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Psychiatry Research: Neuroim

Resting state corticolimbic connectivity abnormalities inunmedicated bipolar disorder and unipolar depression☆

Amit Ananda,b,⁎, Yu Lia, Yang Wangb, Mark J. Lowec, Mario Dzemidzicd

aDepartment of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United StatesbDepartment of Radiology, Indiana University School of Medicine, Indianapolis, IN, United States

cDivision of Radiology, The Cleveland Clinic Foundation, Cleveland, OH, United StatesdDepartment of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States

Received 28 October 2007; received in revised form 27 March 2008; accepted 28 March 2008

Abstract

This study for the first time investigated resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder(BD) and compared them with findings in healthy controls and unipolar major depressive disorder (MDD) patient groups. Restingstate correlations of low frequency BOLD fluctuations (LFBF) in echoplanar functional magnetic resonance (fMRI) data wereacquired from a priori defined regions of interests (ROIs) in the pregenual anterior cingulate cortex (pgACC), dorsomedial thalamus(DMTHAL), pallidostriatum (PST) and amygdala (AMYG), to investigate corticolimbic functional connectivity in unmedicated BDpatients in comparison to healthy subjects and MDD patients. Data were acquired from 11 unmedicated BD patients [six manic(BDM) and five depressed (BDD)], and compared with data available from 15 unmedicated MDD and 15 healthy subjects. BDpatients had significantly decreased pgACC connectivity to the left and right DMTHAL, similar to findings seen in MDD.Additionally, BD patients had decreased pgACC connectivity with the left and right AMYG as well as the left PST. An exploratoryanalysis revealed that both BDD and BDMpatients had decreased connectivity between the pgACC andDMTHAL. The results of thestudy indicate a common finding of decreased corticolimbic functional connectivity in different types of mood disorders.© 2008 Elsevier Ireland Ltd. All rights reserved.

Keywords: fMRI; Resting state connectivity; Bipolar disorder; Depression; Brain imaging

1. Introduction

Converging findings from animal and human studiespoint to the anterior cingulate–pallidostriatal–thalamic–amygdala circuit as a putative corticolimbic mood-

☆ The results of this study have been previously presented at theSociety of Biological Psychiatry meeting in San Diego (2007).⁎ Corresponding author. Outpatient Psychiatry Clinic, University

Hospital Suite #3124, 550 N. University Boulevard, Indianapolis, IN46202, United States. Tel.: +1 317 274 7424; fax: +1 317 274 1497.

E-mail address: aanand@iupui.edu (A. Anand).

0925-4927/$ - see front matter © 2008 Elsevier Ireland Ltd. All rights resedoi:10.1016/j.pscychresns.2008.03.012

regulating circuit (MRC) that may be dysfunctional inmood disorders (Drevets, 1998; Anand and Charney, 2000;Mayberg, 2003). Brain-imaging techniques such as func-tional magnetic resonance imaging (fMRI) and positronemission tomography have shown increased activation inmajor depressive disorder (MDD) of mood-generatinglimbic areas such as the amygdala (AMYG) (Ketter et al.,2001; Sheline et al., 2001; Drevets et al., 2002; Siegle et al.,2002; Anand et al., 2005a), ventral striatum (VST), anddorsomedial thalamus (DMTHAL) (Drevets, 1998; Taberet al., 2004).Other areas of the brain that are also implicated

rved.

190 A. Anand et al. / Psychiatry Research: Neuroimaging 171 (2009) 189–198

are the insula, hippocampus and parahippocampal areas(Mayberg et al., 1999; Phillips et al., 2003; Anand et al.,2005a). Conversely, decreased activation of certain corticalareas have been reported in MDD — in particular, thepregenual and ventral subdivisions of the anterior cingulatecortex (pgACC and vACC) (Drevets et al., 1997; Mayberget al., 1999), the anteromedial prefrontal cortex, theorbitofrontal cortex (OFC), and the dorsolateral prefrontalcortex (DLPFC) (Mayberg et al., 1999; Ketter et al., 2001).

Compared with the literature on MDD, considerablyfewer studies have investigated regional brain activationin bipolar disorder (BD). In studies in which phase ofillness has been characterized, most have been con-ducted in BD depression (BDD), which has also hasbeen reported to be associated with increased limbicactivation and decreased activation of cortical regionssuch as the DLPFC and ACC (Buchsbaum et al., 1986;Baxter et al., 1989; Drevets et al., 1997; Yurgelun-Toddet al., 2000; Ketter et al., 2001; Blumberg et al., 2003;Phillips et al., 2003; Chang et al., 2004). In mania(BDM), increased metabolism of the ventral and dorsalACC, the striatum (Drevets et al., 1997; Blumberg et al.,2000), and the amygdala (Altshuler et al., 2005)[another study reported decreased amygdala activation(Lennox et al., 2004)], and decreased activity of theOFC (Blumberg et al., 1999), have been reported.Strakowski et al. (2004) have reported an abnormality inthe anterior limbic network in BD in response tocognitive stimuli. Compared with healthy subjects,unmedicated euthymic BP patients showed increasedactivation in the limbic and paralimbic areas (para-hippocampus, amygdala and insula) as well as ventralprefrontal regions when performing attentional tasks(Strakowski et al., 2004). Therefore, in BD, abnormal-ities within the prefrontal cortex, subcortical structuressuch as the striatum and thalamus, and medial temporalstructures such as the amygdala and the parahippocam-pus, are likely to be present (Strakowski et al., 2005;Adler et al., 2006).

Methodological issues such as medication status andinadequate identification of the phase of illness may havecontributed to discrepant results in some of the abovestudies. Unmedicated BD subjects are difficult to recruitfor studies. Furthermore, the discrepant findings ofchanges in local activation also suggest that the abnorm-ality may lie at a circuit level in terms of the corticolimbicconnectivity rather than in localized brain regions.

Recently, there has been considerable interest gener-ated from the discovery of spontaneous low frequency(b0.08 Hz) blood oxygen level-dependent (BOLD)fluctuations (LFBF) in resting state in echoplanar imaging(EPI) data (Raichle et al., 2001). It has been recognized

that these LFBFs are not caused by instrumentation orphysiological effects (such as cardiac and respiratorycycles) originating outside the brain (Biswal et al., 1995).It has also been shown that these resting state signalchanges reflect alterations in blood flow and oxygenationthat may be coupled to neuronal activity and that LFBFscorrelate between brain areas of plausible functionalconnectivity (Biswal et al., 1995; Lowe et al., 2000;Cordes et al., 2001; Peltier and Noll, 2002; Hampson etal., 2002; Salvador et al., 2005). Published studies ofconnectivity abnormalities using the LFBF correlationmethod have been reported in neuropsychiatric conditionssuch as attention deficit hyperactivity disorder (ADHD)(Castellanos et al., 2008), schizophrenia (Liang et al.,2006; Garrity et al., 2007; Zhou et al., 2007), Alzheimer'sdisease (Greicius et al., 2004), substance abuse (Li et al.,2000), multiple sclerosis (Lowe et al., 2002), and autism(Cherkassky et al., 2006).

We have previously reported the results of our studyin which corticolimbic connectivity was measured usingthe resting state LFBF correlation method in unmedi-cated MDD patients and healthy subjects (Anand et al.,2005a). The results of this study indicated that restingstate functional connectivity between the pgACC andthe limbic regions – amygdala (AMYG), pallidostria-tum (PST) and dorsomedial thalamus (DMTHAL) – isdecreased in MDD (Anand et al., 2005a,b). In this study,we report, for the first time, corticolimbic connectivityabnormalities in unmedicated BD patients in both themanic and depressed phase of the illness and acomparison with corticolimbic connectivity abnormal-ities previously reported in MDD patients and healthycontrols. Our unitary hypothesis was that mooddyregulation arises from decreased corticolimbic con-nectivity, and hence in BD (whether in the manic or thedepressed phase) decreased connectivity will be seensimilar to that seen in MDD.

2. Methods

2.1. Subjects

Medication-free unipolar depressed (MDD), bipolardepressed (BDD) and bipolar manic (BDM) outpatientswere recruited from the outpatient clinic at UniversityHospital, Indiana University School of Medicine, andby advertisement, from the community. Closelymatched healthy subjects were recruited through adver-tisements. All subjects took part in the study aftersigning an informed consent form approved by theInvestigational Review Board (IRB) at Indiana Uni-versity School of Medicine. Both patients and healthy

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control subjects were paid $50 for screening and $50 foreach MRI scan. Inclusion criteria for MDD patientswere as follows: age 18–60 years and ability to givevoluntary informed consent; satisfy Diagnostic andStatistical Manual fourth edition (DSM-IV) criteria forMajor Depressive Episode; have a 25-item HamiltonDepression Rating Scale (HDRS) (Thase et al., 1991)score N18; satisfy criteria to undergo an MRI scan basedon an MRI screening questionnaire; and be able to bemanaged as outpatients. The inclusion criterion for BDpatients was that they satisfy DSM-IV criteria forBipolar Disorder either in the hypomanic or manic(Young Mania rating Scale (Young et al., 1978)(YMRS) N10) or depressed episode (HDRS N18).Other inclusion criteria were the same as those for MDDpatients. Exclusion criteria for patients were as follows:meeting DSM-IV criteria for schizophrenia, schizoaf-fective disorder, or an anxiety disorder as a primarydiagnosis; use of psychotropic agents in the past2 weeks; use of fluoxetine in the past 4 weeks; beingacutely suicidal or homicidal or requiring inpatienttreatment; meeting DSM-IV criteria for substancedependence within the past year, except caffeine ornicotine; positive urinary toxicology screening at base-line; use of alcohol in the past week; serious medical orneurological illness; current pregnancy or breast-feed-ing; metallic implants or other contraindications to MRI.Inclusion criteria for healthy subjects were as follows:ages 18–60 years and ability to give voluntary informedconsent; no history of psychiatric illness or substanceabuse or dependence; no significant family history ofpsychiatric or neurological illness; not currently takingany prescription or centrally acting medications; no useof alcohol in the past week; and no serious medical orneurological illness. Exclusion criteria for healthysubjects were as follows: under 18 years of age;pregnant or breast-feeding; metallic implants or othercontraindication to MRI.

2.2. Behavioral ratings

Subjects were rated on the 25-item HDRS and theYMRS at the time of the baseline scan.

2.3. MRI data

Scans were performed in either the morning or theearly afternoon.

2.3.1. Image acquisitionImaging data were acquired using a General Electric

(Waukesha, WI) 1.5 T MRI scanner. Subjects were

placed in a birdcage head coil and individually fitted to abite bar partially composed of dental impressioncompound attached to the coil to reduce head motion.For the resting state connectivity scan, the subjects wereasked to keep their eyes closed, stay awake, and notthink of anything in particular. The MRI sequenceincluded a T1-weighted whole brain image using aSpoiled Gradient-Echo Recalled sequence (SPGR)sequence to provide real 1×1×1 mm3 spatial resolution.Next, a T1-weighted axial image, to identify slices forthe various regions of interest (ROIs), was acquired withthe following sequence: TR/TE 500/12 ms; 16 slices;Thickness/Gap 7.0/2.0 mm; matrix 256×128; FOV24×24 cm; 1 NEX. The short TR limits the number ofslices that can be acquired; therefore four non-contiguous axial slices were acquired that covered theareas of interest at the level of the pgACC, DMTHAL/PST and AMYG identified by a trained radiology staffmember (YW) during the scan. Other scans for localbrain activation in response to emotional stimuli andconnectivity scans during steady state exposure toneutral, positive and negative pictures were alsoacquired during the fMRI session as previouslydescribed (Anand et al., 2005a); however, this reportis mainly focused on the results of resting stateconnectivity. During the resting state, awake with eyesclosed, subjects were asked to stay awake and think ofnothing in particular. After the scan, the patients wereinterviewed, and it was assessed whether they compliedwith the instructions or were awake throughout the scan.Subjects who were judged not have complied with theinstructions were excluded from the study.

2.4. Image analysis

The raw imaging data were Hamming-filtered toimprove signal-to-noise ratio with minimal reduction inspatial resolution (Lowe and Sorenson, 1997). Motionwas measured, but motion correction was not performedon the data as it can lead to increase in spatial correlationin LFBF data (Lowe et al., 1998). Moreover, with alimited number of slices motion correction is not reliablewith the usual registration routines.

2.4.1. Selection of regions of interest (ROIs)ROIs were placed by a trained radiology staff

member (YW) corresponding to the a priori definedareas of the MRC (Fig. 1). The pregenual ACC (the areajust anterior to the genu of the corpus callosum) (sub-region of Brodmann area 24) was chosen as thereference ROI as a number of neurological studieshave indicated that this area, as well as the more inferior

Fig. 1. Region of interest (ROI) placement for sampling of low frequency BOLD fluctuations (LFBF) for corticolimbic connectivity analysis. 1. Pregenualanterior cingulate cortex (pgACC); 2, 3: pallidostriatum (PST); 4, 5: dorsomedial thalamus (DMTHAL); 6, 7: amygdala (AMYG).

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subgenual ACC, is involved in the regulation ofemotions (Damasio, 1997; Critchley, 2004), and activityin these areas has been shown to accompany reward-based emotional/motivational processing (Critchley,2004). Another more caudal area of the subgenualACC (Area 25) has also been described in imagingstudies to be involved in emotion regulation (Mayberget al., 2005). We chose to study the pgACC because thesignal there was less likely to be corrupted bysusceptibility artifacts than the more ventral subgenualACC or Area 25. The pallidostriatal ROI was defined asreported by Burruss (2000) and partially coveredputamen and lateral palladium. The DMTHAL ROIwas centered in the medial dorsal posterior part of thethalamus. The AMYG ROI was centered on the AMYGbased on anatomical landmarks for that region. The“draw dataset” function in Analysis of FunctionalNeuroimages (AFNI) software was used to defineROIs as fixed size circles with a radius of 6 mm forthe ACC, DMTHAL and PST and 4 mm for the AMYG(Fig. 1). The radiologist was not aware of the groupstatus while placing ROIs. As non-contiguous EPI sliceswere selected, corresponding to the axial high-resolu-tion T1 images that covered ROIs, the distance betweenthe four EPI slices varied according to individualanatomy and position. ROI time series were averagedusing the AFNI function “3dmaskave.” This functionproduces the time-wise arithmetic average for eachvoxel in an ROI mask. The result is an average timeseries for each ROI.

2.4.2. LFBF correlation analysisThe analysis was done as previously described (Anand

et al., 2005a). Briefly, the first 50 scans were discarded to

allowMR signal to reach steady state and the next 512 timepointswere included in the analysis. Averaged data from allthe voxels within each ROI (as defined above) weredetrended for global signal drifts using previouslydescribed methods (Lowe and Russell, 1999) and thenpassed through a finite-impulse response (FIR) filter toremove all frequencies above 0.08 Hz. This procedureremoves the oxygenation fluctuations from physiologicalprocesses such as direct sampling of respiratory andcardiac-related oxygenation fluctuations (Lowe et al.,1998; Cordes et al., 2001). Next, the Pearson correlationcoefficient (cc) was calculated between the averaged LFBFtime series of pgACC as the reference region with theaveraged time series of each of the limbic ROIs across alltime points (512 time points) (Lowe et al., 1998). Thecorrelation coefficient was then transformed to a t statistic(Lowe et al., 1998; Anand et al., 2005a) to enablecomparison between groups. This t-score was used as themeasure of corticolimbic functional connectivity.

2.5. Statistical analysis

One-way analysis of variance (ANOVA) was per-formed to evaluate the effect of diagnostic groups onpgACC connectivity with the DMTHAL, PST andAMYG on each side. For significant ANOVA results,post hoc analysis was performed using Fisher'sprotected least square difference (Fisher's PLSD).

3. Results

Twelve unmedicated bipolar subjects in either themanic (BDM) or the depressed (BDD) phase completedthe study. One BDD subject's imaging data were

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discarded because of technical difficulties during theacquisition of the scan. The results of the remaining 11BD subjects (6 BDM, 5 BDD) were then compared withthose of 15 unipolar depressed (MDD) subjects and 15healthy subjects who we had previously studied usingthe same paradigm (Anand et al., 2005a). One BDMpatient, after inclusion in the study, reported taking asmall dose of gabapentin for chronic leg pain. The doseof gabapentin was very small, and it was not being takenfor mood stabilization. Exclusion of this patient's datadid not change the results of the study. Therefore, it wasdecided not to exclude this patient. Table 1 presents thedemographics and illness characteristics of each group.The BD group was slightly older (age: 33±12 years)than the MDD group (29±9 years), but the differencewas not significant. A bite bar customized for eachindividual was used to minimize the effect of motion.Mean displacement over surface (Jiang et al., 1995),calculated to assess for motion effects, was 0.33±0.19 mm, 0.20±0.06 mm and 0.18±0.10 mm, respec-tively, in the BD, MDD and healthy subjects groups.

3.1. Resting state connectivity in bipolar disordercompared with unipolar depression and healthysubjects

Both the unipolar depressed group and the bipolargroup had decreased connectivity, compared withhealthy subjects, between the pgACC and the AMYG,THAL and PST on each side (Fig. 2). The ANOVA for

Table 1Demographic and clinical characteristics of subjects

Bipolar (BP) (N=11) Bipolar manic(BPM) (N=6)

Age 33±12 34±14Gender 7 female 4 male 4 female, 2 maleEthnicity 9 Caucasian, 2

African American5 Caucasian, 1African American

YMRS onday of scan

13±8 (range: 1–25) 18±4 (range: 15–25)

25-item HDRSscore on day of scan

18±13 (3–34) 8±6 (3–16)

Number of previousmood episodes

18±5 18±6

Type Bipolar I: 10; Bipolar II: 1 Bipolar I: 5; Bipolar IIDuration of

illness (years)14±10 14±11 years

Drug freeperiod (weeks)

Treatment Naïve: 1Rest: 95±218(range 2–676 weeks)

Treatment Naïve:1 patient,1 taking gabapentinRest of the patients:189±324 (range 24–6

HDRS: Hamilton Depression Rating Scale. YMRS: Young Mania Rating Sc

differences between the three groups was significant forthe right AMYG (F=3.74, df=2, Pb0.04) and for theleft DMTHAL (F=7.27, df=2, Pb0.003) and the rightDMTHAL (F=6.270, df=2, Pb0.005), and there wasa trend for significance for the left AMYG and the leftPST. Post hoc tests of significance revealed that thedifference was significant between BD as a group andhealthy subjects for the pgACC and for the left(Pb0.05) and right AMYG (Pb0.01) as well as theleft PST (Pb0.04) connectivity. Both the BD and theMDD groups had decreased connectivity between thepgACC and the left DMTHAL (BD: Pb0.005; MDD:Pb0.01) and right DMTHAL (BD: Pb0.005; MDD:Pb0.05) as well as the left PST (BD: Pb0.04; MDD:Pb0.07). No significant differences were seen betweenBD and MDD subjects.

3.2. Resting state connectivity in bipolar depressionand bipolar mania compared to healthy subjects

An exploratory analysis, keeping in mind the smallnumber of subjects in each BD subgroup, was conducted.Both BDD and BDM patients exhibited decreasedcorticolimbic connectivity compared with healthy sub-jects. One-way ANOVA for differences between thegroups was significant for the left DMTHAL (F=5,df=3, Pb0.005) and for the right DMTHAL (F=4, df=3,Pb0.005), and there was a trend for significance for theright and left AMYG (Pb0.07). A post hoc t-test revealeda significant decrease for left DMTHAL connectivity

Bipolar depressed(BPD) (N=5)

Unipolar depressed(MDD) (N=15)

Healthy controls(N=15)

32±9 29±9 28±73 female, 2 male 11 female, 4 male 11 female, 4 male4 Caucasian, 1African American

14 Caucasian,1 African American

14 Caucasian,1 African American

6±7 (1–19) NA 0

29±6 (21–34) 31±8 (19–46) 0

18±4 3±2 NA

: 1 All Bipolar I NA NA14±10 years 6±7 years NA

21±21(range 2–52 weeks)

Treatment Naïve:8 patients

NA

76)Rest of the patients:24±33 weeks(Range 2–24 weeks)

ale. Unless otherwise indicated, data are expressed as mean±S.D.

Fig. 2. Cortiolimbic connectivity in major depression (N=15), healthy controls (N=15), bipolar disorder (N=11), bipolar mania (N=6), and bipolardepression (N=5). Post hoc t-tests results for significant differences with each of the mood disorder groups and healthy subjects as described in thetext are denoted by ⁎Pb0.05, ⁎⁎Pb0.01 and ⁎⁎⁎Pb0.005. A. Pregenual anterior cingular cortex (ACC) and dorsomedial thalamus connectivity. B.Pregenual anterior cingular cortex (ACC) and amygdala connectivity. C. Pregenual anterior cingular cortex (ACC) and striatal connectivity.

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between BDD and healthy controls (Pb0.005) and BDMand healthy controls (Pb0.005), for the right DMTHALbetween BDD and healthy controls (Pb0.01) and BDMand healthy controls (Pb0.01). Additionally, significantdifferenceswere found for pgACCconnectivity to the rightAMYG between BDD patients and healthy controls(Pb0.05), and for the left AMYG for BDM patients andhealthy controls (Pb0.05).

4. Discussion

The findings of this study indicated decreasedcorticolimbic connectivity in BD patients compared

with healthy subjects, similar to results previouslyreported for MDD; however, the abnormalities seemedto be more severe in the BD group. This is not asurprising finding as BD is a more severe illness ofmood regulation than MDD. The BD subgroup also hada longer duration of illness and had had more moodepisodes than the MDD group, and it was slightly olderthan the MDD and healthy control groups. The greaterseverity of mood disorder in the BD group could alsoexplain the greater decrease in connectivity in thisgroup. Motion was slightly greater in the bipolar group,but when used as a covariate in the analysis, it did notchange the findings of the study.

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An exploratory analysiswas done (keeping inmind thesmall number of patients in each subgroup of BD) andshowed that theBDDand theBDMsubgroups had similardecreases in corticolimbic connectivity compared withhealthy subjects. Some differences were noted, e.g. thedecreased pgACC-left AMYG connectivity only in BDMand not in BDD, and the decreased pgACC-right AMYGconnectivity in BDD, which will need to investigated infuture studies with a larger number of subjects.

The decreased corticolimbic LFBF correlationsresults indicate possible decreased phase coherencebetween LFBF sampled in the ACC and the limbicregions in BD and MDD patients. Phase synchrony hasbeen related to the integrity of the circuits between twobrain regions (Spencer et al., 2004). Single neuronstudies with intraneuronal electrodes and, to someextent, electroencephalograhic studies have shown thatif two brain regions are locked in phase with each other,their functioning is closely connected (Varela et al.,2001). Hence, decreased phase coherence could beassociated with a decreased regulatory effect of the ACCover the limbic areas leading to mood dysregulation inbipolar and unipolar depression as well as mania.

The decreased corticolimbic connectivity seen inmood disorders across diagnosis and phase of illnesssuggests that the decreased connectivity may be a traitabnormality. However, in a previous study (Anand et al.,2005b), we found that antidepressant treatment leads toan increase in corticolimbic connectivity in MDDpatients, and therefore the connectivity abnormalitymay be state-dependent. To investigate whether thedecreased connectivity is state- or trait-dependent, thesefindings will need to be investigated in BD before andafter treatment and also in unmedicated euthymic BDand MDD patients.

The ROIs were placed within the corticolimbicsystem based on a priori identified and agreed uponanatomical landmarks as discussed in Section 2.4. TheEPI slices that were selected on matching high-resolution T1 images were not contiguous but includedonly slices chosen by the radiologist to cover the ROIs.Therefore, the location of the EPI slices, the distancebetween the four EPI slices, and the placement of theROIs did vary slightly from subject to subject due todifferences in subjects' anatomy and head position. Toplace ROIs in exactly the same location for all subjects,it would have been necessary to normalize the data intoa standardized space and therefore have considerablylarger slice coverage. This was not possible without avery significant increase of the TR. As discussed inSection 2, the data were acquired with a short TR toavoid aliasing effects of fluctuations in the BOLD

signals due to cardiac and respiratory cycles. In futurestudies, to acquire data from the whole brain, methodssuch as recording of cardiac and respiratory cycles alongwith fMRI acquisition with subsequent retrospectivecorrection for effects of these physiological variables onthe BOLD signal could be used (Glover et al., 2000).

The correlation of LFBF between two areas is ameasure of functional connectivity, i.e. that the two arein synchrony (Friston et al., 1993). However, this couldalso occur due to the influence of a third factor that maybe simultaneously affecting both the areas. In the future,to measure the direct effect of one area over another, i.e.to measure effective connectivity, techniques such asstructural equation modeling (SEM) (Seminowicz et al.,2004) or newer techniques such as dynamic causalmodeling (DCM) (Friston et al., 2003) could be used.An investigation of structural connectivity using dif-fusion tensor imaging (DTI) could also shed lighton the relationship between functional and structuralconnectivity.

The analysis performed here is a straightforwardhypothesis-driven analysis based on an a prioriexpectation of involved regions of the brain. The apriori defined ROI approach has the advantage ofreducing the magnitude of correction needed for a largenumber of voxels; one can correct only for a smallnumber of ROIs, thereby considerably increasingstatistical power (Poldrack, 2007). The same analysiswas performed on controls and patients, and statisticallymeaningful conclusions were drawn. Connectivitieswith other regions were not investigated, and noconclusions were drawn regarding regions that werenot examined.

Out of the three a priori identified limbic structureswhose connectivity with the pgACC was investigated inthis study, the connectivity of the pgACC-DMTHALwas present in all mood disorders (Fig. 2). This is notsurprising as the thalamus is an integral part of thecingulate–pallidostriatal–thalamic–amygdala mood-regulating circuit (Taber et al., 2004). The DMTHALhas major connections with the ACC, the ventral PST,and the AMYG, and therefore it is central to the circuit(Taber et al., 2004). Decreases in pgACC connectivitywere also seen for the AMYG. The AMYG is located inthe more ventral part of the brain, and the BOLD signalfrom the AMYG has a lower signal-to-noise ratio due tosusceptibility artifacts. Therefore, the variance for thedata was greater in this region. In future studies, moresophisticated techniques using advanced hardware andtechniques such as z-shimming to reduce susceptibilityartifacts could be used to image the ventral areas of thebrain (Glover, 1999).

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Another limitation of this study was that we did notmeasure differences in gray matter density within ROIsbetween groups due to the small number of subjectsstudied. It is possible that the ROIs may have containedmore or less gray matter in the different groups, leading topartial-volume effects that could have affected the results.In future studies, measurement of gray matter densityusing techniques such as voxel-based morphometry(Ashburner and Friston, 2000) could be used to inves-tigate differences in gray matter density between groups.

The duration of medication-free period for psychia-tric studies is always a compromise between what isideal and what is clinically feasible. We chose aminimum period of 2 weeks for patients to be offmedication (except for fluoxetine, for which we requireda 4-week drug-free period) and inclusion criteria for nosubstance dependence in the past year and a negativeurine drug screen at the time of screening for the study.However, long-term effects of psychotropic agents maystill be present. Future studies will need to be conductedto address this issue with a larger number of subjectswith longer medication-free and substance-free periodsbefore the study.

The findings of this study are consistent with acommon abnormality of corticolimbic functional con-nectivity in bipolar disorder and depression, and theyneed to be confirmed with a larger number of patientsand with more sophisticated techniques to measurefunctional connectivity within the brain.

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