Clinical Neurophysiology 124 (2013) 2421–2430

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Clinical Neurophysiology

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Altered EEG lagged coherence during rest in obsessive–compulsivedisorder

1388-2457/$36.00 � 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.http://dx.doi.org/10.1016/j.clinph.2013.05.031

⇑ Corresponding author. Address: Semmelweißstraße 10, 04103 Leipzig, Ger-many. Tel.: +49 341 9724364; fax: +49 341 9724509.

E-mail address: Sebastian.Olbrich@medizin.uni-leipzig.de (S. Olbrich).

Sebastian Olbrich a,c,⇑, Hanife Olbrich a, Michael Adamaszek b, Ina Jahn a, Ulrich Hegerl a,c,Katarina Stengler a,c

a Clinic for Psychiatry and Psychotherapy, University Hospital Leipzig, Germanyb Center for Neurologic Rehabilitation, University of Leipzig, Germanyc LIFE – Leipzig Research Centre for Civilization Diseases, University of Leipzig, Germany

See Editorial, pages 2289–2290

a r t i c l e i n f o

Article history:Available online 19 August 2013

Keywords:EEGLagged non-linear and linear coherenceFunctional connectivityObsessive compulsive disorder

h i g h l i g h t s

� Region of interest (ROI)-based analysis of intracortical EEG lagged non-linear and linear coherenceduring rest in unmedicated patients with obsessive compulsive disorder (OCD) in comparison tohealthy controls (HC).

� Decreased non-linear but not linear coherence is found in OCD for the beta frequency range for con-nectivity measures between frontal brain areas including the anterior cingulate cortex, the superiorfrontal gyrus and the left medial frontal gyrus.

� Decreased non-linear coherence is only found at high arousal levels when analysis is performedseparately for different EEG-vigilance stages.

a b s t r a c t

Objective: Functional magnetic resonance imaging (fMRI) studies found alterations of functional connec-tivity in obsessive compulsive disorder (OCD). However, there is little knowledge about region of interest(ROI) based electroencephalogram (EEG) connectivity, i.e. lagged non-linear and linear coherence in OCD.Goal of this study was to compare these EEG measures during rest and at different vigilance stagesbetween patients and healthy controls (HC).Methods: A 15 min resting-state EEG was recorded in 30 unmedicated patients and 30 matched HC. Intra-cortical lagged non-linear coherence of the main EEG-frequency bands within a set of frontal ROIs andwithin the default mode network (DMN) were computed and compared using intracortical exact low res-olution electromagnetic tomography (eLORETA) software.Results: Lagged non-linear but not linear coherence was significantly decreased for patients in compar-ison to HC for the beta 2 frequency between frontal brain areas but not within the DMN. When analysingseparate EEG-vigilance stages, only high vigilance stages yielded decreased frontal phase synchronisationat beta and theta frequencies.Conclusions: The results underline an altered neuronal communication within frontal brain areas duringrest in OCD.Significance: These findings encourage further research on connectivity measures as possible biomarkersfor physiological homogeneous subgroups.� 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights

reserved.

1. Introduction

Obsessive compulsive disorder (OCD) is a neuropsychiatricdisorder with a lifetime prevalence between 2% and 4% (Angstet al., 2004). Although symptoms are often trivialized by patients

2422 S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430

(Stengler-Wenzke and Angermeyer, 2005), the disorder goes in linewith an enormous impact on the quality of life with recurring dis-tressing thoughts (obsessions) and time consuming rituals (com-pulsions) that in fact can be identified as senseless but stillcannot be interrupted by the patient. Besides neurotransmitterdysfunctions (e.g. Nikolaus et al., 2010; Hesse et al., 2005) andstructural grey matter alterations (e.g. Radua and Mataix-Cols,2009; Radua et al., 2010) the most compelling and consistent find-ing in OCD is of functional nature and describes alterations inmainly frontal cortical areas as reported by several studies thatuse different brain imaging modalities: Positron emission tomog-raphy (PET) studies consistently revealed a hypermetabolism ofprefrontal cortices, especially of the orbitofrontal cortex (Steinet al., 2006; Menzies et al., 2008). Functional magnetic resonanceimaging (fMRI) studies found an increased blood oxygenation leveldependent (BOLD) signal in frontal areas e.g. within the medialfrontal gyrus (MFG) during cognitive tasks (Yücel et al., 2007). Fur-ther, electrophysiological studies using electroencephalogram(EEG) also report mainly frontal changes of EEG-power with in-creased fast activity within the beta frequency (Velikova et al.,2010), an excess of frontal slow waves within the delta band fre-quency range at frontal electrodes (Pogarell et al., 2006) and withinthe medial frontal gyrus, the anterior cingulate cortex (ACC) andthe lateral frontal gyrus (Koprivová et al., 2011).

Within the last years, especially fMRI based connectivity analy-sis helped to gain more insight into the dysfunctional interactionbetween different brain areas in OCD by using different approachessuch as independent component analysis (Cocchi et al., 2012), dy-namic causal modelling (Schlösser et al., 2010) or regression anal-ysis of voxel time series of the BOLD signal. The results ofconnectivity analysis during rest remain inconsistent with re-ported decreased connectivity within the so called default modenetwork (Jang et al., 2010) and frontal brain areas (Fitzgeraldet al., 2010) but significantly increased functional connectivityalong a ventral corticostriatal axis (Harrison et al., 2009) and be-tween the ventral striatum and parts of the prefrontal cortex (Sakaiet al., 2010). Zhang et al. (2011) reported increased short rangeconnectivity while long range connectivity was decreased. How-ever, all those studies provide evidence of altered functional con-nectivity in OCD including frontal brain areas and regions of theDMN.

Still, so far there is no study to our knowledge that focused onRegion of interest (ROI) – based EEG-connectivity analysis inOCD. Although Velikova et al. (2010) report of decreased inter-hemispheric coherence and reduced delta/beta band couplingand Desarkar et al. (2007) found increased coherence within thetheta band for OCD in comparison to healthy controls, there arelimitations in appreciating these results due to the use of topo-graphic information instead of intracortical source estimates. Sincea combination of EEG-connectivity measures and intracortical ROIbased analysis promises new insights into altered neuronal func-tion in OCD, the aim of this study was to analyse the ROI-basedEEG lagged non-linear and linear coherence. Describing the cou-pling between two brain regions, this approach refers to the ideathat the connection between two areas is supposed to be stronger,the more synchronous the phases (non-linear) or amplitude fluctu-ations (linear) of a specific EEG-frequency band at the two regionsare. It was hypothesised that compared to healthy controls (HC),patients suffering from OCD show altered inter- and intrahemi-spheric EEG lagged non-linear and linear coherence at the fivemain EEG frequency bands delta, theta, alpha, beta 1 and beta 2within (1) the DMN and (2) within a set of frontal brain areas com-prising the ACC, the superior frontal gyrus and the medial frontalgyrus (see Fig. 1). The lagged coherence measures were then com-puted for each artefact free one-second segment of a fifteen minuteresting period.

Since OCD patients have been found to reveal less drops to lowEEG-vigilance stages during rest in comparison to HCs (Olbrichet al., 2013), it was further intended to compare connectivity mea-sures during different levels of tonic brain arousal, i.e. vigilancestages. Therefore each one-second segment of EEG data was classi-fied into one out of six EEG-vigilance stages that reflect differentfunctional brain levels ranging from high alertness after closingthe eyes to sleep onset (De Gennaro et al., 2001; Tsuno et al.,2002) by using the EEG-Vigilance Algorithm Leipzig (Olbrichet al., 2012). EEG lagged non-linear and linear coherence measureswere then computed for each distinct EEG-vigilance stage.

To make the results comparable to findings from other EEG-studies of OCD, it was a further aim to reproduce differences offrontal EEG activity between patients with OCD and HC as reportedby other investigators (Pogarell et al., 2006; Velikova et al., 2010;Koprivová et al., 2011). Thus it was hypothesised that OCD patientswould show increased frontal EEG delta and beta activity.

2. Methods

2.1. Patients and controls

The same group of patients and controls has been studied be-fore by means of EEG-vigilance regulation differences (Olbrichet al., 2013). The study was approved by the local ethics commit-tee. Written informed consent was obtained prior to investigationaccording to the declaration of Helsinki.

2.1.1. PatientsThirty three patients with an obsessive compulsive disorder

according to the criteria of DSM-IV were recruited at the outpatientward of the Department for Mental Health of the University ofLeipzig between 2007 and 2010. OCD diagnosis was assessed byclinical evaluation of a senior physician; symptom severity wasmeasured using YBOCS (Goodman et al., 1989). All patients werefree from psychopharmacological medication at least for fourweeks. Additionally depressive symptoms were assessed usingthe Hamilton depression rating scale (HDRS; Hamilton, 1960).

2.1.2. Healthy controlsEEG recordings from 30 age- and gender matched HC without

psychopharmacological medication were chosen from the data-base of the Department of Psychiatry of the University Leipzig.Controls who met the criteria of DSM-IV, axis I disorders in theStructured Clinical Interview for DSM-IV (Wittchen, 1994) or hada history or actual symptoms of neurological or other medical dis-orders that required actual treatment were not included.

2.2. EEG-recording and artefact correction

Resting EEG was recorded in both groups between 9 a.m. and3 p.m. The participants were comfortably seated in a recliningchair in half lying position (approximately 45� inclination) in adimly lit (light approximately 40 l�), sound attenuated room.The average temperature was maintained at 20–23�C. Fifteen min-utes of resting EEG were recorded with closed eyes using a 40channel QuickAmp amplifier (Brain Products GmbH; Gilching, Ger-many) and 31 electrode sites according to an extended interna-tional 10–20 system (Fp1, Fp2, F3, F4, F7, F8, Fz, FC1, FC2, FC5,FC6, C3, C4, FT9, FT10, T7, T8, Cz, CP5, CP6, TP9, TP10, P3, P4, P7,P8, Pz, O1, O2, PO9, PO10) at a sampling rate of 1 kHz, referencedagainst common average. Impedances were kept below 10 kO.Electrooculogram (EOG) electrodes were placed above the lefteye and below the right eye. EEG data was preprocessed usingBrainVision Analyzer 2.0 software (Brain Products; Gilching, Ger-

Fig. 1. Connectivity analysis was performed for the (1) default mode network (top) including the anterior cingulate gyrus (ACC), the posterior cingulate gyrus (PCC) and theleft and right inferior parietal lobule (IPL) and for the (2) frontal network (bottom) comprising the anterior cingulate cortex (ACC), the left and right superior frontal gyrus(SFG) and the left and right medial frontal gyrus (MFG). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of thisarticle.)

S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430 2423

many). EEG raw data was filtered at 70 Hz (low-pass), 0.5 Hz (high-pass) and 50 Hz (notch-filter, range 5 Hz). EEG-time series thenwere segmented into consecutive one-second intervals. Usingsimultaneous video-recording, each segment was screened foropen eyes to exclude these segments from further analysis, sincethe VIGALL algorithm only is valid for segments with closed eyes.Eye movement artefacts were removed from the EEG by extracting1–3 out of 31 independent components (using Infomax algorithmimplemented in Vision Analyzer 2.01, Brain Products, Gilching,Germany) that clearly represented vertical and horizontal eyemovements or muscle artefacts and had been identified by visual(topographic) inspection of the ICA maps and comparisons withthe EEG and EOG time series (Delorme et al., 2007; Olbrich et al.2011a). Any segment that still contained muscle-, movement-,sweating- or eye-artefacts as revealed by a visual inspection bytwo experienced clinical raters was excluded from further analysis(no subject had more than 15% artefacts).

2.3. EEG-vigilance classification

EEG vigilance was determined for each remaining one-secondsegment using the VIGALL algorithm (for more details, see Olbrich

Table 1Classification of seven different EEG-vigilance stages according to the Vigilance Algorithmwith declining vigilance from top (active wakefulness) to bottom row (sleep). The middle ccorrelates of each stage. Note that stage B2/3 and stage C were pooled together for analyoccurrence of lowest EEG-stage C.

EEG-vigilance stage Electrophysiological correlates

Stage 0 Low-amplitude EEG without slow eStage A1 Dominant alpha-activity at mainly oStage A2 Dominant alpha-activity at occipitalStage A3 Dominant alpha-activity at mainly fStage B1 Low-amplitude EEG with slow eye mStage B2/3 Dominant delta and theta activity, vStage C Sleep spindles or K-complexes

et al., 2013 and Table 1 for a comprehensive description of the clas-sification). Each segment was classified into one of six differentbrain states along a wake – sleep continuum (declining EEG-vigi-lance stages: 0, A1, A2, A3, B1, B2/3 and C whereby stage B2/3and stage C were pooled together due to low occurrence rate ofstage C). Vigilance stage 0 is characterised by desynchronizedEEG without slow eye movements (SEMs), reflecting an alert andmentally active state. During more relaxed wakefulness, stage A1is marked by dominant alpha activity in the occipital areas. Ananteriorisation of alpha activity takes place during decline to lowervigilance stages with stage A2 with increased alpha activity in tem-poral and parietal areas and stage A3 with increased alpha activityin the frontal areas. Successive stage B1 is marked by low ampli-tude non-alpha EEG with SEMs and stage B2/3 by increasing deltaand theta power. Both states go in line with increased sleepinessand loss of consciousness. Finally, sleep onset corresponds to stageC, characterised by sleep spindles and/or K-complexes. This vigi-lance classification is based on the work of Loomis et al. (1937) thatwas refined by Bente (1964) and Roth (1961) and has been con-firmed by other research groups (Santamaria and Chiappa, 1987;Tanaka et al., 1996, 1997; Benca et al., 1999; Corsi-Cabrera et al.,2000; De Gennaro et al., 2001, 2004, 2005; Tsuno et al., 2002; De

Leipzig (VIGALL) for one-second EEG-segments (resting condition with closed eyes)olumn gives the electrophysiological correlates and the right column the behavioural

sis of phase synchronisation during different EEG-vigilance stages due to the seldom

Behaviour

ye movements (SEMs) Mentally active wakefulnessccipital sites Relaxed wakefulnessand frontal sites Relaxed wakefulness

rontal sites Relaxed wakefulnessovements (SEMs) Drowsiness

ertex waves Sleep-onset periodSleep

2424 S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430

Gennaro and Ferrara, 2003). VIGALL has been validated usingsimultaneous EEG/fMRI and EEG/PET approaches. These studies re-port negative correlations between the EEG-vigilance level and theBOLD signal in vast cortical areas and with the glucose metabolismwithin frontotemporal areas (Olbrich et al., 2009; Guenther et al.,2011). Further, the EEG-vigilance classification has been shownto go in parallel with the level of function of the autonomousnervous system (Olbrich et al., 2011b). The discriminative powerof VIGALL also has been proven by identifying a more unstableEEG vigilance regulation in cancer-related fatigue syndrome (Olb-rich et al., 2011b) and a more rigid vigilance regulation in patientssuffering from major depression (MD) in comparison to HCs (He-gerl et al., 2008; Olbrich et al., 2013).

The whole time series of approximately (with excluded artefactsegments) 900 s and, separately, all segments of each EEG-vigi-lance stage of each subject were exported for further analysis.EEG-vigilance based connectivity analysis was only carried outfor subjects that revealed >50 one-second segments of the con-cerned vigilance stage.

2.4. Regions of interest (ROIs)

Regions of interest for connectivity analysis were selected a pri-ori based on the most consistent reports about frontal alterationsof activity and connectivity and upon the well-known regions ofthe DMN (Fransson, 2006; Harrison et al., 2009). The DMN (seeFig. 1, top panel) included the ACC, the medial frontal cortices(MFC), the posterior cingulate cortex (PCC) and the lateral inferiorparietal lobules (IPL). The frontal network (see Fig. 1, lower panel)included the ACC, MFC and the superior/middle frontal cortices(SFG) derived from reports of altered brain function in OCD (e.g.Harrison et al., 2009; Fitzgerald et al., 2010). Regions of interestwere defined using seed points (Table 2) in the given regions withinclusion of all grey matter voxels within a radius of 20 mm. Thenumber of included voxels is given in Table 2. For statistical anal-ysis, time series with the average current density of all voxels fromone ROI were calculated before computation of lagged coherencemeasures between the ROIs.

2.5. eLORETA, lagged non-linear and linear coherence

EEG-source estimate and connectivity analysis has beenperformed using the exact low resolution electromagnetic tomog-raphy (eLORETA; Pascual-Marqui et al., 2011) software, asprovided by Roberto Pascual-Marqui/The KEY Institute forBrain-Mind Research University Hospital of Psychiatry, Zurich(http://www.uzh.ch/keyinst/NewLORETA/LORETA01.htm). The

Table 2Montreal neurological institute (MNI) coordinates of the region of interest (ROI) seed pointslinear coherence analysis.

Network MNI coordinates

X Y Z

Default mode network0 53 00 �53 29

60 �40 27�60 �40 27

Frontal network0 41 0

31 36 38�31 36 38

14 48 �4�14 48 �4

eLORETA algorithm is a linear inverse solution for EEG signalsthat has no localization error to point sources under ideal(noise-free) conditions (Pascual-Marqui et al., 2002).

Connectivity between two regions was defined as the non-lin-ear and linear dependence, i.e. lagged non-linear and linear coher-ence of intracortical EEG-source estimates (Pascual-Marqui, 2007)as it has been used for e.g. detection of long range coherence ofgamma oscillations in schizophrenia (Mulert et al., 2011). Thenon-linear part of this measure is similar to that described byLachaux et al. (1999) and did not take into account any amplitudeinformation. Instead, absolute values of the complex valued (Her-mitian) coherency between the normalised Fourier transformswere calculated.

The lagged non-linear and linear coherence was calculated forthe EEG alpha (8–12 Hz), beta 1 (12.5–20 Hz), beta 2 (20.5–30 Hz), delta (1–3.5 Hz) and theta (4–7.5 Hz) frequency bands.

Since instantaneous coherence can be found due to non-physi-ological effects such as volume conduction or due to the low spatialresolution of the eLORETA solution, only the lagged coherence con-tribution of these measures were considered. Instantaneous coher-ence between two sources might also be found when a third sourcehas impact on two other sources although these two sources arenot influencing each other (Stam et al., 2007) or because of activitywithin the reference electrode in topographical coherence analysis.

The classical non-linear coherence, which comprises instanta-neous and lagged non-linear coherence is defined as (Pascual-Mar-qui, 2007):

u2x;yðxÞ ¼ fx;yðxÞ

�� ��2 ¼ Re½fx;yðxÞ�� �2 þ Im½fx;yðxÞ�

� �2 ð1Þ

with

fx;yðxÞ ¼1

NR

XNR

k¼1

xkðxÞjxkðxÞj

� �y�kðxÞjy�kðxÞj

� �ð2Þ

with xk(x) and yk(x) denoting the discrete Fourier transforms oftwo signals of interest x and y at frequency x at the k-th EEG seg-ment for NR being the number of all included segments. Re[c] isthe real part and Im[c] the imaginary part of a complex number c;[c] denotes the modulus of c; and the superscript ‘‘�’’, a complexconjugate. The used lagged non-linear coherence measure withoutthe contribution of the instantaneous phase is defined as (Pasc-ual-Marqui, 2007):

u2x;yðxÞ ¼

Im½fx;yðxÞ�� �2

1� Re½fx;yðxÞ�� �2 ð3Þ

Respectively, the lagged linear coherence is defined as (Pascual-Marqui, 2007):

within the default mode network (DMN) and the frontal network for EEG lagged non-

Region Number of voxels

Anterior cingulate gyrus (ACC) 145Posterior cingulate gyrus (PCC) 158Right inferior parietal lobule (rIPL) 93Left inferior parietal lobule (lIPL) 99

Anterior cingulate gyrus (ACC) 72Right superior/middle frontal gyrus (rSFG) 101Left superior/middle frontal gyrus (lSFG) 94Right medial frontal gyrus (rMFG) 110Left medial frontal gyrus (lMFG) 115

S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430 2425

q2x;yðxÞ ¼

Im½fx;yðxÞ�� �2

½fx;yðxÞ�½fx;yðxÞ� � Re½fx;yðxÞ�� �2 ð4Þ

Note that fx,y(x) in Eqs. (1–3) requires normalised Fourier trans-forms to exclude amplitude information from further calculationsof non-linear coherence.

Connectivity measures between the DMN ROIs and the frontalnetwork ROIs for the whole resting period as well as for segmentsfrom the six different EEG-vigilance stages were compared be-tween the OCD group and the HCs. To rule out differences ofEEG-power at different frequency bands between patients andhealthy controls as possible sources of altered connectivity mea-sures, the signal to noise ratio (defined as the ratio between EEG-power of the analysed EEG-frequency band and the EEG-powerof all remaining EEG-frequency bands) was compared betweenOCD-patients and HC at all electrodes.

Also EEG power source estimates were computed usingeLORETA for the EEG alpha (8–12 Hz), beta 1 (12.5–20 Hz), beta 2(20.5–30 Hz), delta (1–3.5 Hz) and theta (4–7.5 Hz) frequencybands.

2.6. Statistics

Statistical analysis has been performed using the statistics mod-ule of the eLORETA software package and STATA software (StataStatistical Software: Release 11. College Station, TX). The method-ology used was non-parametric. It was based on estimating, viarandomization, the empirical probability distribution for the max-imum of a t or F statistic, under the null hypothesis. Due to thenon-parametric nature of the method, its validity did not need torely on any assumption of Gaussianity (for further details refer toNichols and Holmes, 2002).

� Lagged non-linear and linear coherence was computed for con-nections between the frontal ROIs and the DMN ROIs during thewhole resting period for the five EEG frequency bands delta,theta, alpha, beta 1 and beta 2. These measures were also com-puted for connections between the frontal ROIs and the DMNROIs at each out of six different EEG-vigilance stages, given thatthe subject showed >50 segments of the analysed vigilancestage. Results from OCD-patients were compared to the HCgroup using t-test statistics. This methodology corrects for mul-tiple testing, i.e. for the collection of tests performed for all pos-sible connections between ROIs (five ROIs = 10 possibleconnections for the set of frontal brain areas, four ROIs = sixpossible connections for the DMN) and number of frequencybands (alpha, beta 1, beta 2, delta and theta) by using nonpara-metric permutation testing as proposed by Nichols and Holmes(2002). A total of 5000 permutations were used for each ran-domization test.� The signal to noise ratio defined as the ratio between power of

each analysed frequency band and the sum of the power of allremaining frequency bands for all EEG-channels was computedand compared between OCD patients and HC using a F statistic.� To test whether the removal of independent components that

had been identified to yield artefact activity has influence onthe results of connectivity measures, computations of thelagged non-linear coherence additionally has been performedwithout ICA correction. Also the number of removed indepen-dent components was compared between groups using a twotailed t-test.� EEG-source estimates from the five frequency bands delta,

theta, alpha, beta 1 and beta 2 were compared between theOCD patients and the HC group using F-statistics. A correctionfor multiple comparisons was applied (Nichols and Holmes,

2002) for the collection of tests performed (derived from thenumber of 6239 voxels of the intracortical solution space andthe number of five distinct frequency bands).

3. Results

Out of 33 recruited patients, 3 were discarded because of HDRSscores >15 since there is evidence about altered functional connec-tivity and activity within the DMN in patients with depression(Greicius et al., 2007; Grimm et al., 2011). The remaining 30 OCDpatients (age 34.6 years; SD 11.9; 17 female) were sex and agematched with 30 healthy controls (age 34.5 years; SD 11.1; 17female). OCD patients revealed an average of 23.5 (SD 5.6) ofYBOCS scores (obsession: 11.2; SD 3.3; compulsion 12.4; SD 2.8)and an average of 9.1 (SD 4.5) of HRDS scores.

The average amount of artefact-segments for patients was 24.4;SD 18.8 and for HCs 17.7; SD 22.4. Hence, the number of includedone-second segments was 875.6; SD 18.48 for OCD patients and882.3; SD 22.01 for HCs.

The average amount of different EEG-vigilance stages and arte-fact-segments for patients and HCs as well as the number of in-cluded patients and HCs for the EEG-vigilance based connectivityanalysis (with >50 segments of a given EEG-vigilance stage) is gi-ven in Table 3. Although our research group found within the samegroup of patients and HCs a decreased decline of EEG-vigilance inpatients by using a repeated measures ANOVA (Olbrich et al.,2013), no significant differences existed between the groups whencomparing the amount of segments of the different EEG-vigilancestages or artefacts.

3.1. Lagged non-linear coherence within frontal brain areas

Analysis of lagged non-linear coherence between the frontalROIs revealed significantly decreased interhemispheric connectiv-ity within the beta 2 EEG frequency band between right (r) SFGand the ACC (t = 3.78, p = 0.0009, two tailed uncorrected; p < 0.05corrected), between the right (r) SFG and left (l) MFG (t = 3.16,p = 0.0028, two tailed uncorrected; p < 0.05 corrected) and a trendof decreased connectivity between the rSFG and the lSFG (t = 2.95,p < 0.0091, two tailed uncorrected; p < 0.1, corrected) for patientssuffering from OCD in comparison to HC (Fig. 2, top panel). Noother frequency band revealed significant differences. As a post-hoc test, all 10 connections within at the beta 2 frequency bandwere pooled for each subject and the resulting averaged non-linearcoherence measures were compared between the groups. OCD pa-tients showed a significantly decreased overall lagged non-linearphase coherence in comparison to HCs (t = 2.51, p = 0.015, twotailed t-test) within the frontal ROI set.

When lagged non-linear coherence was computed for the beta 2frequency band without removal of artefact independent compo-nents, the highest differences between the groups were found forthe same connections as compared to the ICA-based artefactremoval approach (rSFG-ACC: t = 2.26, p = 0.009 two taileduncorrected; p < 0.1 corrected; rSFG-lMFG: t = 2.64, p = 0.0028,two tailed uncorrected; p < 0.1 corrected; rSFG-lSFG: t = 2.86,p = 0.001, two tailed uncorrected; p < 0.1 corrected). No significantdifference was found for the mean change from uncorrected datato ICA-corrected connectivity measures between the groups(t = 0.11, p = 0.92, two tailed t-test). Further, no differences werefound when comparing the amount of removed independent com-ponents between the groups (for OCD: 2.80; SD 0.38 and for HCs:2.83; SD 0.48 components removed with p = 0.77; two tailed test).

To further analyse whether the decreased lagged non-linearcoherence in OCD patients in comparison to HC was dependenton different brain states by means of EEG-vigilance stages, separatecomputations of the lagged non-linear coherence for EEG-vigilance

Table 3Amount of segments from different EEG-vigilance stages and artefact-segments for patients with obsessive compulsive disorder (OCD) and healthy controls (HC). No significantdifferences were found between the groups. Depicted are also the number of subjects for each vigilance stage that revealed >50 one-second segments of a vigilance stage forinclusion into the EEG-vigilance based connectivity analysis.

Vigilance stage OCD HC

Segments [1 s] (SD) Included subjects with >50 segments Segments [1 s] (SD) Included subjects with >50 segments

0 156.4 (233.0) 16 51.3 (62.9) 10A1 281.1 (273.8) 21 234.1 (100.1) 26A2 123.7 (169.8) 14 193.1 (207.0) 21A3 36.7 (71.4) 5 93.1 (127.4) 12B1 190.2 (234.4) 16 140.9 (182.9) 17B23&C 87.5 (75.1) 12 169.8 (176.9) 18X 24.4 (18.8) – 17.7 (22.4) –

Fig. 2. Significant decreased intracortical EEG lagged non-linear coherence in patients with obsessive compulsive disorder in comparison to healthy controls was found forthe beta 2 frequency range within frontal brain areas during the whole resting state of 15 min (top panel). A more detailed analysis revealed significantly decreased laggednon-linear coherence within the beta 1, beta 2 and theta band (lower panel) during high EEG-vigilance stages A1 and A2 (+ = beta 1 with p 6 0.1;⁄ = beta 2 with p 6 0.1,⁄⁄ = beta 2 with p 6 0.05; ⁄⁄⁄ = beta 2 with p 6 0.01 and ## = theta; p 6 0.05, all corrected for multiple comparison; rSFG = right superior frontal gyrus, lSFG = left superiorfrontal gyrus, rMFG = right medial frontal gyrus, lMFG = left medial frontal gyrus, ACC = anterior cingulate gyrus).

2426 S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430

stages 0, A1, A2, A3, B1 and B2/3/C were performed. OCD patientsshowed a significantly decreased connectivity (see Fig. 2, bottom)during stage A1 between the ACC and the rMFG within the beta1 frequency (t = 4.00, p = 0.0005; two tailed uncorrected; p < 0.01corrected), within the beta 2 frequency (t = 3.81, p = 0.0012; twotailed uncorrected; p < 0.05 corrected) and within the thetafrequency (t = 3.10, p = 0.0043; two tailed uncorrected; p < 0.05corrected) in comparison to HC. Also connectivity between thelSFG and the lMFG for the theta frequency band was decreased(t = 3.48, p = 0.0024; two tailed uncorrected; p < 0.05 corrected).During stage A2, again the lagged non-linear coherence betweenthe ACC and the rMFG within the beta 2 frequency was

significantly lowered for patients with OCD (t = 3.60, p = 0.0026;two tailed uncorrected; p < 0.05 corrected).

The signal to noise ratio did not show any significant differencesbetween patients and HCs at any electrode.

3.2. Lagged non-linear coherence within the default mode network

Connectivity analysis for the DMN revealed no significantdifferences between patients and controls for the whole restingperiod. Also when separate analysis was performed for each EEG-vigilance stage, no significant differences between patients andHC were revealed.

S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430 2427

3.3. Lagged linear coherence within the frontal network and thedefault mode network

No significant differences between OCD patients and HCs werefound for any frequency band for any of the ROIs of the frontal net-work or the default mode network.

3.4. The eLORETA source estimates

Significant differences (t = 3.65, p < 0.05, corrected) betweenOCD and HCs were only found for the EEG delta power withinthe medial frontal gyrus with the peak value at MNI coordinatesx = �5, y = 55, z = 0 with increased activity for OCD. In total 97 greymater voxels reached significance (see Fig. 3).

4. Discussion

This study revealed altered non-linear but not linear coherencewith a decreased frontal EEG phase synchronization for the beta 2frequency range during a 15 min resting condition within a set offrontal brain areas including the right SFG, the left MFG and theACC in patients with OCD in comparison to HCs. A detailed analysisof EEG lagged non-linear coherence yielded significant decreasedconnectivity between these frontal brain areas for two out of sixdifferent EEG-vigilance stages, namely high vigilance stages A1and A2 at the beta 1, beta 2 and theta frequency ranges. No differ-ences of lagged non-linear coherence between the groups werefound within the DMN. By means of EEG power source estimates,an increased EEG-delta activity for OCD patients in comparisonto HCs within the MFG was found.

Since observed brain activity patterns that have been assessedvia inverse solutions (such as eLORETA) only provide possible esti-mates of the underlying intracortical neuronal activity, they haveto be interpreted with regard to neuroanatomical and functionalconnectivity (Tenke and Kayser, 2012). Thus, the observation of adecreased lagged non-linear coherence between frontal brain areasin our study fits with findings of other neuroimaging studies thatconsistently report a mainly frontal dysfunction in OCD (e.g. Men-zies et al., 2008). As already mentioned, data of studies that inves-tigated the interaction between different brain regions in OCDusing fMRI during the resting state are not consistent, most prob-ably due to differences in used methods and conditions. Still, somestudies report a decreased connectivity comprising frontal brainareas: Fitzgerald et al. (2010) observed a decreased connectivitybetween the ACC and the right anterior operculum and Jang et al.(2010) reported decreased connectivity between the ACC and themiddle frontal gyrus and the seed region in the PCC. Furthermore,

Fig. 3. Significant (p < 0.05, corrected) EEG-source estimate differences with increasecompulsive disorder in comparison to healthy controls. (For interpretation of the referenarticle.)

Harrison et al. (2009) revealed decreased functional connectivitybetween the striatum and the lateral prefrontal cortex, whileCocchi et al. (2012) found reduced connectivity of the medial andfrontal dorsal cortex mainly during switches between tasks andrest.

While the BOLD signal is correlated with especially with the lo-cal field potential (Logothetis et al., 2001), the EEG lagged coher-ence reflects neuronal interaction at a less coarse temporalresolution than analysis of coherent BOLD signals that depend ona delay of the hemodynamic response function of several seconds.Especially non-linear coherence is a basic process of neural com-munication that has been associated with synaptic plasticity (Felland Axmacher, 2011). Beta and theta non-linear coherence hasbeen found to be important for memory encoding and retrievaland maintenance of information (Fell and Axmacher, 2011; Palvaet al., 2012). In this light, the observation of a decreased laggednon-linear coherence at the beta and theta frequencies suggeststhat frontal brain areas not only yield altered coherence of meta-bolic activity as revealed by fMRI connectivity studies, but neuro-nal populations seem to fail to synchronize at a basic level ofneuronal interaction: The oscillatory cycle. The fact that differ-ences of functional connectivity within the beta and theta fre-quency range were only found at high EEG-vigilance levels andnot during low vigilance stages like stage B1 (with a desynchro-nized EEG pattern with high beta frequency contribution) or stageB2/3 (with high theta power) suggests that the reported alterationsof lagged non-linear coherence are rather related to neuronal activ-ity during consciousness than during drowsiness. However, be-cause the study was done under resting condition, the resultscannot be linked to altered error processing in OCD (e.g. Schlösseret al., 2010) or tension due to symptom provocation (Adler et al.,2000), but rather seem to reflect a trait aspect during rest ofpatients suffering from OCD. Using the same coherence measures,already Lehmann et al. (2012) showed that EEG-based functionalconnectivity is decreased in all frequency bands from delta to gam-ma during meditation in comparison to rest before and after med-itation. Further, Canuet et al. (2011) reported increased non-linearcoherence during rest in patients with schizophrenia-like psycho-sis of epilepsy. These findings suggest that the resting state ismarked by specific patterns of connectivity that can be altered dur-ing different brain states, e.g. during relaxation or during differentneuropsychiatric disorders. However, data of the presented studydo not allow a conclusion, whether increased frontal non-linearcoherence reflect the neural correlate of the increased inner ten-sion or even compulsive thoughts during rest that are often re-ported by patients with OCD or otherwise just an unspecificalteration of brain function.

d EEG-delta power within the medial frontal gyrus for patients with obsessiveces to colour in this figure legend, the reader is referred to the web version of this

2428 S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430

Since patients with OCD have been found to drop less quickly tolow vigilance stages than do HC (Hegerl et al., 2008; Olbrich et al.,2013), one might speculate that altered phase synchronizationmight be an effect of different prevailing EEG-vigilance stages withdifferent dominating EEG rhythms and connectivity during rest.However, decreased connectivity was also present when similarlevels of brain function, i.e. EEG-vigilance stages, were compared.Although these differences were only revealed during EEG vigi-lance stages A1 and A2, i.e. high vigilance stages that occur quicklyafter closing the eyes, the differences of lagged non-linear coher-ence cannot be traced down to differences of vigilance regulationbetween the groups. Still, since the transition from wakefulnessto sleep requires interactions between long range cortical net-works, the decreased non-linear coherence in OCD might also bea correlate of the impaired vigilance regulation found in a previousstudy (Olbrich et al., 2013).

Another EEG study from Velikova et al. (2010) focused on con-nectivity between brain regions in OCD and reported a decreasedinterhemispheric coherence within the EEG alpha frequency band.Although Velikova et al. (2010) used only topographic electrodeinformation for EEG-coherence measures and no differences oflagged non-linear or linear coherence for the EEG-alpha band werefound within the presented study, both studies point toward adecreased interhemispheric connectivity in OCD during rest. Incontrast to that, Desarkar et al. (2007) found increased phasecoherence between occipital and frontal leads in OCD in compari-son to healthy controls. Their findings are hardly comparable, sinceparticipants of the Desakar study were under medication. The re-sults of the presented study increase the knowledge about laggednon-linear coherence in OCD by reporting alterations between esti-mated intracortical sources rather than differences between elec-trode channels.

Despite the observed decreased connectivity within frontalareas, it is remarkable that, as mentioned before, no differencesof lagged non-linear coherence were found within the DMN. Thismight be due to insufficient DMN ROIs since the used seed pointshad been taken from fMRI studies (Fransson, 2006). It is furthernoteworthy that only the non-linear coherence measure yieldedsignificant differences between groups. Since the linear coherencemeasure reflects fluctuations of amplitude while the non-linearpart only contains phase information, these results underline thatboth measures reflect different aspects of neural activity andshould not be lumped together.

As any experienced EEG-rater knows, changes within the betafrequency range are prone to muscle artefacts since muscle activityoften comprises this frequency range. First, any differences in theamount of EEG segments containing muscle artefacts betweenthe groups were ruled out and no difference of intracortical EEGbeta sources between OCD and HC was revealed by eLORETA anal-ysis. Secondly, muscle activity recorded at EEG channels is oftenseen in the frontotemporal to parietal axis due to activity of themusculus temporalis. If these artefacts would have impacted theresults, one would have expected to find differences of laggednon-linear coherence also within the DMN, which was not the case.As a third point, no differences concerning the signal to noise ra-tion have been revealed between the studied groups. Another pro-cedure that has been described to influence the computation ofcoherence measures is the usage of ICA-based artefact correction.Castellanos and Makarov (2006) describe an overestimation oftopographical coherence measures after ICA-based artefact correc-tion due to the mixture of artefact signals and neural activity in theremoved components. Within the presented study, we followed arigid protocol for identifying independent components that repre-sented artefacts. In cases where a clear neural activity was visiblewithin the components, the independent components were recom-puted or not excluded from the data. The fact that results were

more pronounced and significant after correction for multiplecomparisons when using the ICA-based correction might be ex-plained by an increased signal-to-noise ratio after correction ratherthan by a systematic bias. Furthermore, there was no significantdifference of the number of removed independent components be-tween the groups. Therefore, a possible influence of ICA-basedartefact correction on lagged non-linear coherence should have af-fected both groups the same way. This is underlined by the fact,that no difference of the mean changes of the connectivity measurefrom uncorrected data to ICA-corrected lagged non-linear coher-ence computation was found for comparisons between OCD pa-tients and HCs.

The increased EEG delta power in frontal regions for OCD pa-tients in comparison to HC replicates the result from Pogarellet al. (2006). Since no other frequency bands showed significantdifferences, the finding of increased beta activity in OCD in frontalbrain regions as revealed in the study of Velikova et al. (2010) wasnot repeated. This might be in parts explainable by the differentdurations of EEG recordings in the presented study (continuous15 min) and in Velikova’s study (selected segments of 5 min).

A limitation of this study is that frequency bands have not beenadopted individually. Although there were no significant differ-ences between individual alpha peak frequencies of patients andcontrols (results not presented), effects might have been largerby taking into account individual rhythms. Another limitation isthe relatively low spatial resolution of eLORETA and the disabilityto assess subcortical neuronal activity. Therefore, the analysis fo-cused on cortical, large scale connectivity. As a third point it hasto be mentioned, that the presented study was hypothesis drivenand focused on only two networks, the DMN and a frontal network.The reasoning was that (1) the DMN was one of the first restingstate networks that has been described (Gusnard and Raichle,2001), (2) this network can be assessed using different neuroimag-ing modalities such as fMRI or PET and (3) activity within the DMNhas been shown to be altered in different neuropsychiatric disor-ders (e.g. Hamilton, 1960), especially during rest. The frontal net-work has been chosen due to the reported involvement of thesebrain areas in the pathomechanisms related to OCD (e.g. Harrisonet al., 2009; Fitzgerald et al., 2010). Still there exist other networksthat would be worth to be explored, e.g. the frontoparietal network(Stern et al., 2012). In a large enough sample, also an exploratorycoherence analysis between all e.g. all Brodman areas as performedby Canuet et al. (2011) could enhance the knowledge of specificconnectivity patterns in OCD.

As Nunez and Srinivasan (2006) state, a functional connectivitymeasure that takes into account only information about the phaseof an EEG signal is more prone to noise than a measure that relieson phase and amplitude. Since we did not find any differences ofthe signal to noise ratio between the groups, this should not haveaffected the results of the study. Still, future work that uses phase-only synchronization measures for e.g. prediction of treatment out-come should take this into account. Additionally, future studiesshould also include phase-amplitude synchronization and crossfrequency coupling.

5. Conclusion

A lack of lagged non-linear coherence at the beta 2 frequencywithin frontal brain regions provides evidence for an altered inter-and intrahemispheric neuronal communication within these areasduring rest in OCD. This finding encourages future research on con-nectivity measures as possible biomarkers for physiological homo-geneous subgroups in OCD. Still more knowledge is needed aboutthe association between electrophysiological measures such asthe EEG lagged coherence and structural phenomena, e.g. synaptic

S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 2421–2430 2429

plasticity to gain more insight into pathological mechanisms thatare related with OCD.

Acknowledgements

We would like to thank the medical and technical assistants ofthe EEG-laboratory for their support. This study was supported bythe LIFE – Leipzig Research Center for Civilization Diseases, Univer-sity Leipzig, Germany.

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