. Furthermore, few
studies have focused specifically on the physiological, subjective, and reinforcing effects of
these medications. Preclinical evidence suggests that Δ9-THC, clonidine and lithium can
effectively decrease withdrawal behaviours in rodents (Cui et al., 2001). In humans,
nabiximols or Sativex, a cannabis extract with a 1:1 mixture of THC and CBD, was
originally developed as a treatment for multiple sclerosis. However, interestingly, this
medication has been shown to attenuate cannabis withdrawal symptoms. For example, in
a treatment-seeking cohort, nabiximols reduced withdrawal symptoms and improved
treatment retention. However, this medication did not effectively promote the long-term
reductions in cannabis use following medication cessation (Allsop et al., 2014). These
preliminary findings support the potential treatment efficacy of pharmacotherapy and
highlight the need for continued research investigating the combined effects of behavioural
and pharmacological interventions.
1.3 The Endogenous Cannabinoid System
The endocannabinoid (eCB) system is one of the most abundant neuromodulatory
systems in the brain. This system is responsible for maintaining homeostasis within the CNS
through its control over synaptic transmission (Pertwee, 2008). It is made up of two G-
protein-coupled receptors, cannabinoid type 1 (CB1) and cannabinoid type 2 (CB2). The
14
discovery of the type 1 receptor led to the identification of natural occurring ligands or
endocannabinoids, anandamide and 2-arachidonoylglycerol (2-AG). These endocannabinoids
act at CB1 receptors and are synthesized in response to increased intracellular calcium levels
(Howlett et al 2002). Anandamide behaves similarly to THC, yet with less potent and
pervasive actions (Iversen, 2008), acting as a partial agonist at both CB1 and CB2 receptors.
In contrast, 2-AG is a complete agonist at both receptor subtypes. Cannabis, prepared in all
of its forms, cannabis agonists, and endogenous ligands, act primarily on CB1 receptors.
While the two eCB system receptor subtypes are closely related, type 2 receptors are most
often found on immune cells, and are involved in the immunological activity of leukocytes
(Galiegue et al., 1995).
The complex interaction between CB1 receptors and neurotransmitter systems are
thought to mediate the principal effects of cannabis (Freund, Katona, & Piomelli, 2003). CB1
receptors are primarily located on central and peripheral neurons (Tsou, Brown, Sanudo-
Pena, Mackie, & Walker, 1998) and are highly expressed throughout the brain. These brain
regions, including, the hippocampus, cortex, cerebellum, basal ganglia, PFC, and anterior
cingulate, are thought to be associated with cognition and mood regulation (Herkenham,
1991).
At the molecular level, CB1 receptors are localized presynaptically on excitatory and
inhibitory neurons, primarily on the axon terminals of GABA interneurons. These receptors
are thought to mediate inhibition and thus maintain homeostasis by preventing excessive
release of excitatory neurotransmission within the CNS (Eggan & Lewis, 2007). Evidence
suggests that endocannabinoids act as retrograde signal messengers, whereby increases in
postsynaptic intracellular calcium stimulated by specific neurotransmitters can trigger the
15
synthesis and release of endocannabinoids. These molecules are then thought to activate
presynaptic CB1 receptors and inhibit the further release of GABA (Pertwee, 2008; Szabo &
Schlicker, 2005).
CB1 activation through THC and other psychoactive cannabinoids enhances
mesolimbic dopaminergic neurotransmission, which terminates at the PFC (Pistis et al.,
2002), striatum (Bossong et al., 2009), and nucleus accumbens (Gardner, 2005). Converging
clinical and preclinical research has implicated this increase in dopamine in both the
cognitive impairments (Pistis et al., 2002; Pistis, Porcu, Melis, Diana, & Gessa, 2001) and
reinforcing properties associated with cannabis use (Ameri, 1999; Bossong et al., 2009). The
divergent effects of cannabis on GABA and dopamine may also underlie the subjective
opposing stimulant and depressant effects experienced by individual users.
Similarly, the cannabinoids, specifically THC and CBD, have also been shown to
possess divergent properties. THC acts as a partial CB1 receptor agonist with relatively low
receptor efficacy (Compton, Johnson, Melvin, & Martin, 1992; Pertwee, 1997). In contrast,
CBD indirectly stimulates endocannabinoid signalling through supressing fatty acid amide
hydrolase (FAAH), an enzyme involved in the inactivation of endogenous cannabinoids
(Piomelli, Beltramo, Giuffrida, & Stella, 1998). The molecular mechanism underlying CBD
activity is not well understood, however it has been suggested that CBD has the ability to
non-competitively antagonized CB1 and CB2 receptors (Murray, Morrison, Henquet, & Di
Forti, 2007; Thomas et al., 2007). In this capacity, CBD has been shown to influence the
pharmacological activity of THC, thus countering some of the undesirable side effects
associated with THC administration (Englund et al., 2013; Zuardi, Crippa, Hallak, Moreira,
& Guimaraes, 2006). This opposing molecule activity is reflected in the behavioural effects
16
of THC and CBD. For example, THC is known to induce paranoia in individuals with prior
paranoid ideation (Freeman et al., 2015) as well as exacerbate pre-existing psychotic and
affective symptoms in those diagnosed with schizophrenia (D'Souza et al., 2005). In contrast,
CBD has been shown to possess antidepressant (Zanelati, Biojone, Moreira, Guimaraes, &
Joca, 2010), anxiolytic (Bergamaschi et al., 2011; Zuardi, Shirakawa, Finkelfarb, & Karniol,
1982) and neuroprotective properties (Hayakawa et al., 2007).
1.4 Cannabis Use in Schizophrenia
Recent research has focused on cannabis use among individuals with schizophrenia
due in part to its worldwide standing as the most commonly used illicit substance in this
population. With approximately one-third of persons with schizophrenia and other psychoses
reporting daily use (Jablensky, 2000) and one-quarter meeting criteria for a cannabis use
disorders (CUD) (Koskinen, Lohonen, Koponen, Isohanni, & Miettunen, 2010), it is clear
that understanding the molecular and behavioural effects of cannabis use in this population is
essential.
1.4.1 Effects on Prognosis and Course
The presence of co-morbid cannabis use in schizophrenia is associated with
devastating effects on prognosis and clinical severity. These individuals face symptom
exacerbation, higher rates of relapse and poorer treatment compliance and success (D'Souza
et al., 2005; Foti, Kotov, Guey, & Bromet, 2010; Linszen, Dingemans, & Lenior, 1994;
Manrique-Garcia et al., 2014). Interestingly, studies that employ self-report measures
generally find that patients use cannabis to relieve boredom, provide stimulation to feel good,
to get high, or to relax and socialize with peers (Addington & Duchak, 1997; Fowler, Carr,
17
Carter, & Lewin, 1998; Goswami, Mattoo, Basu, & Singh, 2004). Of note, these self-report
findings are susceptible to denial and rationalization. Yet these findings suggest that many
patients are unaware of or may downplay the clear detrimental effects of cannabis use
(McGee, Williams, Poulton, & Moffitt, 2000).
1.4.2 Effects on Cognition
Given the evident harmful effects of cannabis on the clinical course, it would follow
that cannabis may have equally detrimental effects of cognitive functioning; however, current
literature provides contradictory findings. A recent meta-analysis including 10 studies and
572 patients with schizophrenia with and without cannabis use, found that those with a
history of cannabis use demonstrated better cognitive functioning compared to non-users.
Furthermore, in the second study included in this meta-analysis, cannabis using first episode
patients with schizophrenia performed better on several domains including visual memory,
working memory and executive function when compared to first episode non-using patients
(Yucel et al., 2012). Similar results were demonstrated in another meta-analysis focusing
more specifically on the effects of cannabis by controlling for confounding poly substance
use (Rabin, Zakzanis, & George, 2011). Researchers have suggested that these findings may
be attributed to the neurochemical effects of cannabis (Coulston, Perdices, Henderson, &
Gin, 2011), given that the neuronal pathways regulated by the eCB system are thought to
underlie neurocognitive functioning (Gerdeman, Partridge, Lupica, & Lovinger, 2003).
Alternatively, others have proposed that these cannabis-using patients may represent a
subgroup of patients with better premorbid cognitive functioning and social skills (Dixon,
Haas, Weiden, Sweeney, & Frances, 1991; Leeson, Harrison, Ron, Barnes, & Joyce, 2011;
Rodriguez-Sanchez et al., 2010). As such, individuals in the subgroup are thought to possess
18
a potentially less severe form of the disorder, developing psychosis following the initiation of
cannabis use (Yucel et al., 2012).
In contrast, several studies have demonstrated that cannabis use may further impair
cognitive performance in patients with schizophrenia. This finding has been replicated in
studies employing lab-based (D'Souza et al., 2005), cross-sectional (Ringen et al., 2010), and
longitudinal approaches (Mata et al., 2008). Finally, several studies have been unable to
show that this comorbidity has a significant or consequential effect on cognition (Jockers-
Scherubl et al., 2007; Sevy et al., 2007). These divergent findings may be attributed in part,
to a lack of control for confounding variables within studies as well as methodological
discrepancies between studies.
1.4.3 Underlying Neurobiological Connection
The once popular hypothesis of ‘self-medication’ suggested the psychiatric illnesses
put those at risk for later cannabis use as a means to ‘self-medicate’ and alleviate unpleasant
symptoms of the disorder and medication side effects (Wittchen et al., 2007; Musty &
Kaback, 1995). However, more recent evidence has shown that self-medication does not
adequately account for the pattern of cannabis use among patients with schizophrenia
(Kolliakou et al., 2015). As such, researchers have begun to investigate other potential
explanations for this common co-morbidity.
In spite of its prevalence, many unknowns still exist regarding the neurophysiological
impact of cannabis use among individuals with schizophrenia. Aberrant eCB system
functioning has been implicated in the detrimental effects of cannabis use on the course of
illness. Genetic variants of cannabinoid-related genes, including the cannabinoid receptor
19
gene (CNR1), represent likely candidates underlying the interaction between cannabis use
and schizophrenia (Martinez-Gras et al., 2006). Furthermore, disturbances in the eCB system
have been demonstrated through elevated anandamide levels in the cerebrospinal fluid in
both prodromal and untreated patients with schizophrenia (De Marchi et al., 2003; Koethe et
al., 2009). Additionally, increases in cannabinoid type 1 receptor (CB1R) densities have been
observed in this population (Ceccarini et al., 2013; Dean, Sundram, Bradbury, Scarr, &
Copolov, 2001).
The GABAergic system has also been implicated in this co-morbidity. Aberrant
GABA functioning seen in patients with schizophrenia may be further exacerbated by the
inhibitory influence of cannabis on this neurotransmitter. In fact, it has been suggested the
deficits in GABAergic activity in patients, may increase the likelihood of engaging in
cannabis use (Rabin, 2014). Evidence supporting this suggestion comes from the overlapping
influence of GABA on cognitive function both in individuals with schizophrenia and
cannabis users (Benes, 1998; Di Lazzaro, Ziemann, & Lemon, 2008; Hill, Froc, Fox,
Gorzalka, & Christie, 2004).
1.5 Cortical Inhibition
1.5.1 Indexing Cortical Inhibition using Transcranial Magnetic
Stimulation
Cortical inhibition refers to the neurophysiological process whereby GABA
inhibitory interneurons selectively attenuate the activity of pyramidal neurons in the cortex.
One technique used to evaluate cortical inhibition is transcranial magnetic stimulation
(TMS). TMS was developed as a non-invasive technique used to assess the function of the
corticospinal tract in vivo (Kobayashi & Pascual-Leone, 2003). TMS is based on the concept
20
of electromagnetic induction as outlined by Faraday’s Law, whereby an intense current
pulses within the coil, producing a magnetic field orthogonal to the plane of the coil. This
field passes unimpeded through the skull and in turn induces an electric field tangential to the
skull and in an opposite direction to that of the current in the coil. The horizontal orientation
of the current favorably activates horizontally-oriented interneurons. Thus, pyramidal
neurons are activated transynaptically (Rothwell & Rosenkranz, 2005).
Clinicians and researchers have used TMS for both therapeutic and diagnostic
applications. In terms of its diagnostic ability, combining TMS with electromyography
(EMG) allows for the exploration of cortical GABAergic function. Stimulation in the motor
cortex by a single TMS pulse produces a corresponding contralateral motor evoked potential
(MEP). The ultimate effect of the stimulation depends on several important factors, including
the shape of the magnetic coil, the intensity of the current pulse driven through the coil and
the interstimulus interval separating the various pulses. Commonly used stimulation
parameters include the focal figure-of-eight coil with stimulation intensities corresponding to
the 1 millivolt (mV), i.e. the intensity needed to produce a MEP of 1 mV peak to peak
amplitude. These TMS paradigms have been shown to reliably assess the activity of the
GABAA and GABAB receptor functioning in the motor cortex in human subjects (Chen,
2000; Fitzgerald et al, 2002a).
More specifically, paired pulse TMS (ppTMS) has been used to reliably assess both
cortical inhibition and excitation and thus the function of several neurotransmitter systems.
For example, cortical excitation can be indexed through the resting motor threshold (RMT)
and intracortical facilitation (ICF). The RMT is defined as the minimal intensity that
produces a MEP of >50 μV in 5 of 10 trials in the relaxed muscle of interest. In contrast, ICF
21
is measured by administering a subthreshold pulse prior to the onset of the test pulse by 7 to
20 milliseconds (Kujirai et al., 1993; Ziemann, Rothwell, & Ridding, 1996). There is strong
evidence to suggest that cortical facilitation is mediated in part, by glutamatergic
neurotransmission (Clark, Randall, & Collingridge, 1994).
Cortical inhibition is also assessed through paired pulse paradigms, differing
primarily in interstimulus intervals. For example, short-interval intracortical inhibition
(SICI), a measure of GABAA activity, is measured by administering a subthreshold pulse
proceeded by a suprathreshold test pulse within 1 to 5 milliseconds. GABAB in contrast, can
be measured through two different paradigms. The first is a ppTMS called long interval
cortical inhibition (LICI), which utilizes two suprathreshold stimuli at interstimulus intervals
ranging from 100 to 200 milliseconds. The second paradigm, cortical silent period (CSP),
incorporates motor cortical stimulation superimposed on background EMG activity resulting
in a cessation of EMG activity. Thus the duration of this silent period represents an index of
GABAB activity (Cantello, Gianelli, Civardi, & Mutani, 1992).
Evidence regarding the involvement of GABAA and GABAB underlying SICI and
LICI, respectively, comes from the time course of effects associated with these
neurotransmitters. Cortical stimulation has been shown to produce disynaptic slow and fast
inhibitory postsynaptic potentials (IPSPs) (Davies, Davies, & Collingridge, 1990). These
fast IPSPs are thought to be mediated by GABAA activity as they last approximately 20 ms,
whereas slow IPSPs, mediated by GABAB receptors, peak around 150–200 ms. This time
course is reflected in the interstimulus intervals associated with SICI (1-5 ms) and LICI (100-
200)(Sanger, Garg, & Chen, 2001). Additional support from pharmacological studies has
revealed that administration of GABAA receptor agonists, specifically lorazepam, reliably
22
enhance SICI whereas baclofen, a GABAB receptor agonist prolongs CSP (Di Lazzaro et al.,
2008).
1.5.2 Cortical Inhibitory Deficits in Schizophrenia
While TMS has provided a reliable index of cortical inhibition and excitation in the
motor cortex in healthy controls (Chen, 2000) its more important use has been to better
understand changes in cortical functioning in individuals with mental illness (Daskalakis,
Christensen, Chen, et al., 2002; Di Lazzaro et al., 2004; Maeda, Keenan, & Pascual-Leone,
2000). Aberrant cortical inhibition has been implicated in the pathophysiology of
schizophrenia (Lewis, Pierri, Volk, Melchitzky, & Woo, 1999). Many early studies
investigating the physiological impact of schizophrenia in the motor cortex have reliably
demonstrated dysfunctional inhibition, yet their initial understanding of the mechanisms
associated with these deficits was limited (Davey, Puri, Lewis, Lewis, & Ellaway, 1997;
Puri, Davey, Ellaway, & Lewis, 1996).
Advances in our understanding of the specific neurotransmitters indexed through each
TMS paradigm has allowed for a better understanding of the neurotransmitter deficits
underlying schizophrenia as well as the impact of antipsychotic medication on these deficits.
For example, Daskalakis and colleagues compared the RMT, CSP, SICI and ICF in 15
unmedicated patients with schizophrenia, 15 medicated patients with schizophrenia, and 15
healthy controls. The researchers found that the unmedicated patients exhibited the most
profound cortical deficits on measures of CSP and SICI. Additionally, fewer deficits were
observed among the medicated group when compared to the healthy controls (Daskalakis,
Christensen, Chen, et al., 2002). These findings have been replicated in first episode patients
as well as those with chronic schizophrenia (Fitzgerald, Brown, Daskalakis, & Kulkarni,
23
2002a; Fitzgerald et al., 2003; Wobrock et al., 2009). One of the more reliable and interesting
findings regarding CSP comes from studies investigating the effects of antipsychotic
medication on GABAB-mediated inhibition (Kaster et al., 2015). One such study found a
significant lengthening of the CSP in patients treated with clozapine when compared to both
unmedicated patients and controls (Daskalakis, Christensen, et al., 2008). Of note, a similar
study demonstrated that this CSP finding was clozapine specific, as patients treated with any
other antipsychotic medications failed to demonstrate this lengthened silent period (Liu,
Fitzgerald, Daigle, Chen, & Daskalakis, 2009). These results have significant implications
for the treatment of schizophrenia and highlight the importance of underlying GABAA and
GABAB abnormalities in the pathophysiology of this disorder.
Reduced SICI among individuals with schizophrenia remains the most consistently
replicated finding. These deficits have been demonstrated in patients with varying levels of
illness severity and duration, including those at risk for developing this disorder (Hasan et al.,
2012), first episode patients (Wobrock et al., 2008; Wobrock et al., 2009) and individuals
with chronic schizophrenia (Daskalakis, Christensen, Chen, et al., 2002; Fitzgerald, Brown,
et al., 2002a; Pascual-Leone, Manoach, Birnbaum, & Goff, 2002). In a recent meta-analysis
conducted by Radhu and colleagues (2013), researchers found significant deficits in SICI in
patients with schizophrenia, after controlling for age and medication, using a meta-
regression. Furthermore, this finding showed specificity as a characteristic of schizophrenia,
when compared to both patients with major depression and those with obsessive-compulsive
disorder (Radhu et al., 2013).
However, some studies have been unable to find significant diagnostic differences in
SICI (Daskalakis, Christensen, et al., 2008; Eichhammer et al., 2004; Liu et al., 2009) and
24
ICF (Eichhammer et al., 2004; Hasan et al., 2012; Wobrock et al., 2008) when comparing
patients to controls. Finally, there are inconsistencies in the current literature regarding
GABAB-mediated inhibitory control in patients. For example, studies have revealed
decreased (Daskalakis, Christensen, Chen, et al., 2002; Fitzgerald, Brown, Daskalakis,
deCastella, & Kulkarni, 2002) increased (Bajbouj et al., 2004; Soubasi et al., 2010) and no
differences (Davey et al., 1997; Frantseva et al., 2008; Puri et al., 1996) on measures of LICI
and CSP.
Importantly, there is strong evidence to suggest that these cortical inhibitory deficits
are associated with symptoms of schizophrenia. Liu and colleagues found that CSP duration
was significantly inversely associated to negative symptoms, while positive symptoms were
correlated with SICI. This finding was most pronounced in the unmedicated group (Liu et al.,
2009). Thus these findings highlight the differential role that GABAA and GABAB likely
have on the symptoms of schizophrenia. In a related study, researchers investigated the
underlying association between abnormal cortical inhibition and social cognitive deficits.
SICI and LICI and several dimensions of social cognition including, social perception and
emotion processing, were evaluated in 33 unmedicated patients, 21 medicated patients and 45
controls. Inline with previous studies, SICI deficits were observed in the unmedicated group
indicating less inhibitory response. Additionally, among the unmedicated group, deficits in
SICI were associated with poorer scores on the measures of global social cognition and
emotion processing, thus demonstrating the potential contribution of aberrant cortical
inhibition to deficits of executive function (Mehta, Thirthalli, Basavaraju, & Gangadhar,
2014).
25
A major limitation to the above-mentioned TMS findings is that such measures were
recorded in the motor cortex. Recent technological advances have allowed for the evaluation
of cortical inhibition directly from the DLPFC through combined TMS with EEG
(Daskalakis, Farzan, et al., 2008; Fitzgerald et al., 2008). Assessing cortical inhibition in the
DLPFC is of particular importance in the field of mental illness research, as this region is
associated with the pathophysiology, and cognitive deficits underlying SCZ and is the region
where CBI receptors are densely populated.
Although this method is fairly novel, its validity and reliability have been well
established in healthy subjects by comparing cortical evoked activity as measured through
TMS-EEG with EMG activity from motor cortical inhibition (Farzan et al., 2010b). Using a
pharmaco-TMS-EEG approach, researchers have been able to study the direct role of
GABAergic neurotransmission through TMS-evoked EEG potentials (Premoli et al., 2014).
Furthermore, this technique allows for the decomposition of the EEG signal into five well-
established frequency bands to evaluate their inhibitory activity. Using this technique in
patients with schizophrenia, previous research has demonstrated that DLPFC gamma
inhibitory activity (30-50 Hz) is selectively impaired in individuals with schizophrenia
compared to patients with bipolar disorder (Farzan et al., 2010b), obsessive-compulsive
disorder and healthy individuals (Radhu et al 2015). This finding, along with additional
evidence of decreased cortical inhibition in schizophrenia, provides further support for the
role of the possibility that altered cortical connectivity may underlie the pathophysiology of
schizophrenia (Frantseva et al., 2014).
26
1.5.3 Cortical Inhibitory Deficits with Cannabis Use
There is currently a paucity of research investigating the functional effects of
cannabis use on cortical inhibition. Only one study has sought to determine whether
individuals with chronic cannabis use demonstrate abnormalities in cortical inhibition.
Fitzgerald et al (2009) examined the level of use (heavy versus light users) in forty-two
cannabis users compared to 19 non-using controls on measures of cortical inhibition and
excitation. Heavy cannabis use was defined as those who used 7 or more times a week and
had consumed within 48 hours prior to testing. In contrast, light users were those who had
used between 1 to 4 times a week, and had used sometime within the last 7 days of testing
(Block & Ghoneim, 1993). TMS paradigms included SICI, ICF, LICI and CSP and THC
plasma levels were measured prior to the TMS procedure. The researchers found that both
heavy and light cannabis users demonstrated reductions in SICI compared to healthy
controls, with no other differences on the additional measures of inhibition or excitation.
Given that both heavy and light users exhibited comparable SICI deficits, the researchers
suggest that this finding is likely dependent on long term neurobiological changes associated
with lifetime cannabis use rather than the direct effect of cannabis on GABAA activity. Thus,
long-term cannabis use likely results in the down regulation of cortical inhibitory activity
(Fitzgerald, Williams, & Daskalakis, 2009).
1.5.4 The Combined Effects of Cannabis and Schizophrenia on
Cortical Inhibition
To date, only one study has investigated the effects of cannabis use on cortical
inhibition in patients with schizophrenia. Twelve first-episode patients with a history of co-
morbid cannabis abuse and 17 first-episode patients with no history of cannabis use
27
completed several TMS motor cortical paradigms including RMT, SICI, ICF, and CSP.
Researchers found that stimulation to the right motor cortex in the cannabis-using group
evoked lower cortical inhibition through SICI and enhanced facilitation as measured by ICF.
In line with Fitzgerald et al. (2009), no group differences were observed in RMT or CSP
duration. Interestingly, there were no significant correlations between psychopathology,
disease characteristics, or the extent of cannabis abuse, with TMS parameters. Researchers
suggested that their results likely reflect the ability of cannabis to exacerbate reduced cortical
inhibition and enhanced facilitation in patients with schizophrenia (Wobrock et al., 2010). It
is clear that continued research is needed to verify these results and determine if these same
findings can be replicated in individuals with varying illness severity and duration.
1.6 Working Memory
1.6.1 The Neurophysiology of Working Memory
A key component of cognition that has garnered significant attention is working
memory. Working memory is commonly defined by the ability to maintain and manipulate
information for short periods of time (Baddeley & Hitch, 1974b; Barak & Tsodyks, 2014).
As a higher order cognitive function, working memory is thought to be central to, and
interact with several additional cognitive domains including learning, comprehension and
reasoning (D'Esposito, 2007). As a theoretical construct, working memory has been defined
through multiple models, based primarily on the discipline it is associated with, including
cognitive psychology and cognitive neuroscience (Baddeley, 2003). One of the most
significant contributions to the field was put forth by Baddeley and Hitch, who in 1974,
described a model that combined the ability to both mediate executive control and maintain
temporarily stored information (Baddeley & Hitch, 1974a).
28
Current working memory models came about following early preclinical
observations, which guided their understanding of the brain regions associated with this
cognitive domain. In 1936, Carlyle Jacobsen proposed that the PFC was integral to working
memory functioning, following the observation that damage to this area in primates led to
significant memory deficits. Interestingly, he found that the monkeys could complete
complex cognitive tasks as long as the stimulus of interest remained present. However, the
introduction of a delay period or the removal of the stimulus significantly decreased
performance to chance (Jacobsen, 1936). Later work conducted by Joaquin Fuster supported
this initial observation, whereby prefrontal neurons in monkeys were found to fire most
rapidly during the delay period (Fuster, 1973). These early findings posed as the foundation
for later working memory models, which suggest that the PFC contributes to working
memory by filtering out extraneous information as well as maintaining and organizing key
information in order to aid in goal directed behaviours (Cohen et al., 1997).
Methodological advancements have revealed that working memory function is not
based solely on the PFC, but rather a complex network spanning frontal and parietal areas
(Honey et al., 2002). However, there still exists debate regarding the role of each brain
region. It has been suggested that the PFC may be involved in attentional activation, selecting
strategies, and the manipulation of memory, where as the posterior areas are thought to play a
role in the maintenance of information (Curtis & D'Esposito, 2003; Postle, 2006). However,
neuroimaging and position emission tomography (PET) studies have provided a different
method of categorization suggesting that the PFC may be associated with more specific types
of memory, for example verbal working memory (Jonides et al., 1998; Smith, Jonides,
Marshuetz, & Koeppe, 1998) Such lack of consensus regarding the functional brain regions
29
associated with working memory may be attributed to the type of working memory assessed
or task difficulty.
Further complicating our understanding of the physiology of working memory lies
within the complex coordination between excitatory and inhibitory neurotransmitter circuits.
Goldman-Rakic and others have shown that neuronal prefrontal circuits aid in informational
maintenance through recurrent excitatory glutamate outputs that fire throughout the delay
period (Goldman-Rakic, 1995). These circuits are thought to be “tuned” by inhibitory
GABAergic interneurons (Rao, Williams, & Goldman-Rakic, 2000). Thus GABA is believed
to synchronize neuronal activity during working memory processes (Lewis, Hashimoto, &
Volk, 2005). In support of this, Sawaguchi et al (1989) injected bicuculline, a GABA
antagonist, into the DLPFC of monkeys, which lead to disrupted working memory
performance (Sawaguchi, Matsumura, & Kubota, 1989). However, this finding is not specific
to GABA; rather, research suggests that the coordination of several key neurotransmitter
systems are necessary for optimal working memory performance (Robbins & Arnsten, 2009).
1.6.2 Indexing Working Memory
1.6.2.1 Neurocognitive Assessments
There are numerous well-established neuropsychological batteries used to assess
working memory, which allow for the assessment of several memory domains both verbal
and non-verbal (e.g., visuospatial, and audiospatial). Common non-verbal tasks include the
delayed match to sample task and the spatial delay response. These non-verbal tasks are
generally comprised of a brief presentation of a stimulus followed by a delay period. The
participant is then required to either determine the original position of the stimulus (i.e.,
30
spatial delay response) or determine if an additional stimulus presented is the same as the
initial one (i.e. delayed match to sample). In contrast, common verbal tasks include the
number-letter-span, the Sternberg task and the N-back task. These tasks require participants
to either maintain and manipulate a string of numbers or letters, through repetition and
reordering (i.e., number-letter-span) or determine if the current letter/number presented was
included in the original string (i.e. the Sternberg task). Finally, in the N-back task, letters are
presented sequentially and participants are asked to determine if the same letter was
presented N (i.e. 0-3) trials back, where 0 represents a control condition measuring reaction
time only. Importantly, the N-back requires continuous updating with each trial,
differentiating itself from other working memory tasks. Furthermore, the N-back task is
commonly used in neurophysiologic-based studies given that it requires minimal movement
for responses, thus decreasing the amount of muscle movement noise in the data.
1.6.2.2 Neurophysiological Assessments
Advances in brain imaging and recording techniques have provided novel insight into
the neurophysiological underpinnings associated with working memory.
Electroencephalography (EEG) is a commonly used technique, due to its high temporal
resolution, ease of use, and low costs. This technique records brain activity from electrodes
placed on the scalp, thus measuring a summation of synaptic activity from thousand of
neurons in a similar spatial orientation from the outer layers of the brain. EEG is able to
capture oscillatory activity in five discrete frequency bands including delta (1-4 Hz), theta (4-
7 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-50 Hz). Each oscillatory frequency
is associated with specific brain states. For example, higher frequencies have been shown to
be associated with the awake brain including cognitive tasks such as working memory, while
31
lower oscillatory activities are associated with the different stages of sleep (Buzsaki, 2006;
Buzsaki & Draguhn, 2004). Synchrony among large neuronal networks is thought to be a
critical component of healthy cognitive function (Engel, Fries, & Singer, 2001; Uhlhaas &
Singer, 2006).
Gamma activity specifically, has garnered significant attention due to its involvement
in working memory processes, as demonstrated in vivo through EEG and MEG studies
(Mainy et al., 2007; Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998). Increases in
gamma power are associated with increasing working memory load, most noticeably in the
frontal brain region encompassing the DLPFC (Howard et al., 2003; Meltzer et al., 2008;
Pesaran, Pezaris, Sahani, Mitra, & Andersen, 2002). Evidence suggests that GABAA receptor
mediated IPSPs influence the generation of gamma oscillations (Bartos, Vida, & Jonas, 2007;
Wang & Buzsaki, 1996; Whittington, Traub, & Jefferys, 1995), whereas GABAB is involved
in the modulation of gamma oscillations (Brown, Davies, & Randall, 2007; Leung & Shen,
2007).
1.6.3 Working Memory Deficits in Schizophrenia
Working memory deficits among individuals with schizophrenia are one of the most
replicable and robust findings in this population. A meta-analysis was conducted across 187
studies in patients with schizophrenia on three heterogeneous domains of working memory
including verbal working memory, visuospatial memory and executive working memory.
Researchers found clear deficits across all memory domains in the patient group compared to
controls. Of note, no relationship was found between IQ and working memory performance,
thus confirming that these deficits are not simply a function of low IQ (Forbes, Carrick,
McIntosh, & Lawrie, 2009).
32
Neurophysiological research has suggested that in individuals with schizophrenia,
working memory deficits may be attributed to dysfunction of the DLPFC, aberrant brain
network connectivity, as well as disruptions in neurotransmitter signalling and neuronal
synchrony (Cho, Konecky, & Carter, 2006; Lewis, Curley, Glausier, & Volk, 2012; Lewis &
Moghaddam, 2006; Sun et al., 2011; Uhlhaas & Singer, 2010). However, inconsistent
findings exist in the literature. For example, a meta-analysis including 12 neuroimaging
studies in patients with schizophrenia found significantly lower DLPFC activation in patients
during the N-back task (Glahn et al., 2005). However, a less conclusive meta-analysis
including 29 studies failed to find a significant difference in frontal activation in patients
compared to controls during a memory task (Van Snellenberg, Torres, & Thornton, 2006).
Additionally, several studies have demonstrated abnormal neural oscillatory activity
in individuals with schizophrenia, predominantly in gamma frequency band (Farzan et al.,
2010a; Gandal et al., 2010; Uhlhaas et al., 2006). EEG studies in epileptic patients (Howard
et al., 2003) and healthy subjects (Barr et al., 2009; Cho et al., 2006) have reported increases
in the modulation of gamma oscillations in the DLPFC with increased working memory load.
In contrast, patients with schizophrenia seem to exhibit little to no gamma modulation in
response to changing cognitive demands (Barr et al., 2010; Cho et al., 2006). It has been
suggested that patients therefore inefficiently enlist complex cognitive processes to carry out
tasks with low cognitive demands whereas healthy controls vary cognitive strategies in
response to task difficulty (Basar-Eroglu et al., 2007). However, inconsistencies in the
literature exist regarding gamma oscillatory activity evoked during working memory tasks in
patients (Basar-Eroglu et al., 2007; Cho et al., 2006; Haenschel et al., 2009; Lewis et al.,
2008). For example, Barr et al., (2010) demonstrated that compared to healthy controls,
33
patients with schizophrenia elicited excessive frontal gamma power, specifically during the
3-back condition of the N-back task. Such excessive gamma power was observed at
equivalent performance levels between patients and healthy controls. Interesting, the authors
also found that task performance was inversely correlated to negative symptoms but not with
positive symptoms (Barr et al., 2010). In contrast, a recent study conducted by Chen et al.,
(2014), found that deficits in gamma band amplitude in patients with schizophrenia during a
modified Sternberg working memory task was associated with poorer performance.
Moreover, working memory-induced gamma oscillations were dependent on baseline GABA
levels across all participants (Chen et al., 2014). These findings provide further support for
the importance of GABA function in the production of working memory specific gamma
oscillations, even in those with aberrant system and cognitive functioning. While
discrepancies in the research exist, taken together these findings suggest that neurobiological
alterations in PFC activation, neurotransmitters and neural oscillations contribute to working
memory deficits in schizophrenia.
1.6.4 Cannabis’ Effects on Working Memory
Only recently have researchers begun to accurately assess the effects of cannabis on
working memory, better understanding and controlling for important confounding factors
including frequency of previous cannabis use (Gruber, Sagar, Dahlgren, Racine, & Lukas,
2012; Solowij et al., 2011), and strain of cannabis consumed (i.e., the ratio of THC:CBD)
(Morgan, Schafer, Freeman, & Curran, 2010). These well controlled studies in conjunction
with advances in neuroimaging methodologies have provided a more conclusive
understanding of the acute and long-term effects of cannabis as well as the potential
mechanisms underlying this relationship.
34
There is an abundance of research demonstrating that acute administration of THC
has a negative, dose dependent impact on working memory (Ilan, Gevins, Coleman, ElSohly,
& de Wit, 2005; Silva de Melo et al., 2005). Importantly, these findings suggest that despite
acute impairments following cannabis administration, long-term memory function seems to
remain unaffected. Furthermore, it appeared that acute cannabis intoxication did not
significantly impair working memory in heavy users, unless they were administered high
doses of potent cannabis (i.e., high THC content) (Curran, Brignell, Fletcher, Middleton, &
Henry, 2002). This divergent effect in experienced users implies that drug tolerance is
associated with less impairing side effects.
In contrast to the acute effects, conclusions regarding the long-term impact of chronic
cannabis use on working memory are less consistent, due in part to interindividual and
intraindividual variability in cannabis use. More recent evidence has suggested that memory
deficits persist in individuals who use cannabis frequently and over a long period (for full
review see (Schoeler & Bhattacharyya, 2013)). Moreover, early initiation of cannabis use
(prior to the age of 17) and strains high in THC, seem to increase the risk of long-term
memory dysfunction. These findings are especially troubling given the younger onset of
cannabis use as well as recent increases in cannabis potency (Mehmedic et al., 2010).
In line with this research, neurobiological and neuroimaging studies have
demonstrated that long-term, early cannabis use lead to the most significant adverse and
persistent effects on morphology and brain function, beyond that of acute intoxication
(Becker, Wagner, Gouzoulis-Mayfrank, Spuentrup, & Daumann, 2010; Lopez-Larson et al.,
2011; Wilson et al., 2000; Yucel et al., 2010). These neuroimaging studies have also found
that normal levels of working memory accuracy were associated with a common pattern of
35
increased overall brain activation, following cannabis use. This enhanced activity in
combination with accurate performance suggests neurophysiological inefficiency, whereby
increased neural effort is needed in regions normally recruited for task completion. However,
it may also suggest that there is a compensatory mechanism involving the recruitment of
regions not normally associated with the specific task. Researchers have suggested that this
may be indicative of a change in strategy to meet task demands (Bossong, Jager,
Bhattacharyya, & Allen, 2014).
One way in which researchers have attempted to better understand the long-term
effects of cannabis use on neurophysiological and cognitive function is by employing
abstinence paradigms as these studies allow for the assessment of reversibility. In a
longitudinal study, Pope and colleagues compared neurocognitive function in heavy cannabis
users compared to controls, over 1 month of abstinence. As hypothesized, significant
memory impairments in the cannabis-using group on days 0, 1, and 7 were observed;
however, the groups were no longer significantly different at day 28, suggesting that the
effects of cannabis on memory may not persist following the complete elimination of
cannabis from the body. In line with this initial observation, several studies have reported
that memory functioning is restored in “regular” cannabis users following abstinence periods
ranging from 48 hours to one month (Jager, Kahn, Van Den Brink, Van Ree, & Ramsey,
2006; Schweinsburg et al., 2005; Schweinsburg et al., 2010).
It is important to take into consideration the potential moderating effects of factors in
addition to current cannabis use on working memory, including previous exposure to
cannabis and additional drugs, frequency and recency of cannabis use, dose of THC
administered, THC:CBD ratio, and the particular assessment of memory function. Further
36
difficulty in interpreting these results lies within the heterogeneity of the selected samples,
especially in terms of the criteria used to categorize participants. Previous studies have
included groups of former cannabis users, current regular cannabis users, or heavy cannabis
users, long-term and short-term cannabis users, or early-onset and late-onset cannabis users
(Schoeler & Bhattacharyya, 2013). Overall, it is clear that further research employing diverse
methodologies and controlling for confounding variables is necessary for elucidating the
acute and long-term effects of cannabis use on working memory.
1.6.5 The Combined Effects of Cannabis Use and Schizophrenia on
Working Memory
The relationship between cannabis use and working memory in individuals with
schizophrenia has proven to be more complex than was initially assumed, as current research
provides inconsistent evidence. D’Souza and colleagues (2005) examined the dose-related
effects of intravenous THC using a double blind, randomized, placebo-controlled design in
patients with schizophrenia and controls. Compared to those given the placebo, both patients
and controls given THC exhibited impaired verbal memory, with more profound deficits
observed in the patient group (D'Souza et al., 2005). Similarly, Ringen et al., (2010)
evaluated the relationship between acute cannabis use within the last six months and
cognitive function including verbal and working memory in 140 patients with schizophrenia.
Consistent with previous findings, the researchers found that cannabis use lead to poor verbal
learning and working memory performance compared to abstainers (Ringen et al., 2010).
In contrast, there is a growing body of evidence suggesting that chronic cannabis
users with schizophrenia may perform better on memory tasks than patients with no history
of drug use. Schnell and colleagues (2009) sought to investigate both the enduring effects of
37
cannabis consumption and the specific patterns of use on cognition. As such, a cognitive test
battery was administered to 35 patients with schizophrenia with a lifetime diagnosis of co-
morbid cannabis use disorder and 34 patients with no current or past history of cannabis use.
Interestingly, cannabis-using patients performed better on the memory tasks, and more
frequent cannabis use was associated with better performance, dose-dependently (Schnell,
Koethe, Daumann, & Gouzoulis-Mayfrank, 2009). This finding has been replicated in first-
episode patients with schizophrenia (Yucel et al., 2012). This body of work was further
supported by a longitudinal study conducted by Stirling and colleagues (2005). The
researchers found that patients who continued cannabis use during the follow-up period
demonstrated a “sparing” of neurocognitive functioning (Stirling, Lewis, Hopkins, & White,
2005). Taken together, these findings outline the divergent and complex effects of acute and
chronic cannabis use in this patient population and highlight the need for continued research.
1.7 Overview of Current Study Objectives and Hypotheses
Given the high rates of cannabis abuse and dependence among patients with
schizophrenia and the harmful effects of cannabis on illness onset, prognosis and treatment
success (D'Souza et al., 2005; Foti et al., 2010; Linszen et al., 1994; Manrique-Garcia et al.,
2014), research investigating the neurophysiological effects of cannabis use in patients with
schizophrenia is of great importance. Previous neurochemical evidence has suggested that the
effects of cannabis on the human brain lie within the complex interaction between the
cannabinoid system and inhibitory neuronal networks. More specifically, cannabis acts
through the GABAergic system (Eggan & Lewis, 2007), a system vital to inhibitory control
and working memory associated gamma oscillations (Bartos et al., 2007; Brown et al., 2007;
Fitzgerald et al., 2009). In support of this, previous research has demonstrated that cannabis
38
impairs cortical inhibition and working memory in otherwise healthy subjects (Ilan et al.,
2005; Silva de Melo et al., 2005) and further exacerbates already present inhibitory deficits in
patients with schizophrenia (Wobrock et al., 2010). Given that the majority these studies are
only conducted cross-sectionally, our understanding is limited to the acute effects of cannabis
in these individuals. Thus it is clear that longitudinal studies, employing abstinence
paradigms among currently dependent patients, are needed to evaluate the long-term
modulatory effects of cannabis on measures of cortical inhibition and working memory. To
our knowledge, this is the first study to address the complex relationship between cannabis,
cognition, and cortical inhibition in patients with schizophrenia using a within- and between-
subjects longitudinal design. The findings of this study may further our understanding of the
mechanisms underlying cannabis use among patients will likely help guide novel treatment
approaches.
Therefore, the aim of this current study is to examine the neurophysiological effects
of cannabis dependence and abstinence in cannabis dependent patients with schizophrenia
and non-psychiatric controls. Firstly, at baseline, we will be evaluating the differential effects
of cannabis dependence cross-sectionally, on cortical inhibition in patients compared to
controls. Through several well-established TMS paradigms, including SICI, LICI, CSP and
ICF, we will be able to differentially index GABAA, GABAB and NMDA receptor function
of the motor cortex. We hypothesize that patients will demonstrate reduced cortical inhibition
and excitation indexed through SICI, LICI and ICF measures, as well as a shorter silent
period, as assessed through CSP. The second objective of the current study is to evaluate the
differential effects of cannabis dependence on working memory performance and gamma
oscillatory activity in patients and controls, cross-sectionally, using the N-back task while
39
EEG is recorded. We hypothesize that patients will demonstrate poorer working memory
performance compared to controls. Moreover, we expect to see excessive gamma oscillatory
activity specifically in the frontal region in patients with schizophrenia.
As an exploratory analysis, an additional objective of the current study is to evaluate
the changes, longitudinally, in cortical inhibition and working memory following a 28-day
abstinence period. Thus we will compare TMS measures prior to and following abstinence in
the patients and controls who are able to successfully abstain from cannabis use. We
hypothesize that following abstinence, both patients and controls will exhibit improved
cortical inhibition, with the greatest improvements in patients. Furthermore, we will compare
working memory performance and gamma oscillatory activity, prior to and following the
cannabis abstinence period. We hypothesize that abstinence will improve working memory
performance in both groups compared to baseline, and reduce gamma activity in the patient
population.
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Chapter 2
Methods
2.1 Study Design
This study is a cross-sectional and longitudinal human laboratory study investigating
the effects of cannabis dependence and abstinence on cortical inhibition and working
memory associated gamma oscillations in cannabis dependent patients with schizophrenia
compared to cannabis dependent non-psychiatric controls. A total of 13 participants (7
controls and 6 patients) completed both the pre and post abstinence TMS testing sessions,
while an additional 10 participants (13 controls and 10 patients) completed both working
memory testing session (see figure 3.2). The study duration was approximately 4 weeks; this
included a screening visit, baseline TMS and working memory visit, and a final TMS
working memory test session following the 28-day abstinence period. Included in this
abstinence period was weekly TMS testing along with twice-weekly urine drug testing (see
figure 2.1). In depth procedural details are provided in section 2.4.
41
Figure 2.1
Study Design
Figure 2.1: Outline of overall study design including screening assessments along with pre and post TMS and working memory assessments and weekly motor CI measures. Additionally, urine collection took place twice weekly throughout the abstinence period.
This study was approved by the Centre for Addiction and Mental Health’s (CAMH)
Research Ethics Board (REB #169/2011) and in accordance to the declaration of Helsinki.
Written informed consent was obtained for each participant as approved by the CAMH
Research Ethics Board (see Appendix B). Patients with schizophrenia were recruited through
CAMH via flyers, word-of-mouth, various outpatient clinics, and referrals. Healthy controls
were recruited through flyers, referrals from other studies and online advertisements on
Craigslist, and Kijiji.
42
2.2 Participants
2.2.1 Power analysis
Initially, we aimed to assess cannabis abstinence in 12 patients with schizophrenia
and 12 healthy controls, based on an anticipated 25% improvement (Cohen’s d=0.73) in
cortical inhibition in patients compared to controls at the end of 28 days of cannabis
abstinence (with α=0.05 and power=80%). Furthermore, an estimated attrition rate of 20%
per group is expected resulting in 10 completers for each group.
2.2.2 Sample
All participants were between the ages of 16 to 55. Additionally, participants had to
meet criteria for cannabis dependence, as confirmed by the Structured Clinical Interview of
the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (SCID for DSM-
IV-TR, (APA, 2000a) and could not be treatment seeking for such dependence. Current and
lifetime cannabis use and dependence were assessed using several self-report and clinical
interview measures. Current cannabinoid levels were assessed using the Narcocheck, a semi
quantitative urine drug screen, which detects the presence of cannabis, ranging from 25 to
150 ng/ml. All participants were males, allowing us to focus on the most representative
sample of the target population (Goldstein, Seidman, Santangelo, Knapp, & Tsuang, 1994).
Further, all participants were current daily cigarette smokers, given that cigarette use is
common among cannabis users (Patton, Coffey, Carlin, Sawyer, & Wakefield, 2006) and
patients with schizophrenia (Kalman, Morissette, & George, 2005; Lasser et al., 2000),
including co-morbid nicotine use was essential. Tobacco smoking status was assessed via
self-report and was biochemically verified with expired breath carbon monoxide levels
(Vitalograph, Lenexa, KS).
43
A diagnosis of schizophrenia or schizoaffective disorder was confirmed using the
criteria outlined in the SCID for DSM-IV-TR (APA, 2000a). Additionally, inclusion criteria
outlined that schizophrenia patients had to be psychiatrically stable, requiring a score <70 on
the Positive and Negative Syndrome Scale (Kay, Fiszbein, & Opler, 1987) (PANSS), and on
a stable dose of antipsychotic medication for a least one month prior to the time of their
assessment. In contrast, healthy controls could not meet SCID for DSM-IV TR criteria for
any current or past Axis I psychiatric disorder except for cannabis dependence or past major
depression, with at least one year of symptom remission. Additionally, healthy controls could
not be taking any psychotropic medications, or have a first-degree relative with psychosis.
Participants were excluded if they met criteria for abuse or dependence for alcohol or
any illicit substances within the past 6 months, with the exception of cannabis, nicotine or
caffeine. The absence of additional substances was verified using Medtox urine toxicology
screens (7-panel-Cannabinoids, Opiates, Amphetamine, Cocaine, Phencyclidine,
Barbiturates, and Benzodiazepines). All measures will be discussed in detail in the following
section. Additional general exclusion criteria included a history of head injury or loss of
consciousness, a neurological or medical condition deemed to affect cognitive function and a
personal or first-degree relative with a history of seizures or syncope. Furthermore, all
participants had to have a Full Scale IQ ≥ 80 as determined by the WTAR (Wechsler, 2001)
and a Test of Memory Malingering (TOMM) score ≥ 45.
44
2.3 Measures
2.3.1 Clinical interview measures
Structured Clinical Interview for the DSM-IV-TR (SCID-IV)
The SCID-IV (First, 2002) is a semi-structured interview used to diagnose a current
or lifetime axis I disorder. In the current study, the SCID was used to verify a diagnosis of
schizophrenia or schizoaffective disorder in our patient group and subsequently confirm that
the control group did not meet criteria for any axis I disorder. The SCID also allowed for a
definitive diagnosis for cannabis dependence in all participants.
Positive and Negative Syndrome Scale (PANSS)
The PANSS (Kay et al., 1987) is a 30-item medical scale used to evaluate symptom
severity in individuals with schizophrenia. Through clinical interview, the occurrence and
severity of positive symptoms, negative symptoms and general psychopathology are
assessed. All items are rated using a 7-point scale (from 1=absent to 7=extreme). The PANSS
was chosen due to its high inter-rater reliability (Kay, Opler, & Lindenmayer, 1988) and
suitable construct validity (Kay et al., 1987). Administration occurred at screen and weekly
throughout the abstinence period.
Addiction Severity Index (ASI)
The ASI (McLellan, Alterman, Cacciola, Metzger, & O'Brien, 1992) is a semi-
structured interview used to assess substance abuse and outline the need for new or additional
treatment approaches based on the duration, severity and number of symptoms. The ASI
evaluates several potential problem areas referencing both the past 30 days and one’s
lifetime. These problem domains include psychiatric and medical status, employment and
45
support, drug and alcohol use, family/social status, and legal status. Using a ten point scale
(from 0-1 No real problem, treatment not indicated to 8-9 Extreme problem, treatment
absolutely necessary) interviewer severity ratings (ISR) are assigned regarding the severity of
problems in each of the seven domains. The ISRs are used to calculate composite scores
based on the individuals’ responses corresponding to their experiences in the last 30 days.
The ASI was administered at screen in all potential participants.
Calgary Depression Scale for Schizophrenia (CDSS)
The CDSS is semi-structured interview, developed to assess the severity of
depression symptomatology in individuals with schizophrenia (McLellan et al., 1992). The
CDSS is a 9-item measure rated from 0 (absent) to 3 (severe). A total score greater than 4
suggests the presence of minor depression, whereas scores greater than 7 are indicative of
major depression. The CDSS was administered at screen in the patient group only.
2.3.2 Premorbid IQ
Wechsler Test of Adult Reading (WTAR)
The WTAR (Wechsler, 2001) was developed to assess premorbid intelligence. This
assessment is comprised of 50 irregularly spelled words, and each correctly pronounced word
is given a score of 1. The irregularity of the words makes it difficult to correctly pronounce
without having previous knowledge of the words. Thus the participant’s vocabulary, and by
extension IQ, can be assessed. Raw scores were standardized by age.
46
2.3.3 Extrapyramidal Side Effects
Abnormal Involuntary Movement Scale (AIMS)
The AIMS (Guy & Cleary, 1976) was designed to measures dyskinetic symptoms, a
common side effect associated with long-term antipsychotic treatment in patients with
schizophrenia. The AIMS allows for early detection and ongoing surveillance of tardive
dyskinesia (TD). This 12-item assessment of involuntary movements is rated on a 5-point
scale ranging from 0 (none) to 4 (severe), except for items 11 and 12 (dental care) which are
answered with either a “yes” or a “no”. A score of 2 or higher, i.e. mild movements in two
categories or moderate movements in one area, (Barnes, 1989) suggest the presence of TD.
Barnes Akathisia Rating Scale (BARS)
The BARS (Barnes, 1989) is used to assess neuroleptic-induced akathisia, a
syndrome of motor restlessness, in individuals with schizophrenia. The BARS incorporates
subjective reports of awareness and distress related to akathisia, along with objective motor
restlessness observations rated using a 4-point scale. Finally, global clinical assessment is
rated using a 5-point scale.
Simpson Angus Rating Scale (SARS)
The SARS (Simpson & Angus, 1970) is used to assess neuroleptic-induced
parkinsonism, evaluating gait (hypokinesia), rigidity and glabella tap, as well as tremors and
salivation. The SARS is a 10-item measurement rated on a 4-point scale.
47
2.3.4 Substance Use Measures
The Alcohol Use Disorders Identification Test (AUDIT)
The AUDIT (Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) was developed
by the World Health Organization to quickly screen for excessive alcohol use. The ten-item
measure assesses alcohol consumption, alcohol dependence and other alcohol related
problems. Men who achieve a score of 8 or more (7 for women) out of a possible score of 40,
likely partake in hazardous or harmful drinking behaviours, whereas a score of 20 or more is
indicative of alcohol dependence.
Timeline Follow Back (TLFB)
The TLFB (Sobell, Sobell, Leo, & Cancilla, 1988) is used as an estimate of alcohol,
marijuana, tobacco and caffeine use over a one-week period. Participants were asked to
retrospectively assess the frequency of substance use on a day-to-day basis. Given the
importance of cannabis use in this study specifically, participants were also asked to estimate
the number of times they had purchased cannabis in the last week, as well as the number or
grams and amount of money spent on cannabis.
Fagerstrom Test of Nicotine Dependence (FTND)
The FTND (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1991) is one of the most
commonly used measures, developed to assess self-reported nicotine dependence. This brief
6-item questionnaire ranges in scores from 0 to 10, where higher scores correspond to more
severe nicotine dependence. Importantly, the FTND has been shown to have adequate
reliability in smokers with and without schizophrenia (Weinberger et al., 2007).
48
Joint Years
Joint years was used as a measure of cumulative exposure to cannabis. This measure
was adapted from a commonly used measure in nicotine research called cigarette years. A
joint-year was defined as smoking on average one joint per day for 1 year (e.g., 1 joint year =
365 joints smoked in one year).
Urine Drug Screen
The Narcocheck is a semi-quantitative urine drug-screening test that has 5 levels of
cannabis detection ranging from 25 to 150 ng/ml. This test is able to detect marijuana,
hashish and cannabis resin present in the users urine. In this study, the Narcocheck was used
once weekly throughout the abstinence period to assess for changes in cannabis using
behaviours and verify abstinence at day 28.
2.3.5 Cortical Inhibition
Participants were instructed to abstain from cannabis 12 hours prior to the TMS
session; however, ad libitum cigarette use was allowed throughout the test session to avoid
nicotine withdrawal. During testing, participants were seated in a comfortable chair with their
arms supported passively by a pillow, and were asked to maintain relaxation throughout the
session. Surface EMG was recorded from the abductor pollicis brevis (APB) muscle of the
right hand with disposable disc electrodes placed in a tendon-belly arrangement over the bulk
of the muscle. An additional metal ground electrode was placed on the inside of the forearm
distal to the wrist. EMG was monitored on a computer screen and the signal was amplified
(Intronix Technologies Corporation Model 2024F, Bolton, ON, Canada), filtered (band pass
49
2 Hz–2.5 kHz), digitized at 5 kHz (Micro 1401, Cambridge Electronics Design, Cambridge,
UK) and stored in a laboratory computer for offline analysis.
TMS was applied to the hand area of the left motor cortex with a 7 cm figure-of-eight
magnetic coil and two Magstim 200 magnetic stimulators (Magstim, Whitland, Dyfed,
Wales). The coil was held tangentially on the head with the handle pointing backward
midline at a 45° lateral angle. The optimal coil position was determined for each subject
individually and was marked to ensure consistent positioning. The RMT was defined as the
minimal intensity that produced a MEP of >50 μV in 5 of 10 trials in relaxed the APB muscle
(Kujirai et al., 1993). The output intensity required to elicit a MEP 1 mV peak to peak was
determined (Kujirai et al., 1993).
SICI and ICF were assessed using a standard paired pulse procedure (Kujirai et al.,
1993). A subthreshold conditioning stimulus (CS), which was set at 80% of RMT, preceded a
suprathreshold test stimulus (TS). The TS was delivered at an intensity that produced an
average 1mV peak-to-peak amplitude (Kujirai et al., 1993). The CS was applied to the motor
cortex before the TS at one of five randomized ISIs: 2 ms and 4 ms for SICI and 10 ms,
15 ms, and 20 ms for ICF along with the TS alone. Seventy-two trials were performed, 12 for
each condition. Changes in the TS MEP amplitude at each ISI were expressed as a
percentage of the mean unconditioned MEP amplitude (Daskalakis, Christensen, Fitzgerald,
& Chen, 2002).
For LICI, a suprathreshold CS preceded a suprathreshold TS (set at the adjusted 1 mv
peak-to-peak) at one of three randomized ISIs: 100 ms, 150 ms and 200 ms. Forty trials were
performed, 10 for each ISI as well as 10 for the TS alone. Finally, the measurement of the
50
CSP duration was obtained in moderately tonically active APB muscle (i.e., 20% of
maximum contraction) by stimulating the motor cortex with an intensity of 140% of the
RMT. Ten trials were performed. CSP duration was measured as the time from the MEP
onset to the return of any voluntary EMG activity, referred to as the absolute CSP. The CSP
was determined with our previously published automated method (Daskalakis et al., 2003).
Using CFSview in CED Signal 2.0, the 10 trials were averaged off- line and CSP duration
was measured on an averaged recording. The absolute CSP value was calculated as the time
of offset of the period of EMG activity suppression minus the time of onset. The order of
administration of SICI/ICF, LICI and CSP were counterbalanced for each participant (refer
to figure 2.2).
51
Figure 2.2
Motor Cortical Inhibition Paradigms
Figure 2.2. Adapted from (Barr, Fitzgerald, Farzan, George, & Daskalakis, 2008). EMG recordings following: A) SICI a conditioning stimulus (CS) precedes the TS by 2–5 ms to and inhibits corresponding MEP B) LICI, the CS precedes the TS by approximately 100–200 ms and inhibits the corresponding MEP C) CSP induced following a 140% suprathreshold TS while the right hand muscle is tonically activated D) ICF a conditioning stimulus (CS) precedes the TS by 10-20 sec.
A
B
C
D
52
2.3.6 Working Memory
N-Back Task
Subjects performed the N-back task while EEG activity was recorded (STIM2,
Neuroscan, U.S.A.). Black capital letters were presented on a computer monitor one at a time
in a continuous sequence. Participants were instructed to respond by pushing a button (2;
target) if the present stimulus was identical to the stimulus presented N (1 or 3) trials back;
otherwise, participants pushed a different button (1; non-target). Each letter was present on
the screen for 250 ms, followed by a 3000 ms time frame to respond. Stimuli were presented
for 15 minutes in the 1-back condition and 30 minutes in the 3-back, ensuring that there were
a satisfactory number of correct responses for data analysis (refer to figure 2.3). The number
of target letters in each condition was: 46 of 198 (23.2%) in the 1-back and 59 of 400 trials
(14.6%) in the 3-back condition. Outcome measures included task accuracy and response
time. The order of conditions were randomized and counterbalanced across subjects to
prevent order effects.
EEG Measurement of Induced γ Oscillatory Activity
Induced oscillatory responses are non-phase-locked to the stimulus onset with a
latency within the first 500 ms of stimulus onset, which varied trial-by-trial (Tallon-Baudry,
Kreiter, & Bertrand, 1999). In contrast to induced activity, evoked oscillatory activity is
phase-locked to the stimulus onset, with a fixed latency within 100 ms of the stimulus onset
(Tallon-Baudry et al., 1999). Although both evoked and induced gamma activities are
associated with working memory (Barr et al., 2008; Basar-Eroglu et al., 2007; Howard et al.,
2003), it has been suggested that induced gamma band activity may be more closely related
to the maintenance of information in working memory, whereas evoked oscillations are
53
considered less related to higher cognitive functions (Tallon-Baudry et al., 1999). Several
studies have demonstrated that evoked oscillations are more strongly influenced by attention
(Tiitinen et al., 1993) and task instructions (Hermann, 1993). As such, we measured mean
induced gamma power from frontal electrodes (Fpz, Fp1, Fp2, AFz, AF3, AF4, AF7, AF8,
Fz, F1, F2, F3, F4, F5, F6, F7, F8) while subjects completed the N-back task.
Figure 2.3
N-Back Task
Figure 2.3. Adapted from (Barr et al., 2013) A) An example of the 1- and 3-back conditions that were completed by patients and controls. Participants were asked to push a button if the current letter was identical to the letter presented N trials back and push a different button for the non-targets. Only correct responses for target (TC) were included in the data analysis. B) The timing of one trial for a total time of 3000 ms including the presentation of a letter separated by a + sign followed by a subsequent letter. The data gamma power were measured following the presentation of the stimulus (250 ms) until 750 ms.
54
EEG Recording
EEG data were acquired using a 64-electrode cap and Synamps2 DC-coupled EEG
system (Compumedics, U.S.A.). Data were recorded at a rate of 1000 Hz DC and with a 0.3
to 200 Hz band pass hardware filter. Electrode impedances were lowered to < 5 kΩ. All
channels were referenced to the mastoid electrodes, TP7 and TP8.
Offline EEG Processing
EEG recordings were processed offline using MATLAB (The MathWorks Inc.
Natick, MA, USA) and EEGLAB toolbox (Delorme reference). Data were down sampled to
1 kHz and filtered by using second order, Butterworth, zero-phase shift 1–55 Hz band pass
filter. Second, EEG data was segmented -1400 ms to +3100 ms relative to the stimulus onset.
Next, a channels-by-trials matrix of all ones was created and assigned the value to zero if an
epoch had: (1) amplitude larger than +/- 150 µV; (2) power spectrum that violated 1/f power
law; or (3) standard deviation 3 times larger than average of all trials. A channel was rejected
if its corresponding row had more than 60% of columns (i.e., trials) coded as zeros and an
epoch was removed if its corresponding column had more than 20% of rows (i.e., channels)
coded as zeros. Epochs were then manually inspected and trials containing irregularities were
removed. Finally, an independent component analysis (ICA) (EEGLAB toolbox; Infomax
algorithm) was performed to remove eye-blink traces, muscle artifacts, and other noise from
the EEG data.
Gamma power was analyzed for the target trials that were responded to correctly
across all N-back conditions. The raw data was filtered for gamma (30-50 Hz) frequency
ranges with zero-phase shift. We then calculated the time series for gamma amplitude using
the Hilbert transform. Additionally, we created concatenated signal of 5000 ms using
55
randomly selected epochs for each subject, each trial type, and at each electrode, as this
corresponds with induced gamma power.
2.3.7 Cannabis Abstinence Paradigm
Abstinence was assessed weekly through the administration of NarcoCheck.
However, to our knowledge this test has not been measured in a clinical research setting and
thus the NarcoCheck was only used to capture decreasing THC levels or alternatively any
sudden increases throughout the 28-day abstinence period. End-point abstinence was
assessed using MEDTOX urine toxicology. Abstinent individuals were those who did not use
cannabis within the 28-day period and demonstrated endpoint detection THC-COOH levels
below 50ng/ml, which is the accepted cut-off level in the literature (Huestis, Mitchell, &
Cone, 1995). If participants did not meet these criteria, they were classified as relapsers or
non-abstainers. All stored urine from those classified as relapses was sent to our CAMH
clinical laboratory and was further analyzed by gas chromatography-mass spectroscopy to
obtain quantitative THC-COOH and creatinine concentrations. This analysis confirmed that
relapsers did in fact use cannabis over the abstinence period.
Contingency management and brief behavioural therapy were administered weekly to
aid in cannabis abstinence. The basic model of contingency managment promotes
behavioural change through the administration of positive or negative contingencies. In the
current study, we used a cost-effective strategy called the “fishbowl” technique whereby
participants who demonstrated decreasing THC levels through urinalysis, were able to select
a slip of paper from a fish bowl. The bowl will contain 250 slips of paper, half of which were
nonwinning slips that said Sorry, try again while the winning slips could be exchanged for
prizes that varied in value. Of the winning slips, 109 were small prizes consisting of $1-5
56
coupons to local restaurants, 15 were large prizes, worth up to a maximum of $20 in value
and 1 of the slips was a jumbo prize valued at $100. In addition to the weekly contingency
management, on day 29, participants received a $3000 bonus if urine results were indicative
of cannabis abstinence.
Furthermore, individual weekly therapy sessions were administered on days 1, 8, 15
and 22 in order to aid in study attendance and abstinence. A combination of psychoeducation,
motivational interviewing, coping skills therapy, and cognitive-behavioural relapse
prevention were implemented, given research suggesting that a combination of interventions
leads to the highest rates of abstinence success (Budney et al., 2000). Early sessions focused
on building a rapport and provided psychoeducation in order to heighten awareness of the
personal consequences associated with cannabis use. Additionally, motivation interviewing
was used to increase participants’ willingness to stop using. Finally, later sessions focused on
coping strategies and relapse prevention techniques. These therapy sessions lasted
approximately 30 minutes and were conducted by trained research assistants in the CAMH
Schizophrenia Program. .
2.4 Study Procedure
Once potential participants were identified, a telephone screen was completed, which
briefly assessed eligibility based on current and past medical and substance use. Those who
were deemed eligible were invited into the Biobehavioural Addictions and Concurrent
Disorders Research Laboratory (BACRDL; Principal Investigator: Dr. Tony P. George,
M.D., FRCPC) at CAMH for an additional screening assessment. Upon arrival for a
screening assessment, participants were asked to complete the informed consent as well as a
post-consent quiz to verify their understanding of the consent. Following this, demographic
57
information, IQ testing, as well as measures of depression (HAM-D), drug use (ASI, TLFB,
AUDIT), carbon monoxide levels and Medtox™ urine toxicology were administered. For
schizophrenia patients, clinical assessments were conducted including the SCID-IV, PANSS,
with the AIMS, SARS, and BARS. The candidate, Michelle Goodman conducted screening
measures along with Rachel Rabin, who primarily conducted the SCID, PANSS, and ASI
assessments.
Data presented are a subset of a larger study, therefore only baseline and post
abstinence testing sessions will be included. All testing sessions were conducted at the
Temerty Centre for Therapeutic Intervention, CAMH. Participants were instructed to abstain
from using cannabis 12 hours prior to baseline testing. During the baseline visit, motor
cortical inhibition was administered including SICI, LICI, ICF and CSP. Following this, N-
Back EEG was collected for the 1 and 3-Back conditions. This testing session was repeated
following the 28-day abstinence period. Participants were compensated for their time at a rate
of $10 per hour, which they received at the end of every testing session, after approximately
3 hours.
2.5 Data Analysis
2.5.1 Study Sample
Independent t-tests and chi-squared tests were used to analyze differences between
the two diagnostic groups on demographic variables including age, years of education, IQ,
daily cannabis and cigarette use and joint years.
58
2.5.2 Cortical Inhibition
All data were analyzed using the Statistical Program for Social Sciences (SPSS)
version 21.0 (SPSS Inc., Chicago, Ill). All tests were two-tailed and the level of significance
was set at α = 0.05. Separate repeated measures ANOVAs were conducted for SICI, LICI
and ICF with diagnosis (patients versus controls) as the between subjects factor and ISI (SICI
ISI: 2 ms and 4 ms, ICF ISI: 10 ms, 15 ms and 20 ms, LICI ISI: 100 ms, 150 ms and 200 ms)
as the within subjects factor. Independent t-tests were conducted for RMT and CSP. Post-hoc
tests were conducted where appropriate with an adjusted Bonferroni corrected alpha level to
account for multiple comparisons. Pearson correlation analyses were conducted on each of
the TMS paradigms and demographic variables, and were analyzed within groups when
significant.
2.5.3 Working Memory
Baseline working memory performance (accuracy and reaction time) and gamma
oscillatory activity were analyzed using separate repeated measures ANOVAS with diagnosis
(schizophrenia vs. healthy controls) as the between-subjects factor and N-back load (1- and
3-back) as the within-subjects factor. Pearson correlation analyses were conducted on
performance and oscillatory activity on the 1-back and 3-back condition and demographic
variables, and were analyzed within groups when significant.
59
Chapter 3 Results
3.1 Baseline Results
3.1.1 Demographics
A total sample of 26 individuals participated in the baseline testing session of this
study, recruited over a 24-month period. Twelve of these participants met diagnostic criteria
for schizophrenia or schizoaffective disorder and fourteen were non-psychiatric healthy
controls. Of these participants, all completed baseline N-Back testing. However, 3 patients
with schizophrenia and 5 healthy controls failed to meet eligibility for TMS testing due to
history of concussions and other head injuries, history of seizures, as well as 1 control
participant who did not want to take part in this portion of the study (refer to figure 3.2).
All patients were clinically stable, with PANSS scores below 70. Ten of these
patients were on atypical antipsychotic medications including: risperidone (4), quetiapine (3)
olanzapine (1), clozapine (1), and paliperidone (1). Furthermore, 1 patient was on the typical
antipsychotics flupentixol, and 1 patient received both fluphenazine and quetiapine. In
addition to antipsychotic medications, several patients were on antidepressants for symptoms
of depression or anxiety including escitalopram, lorazepam and citalopram and trazodone.
Finally, 1 of these patients was on the anticholigeric medication benzotropine as well as
several additional cholesterol and diabetes medications including metformin, nicotinic acid,
atorvastatin, indapamide, and glyburide.
60
Given that sample sizes differed between cortical inhibition and working memory
assessments, separate demographic tables were presented for our TMS and working memory
findings. The following demographics included all baseline participants who completed the
working memory assessment (for baseline TMS demographics refer to table 3.1). As
expected, group differences were revealed on measures of IQ as measured by the WTAR,
with higher average IQ scores in the control group (t(25) = -3.061, p =.005). Group
differences were revealed on years of education, with fewer years seen in the patient group
(t(25) = -2.116, p = .044). Importantly, there were no significant differences in daily cigarette
use (p = .520), daily cannabis use (p = .971) and joint years (p =.157) (refer to table 3.3).
Interestingly, IQ was negatively correlated with joint years (r (25) = -.393, p = .043). This
correlation only remained significant when all participants were included.
Figure 3.1. Significant negative correlation between IQ and joint years r (25) = -.393, p =.043, Thus higher IQ was associated with fewer joint years.
0
5
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30
60 70 80 90 100 110 120
Join
t Yea
rs
IQ
61
Figure 3.2
Consort Diagram for patients with schizophrenia (SZ) and healthy controls (HC)
Assessed for eligibility via phone screen (N=123)
Baseline TMS sample SZ= 9 HC= 10
Baseline N-Back sample SZ= 12 HC= 14
Initially eligible (N=71)
Total sample (N= 26)
Excluded (n = 47): Concurrent Substance use or Axis I, PANSS > 70, FSIQ < 80, or Negative for THC
Completed TMS sample SZ= 6 HC= 7
Completed N-Back sample SZ= 9 HC= 13
62
3.1.2 Baseline TMS Assessments Table 3.1 Baseline TMS demographics, clinical means, and standard deviations by diagnosis
_______________________________________________________________________ HC (N = 10) SZ (N = 9) p-value . Age (years) 30.54 ± 6.98 30.33 ± 9.63 .955 Race Caucasian 70% (7) 11.1% (1) skskdn African American 20% (2) 66.7% (6) Other 10% (1) 22.2% (2) Education (years) 12.3 ± 2.00 11.0 ± 2.18 .191 IQ 99.54 ± 9.43 91.67 ± 1.01 .089 PANSS positive - 13.78 ± 2.95 negative - 11.89 ± 2.71 general - 27.44 ± 4.5 total - 53.11 ± 8.31 CPZ equivalent (mg/day) - 531.92 ± 262.65 CPD 11.99 ± 1.02 9.52 ± 5.91 .592 GPD 1.86 ± 1.27 1.30 ± .88 .276 Joint years 8.75 ± 4.8 12.35 ± 9.36 .281 ________________________________________________________________________
Values are expressed as mean ± SD HC = Healthy controls, SZ = patient with schizophrenia CPZ = chlorpromazine, CPD = cigarettes per day, GPD = grams per day
3.1.2.1 Baseline Inhibitory Assessments: SICI, LICI, and CSP
To evaluate baseline differences in GABAA inhibitory activity between patients and
controls (SICI), a repeated measures ANOVA was conducted with ISI (2 ms versus 4 ms) as
the within-subjects variable and diagnosis (patients and controls) as the between-subjects
variable. This analysis revealed a significant main effect of ISI (F(1,17) = 9.244, p = .007)
(see figure 3.3). A paired samples t-test revealed greater inhibition at 2 ms compared to 4 ms
(t(18) = 3.123, p =0.006). No significant ISI x diagnosis interaction was found (p = .888),
63
however there was a trend towards a significant diagnosis effect (F(1,17) = 3.473, p = 0.080),
with greater inhibition in patients.
Figure 3.3. Baseline short interval cortical inhibition (SICI) between patients (SZ) and healthy controls (HC) across 2 ms (SICI2) and 4 ms (SIC4) ISIs. Results revealed a significant main effect of ISI F(1,17) = 9.244, p = .007. Bars represent (±) 1 standard deviation. Asterisks (*) represent significance.
To evaluate baseline differences in GABAB inhibitory activity between patients and
controls measured through LICI, a repeated measures ANOVA was conducted with ISI (100
ms versus 150 ms versus 200 ms) as the within-subjects variable and diagnosis (patients
versus controls) as the between-subjects variable. Results revealed a significant main effect
of ISI (F(2,38) = 8.876, p = .001) (see figure 3.4). A paired samples t-test found significant
differences between150 and 200 ms (t(20) = 3.180, p = 0.005) and 100 and 200 ms (t(2) =
2.366, p = 0.028). Only the first comparison remained significant following an adjustment for
multiple comparisons (Bonferroni correction) with greater inhibition in the 150 ms condition.
No significant diagnosis effect was found (p = .928), however a trend towards a significant
ISI x diagnosis interaction (F(2,38) = 2.778, p = 0.075) was revealed.
-‐80
-‐60
-‐40
-‐20
0
20
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120
SICI2 SICI4
HC
SZ
Interstimulus Interval
*
Cor
tical
Inhi
bitio
n (%
ME
P re
duct
ion)
64
Figure 3.4. Baseline long interval cortical inhibition (LICI) between patients (SZ) and healthy controls (HC) across 100 ms (LICI100), 150 ms (LICI150) and 200 (LICI200). Results revealed a significant main effect of ISI (F(2,38) = 8.876, p = .001). Bars represent (±) 1 standard deviation. Asterisks (*) represent significance.
-‐60
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LICI100 LICI150 LICI200
HC
SZ
Cor
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ME
P re
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ion)
Interstimulus Interval
* *
65
An additional measure of GABAB inhibitory activity was assessed using an
independent t-test with CSP as the test variable and diagnosis as the grouping variable.
Results revealed significantly longer baseline CSP in patients (M = 0.167, SD = 0.030)
compared to controls (M = 0.135, SD = 0.010 SD) (t(18) = 2.257, p = 0.037).
Figure 3.5. Baseline cortical silent period (CSP) for patients (SZ) and healthy controls (HC). Significant differences were found between diagnosis regarding the lengths of silent periods (t(18) = 2.257, p = 0.037). Bars represent (±) 1 standard deviation. Asterisks (*) represent significance.
0
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nt P
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urat
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(sec
onds
) *
66
3.1.2.2 Baseline Excitatory Assessments: RMT and ICF
To examine potential baseline differences in cortical excitatory activity between
patients and controls, we conducted an independent t-test on RMT. Results revealed no
significant diagnostic differences (p = 0.207). Additionally, a repeated measures ANOVA
was conducted on ICF to assess NMDA cortical excitation. There were also no significant
differences in ICF between controls and patients (p = 0.278); however, a large effect size
was revealed for the 20 ms interval (Cohen’s d = .951;refer to table 3.2).
Figure 3.6. Baseline cortical facilitation (ICF) between patients (SZ) and healthy controls (HC) across 10 ms (ICF10), 15 ms (ICF15) and 20 ms (ICF20). No significant differences were found at any ISI. Bars represent (±) 1 standard deviation.
0
50
100
150
200
250
ICF10 ICF15 ICF20
HC
SZ
Interstimulus interval
Cor
tical
Fac
ilita
tion
(%M
EP e
nhan
cem
ent)
67
Table 3.2 Cohen’s D and p-values for TMS Measures
.
TMS Measure ISI HC (N = 10) SZ (N = 9) Cohen’s d p-value . .
SICI 2 ms 29.5 ± 62.8 72.2 ± 24.0 .915 .065 4 ms 14.9 ± 70.3 54.1 ± 27.5 .794 .109
LICI 100 ms 58.3 ± 43.1 58.2 ± 70.4 .065 .881 150 ms 61.4 ± 38.9 67.6 ± 63.9 .177 .683 200 ms 50.2 ± 39.3 24.0 ± 66.2 .342 .429
CSP - 0.13 ± 0.03 0.16 ± 0.03 .926 .037
ICF 10 ms 130.2 ± 64.7 148.2 ± 57.8 .198 .109 15 ms 128.4 ± 54.1 150.7 ± 52.7 .336 .668 20 ms 103.3 ± 44.8 147.9 ± 51.1 .951 .054
___________________________________________________________________________ Bolded values indicate a medium to large effect size (>.5)
Percent inhibition or facilitation are expressed as mean ± SD. Cohen’s D values and p-values comparing short interval cortical inhibition (SICI), long interval cortical inhibition (LICI), cortical silent period (CSP) and intracortical facilitation (ICF) between patients (SZ) and healthy controls (HC). Bolded results indicate that there is a strong or significant effect for SICI (2 ms and 4 ms ISI), CSP and ICF (20ms ISI).
68
3.1.3 Baseline Working Memory Assessments
Table 3.3
Baseline N-Back demographics, clinical means, and standard deviations by diagnosis _______________________________________________________________________
HC (N = 14) SZ (N = 12) p-value . Age (years) 28.93 ± 6.62 32.00 ± 1.02 .353 Race Caucasian 78.6% (11) 25.1% (3) skskdn African American 14.3%(2) 50.0% (6) Other 7.1% (1) 16.7% (2) Education (years) 13.1 ± 2.67 11.13 ± 2.02 .044* IQ 102.40 ± 9.43 91.17 ± 9.37 .005* PANSS positive 13.67 ± 3.06 negative 11.58 ± 2.61 general 27.00 ± 4.77 total 52.25 ± 9.01 CPZ equivalents (mg/day) 463.21 ± 302.06 CPD 10.43 ± 9.05 12.56 ± 7.55 .520 GPD 1.55 ± 1.20 1.53 ± .89 .971 Joint years 7.94 ± 4.50 11.68 ± 8.60 .157 ________________________________________________________________________
Values are expressed as mean ± SD Results that are significant at the 0.05 level are marked with an (*) HC = healthy controls, SZ = patients with schizophrenia, CPZ = chlorpromazine, CPD = cigarettes per day, GPD = grams per day
3.1.3.1 Baseline Working Memory Performance
In total, 12 patients with schizophrenia and 14 healthy controls were included in our
baseline analysis. As expected, a repeated measures ANOVA revealed a main effect of N-
back load, between the 1-back condition (M = 85.45, SD = 12.09) and the 3-back condition
(M = 49.67, SD = 23.09) (F(1,24) = 97.021, p > 0.001). Both groups demonstrated
significantly lower scores on the 3-back, as measured by a paired t-test (t (25) = 10.019, p <
0.001)(refer to figure 3.7). No significant N-back load x diagnosis interaction was found (p =
69
0.695). No significant effect of diagnosis was revealed (p = 0.186). A repeated measures
ANOVA on reaction time (ms) found a main effect of load, between the 1-back condition (M
= 821.23, SD = 166.76) and the 3-back condition (M = 1041.40, SD = 258.80) (F(1,24) =
23.835, p > 0.001). As expected, paired samples t-test revealed longer reaction times in the 3-
back condition (t (25) = -5.056, p < 0.001).
Figure 3.7. Baseline N-back performance between patients (SZ) and healthy controls (HC) and the 1-back and 3-back conditions. Performance is significantly lower in the 3-back condition across both groups F(1,24) = 23.835, p > 0.001. No performance differences were found in the 1-back condition or 3-back condition between groups. Bars represent (±) 1 standard deviation. Asterisks (*) represent significance.
3.1.3.2 Baseline Gamma Power
To evaluate baseline gamma oscillatory activity elicited during a working memory
task, a repeated measures ANOVA was conducted for the frontal electrodes encompassing
the DLPFC (Fpz, Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8)
and for the target correct responses only, with N-back condition (1 and 3-back) as the within-
0
20
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80
100
120
1-Back 3-Back
HC
SZ
N-Back Condition
Mea
n Ta
rget
Cor
rect
Res
pons
e (%
)
*
70
subjects variable and diagnosis (patients versus controls) as the between-subjects variable.
Results did not revealed a significant main effect of memory load (p = .589) nor did we find
a significant N-back load x diagnosis interaction (p = .390). Similarly no significant effect of
diagnosis was revealed (p = .823).
1-Back 3-Back
HC SZ HC SZ
Figure 3.8. Mean gamma (30-50 Hz) oscillatory power from target correct (TC) responses elicited across 1-back and 3-back conditions in patients (SZ) compared to healthy controls (HC). Bar graphs represent mean gamma power across the frontal electrodes (Fpz, Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8). Bars represent (±) 1 standard deviation. Topographical illustration of mean gamma power elicited during 1- and 3-back in patients with SCZ compared to HS. Greater gamma power is represented by hot or red colours.
0
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71
3.2 Exploratory Longitudinal Results
The following section is exploratory given that this study is too underpowered to
assess the effects of cannabis abstinence in patients and controls. Abstinent individuals were
defined as those who did not use cannabis within the 28-day period and who demonstrated
endpoint THC-COOH levels below 50ng/ml (for more details refer to section 2.3.7).
Urinalysis was conducted for most of the participants in the study and reported as
normalized-creatinine THC-COOH levels. THC-COOH is the main non-psychoactive
metabolite detected in the urine of cannabis users. Normalizing THC-COOH concentrations
to urinary creatinine controls for individual variability in hydration and urine output (Lafolie
et al., 1991). It should be noted that at baseline, healthy control abstainers had higher levels
of THC-COOH as compared to patient abstainers. Regardless of diagnosis, at baseline,
relapsers demonstrated higher average THC-COOH levels than the abstainer.
Table 3.4 Creatinine-normalized THC-COOH levels in patient and control abstainers and relapsers
. Group Diagnosis Baseline THCCOOH Post THCCOOH
(ng/ml) (ng/ml) .
Abstainers HC (N = 7) 77.68 ± 110.70 2.83 ± 5.40
SZ (N = 3) 21.9 ± 9.53 1.13 ± 1.19
Relapsers HC (N = 2) 157.35 ± 68.93 127.45 ± 130.74
SZ (N = 3) 86.9 ± 66.93 50.17 ± 78.2 ___________________________________________________________________________
Urinalysis of creatinine-normalized TH-CCOOH levels prior to and following a 28-day abstinence period in patients (SZ) and healthy controls (HC). THC-COOH is the main non-psychoactive metabolite, detected in the urine of cannabis users. Values above 50ng/ml are representative of “relapse” or new cannabis use.
72
3.2.1 Longitudinal TMS Assessments Six patients with schizophrenia and 7 controls participated in the abstinence TMS
testing session. Of the patients who participated, 4 successfully abstained, while 2 relapsed
and for the controls, 3 successfully abstained and 4 relapsed (see Table 3.4).
Table 3.5 Post TMS demographics, clinical means, and standard deviations of the sample by diagnosis _______________________________________________________________________ HC abst HC non-abst SZ abst SZ non-abst
(N = 3) (N = 4) (N = 4) (N = 2) . Age (years) 29.0 ± 5.57 32.0 ± 6.16 32.75± 13.2 26.5 ± .707 Race Caucasian 66.7% (2) 66.7% (2) - 50% (1) skskdn African American - 33.3% (1) 75% (3) 50% (1) Other 33.3% (1) - 25% (1) - Education (years) 12.0 ± 3.0 12.0 ± 1.41 12.75 ± 1.5 11.0 ± 1.41 IQ 98.67 ± 8.08 95.75 ± 8.90 92.25 ± 11.4 92.0 ± 5.6 PANSS positive - - 14.75 ± 3.10 14.5 ± 4.95 negative - - 11.0 ± 2.83 11.0 ± 2.83 general - - 29.5 ± 4.20 25.0 ± 5.66 total - - 55.25 ± 9.29 50.5 ± 13.0 CPZ equivalents (mg/day) - - 549.7 ± 4111.9 250 ± 353. 5 CPD 18.86 ± 18.41 9.43 ± 6.31 7.22 ± 3.96 16.0 ± 5.6 GPD 1.83 ± 1.56 2.74 ± 1.30 .748 ± .227 2.31 ± 1.60 Joint years 12.11 ± 4.99 10.35 ± 3.15 13.65 ± 13.12 9.66 ± 4.73 __________________________________________________________________________
Values are expressed as mean ± SD HC = healthy controls, SZ = patients with schizophrenia Abst = abstainer and non-abst = relapse CPZ = chlorpromazine, CPD = cigarettes per day, GPD = grams per day
3.2.1.1 Longitudinal Inhibitory Assessments: SICI, LICI, and CSP
We aimed to evaluate the potential longitudinal changes in GABAA inhibitory activity
through SICI. We compared the reduction in cortical inhibition at baseline and following 28
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days of abstinence in patients with schizophrenia and healthy controls. Furthermore, we
separately graphed patients and controls who successfully abstained (figure 3.9 A) from
those who relapsed (figure 3.9 B).
Figure 3.9. Longitudinal data for short interval cortical inhibition (SICI) between patients (SZ) and healthy controls (HC) across 2 ms (SICI2) and 4 ms (SIC4) ISIs at baseline and at following 28-days of abstinence. A represents a graph of abstainer HC and SZ, B represents a graph of relapser HC and SZ. Bars represent (±) 1 standard deviation.
To evaluate longitudinal changes in GABAB inhibitory activity, we administered
LICI and CSP in patients and controls. We compared the reduction in cortical inhibition
(figure 3.10) as well as the change in silent period length (figure 3.11) at baseline and
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following 28 days of abstinence separately in patients and controls who successfully
abstained (figure 3.10 A, 3.11A) and those who relapsed (figure 3.10 B, 3.11 B).
Figure 3.10. Longitudinal data comparing long interval cortical inhibition (LICI) between patients (SZ) and healthy controls (HC) across 100 ms (LICI100), 150 ms (LICI150) and 200 (LICI200) at baseline and following 28-days of abstinence. A represents a graph of abstainer HC and SZ, B represents a graph of relapser HC and SZ. Bars represent (±) 1 standard deviation.
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Figure 3.11. Longitudinal data comparing cortical silent period (CSP) between patients (SZ) and healthy controls at baseline and following 28-days of abstinence. A represents a graph of abstainer HC and SZ, B represents a graph of relapser HC and SZ. Bars represent (±) 1 standard deviation.
3.2.1.2 Longitudinal Excitatory Assessment: ICF
We also evaluated the potential longitudinal changes in NMDA excitatory activity
through ICF. We compared the enhancement in cortical excitation at baseline and following
28 days of abstinence in patients with schizophrenia and healthy controls. We separately
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graphed patients and controls who successfully abstained (figure 3.12 A) from those who
relapsed (figure 3.12 B).
Figure 3.12. Longitudinal data comparing intracortical faciliatation ICF) between patients (SZ) and healthy controls (HC) across 10 ms (ICF10), 15 ms (ICF15) and 20 ms (ICF20) at baseline and following 28-day abstinence. A represents a graph of abstainer HC and SZ, B represents a graph of relapser HC and SZ. Bars represent (±) 1 standard deviation.
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3.2.2 Longitudinal Working Memory Assessments Ten patients with schizophrenia and 13 controls participated in the longitudinal TMS
testing session. Of the patients who participated, 6 successfully abstained while 4 relapsed,
and for the controls, 8 successfully abstained and 5 relapsed (see Table 3.5).
Table 3.6 Post WM demographics, clinical means, and (±) 1 standard deviations by diagnosis
_________________________________________________________________________ HC abst HC non-abst SZ abst SZ non-abst
(N = 8) (N = 5) (N = 6) (N = 4) . Age (years) 26.25 ± 5.20 31.2 ± 5.63 32.2± 11.50 33.5 ± 11.79 Race Caucasian 87.5% (7) 66.7% (2) 16.7% (1) 50% (1) skskdn African American - 33.3% (1) 50% (3) 50% (1) Other 33.3% (1) - 16.7% (1) - Education (years) 12.5 ± 3.35 12.8 ± 2.17 12.90 ± 1.34 10.75 ± .957 IQ 105.88 ± 8.03 97.8 ± 9.04 90.6 ± 10.57 92.25 ± 6.24 PANSS positive - - 14.0 ± 3.16 14.5 ± 4.04 negative - - 10.4 ± 2.79 11.5 ± 1.91 general - - 28.0 ± 4.95 26.25 ± 5.74 total - - 55.4 ± 10.26 52.25 ± 11.44 CPZ equivalents (mg/day) - - 651.33 ± 238.1 412.5 ± 287.2 CPD 10.93 ± 11.89 9.03 ± 5.54 10.77 ± 8.66 18.0 ± 4.0 GPD 1.24 ± .998 2.39 ± 1.37 1.13± .869 2.17 ± .98 Joint years 7.03 ± 5.35 8.99 ± 4.08 11.81 ± 12.07 10.97 ± 5.33 __________________________________________________________________________
Values are expressed as mean ± SD HC = healthy controls and SZ = patients with schizophrenia Abst = abstainer and non-abst = relapse CPZ = chlorpromazine, CPD = cigarettes per day, GPD = grams per day
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3.2.2.1 Longitudinal Working Memory Performance
The N-back task was administered at baseline and following 28 days of cannabis
abstinence in patient and control abstainers and relapsers. Accuracy for target correct
responses on the 1 and 3-back conditions were graphed separately.
Figure 3.13. Longitudinal N-back performance between patients (SZ) and healthy controls (HC) abstainers and relapsers at baseline and following 28-day abstinence on the 1-back and 3-back conditions. Bars represent (±) 1 standard deviation
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3.2.2.2 Longitudinal Gamma Power
Mean gamma oscillatory power was measured at baseline and following abstinence in
patient and control abstainers and relapsers. Topographs illustrated the mean gamma power
elicited during the 1- and 3-back conditions.
Mean Gamma 1-Back (µV2)
HC
SZ
Baseline Abstinence Relapse
Mean Gamma 3-Back (µV2)
HC
SZ
Baseline Abstinence Relapse
Figure 3.14. Topographical illustration of mean gamma power elicited during 1- and 3-back in patients (SZ) and healthy control (HC). Included are topo plots at baseline and following a 28-day abstinence period in abstainers and relapsers. Greater gamma power is represented by hot or red colours.
N = 13 N = 8 N =5
Basel Abstinent Relapse
5 Second Time Window, Mastoid Reference N = 10 N = 6 N =4
N = 13 N = 8 N =5
N = 10 N = 6 N =4
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Chapter 4 Discussion
4.1 General Discussion
Given the high rates of cannabis abuse and dependence among patients with
schizophrenia and the detrimental effects of cannabis on illness onset, prognosis and
treatment success (D'Souza et al., 2005; Foti et al., 2010; Linszen et al., 1994; Manrique-
Garcia et al., 2014), uncovering the mechanism underlying this common co-morbidity is of
great value. As such, the aim of this study was to investigate the neurophysiological effects
of cannabis dependence in patients with schizophrenia and non-psychiatric controls. To our
knowledge, this was the first study to address the complex relationship between cannabis,
cognition, and cortical inhibition in patients with schizophrenia using a within- and between-
subjects longitudinal design. Given that cannabis inhibits GABAergic neurotransmission,
which is necessary for both cortical inhibition and working memory, we hypothesized that
cannabis dependence would further impair GABAergic deficits present in patients with
schizophrenia. We assessed GABA inhibitory neurotransmission through several well-
established TMS paradigms as well as indirectly through working memory performance and
gamma oscillatory activity. As an exploratory secondary analysis, we evaluated longitudinal
changes in cortical inhibition and working memory following a 28-day cannabis abstinence
period. We hypothesized that cortical inhibition and working memory deficits would be
potentiated with cannabis abstinence in both patients and non-psychiatric controls.
The main finding in the current study was greater GABAB mediated inhibitory
neurotransmission, as measured by CSP, in cannabis dependent patients compared to
dependent healthy controls. In regards to SICI, while no significant effect of diagnosis was
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found, due in part to the large within-group variability, our results suggest that cannabis
dependent patients may have more GABAA mediated inhibition compared to controls. No
significant differences were found on LICI or on our measures of cortical excitation,
including ICF and RMT. With regards to our working memory findings, results suggest that
patient and control cannabis users perform similarly on the N-back task, along with
comparable levels of frontal gamma oscillatory activity. Taken together, these results suggest
that cannabis dependence has differential effects on cortical inhibition in controls compared
to patients with schizophrenia. While these contradict our initial hypotheses, this preliminary
research provides novel insight into the mechanisms underlying co-morbid cannabis use
among individuals with schizophrenia.
4.1.1 Study Sample
In the current study, our patient group demonstrated a significantly lower IQ and
fewer years of education as compared to the control group. This finding is in accordance with
previous cross-sectional studies both in patients with schizophrenia (Kremen, Seidman,
Faraone, & Tsuang, 2001) and in chronic cannabis users (Arseneault, Cannon, Witton, &
Murray, 2004); therefore, this sample seems to be representative of the target population.
Additionally, all participants were male. While this does limit the generalizability of the
study, cannabis use has been shown to be especially common in young males experiencing
their first-episode of psychosis (Koskinen et al., 2010).
Of note, this was one of the first studies conducted in cannabis dependent individuals,
to employ a longitudinal abstinence paradigm, with twice-weekly urinalysis. Additionally,
we accounted for important confounding variables associated with substance use. This
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included, frequency and recency of use as well as the amount of money spent weekly on
cannabis, which is thought to indirectly index of the quality of cannabis consumed.
Furthermore, unlike previous research in this field, we implemented a clear definition of
cannabis dependence, whereby all participants had to be daily cannabis users for a minimum
of 1 year. Additionally, participants had to meet DSM-IV criteria for cannabis dependence
and could not currently consume or meet dependence for, any additional illicit substances
within the past 6 months. Finally, we also controlled for co-morbid cigarette use unlike
previous research, given its common co-occurrence in this population (Kalman et al., 2005;
Patton et al., 2006). Importantly, daily cannabis and cigarette consumption did not differ
between controls and patients.
4.1.2 TMS Findings
The main finding in the current study was increased GABAB mediated inhibitory
control, as measured by CSP, in cannabis dependent patients compared to dependent healthy
controls. This finding contradicts the previous two studies investigating the effects of
cannabis use on cortical inhibition in first episode patients with schizophrenia (Wobrock et
al., 2010) and controls (Fitzgerald et al., 2009), as neither study found significant differences
on this measure. CSP is considered to be one of the more robust and consistent TMS
measures; however recent observations have brought to light large variability between
studies in terms of the effects of schizophrenia on CSP. For example, early research
demonstrated GABAB specific CSP deficits in patients with schizophrenia (Eichhammer et
al., 2004; Fitzgerald, Brown, et al., 2002a). However, consistent with the current study, more
recent research has found a lengthening of the CSP length in patients with schizophrenia
(Hasan et al., 2012; Strube et al., 2014; Wobrock et al., 2009).
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In comparison to previously published CSP values, our cannabis dependent controls
and patients displayed both longer than “average” (Hasan et al., 2012) and shorter than
“average” (Strube et al., 2014) CSP lengths. Therefore, in the context of previous research,
this suggests that no clear unidirectional comparisons regarding the effects of cannabis use
on cortical silent period in patients or controls can be drawn. Methodological differences,
including severity and duration of illness, stimulation intensity and the effects of medication,
likely contribute to these divergent findings. For example, stimulation intensity in both of
these previous studies was set to the participant’s 1mV (which is typically 120% of their
RMT), whereas in the current study, stimulation intensity was set to 140% of the RMT
(Hasan et al., 2012; Strube et al., 2014). Additionally, clozapine has been shown to
significantly lengthen CSP in patients with schizophrenia (Daskalakis, Christensen, et al.,
2008). In the current study, we included one patient who was treated with clozapine, and
given that no outliers were removed from analysis, we cannot rule out the possibility that this
participant is driving the current finding. In summary, our results suggest that cannabis
dependent patients with schizophrenia exhibit greater GABAB mediated inhibition compared
to cannabis dependent controls.
Regarding our SICI findings, unlike previous research and contrary to our hypothesis,
we did not find reduced SICI in cannabis dependent patients. In fact, with a trend towards a
significant diagnosis effect, as well as a large effect size, our results suggest the opposite
effect of cannabis on GABAA mediated cortical inhibition, with enhanced inhibition in
patients. This large effect size yet lack of statistical significance is likely due to small sample
sizes and large variability within groups. Interestingly, when compared to the heavy using
dependent controls in Fitzgerald’s study, our current control group demonstrated similarly
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low levels of GABAA mediated cortical inhibition (Fitzgerald et al., 2009). Additionally,
when compared to previously published values regarding the percentage of inhibition in
Fitzgerald’s healthy, non-cannabis using sample, controls in the current study demonstrate
lower levels of inhibition (Fitzgerald et al., 2009).
In contrast, when compared to Wobrock’s previously published SICI findings in first-
episode cannabis-using patients, cannabis dependent patients in the current study
demonstrated a greater amount of cortical inhibition. Similarly, when compared to published
findings in both recent onset and chronically ill non–using patients with schizophrenia
(Strube et al., 2014), cannabis dependent patients in the current study also demonstrated more
cortical inhibition. Of note, these comparisons must be interpreted with caution, given that
they were made across studies, which introduces a number of uncontrolled variables. For
example, in Wobrock’s study, dependence was assessed with the European Addiction
Severity Index as well as the DSM-IV criteria of substance abuse/dependence, however,
these cannabis users only had to use weekly and more than 20 times in their lifetime. Given
such divergence in cannabis dependence criteria, it is not possible to draw direct comparisons
regarding cortical inhibitory results from the current study with that of the previous findings.
In the context of previously published data (Fitzgerald et al., 2009; Strube et al.,
2014; Wobrock et al., 2010), these SICI results suggest that cannabis may reduce GABAA
inhibition in controls whereas cannabis may have the opposite effect on inhibition in patients.
In support of this, our group recently demonstrated this finding with preliminary data. This
data showed that cannabis using patients had more cortical inhibition as measured by SICI
compared to non-using patients, whereas control cannabis users demonstrated significantly
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less inhibition as compared to non-using controls (Goodman, 2015). While the previous
studies mentioned above all assessed SICI, these findings must be considered with caution,
given that differences within these SICI paradigms, participant pool and study designs likely
exist. Thus, these comparisons go beyond the scope of the current project.
In line with previous findings in cannabis using controls compared to non-users
(Fitzgerald et al., 2009) as well as in medicated and unmedicated patients compared to
controls (Fitzgerald et al., 2003), no LICI diagnostic differences were revealed in the current
study. Given that both LICI and CSP are thought to index GABAB mediated inhibition, and
significant differences in CSP were observed in the current study, it would suggest that LICI
might not be sensitive enough to detect these potential diagnostic differences. These
divergent effects may also be due to the differential mechanisms underling these TMS
paradigms. For example, CSP assesses the duration of inhibition, whereas LICI indexes the
magnitude of inhibition at a single time point (McDonnell, Orekhov, & Ziemann, 2006).
Furthermore, CSP has been shown to be associated with inhibition at the level of the spinal
cord, whereas long interval inhibition may take place more so at the level of the cortex
(Chen, Lozano, & Ashby, 1999).
While no significant diagnostic differences were observed on measures of cortical
excitation including RMT and ICF, a large effect size at 20 milliseconds for ICF was
revealed. The previous study conducted by Wobrock and colleagues (2010) found enhanced
ICF in cannabis dependent first-episode patients with schizophrenia (Wobrock et al., 2010).
In the current study, we found a trend towards higher facilitation in the cannabis dependent
patient group, although these differences were not significant. It has been suggested that
enhanced ICF in these patients might best be explained by an increased glutamatergic input
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along with disturbances in cholinergic intracortical neuronal circuits (Wobrock et al., 2010).
While no study has looked specifically at the effects of chronic cannabis use on cortical
facilitation, previous research has shown that nicotine, alcohol and cocaine users demonstrate
cortical hypoexcitability (Barr et al., 2008; Boutros et al., 2005; Conte et al., 2008; Lang,
Hasan, Sueske, Paulus, & Nitsche, 2008). Given that these previous studies were conducted
in otherwise healthy controls, we cannot rule out the possibility that these divergent findings
may be due, in part, to the underlying brain abnormalities in NMDA receptor functioning
seen in patients with schizophrenia (Falkai, Wobrock, Schneider-Axmann, & Gruber, 2008).
Additionally, several of these previous studies only examined the acute drug effects on
cortical facilitation and did not employ the appropriate abstinence period, thus testing
participants while intoxicated. As such, it is clear that continued research investigating the
effects of cannabis use on cortical excitability is necessary.
In summary, these findings suggest that cannabis has differential effects in controls
and patients in terms of GABAB inhibitory functioning, demonstrated through a significantly
longer CSP in patients. The large effect sizes and trends towards significant diagnostic
effects on measures of GABAA and NMDA, suggest that with a larger sample size, these
differences may become more evident. While these findings oppose our initial hypotheses,
they depict an interesting picture of the neurophysiological effects of cannabis dependence
among patients with schizophrenia. Given the paucity of research in this field, it is clear that
further research confirming this finding is necessary.
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4.1.3 Working Memory Findings
Contrary to our initial hypothesis, cannabis dependent patients with schizophrenia
performed as well as cannabis dependent controls on both the 1 and 3-back conditions of the
N-back verbal working memory task. Previous research has suggested that patients with
schizophrenia consistently perform worse than controls on working memory tasks. In fact,
working memory deficits are among the most replicable and robust findings in this
population (Forbes et al., 2009). Comparisons have been drawn between the cognitive
impairments seen in patients with schizophrenia with that of cannabis users; these cognitive
domains included attention, learning and inhibition (Radhakrishnan et al., 2014; Solowij &
Michie, 2007). Thus it would follow that cannabis use among patients with schizophrenia
would exacerbate already present working memory deficits. However, there is a growing
body of evidence suggesting that chronic cannabis users with schizophrenia perform better
on memory tasks than patients with no history of drug use (Rabin et al., 2011; Schnell et al.,
2009; Stirling et al., 2005; Yucel et al., 2012). Thus, in order to determine exactly how
cannabis dependence impacts working memory deficits in patients with schizophrenia, the
current sample must be compared to a non-dependent patient sample.
Current research has suggested that under low working memory load conditions (i.e.,
the 1-back), accuracy in patients and controls is comparable; however, increased memory
load was associated with a deterioration in patients’ performance (Barr et al., 2013; Carter et
al., 1998). Unfortunately, few studies have administered the most challenging 3-back
condition to patients with schizophrenia. In one study conducted by Barr et al. (2013),
researchers aimed to evaluate the effects of a non-invasive brain stimulation technique called
repetitive transcranial magnetic stimulation (rTMS) on working memory performance in
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schizophrenia patients. Prior to and following sham stimulation, 3-back accuracy slightly
decreased from 36 to 33 percent (Barr et al., 2013). In an earlier study conducted by Jansma
et al. (2004), researchers found that half of their patient sample performed near chance on the
3-back condition, with scores lower than 35 percent (Jansma, Ramsey, van der Wee, & Kahn,
2004). In contrast, cannabis dependent patients in the current study scored above average at
82 percent on the 1-back and 44 percent on the 3-back. It should be noted that in the current
study, no outliers were removed, thus we included one extreme outlier who performed well
below chance on the 3-back condition. Excluding this patient’s data increased 3-back
performance to 49 percent. Taken together, these findings suggest that cannabis dependent
patients may perform better on the 3-back condition than non-using patients.
Similarly to the cognitive deficits observed in non-using patients with schizophrenia,
converging neuroimaging, neurobiological and longitudinal research has suggested that
memory deficits persist in otherwise healthy individuals who use cannabis at an early age,
frequently, and over a long period of time (Becker et al., 2010; Lopez-Larson et al., 2011;
Mehmedic et al., 2010; Yucel et al., 2010). To our knowledge, only one study has
administered the 3-back condition in cannabis using non-psychiatric controls. In this previous
study, N-back accuracy was compared among daily cannabis users (>7 joints per week for a
minimum duration of 3 years) (Verdejo-Garcia et al., 2013). Unlike the current study,
researchers reported probability of hit (i.e., number of hits/number of hits+number of
misses). The 1-back probability of hit was 91 percent in cannabis users, while their 3-back
probability of hit was significantly lower in at 61 percent. These scores were similar to, yet
slightly better than that of the cannabis dependent controls in the current study, who scored
an 89 percent on the 1-back and a 54 percent on the 3-back. It should be noted that the
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accuracy from these two studies cannot be directly compared, given the different methods of
reporting performance (i.e., % accuracy versus probability of hit) and number of available
target correct responses. Unlike the current study, the cannabis-using group had to abstain for
72 hours, which coincides with the height of cannabis withdrawal symptoms (Budney,
Moore, Vandrey, & Hughes, 2003); however, these withdrawal symptoms were medically
controlled (Verdejo-Garcia et al., 2013). Thus these slight differences in performance could
be due to the period of cannabis abstinence, as well as the divergent methods of reporting
performance.
Compared to non-using controls, cannabis dependent controls in the current study
seemed to performe worse on the more challenging 3-back condition. In healthy controls, 3-
back accuracy has been shown to range from above 80% (Prilipko et al., 2011; Sanchez-
Carrion et al., 2008) to approximately 70% (Barr et al., 2010; Rose & Ebmeier, 2006).
Therefore, these results suggest that cannabis dependence in healthy controls impairs
working memory performance as compared to non-using controls. This phenomenon is
generally supported in the literature, whereby memory deficits persist beyond acute
intoxication in chronic users (for full review see (Schoeler & Bhattacharyya, 2013)).
Regarding the effects of cannabis dependence on baseline gamma oscillatory activity,
we initially hypothesized that cannabis dependence among patients would further exacerbate
their excessive gamma activity. However, our results revealed that patients and controls
exhibited similarly high level of gamma activity in the prefrontal cortex. One study
conducted by our group revealed excessive oscillations in patients compared to controls
during the 3-back condition (Barr et al., 2010). The researchers suggested that this finding
may be due in part to GABAergic deficits, specifically through the action of GABAB,
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ultimately leading to a lack of inhibition of gamma oscillations (Farzan et al., 2010a). In the
current study, a similar lack of modulation was observed in both controls and patients, given
that gamma oscillatory activity was as high in the 1-back condition as the 3-back. This
suggests that cannabis may have similar underlying impairing effects on neural oscillations
as those seen in patients with schizophrenia. Specifically this lack of gamma modulation may
be due, in part to GABAB dysfunction, which was observed among our control cannabis
using participants.
Similarly to the excessive oscillatory activity among patients, cannabis use has been
shown to increase gamma oscillations in healthy controls. The CB1R receptor is primarily
located on GABAergic inhibitory interneurons, and exogenous activation of these receptors
through THC, for example, suppresses GABA (Freund et al., 2003). Thus, it has been
suggested that cannabis may increase neuronal synchrony similarly to that of a GABA
receptor antagonist. These antagonists have been shown to lead to convulsive seizures
(Steriade, Amzica, Neckelmann, & Timofeev, 1998), due to excessive or hypersynchronous
neuronal activity in the brain (Shusterman & Troy, 2008). Moreover, models of epilepsy
utilize cannabinoids due to their pro-convulsant side effects (Clement, Hawkins, Lichtman, &
Cravatt, 2003). In line with this research, cannabis dependent controls in the current study
demonstrated increased levels of gamma oscillatory activity compared to previously
published results in non-using controls (Barr et al., 2010). Thus, it is possible that the lack of
diagnostic differences in gamma activity in the current study could be attributed to excessive
oscillations both in control cannabis users and patients with schizophrenia.
Thus taken together, these findings suggest that aberrant gamma oscillatory activity
may underlie cognitive deficits associated with both schizophrenia and cannabis use. Based
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on our working memory performance results, it may seem as though there are no differential
diagnostic effects of cannabis dependence, however the potential effects of cannabis become
clearer when compared to previously published 3-back accuracy data conducted in a group of
non-cannabis using patients and controls. As such, these findings suggest that cannabis use
may be more impairing in control users, where as cannabis dependent patients with
schizophrenia seem to performed better than non-using patients. Given that this is the first
study evaluating working memory performance combined with gamma activity in this
population, it is clear that further research is warranted.
4.1.4 Exploratory Findings To date, all studies that have investigated the effects of cannabis either on cortical
inhibition or working memory associated oscillatory activity in patients with schizophrenia
have employed cross-sectional designs. As such, the current study aimed to investigate the
longitudinal neurophysiological effects of cannabis throughout a 28-day cannabis abstinence
period. Employing an abstinence paradigm allows for the evaluation of the long-term effects
of cannabis, beyond that of acute intoxication and withdrawal. Given that cannabis likely
differentially modulates cortical functioning in patients and controls, we directly compared
these effects in cannabis dependent individuals with and without schizophrenia. Additionally,
within each of these groups, valuable information was also drawn from comparing those who
successfully abstained and those who relapsed.
In the current study, the small sample size limited our ability to draw conclusions
regarding the longitudinal effects of cannabis abstinence. This was especially true for our
TMS findings, given the large within group variability and lack of clear or consistent
inhibitory patterns. However, there is one interesting preliminary LICI finding in abstaining
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patients versus those who relapsed. Results revealed an increase in GABAB mediated
inhibition following 28-day abstinence in patients who successfully abstained. In contrast,
the reverse was found in patients who relapsed, whereby, on day 28, relapsers demonstrated a
decrease in inhibition. While it is an interesting finding, it is clear that continued research is
needed in order to validate this potential pattern with a greater number of participants.
Regarding the working memory findings, although subtle, results revealed a decrease
in accuracy on both the 1 and 3-back conditions among patient abstainers. In line with this
finding, these patients also demonstrated slight decreases in frontal gamma oscillatory
activity. While this finding may suggest that patient’s optimal level of gamma activity and
accuracy is present during acute cannabis abstinence (i.e. at baseline), continued research is
needed, as these findings are very preliminary. An additional interesting finding within the 3-
back condition in both patients and controls, was that those who successfully abstained from
cannabis performed better at baseline than those who went on to relapse (refer to figure
3.13). This may suggest that N-back accuracy, or more broadly cognitive functioning, may
predict one’s ability to successfully abstain from cannabis. While this relationship has never
been studied in cannabis users, in cigarette smokers with schizophrenia, poor working
memory performance has been shown to predict relapse (Dolan et al., 2004). These
preliminary findings highlight the need for continued research on the effects of both cannabis
dependence and abstinence in order to better understand the long-term neurophysiological
impact of cannabis on the brain.
4.1.5 Potential Mechanisms While the exact mechanisms underlying the effects of cannabis in individuals with
schizophrenia are not well understood, overlapping neurocognitive and neurophysiological
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research suggests that interferences in the homeostatic role of the eCB system may underlie
this common co-morbidity. The eCB system acts through synaptic retrograde signalling to
ultimately control the release of other neurotransmitters, including GABA, glutamate and
dopamine (Schlicker and Kathmann, 2001). Activation of the CB1 receptor, through either its
endogenous ligands anandamide and 2-AG, or exogenous cannabis use, mediates the
production and release of inhibitory and excitatory neurotransmitters key to cortical
inhibition, higher order cognitive functions and synaptic plasticity (i.e., learning and
memory) (Chevaleyre, Takahashi, & Castillo, 2006; Howlett et al., 2004; Piomelli, 2003).
Exposure to exogenous cannabinoids, especially during adolescence, has been shown to
significantly disrupt the protective effect of the eCB system (James, James, & Thwaites,
2013). Chronic cannabis use leading to the development of dependence and tolerance has
been shown to alter the functionality of the eCB system; however the exact neurochemical
and neurophysiological effects of chronic cannabis use are still largely unknown (Hoffman,
Oz, Caulder, & Lupica, 2003; Sim-Selley, 2003). Given that cannabis is known to suppress
GABAergic inhibitory neurotransmission (Iversen, 2003) we hypothesized that, cannabis
dependence would lead to deficits in cortical inhibition. In line with this hypothesis, cannabis
dependent controls in the current study demonstrated less GABAB mediated cortical
inhibition when compared to cannabis dependent patients with schizophrenia.
Interest in the involvement of the eCB system underlying cognitive processes was
motivated by evidence demonstrating that CB1 receptors are highly expressed in the
hippocampus (Herkenham, 1991), a brain region vital to learning and memory. Evidence has
consistently demonstrated that cannabinoids manipulate the various stages of learning and
memory, including acquisition, consolidation, and retrieval (Ilan et al., 2005; Silva de Melo
94
et al., 2005). These behavioural effects are thought to be produced, in part, by an imbalance
in GABAergic neuronal transmission in the PFC (Pistis et al., 2002). As such, we
hypothesized that cannabis dependence in patients and controls would increase gamma
oscillatory activity and lead to poorer working memory performance. Based on previously
published 3-back accuracy results in healthy controls (Barr et al., 2010), we found that
cannabis dependent controls in the current study demonstrated lower working memory
accuracy as well as high frontal gamma oscillatory activity. Thus we propose that these
cortical inhibitory and working memory deficits seen in non-psychiatric cannabis dependent
controls are likely due to the suppressing effects of cannabis use on GABA inhibitory
neurotransmission.
Regarding the patient sample, we hypothesized that cannabis dependence would
further exacerbate GABAergic deficits, leading to aberrant cortical inhibition and poor
working memory performance. However, our results revealed that cannabis dependent
patients actually demonstrated improved cortical inhibition and similar working memory
performance to our control group. Thus this implies that chronic cannabis use does not
necessarily have the same neurophysiological effects in controls and patients and highlights
the complex relationship between schizophrenia and cannabis dependence. There are a
number of plausible reasons as to why we did not confirm our hypotheses. Firstly, there is
large body of evidence illustrating alterations in the eCB system in patients with
schizophrenia, regardless of cannabis consumption. For example, post-mortem studies in
patients demonstrated an increased density of CB1 receptors in DLPFC (Dean et al., 2001)
and anterior cingulate (Zavitsanou, Garrick, & Huang, 2004) as well as elevated levels of
anandamide in the cerebrospinal fluid (CSF) in acute schizophrenia (Giuffrida et al., 2004).
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Thus these pre-existing eCB alterations likely influence the “normal” effects of cannabis use
among patients.
Secondly, it is possible that patients develop a compensatory mechanism in response
to these underlying eCB system deficits. This may therefore account for the improved
cortical inhibition and working memory performance in cannabis dependent patients
observed in the current study. The existence of a compensatory mechanism in patients has
previously been suggested. For example, one study found that in the DLPFC of patients with
schizophrenia, GABAA receptors were up-regulated at pyramidal axons, which was thought
to develop in response to deficient GABA release from chandelier axon terminals (Jensen,
Chiu, Sokolova, Lester, & Mody, 2003). Researchers have interpreted this finding to
represent a compensatory response to GABA deficits (Lewis et al., 2005). Similarly, Bossong
and colleagues proposed that chronic cannabis use might lead to a compensatory mechanism,
involving the recruitment of regions not normally associated with a working memory task
(Bossong et al., 2014). Thus these findings suggest that chronic cannabis using patients
develop a GABA specific compensatory mechanism, which may in part, account for our
current unexpected findings in patients.
An additional potential reason as to why cannabis dependent patients demonstrated
better cortical inhibition and working memory performance compared to non-using patients
is drawn from social cognitive research. Several researchers have suggested that cannabis-
using patients with schizophrenia may represent a subset individuals with this disorder,
following the observation that these patients reliably performed better on cognitive tasks than
non-using patients (Rodriguez-Sanchez et al., 2010; Yucel et al., 2012). It has been suggested
that cannabis-using patients may be cognitively less vulnerable given that their onset of
96
psychosis may not have developed without the initiation of cannabis use. These individuals
may therefore represent a ‘healthier’ group (Arndt, Tyrrell, Flaum, & Andreasen, 1992), who
are able to lead the complex life of a drug user (Joyal, Halle, Lapierre, & Hodgins, 2003;
Rodriguez-Sanchez et al., 2010). Given that these cannabis-using patients may have better
premorbid social and cognitive functioning, it is possible that they may not have as profound
GABAergic deficits as their non-using counterparts. In line with this hypothesis, preliminary
data from our group have demonstrated that cannabis dependent patients have better GABAA
mediated cortical inhibition than non-cannabis dependent patients (Goodman, 2015).
Therefore, the current findings of enhanced cortical inhibition and working memory
performance in cannabis dependent patients may provide preliminary support for the
hypothesis that patients who present with co-morbid cannabis use represent a subset of the
patient population with better premorbid social functioning may.
The fact that this GABAergic mechanism cannot adequately account for all of the
observed findings in patients and controls highlights the complex neurophysiological
interactions between mental illness and drug use. Of note, this current mechanism does not
take into consideration the widespread effects of cannabis on dopamine, glutamate, and other
neurotransmitters. Similar to the pathophysiological theories underlying schizophrenia,
deficits in one neurotransmitter alone may not sufficiently account for this complex co-
morbidity. Furthermore, the majority of neurochemical and neurophysiological research is
based on the acute effects of cannabis among patients with schizophrenia and many
unknowns still exist regarding the long-term effects of chronic cannabis use. Thus, without a
better understanding of these unknowns, we are limited in our interpretation of the
underlying neurophysiological and neurochemical mechanisms.
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4.2 Limitations These results should be interpreted in light of several limitations. One of the most
noticeable weaknesses in the current study was the small sample size. This was especially
true for our longitudinal analysis, given that our small sample was further subdivided into
abstainers and relapses. Nevertheless, the 3-back working memory load condition may
capture the divergent neurophysiological effects in abstainers versus relapsers with continued
testing. With such a small sample size, we chose not to remove outliers from the study,
which likely contributed to the large variability seen within our TMS measures. An
additional and significant limitation was the lack of a non cannabis-using control group, for
both our patients and controls. This lack of a true control group likely masked the true
neurophysiological effects of cannabis use.
Although our patient and control groups were well matched in terms of age, cigarette
use as well as daily and lifetime cannabis use, there were several limitations within our
sample. Firstly, the study included only male participants, thus reducing the generalizability
of our findings. A key reason why only males were selected for this study was due to
previous research suggesting that cannabis is especially common in young males
experiencing their first-episode of psychosis (Koskinen et al., 2010). Thus, these findings
highlight the difficulties in recruiting cannabis dependent female patients. Further differences
in our sample were observed in the breakdown of racial backgrounds between our two
groups, with a higher percentage of Caucasian controls and non-Caucasian patients. While
these differences were not significant, it is possible that cannabis metabolism may be
influenced by race. To date, no studies have looked into this specifically in cannabis users
however, research has suggested that the pharmacodynamics and pharmacokinetics of other
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drugs vary based on race (Chen, 2006).
Secondly, while we collected data regarding current and past patterns of cannabis use,
we did not differentiate between the strains of cannabis nor did we chemically verify the
potency of cannabis used by our participants. It has been well-documented that the
neuropharmacological effects of cannabis vary widely across the different strains due to the
varying ratios of the cannabinoid constituents (Potter, Clark, & Brown, 2008). It should be
noted, however, that other than the joint year and IQ negative correlation, we did not find a
significant correlation between the TMS or working memory assessments and cannabis
consumption, including grams per day and joint years.
Thirdly, it should be considered that while participants were excluded if they met
criteria for other substance abuse or dependence 6 months prior to testing, several
participants had a past diagnosis of an alcohol or substance use disorder. Lifetime substance
dependence has been shown to have long-term effects on neurocognition (Potter, Downey, &
Stough, 2013; Verdejo-Garcia, Bechara, Recknor, & Perez-Garcia, 2006) and cortical
functioning (Bolla et al., 2003; Ersche et al., 2005) even following the complete elimination
of the substance from the body. Additionally, all participants were current cigarette users,
given that nicotine dependence commonly co-occurs in cannabis users (Patton et al., 2006)
and patients with schizophrenia (Kalman et al., 2005; Lasser et al., 2000). In patients,
nicotine use is known to selectively enhance neurocognitive function (Sacco et al., 2005),
thus the presence of nicotine in our participants likely confounds our working memory
performance results and potentially our neurophysiological findings. Importantly, in the
current study, the frequency of past substance dependence and daily cigarette use did not
significantly differ between patients and controls. However, in order to truly elucidate the
99
effects of cannabis, future studies should better control for these confounding variables.
Additionally, in the patient group specifically, it should be noted that antipsychotic
medications pose as a considerable confounding factor (Davey et al., 1997). Differences in
RMT have been found in patients treated with risperidone compared to those on olanzapine
(Fitzgerald, Brown, Daskalakis, & Kulkarni, 2002b). Similarly, for paired-pulse
measurements, studies have shown that clozapine significantly lengthens CSP (Daskalakis,
Christensen, et al., 2008) where as benzodiazepines alter GABA specific TMS paradigms (Di
Lazzaro et al., 2008). Furthermore, these medications likely influenced our working memory
findings. For example, studies have shown enhanced 40 Hz oscillatory activity (Hong et al.,
2004) and cognitive performance (Bilder et al., 2002; Meltzer & McGurk, 1999; Sharma,
Hughes, Soni, & Kumari, 2003) in patients on second-generation antipsychotics. In response
to these findings, we did not include any patient on benzodiazepines for the TMS portion of
the study; however, participants on a variety of antipsychotics were included and therefore
pose as a limitation to the current study.
One final limitation of the current study was the task used to assess working memory
functioning. The N-back task does not allow for the evaluation of specific working memory
subprocesses and previous research has demonstrated oscillatory alterations within these
subprocesses (Haenschel et al., 2009). Therefore, we were unable to determine if cannabis
differentially modulated the encoding, maintenance, manipulation, or retrieval stages of
memory. Additionally, repeated working memory and EEG testing has been shown to be
influenced by within-subject variability. A recent study revealed that while the overall score,
cortical activation, and alertness, showed long-term stability within subjects, these measures
were sensitive at the level of the individual. Cognitive function generally varied due to
100
within-day fluctuations including alcohol, marijuana and caffeine use; these fluctuations
were greatest when testing sessions took place in the afternoon (Gevins et al., 2012). While
participants in the current study were instructed not to use alcohol, caffeine, or marijuana on
the testing day, these factors as well as variability within testing times likely influence the
current findings.
4.3 Conclusions To our knowledge, this was the first study to address the complex relationship
between cannabis, cognition, and cortical inhibition in cannabis dependent patients with
schizophrenia using a within- and between-subjects longitudinal design. We hypothesized
that patients would demonstrate reduced cortical inhibition and excitation indexed through
SICI, LICI and ICF measures, as well as a shorter silent period, as assessed through CSP. We
also hypothesized that patients would demonstrate poorer working memory performance
compared to controls as well as excessive gamma oscillatory activity specifically in the
frontal region in patients with schizophrenia. Finally, in regards to our longitudinal data, we
hypothesized that following abstinence, both patients and controls would exhibit improved
cortical inhibition and working memory performance, with the greatest improvements in
patients.
However, contrary to previous findings (Fitzgerald et al., 2009; Wobrock et al., 2010)
at baseline, cannabis dependent patients demonstrated significantly greater GABAB function,
as measured by CSP. Additionally in the current study, no diagnostic differences were found
on baseline measures of working memory accuracy or gamma oscillatory activity. When
compared to a non-using sample, our current dependent controls performed worse on the 3-
back condition of the N-back whereas cannabis dependent patients performed better than
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non-using patients. Taken together, these findings would suggest that cannabis dependence
might have more impairing effects, specifically on GABAB mediated inhibition, in controls
compared to patients.
Given that cannabis reduces GABA function, and GABAergic neurotransmission
underlies both cortical inhibition and cognition, it follows that cannabis dependence would
lead to deficits in these domains. This pattern was clearly observed in our control group.
However, this mechanism cannot account for our findings in cannabis dependent patients.
Converging evidence suggests that cannabis-using patients may represent a subset of
schizophrenia patients with better premorbid cognitive and social functioning, and therefore,
potentially less impaired cortical inhibition. It is clear that continued research is necessary in
order to better understand the mechanism underlying this common co-morbidity to work
towards developing more specialized treatment approaches.
4.4 Future Directions Given that this is the first study evaluating the effects of cannabis dependence and
abstinence on cortical inhibition and working memory in patients with schizophrenia, it is
clear that future investigation is needed in order to address the aforementioned limitations.
Other than our CSP findings, the baseline analysis did not yield statistically significant
differences between controls and patients, suggesting that continued research is needed in
order to elucidate the potential differential effects of cannabis. This also highlights the
heterogeneity of both schizophrenia (Davidson & McGlashan, 1997) and cannabis use
(Iversen, 2003). Given the large interindividual and intraindividual variation over time in this
population, much larger samples may be needed in order to elucidate group differences.
Furthermore, future research would benefit from enlisting a true control group of non-using
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patients or controls. This four-way comparison would provide additional insight into the
differential effects of cannabis use on patients with schizophrenia and non-psychiatric
controls.
The current study sought to investigate the effects of the cannabis-psychosis co-
morbidity primarily on GABAergic functioning, however, both cannabis and schizophrenia
are known to influence a range of neurotransmitters, especially dopamine. For example, CB1
activation through THC is known to enhance mesolimbic dopaminergic neurotransmission.
Converging clinical and preclinical research has implicated this increase in dopamine in both
the cognitive impairments (Pistis et al., 2002; Pistis et al., 2001) and reinforcing properties
associated with cannabis use (Ameri, 1999; Bossong et al., 2009). While PET studies
investigating the effects of chronic and recreational cannabis use exist, studies focusing on
the long-term neurophysiological effects in abstinent users are limited. Thus, given the
widespread neurochemical effects of both cannabis use and schizophrenia, in order to gain a
true understanding of the neurophysiological effects in this population, future research should
take into consideration additional neurotransmitter systems.
Similarly, future research should also focus on additional oscillatory frequencies
including theta gamma coupling, which has also been implicated in working memory
functioning (Canolty et al., 2006). There is a paucity of research investigating the
functionality of theta-gamma coupling in patients with schizophrenia, and to our knowledge,
this topic has not been investigated in cannabis users. In the current study, we chose the
prefrontal cortex as the optimal site to investigate gamma oscillatory activity from, given its
involvement in working memory processes. However, neuroimaging studies have shown that
working memory functioning relies on a complex network spanning frontal and posterior
103
parietal areas (Honey et al., 2002). Research has suggested that the prefrontal cortex may be
involved in retrieval whereas the posterior regions are associated with information storage
(Curtis & D'Esposito, 2003). Unfortunately, the N-back task used in the current study does
not allow for the investigation of the distinct subprocesses associated with working memory
functioning. Taken together, this highlights the need for future research examining additional
oscillatory frequencies spanning the frontal and parietal working memory networks. In order
to better understand the exact influence of cannabis and schizophrenia on working memory,
future studies should employ a memory task that allows for such assessments.
An additional and extremely important future direction would be to assess cortical
inhibition from the DLPFC, given its direct involvement in the pathophysiology of
schizophrenia (Barch et al., 2001) and evidence revealing eCB alterations in this region in
patients (Dean et al., 2001). Moreover, many of the cognitive side effects associated with
cannabis use are thought to be derived from CB1 receptors in the DLPFC. Recent
technological advances have allowed for the combination of TMS with EEG. Using a
pharmaco-TMS-EEG approach, researchers have been able to study the direct role of
GABAergic neurotransmission through TMS-evoked EEG potentials (Premoli et al., 2014).
Therefore, this technique would likely provide valuable insight into the mechanism
underlying co-morbid cannabis use in patients with schizophrenia.
Future research would also benefit from employing longitudinal abstinence
paradigms, which not only provide insight into the long-term neurophysiological effects of
cannabis but also control for some of the confounding variables associated with between-
subject study designs. Employing a longitudinal design may provide clarification as to
whether the observed results were due to the residual effects of THC in the body, acute
104
cannabis withdrawal, long-term cannabis consumption, or premorbid neurodevelopmental
dysfunction seen in patients with schizophrenia.
Furthermore, in order to understand its effects within complex co-morbid populations,
we must first better understand the neurochemical and neurophysiological impact associated
with cannabis use. Additionally, further research specifically focusing on the long-term
effects of chronic use, and early initiation of cannabis use is needed as the clinical
importance of cannabis research lies within this problematic pattern of use. Thus longitudinal
research should also focus more so on naturalistic substance use in the general population
rather than acute administration of THC using lab-based methodologies. While these
naturalistic studies introduce variability, in terms of the strain and pattern of cannabis used as
well as history of cannabis and other drug use, they also allow for more generalizable and
relevant findings. This variability within the cannabis using population highlights the fact
that cannabis research is still in its infancy and continued investigation is needed.
Elucidating the neurophysiological effects of cannabis dependence would provide
novel and valuable insight into this common co-morbidity. This field would benefit from
multidisciplinary research both neurophysiological and cognitive based, in order to
ultimately catalyze future research regarding intervention and treatment strategies. It is
evident that there is a need for biologically-based investigation of mechanisms underlying
this common co-morbidity. As such, more funding is needed in order to allow for a more
intense investigation in this area. While the findings from the current study are preliminary,
they provide a first step towards better understanding the impact of cannabis on patients with
schizophrenia. Whether continued research ultimately determines that cannabis use has more
impairing neurophysiological effects on patients with schizophrenia or seemingly positive
105
effects, the findings will be of value. However, it is clear that given the widespread use of
cannabis among patients with schizophrenia, the known negative effects on illness course
and the lack of specific treatment approaches targeted at cannabis dependent patients, further
investigation if of great importance.
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Appendix A: Telephone Screen
Screen ID: Sz Screen date:
Interviewer’s Initials
Eligible Recontact
Y N
Y N
Name (first/last): Phone no H/W/C.:
Age: ________ (16 to 55) DOB (dd/m/yr) :__________________________ Gender: M (exclude females)
Referral Source: ______________
SMOKING:
Do you smoke tobacco? No (exclude), Yes.
On average, how many cigarettes do you smoke in a day/week?
PSYCHIATRIC HISTORY: (Diagnosis of schizophrenia or schizoaffective disorder, currently in TX)
Have you ever had any psychiatric treatment? No Yes, currently
Diagnosis? ____________________ Clinician: ______________________ Program: ______________
Last hospitalization for psychiatric symptoms? ________________________
Current Medications (dose and recent changes) _____________________________________________
(Must be on stable antipsychotic dose for at least one month, if not, re-contact)
Have you ever had a heady injury with LOC? No Yes details
Did you black-out? For how long? Did you need to be hospitalized?
Are you experiencing medical problems at this time? No Yes details
Do you have any metal in your body? No Yes details
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CURRENT SUBSTANCE USE:
Marijuana: ............................................... No (exclude) Yes, assess for current dependence:
Frequency and duration of use:________________________
• substance is taken in larger amounts or over long period than intended
• persistent desire to quit or cut down • majority of time spent involved in activities of obtaining/using/recovering • social, occupational, recreational activities given up or reduced • continued use despite physical and psychological problems • tolerance: needed for same effect/ ↓effect with same amount • withdrawal
Yes to 3 of above symptoms No (exclude), Yes
Are you currently actively trying to quite marijuana? (treatment-seeking are not eligible and should be referred elsewhere)
No Yes (exclude),
CURRENT/PAST SUBSTANCE USE:
Alcohol: ..................................................... Current:_____________ (exclude: M>14/ wk) Past: ____________
Cocaine: ..................................................... Current: Y (exclude) N Past: _____________________
Opiates (heroin, p-dope, percocets): .............. Current: Y (exclude) N Past: _____________________
Methadone: ................................................ Current: Y (exclude) N Past: _____________________
“Pills” (valium, xanax, etc): .......................... Current: Y (exclude) N Past: _____________________
Other (acid, speed, illy, etc): ......................... Current Y (exclude) N Past: _____________________
Cannabis Abstinence Study Eligibility:
No Yes Yes, possibly Availability:
Would you be willing to quite marijuana for a one-month period?
IN-PERSON appointment SCHEDULED: No Yes TBA (pending)
If Yes: Appointment date, time, RA:
Consent to contact for participation in future studies or add to Sz research registry
Y N
Y N
Y N
Y N
Y N
Y N
Y N
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Appendix B: Study Consent
STUDY INFORMATION AND CONSENT FORM:
Effects of Cannabis Abstinence on Neurocognition in Schizophrenia: Neurophysiology Assessment
Investigators Contact Phone Number Tony P. George, MD, FRCPC 416-535-8501 x 34544
Zafiris J. Daskalakis, MD, PhD, FRCPC 416-535- 8501 x 34319
Rachel A. Rabin, MSc 416-535-8501 x 36115
Mera S. Barr, PhD 416-535-8501 x 33095
Study Purpose:
The use of cannabis influences the brain’s ability to concentrate, pay attention, reason, and remember. Collectively these are known as cognitive functions. How cannabis affects these cognitive functions are still unclear.
Cannabis may influence brain mechanisms called cortical inhibition and plasticity, which in turn, influences cognitive function. Cortical inhibition is a brain process that is designed to filter excessive information in our environment in order to process information efficiently. Plasticity is involved in learning and memory and. It has been discovered that cortical inhibition and plasticity may impaired in the brains of people who use cannabis. Cortical inhibition and plasticity is also impaired in patients with schizophrenia who suffer from cognitive impairment as part of their illness.
Transcranial magnetic stimulation (TMS) is a method used to measure brain inhibition. TMS excites nerves over the area of the brain that is responsible for thinking and planning. When the nerves are stimulated, this causes brain activity to change, which will be recorded with electromyography (EMG) and during electroencephalography (EEG), which will be analyzed later.
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The purpose of this study is to measure cortical inhibition, plasticity and cognitive function in patients and control participants who are cannabis dependent. These measures will then be evaluated during a one month period of abstinence. We hope that this study will help us understand the interaction between cannabis, cognition, and schizophrenia. This is not part of any treatment plan.
Procedures: You are being asked to provide informed consent. After reading through this form, you will be given a chance to ask questions. All study procedures will be completed at Dr. George’s BACDRL lab (1st floor, 33 Russell Street, Toronto, ON, Room 1910A) and at the Temerty Centre for Therapeutic Brain Intervention (1001 Queen Street West, Unit 4-1, Room 124) at the Centre for Addiction and Mental Health. The study will take place over a 1-month period + a one-month follow-up, where you will be asked to come into the lab. These sessions will either take place on the same day as primary assessments or can be done within the same week on a separate day. The assessments will be conducted by Ms Michelle Goodman, Ms. Rachel Rabin and Dr. Mera Barr. Ms Goodman and Rabin are graduate students in the Institute of Medical Science at the University of Toronto and the Centre for Addiction and Mental Health, Schizophrenia Program, and this study is part of their graduate research program. Rachel Rabin will lead the project and be responsible for recruitment, enrolment and screening of participants, as well as assessments and cognitive testing. Ms. Michelle Goodman will be responsible for the neurophysiological assessment conducted at the Temerty Centre. Dr. Mera Barr is an Independent Scientist in the Schizophrenia Division of the Complex Mental Illness Program at CAMH. Dr. Mera Barr will oversee the assessment of cortical inhibition and plasticity with TMS and measure the memory task with electroencephalography (EEG). Drs. George and Daskalakis are research psychiatrists in the CAMH Schizophrenia Division who act as supervising and medically qualified investigators for this study.
DT-MRI
The sessions to measure cortical inhibition in your brain will be conducted at baseline prior to the initiation of cannabis abstinence, once a week for 4 weeks (during the one month abstinence), and one month post abstinence.
• The MRI will be done at the Centre for Addiction and Mental Health. The MRI scanning is a technique of taking pictures of the brain that gives information on brain structure and brain function.
• Before the scan begins, you will be asked to remove all metal that you are wearing because MRI generates images using a strong magnetic field and this can damage some electrical devices.
• You will then be asked to lie on a padded bed that will be moved into a tunnel-like
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machine for the MRI scan. The tunnel is not very large, and the study requires you to lie still in the scanner.
• You should try to remain as still as possible during the scan. Movements will not be dangerous to you in any way, but would blur the picture of your brain.
• You will be in the scanner for about 60 minutes. • You might hear moderately loud knocking or beeping during the scan when the MRI
machine is in operation. These sounds reflect the normal functioning of the MRI. • MRI scanning is not associated with any known risks to your health, and there is no
proof that there will be short-term or long-term side effects. • This will be done before and after 28 days of abstinence.
TMS TMS sessions will measure cortical inhibition and plasticity in your brain. At baseline and following cannabis abstinence, we will measure cortical inhibition and plasticity using TMS and EEG. Once a week during the one month abstinence (4 testing sessions) and 1 month post abstinence we will conduct the TMS session without EEG. The TMS and EEG sessions at baseline and following cannabis abstinence will take approximately 8 hours. The weekly TMS sessions and one month follow up will take approximately 30 minutes.
Cortical Inhibition
• We will attach soft foam electrodes to the skin surface over your hand muscles, and then connect these to a recorder that will record the activity of your hand muscles (for the first part, then TMS will be applied to the front of your head)
• A magnetic coil will be held on the surface of your scalp • To obtain brain inhibition, different modes of TMS will be applied to the head
surface and EMG will be recorded from your hand muscle. • When the magnetic stimulation is applied you will feel a twitch or small movement in
your hand but no pain. • There are many pulses but each pulse is very short and not too close to the next.
Plasticity
• This set up will be the same as measuring cortical inhibition but with an additional electrode placed on your right wrist that will deliver low level electrical stimuli prior to the delivery of the TMS pulse
• There will be several pulses with TMS alone and TMS combined with the electrical stimulus
• This will take approximately 3-4 hours.
EEG
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The sessions to measure TMS cortical inhibition, working memory, and mismatch negativity will be conducted at baseline prior to the initiation of cannabis abstinence and week 4 of abstinence.
• EEG or electroencephalography is a measurement of the electrical activity of the brain. • We will use this to look at brain activity during TMS brain stimulation. • You will be seated in a comfortable chair and will have on a cap containing many
electrodes that record your brain activity. It takes approximately 30 minutes to put on the cap and to prepare for recording.
• There is gel on the inside of the cap that may be sticky. Shampoo will be provided after the testing session to wash your hair.
• You will be asked to rest comfortably while the TMS is applied and the computer is recording information. The combination of TMS and EEG will take approximately 4.5 hours.
Working Memory Task
• We will use this to look at brain activity while at rest and during a working memory task.
• You will be seated in a comfortable chair and will have on a cap containing many recorders that record your brain activity. It takes approximately 15 minutes to put on the cap and get it ready for recording.
• There is gel on the inside of the cap that may be sticky; you will be allowed to rinse it out after the test.
• The recorders are attached to a computer to record the information. • This will last approximately 1.5 hours.
Mismatch Negativity Task (MMN)
• We will use this to look at brain activity that takes place when you are not directly paying attention to auditory stimuli.
• You will be seated in a comfortable chair with the EEG cap and two foam earphones placed in the ears.
• Auditory tones will be played in both ears while you watch a silent cartoon video. • This will last approximately 45 minutes.
Are there any side effects or pain associated with TMS, EEG, or MRI?
There may be some mild side effects with transcranial magnetic stimulation. At certain positions on the head, the stimulation may cause your eyes to blink or a brief contraction of the scalp, neck, trunk or upper arm muscles. You may find these contractions annoying, but they should not be painful. Some people may experience mild headache or shoulder stiffness after testing, however, these symptoms will usually go away in time. Acetaminophen
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(Tylenol) is effective in treating these side effects. If you have further concerns you may contact your study doctor at any time. The magnetic stimulator makes a clicking noise when it stimulates. Since this may be distracting, you will be offered ear-plugs which mute the sound. Magnetic brain stimulation has been used on thousands of individuals in the United States, Canada and Europe over several years without any serious problems. There are no serious side effects for EEG. Some people report a headache following the procedure but this is only due to the pressure of wearing a cap and not the procedure itself. MRI is a non-invasive procedure. Certain items such as a pacemaker and having surgical staples or other ferrous metal in or near the head are dangerous with an MRI. For this reason you will be screened using the precautionary screen for these items designed by the MRI Unit at CAMH.
Risks
You should be aware that the magnetic fields generated by the stimulator may damage magnetic cards, watches and some electrical devices. Please remove any such items before testing.
Exposure to magnetic stimulation or any strong magnetic field is not permitted in people who have a pacemaker, an implanted medication pump, a metal plate in the skull, or metal objects inside the eye or skull (for example, after brain surgery or a shrapnel wound). Please inform the investigators if you might have any of these.
Magnetic brain stimulation has caused brief epileptic seizures in a few patients with stroke and epilepsy out of thousands of people tested. This has never happened with the form of TMS you will be receiving. Patients with epilepsy and patients with stroke can have an increased risk of seizures. Therefore, there is a very small possibility that magnetic stimulation may cause seizures in persons with a heightened risk. The investigators cannot promise that this will never happen in the future; however, the probability of this occurring in the experiment is low. As we have stated above, these tests have been used in thousands of healthy volunteers all over the world and there is no report of a seizure in healthy people.
Risks of TMS
Common Headache and Scalp pain
Tingling sensation and/or heat at coil location
Remote
Effects of Hearing (earplugs are worn to prevent this)
Syncope
Seizure (no reports in current literature during paired-pulse TMS)
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You might feel slight fatigue during the testing sessions. While a cannabis withdrawal syndrome has been reported it does not include significant physical, medical, or psychiatric problems. You may experience irritability, anger, depression, difficulty sleeping, craving, and decreased appetite. Headaches, physical tension, sweating, stomach pain, and general physical discomfort have also been observed during cannabis withdrawal, but are less common.
Benefits:
The information you provide will improve our knowledge about the effects of cannabis abstinence in individuals with schizophrenia, and might encourage you to want to quit using cannabis. Voluntary Participation & Subject Obligations: You are free to choose not to participate, and may withdraw from this study at any time. If you withdraw, it will not affect your ability to receive treatment at CAMH. Study staff/investigators may, at their discretion, end your participation in this study at any time. If you are an existing CAMH client, you will also be asked to give the research team permission to access your medical records for the purpose of confirming your medication and treatment status. Study Provisions: You will be paid for attending and completion of each study visit at a rate of $10/ hour for a total of 9 hours. If all visits are attended for a total of $220.
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Payment Schedule: $10/hour for a total of $200
Assessment
Length (UP TO; h)
Assessment
Completion (UP TO)
Baseline: PAS,TMS-EEG, Working Memory, MMN, and fMRI
9 hours $90
Wk 1 TMS 1 hour $10
Wk 2 TMS 1 hour $10
Wk 3 TMS 1 hour $10
Wk 4: PAS TMS-EEG Working Memory, MMN, and fMRI
9 hours $90
Wk 8: TMS 1 hours $10
TOTAL 22 hours $220
Confidentiality:
If you decide to take part in this research study, you will be required to answer some questions about your drug use and problems you may be having relating to drug use. Your answers to these questions, as well as other data collected will only be used by the study investigators and their designates and will remain confidential to the extent permitted by law.
• As part of continuing review of the research, your study records may be assessed on behalf of the Research Ethics Board. A person from the research ethics team may contact you to ask you questions about the research study and your consent to participate. The person assessing your file or contacting you must maintain your confidentiality to the extent permitted bylaw.
• In accordance with federal requirements, CAMH will maintain archived study records for 10 years. However, those documents that contain personal identifiers (i.e. consent forms) will be stored separately from data files.
• General results of this study might be published, but will not identify you by name.
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As part of the Research Services Quality Assurance Program, this study may be monitored and/or audited by a member of the Quality Assurance Team. Your research records and CAMH records may be reviewed during which confidentiality will be maintained as per CAMH policies and extent permitted by law.
Contact: If you have any further questions or desire further information about this study, you may contact Rachel Rabin at 416-535-8501 x 36115, Dr. Mera Barr at 416-535-8501 x 33095 or the Principal Investigator Dr. Tony George at 416-535-8501 x 34544. If you have any questions about your rights as a study participant, you may contact Dr. Padraig Darby, Chair of the Research Ethics Board, Centre for Addiction & Mental Health, at 416-535-8501 (x36876).
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AGREEMENT TO PARTICIPATE
I, ___________________________________have read (or had read to me) the information form for the study named Effects of Cannabis Abstinence on Neurophysiology in Schizophrenia: Neurophysiology Assessment. I understand that my role is that of a participant in this study. I have been given an opportunity to ask questions about this study. Any questions that I have had, have been answered to my satisfaction, so that I now understand the study procedures, the potential risks of participating, and my right to the confidential treatment of the information that is collected about me. I also understand that my participation in this study is entirely voluntary, and that I may refuse to participate or withdraw from the study at any time, without any consequences for my continuing care. By signing this consent form, I do not waive any of my legal rights nor relieve the investigators/institution from legal responsibilities. If I have any questions about my rights as a study participant, I may contact Dr. Padraig Darby, Chair of the Research Ethics Board, Centre for Addiction and Mental Health, at 416- 535 8501, extension 6876.
I have received a copy of this consent form for my own record.
Participant’s Initials:________
Participant Name: Person who conducted informed consent discussion:
Print name Print name
Date:
Signature of Participant Signature of Witness
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Appendix C: Case Report Form
Effects of Cannabis Abstinence on Neurocognition in Schizophrenia: Neurophysiology Assessment
Subject ID: ___________________________
Testing Completed by: __________________
Time Point: Week 0
Date: ________________________________
Time of Last cannabis smoked: ___________ AM PM
Testing Time Start: _____________________ AM PM
Testing Time End: ______________________ AM PM
Without Cap
Resting Motor Threshold: ________________
1 mV Peak to Peak: ________________
SICI: ___________________________________________________________________
LICI: ___________________________________________________________________
CSP: ___________________________________________________________________
With Cap
Resting Motor Threshold: ________________
1 mV Peak to Peak: ________________
Rest:
LICI Motor Single: _______________________________________________________
LICI Motor Paired: _______________________________________________________
ICF Motor Paired: ________________________________________________________
LICI DLPFC Single: ______________________________________________________
LICI DLPFC Paired: ______________________________________________________
ICF DLPFC Paired: _______________________________________________________
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Nback 1-back: ___________________________________________________________
3-back: ___________________________________________________________
Time Point: Week 0
Comments: ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
