Clinical Neurophysiology 124 (2013) 497–502

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Clinical Neurophysiology

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EEG-vigilance regulation during the resting state in obsessive–compulsive disorder

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

a Clinic for Psychiatry and Psychotherapy, University Hospital Leipzig, Germanyb Clinic for Neurology, University Hospital Leipzig, Germanyc Centre for Neurologic Rehabilitation, University of Leipzig, Germany

a r t i c l e i n f o

Article history:Accepted 30 August 2012Available online 26 September 2012

Keywords:EEGVigilance-regulationObsessive compulsive disorderHealthy controls

1388-2457/$36.00 � 2012 Published by Elsevier Irelahttp://dx.doi.org/10.1016/j.clinph.2012.08.018

⇑ Corresponding author. Address: SemmelweißGermany. Tel.: +49 341 9724304; fax: +49 341 972

E-mail address: Sebastian.Olbrich@medizin.uni-lei

h i g h l i g h t s

� EEG-vigilance regulation during the resting state is a biological marker of neuropsychiatric disorders.� EEG-vigilance regulation is found to be more stable in obsessive compulsive disorder in comparison tohealthy controls.� Results encourage to use the EEG-vigilance measure for studying treatment response and as a controlvariable in neuroimaging studies of OCD.

a b s t r a c t

Objective: Obsessive compulsive disorder (OCD) has been associated with disturbed sleep-wake cyclesand cortical hypermetabolism. However, it still remains unclear whether OCD is associated with a dys-regulation of vigilance (i.e. ‘‘brain arousal’’). VIGALL (Vigilance Algorithm Leipzig) is an EEG-based toolto assess vigilance dynamics. Aim of this study is to test the hypothesis that during resting state lessdeclines to lower vigilance stages are found in unmedicated patients with OCD (n = 30) compared tohealthy controls (HCs, n = 30).Methods: Fifteen minutes of resting EEG were recorded; preceding sleep duration, nicotine/caffeine con-sumption and severity of OCD and depressive symptoms were assessed.Results: Significant differences of EEG-vigilance were found for the factor ‘‘group’’ (OCD vs. HC), factor‘‘time’’ and interaction ‘‘group � time’’ in a repeated measures ANOVA with increased EEG-vigilance inOCD patients. OCD patients showed significantly fewer transitions between EEG-vigilance stages.Conclusions: In line with findings of disturbed sleep regulation, OCD is associated with altered EEG-vig-ilance regulation with a failure of declining toward low vigilance stages during rest.Significance: These results encourage the use of EEG-vigilance regulation for determining subgroups fore.g. studying treatment response and suggest awareness for possible vigilance effects in neuroimagingstudies of OCD.

� 2012 Published by Elsevier Ireland Ltd. on behalf of International Federation of ClinicalNeurophysiology.

1. Introduction

During the past decades, biological concepts emerged thatshifted the understanding of obsessive compulsive disorder (OCD)away from being a maladaptive response to intrapsychic unre-solved conflicts toward a disease with assessable structural andphysiological alterations (Goodman, 2010). Beside the clinical coresymptoms of the disorder with intrusive and recurrent thoughts asthe obsessive component and/or repetitive ritualistic behaviour asthe compulsive part of the disease, OCD-related pathophysiology

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also revealed an association with disturbed sleep-wake cycles.Although some results of studies analysing the sleep in OCD remainheterogeneous, e.g. the often observed but somehow argued REMsleep latency alterations in OCD (Insel et al., 1982; Hohagen et al.,1994; Robinson et al., 1998), other findings seem to be more consis-tent. Especially total sleep time has been found to be decreased inOCD in comparison to healthy controls (Insel et al., 1982; Hohagenet al., 1994; Voderholzer et al., 2007; Alfano and Kerri, 2011) and incomparison to populations suffering from other psychiatric disor-ders (Ivarsson and Larsson, 2009). Apart from a decreased timespent asleep, it has been suggested that OCD is related also to asleep phase shift which is defined as a delayed time to go to bedat night and getting up late during daytime (Mukhopadhyayet al., 2008; Turner et al., 2007). More evidence for disturbed sleep

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in OCD emerges from a study reporting that over 56% of a youthOCD population had difficulties to fall asleep as reported by theirparents (Storch et al., 2008).

The disturbances of sleep–wake rhythm with delayed sleepperiods, difficulties to fall asleep and a decreased sleep time canbe assumed to be a dysregulation of brain function during the in-tended resting phase. This impaired regulation of cortical activitywithin circadian rhythms and adaption to environmental require-ments in OCD is underpinned by findings retrieved from differentneuroimaging modalities. Positron emission tomography (PET)studies using [18F] 2-fluoro-2-deoxyglucose revealed an increasedcerebral glucose metabolism in OCD mainly in frontal brain areasduring the resting state (Baxter et al., 1987; Nordahl et al., 1989).Also results from functional magnetic resonance imaging (fMRI)studies showed increased activity by means of blood oxygenationlevel dependent (BOLD) signals in different cortical areas in OCDduring cognitive tasks and symptom provocation as well as in-creased low frequency fluctuations of the BOLD signal during rest(Hou et al., 2012). Further, the results from an EEG-study (Koprivo-vá et al., 2011) yielded increased theta-activity in frontal areas inOCD. These findings have been interpreted as cortical hyperactiva-tion and hyperactivity in OCD (Nakao et al., 2005; Simon et al.,2010; Yücel et al., 2007). Still it remains unclear whether theabovementioned findings of hypermetabolism and hyperactivityindicate that OCD is associated with an increased vigilance, i.e. to-nic brain arousal during rest as suggested by the abovementionedfindings of sleep alterations.

An EEG-based algorithm called VIGALL (Vigilance AlgorithmLeipzig) for assessment of brain arousal, i.e. vigilance and its regu-lation during rest has been developed and validated using simulta-neous EEG/fMRI and EEG/PET approaches. These studies reportnegative 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., 2011). The discriminative powerof VIGALL also has been proven by identifying a more unstableEEG-vigilance regulation in cancer-related fatigue syndrome(Olbrich et al., 2012) and a more rigid vigilance regulation inpatients suffering from major depression (MD) in comparison toHCs (Hegerl et al., 2012). In an earlier study, Hegerl et al. (2008)showed that during a 5 min resting period, OCD patients showedsignificantly increased amounts of high vigilance stages in compar-ison to patients with borderline personality disorder but not incomparison to healthy controls. Using a longer recording period,refined methods and a confirmatory statistics, the aim of the pre-sented study was to test the hypothesis that during 15 min of rest-ing state with closed eyes a more stable vigilance regulation withless declines to lower vigilance stages is found in unmedicatedpatients with OCD compared to matched healthy controls (HCs).

2. Methods

2.1. Subjects

2.1.1. PatientsPatients with OCD were recruited at the outpatient ward of the

Department for Mental Health of the University of Leipzig between2007 and 2010. OCD diagnosis was assessed according to the crite-ria of DSM-IV by clinical evaluation of a senior physician; symptomseverity was measured using the Yale-Brown Obsessive Compul-sive Scale (Y-BOCS; (Goodman et al., 1989). Additionally depressivesymptoms were assessed using the Hamilton depression ratingscale (HDRS-21; Hamilton, 1960). Patients were included, if they

had at least 17 points in the YBOCS. Exclusion criteria for patientswere (1) a clinical significant neurological or other medical disor-der (e.g. dementia or organic brain disorder), (2) use of centrallyactive medications in the last 4 weeks and (3) a HRDS-21 score>15 due to evidence about increased amounts of high EEG-vigi-lance stages in Major Depression (Hegerl et al., 2012). This cut-off-score was chosen since almost all patients suffering from OCDshow depressive symptoms although not fulfilling criteria for adepressive episode. However, no patient with a depressive episodeaccording to DSM-IV was included. Amongst the screening sampleof 65 patients, 34 met the inclusion criteria. Four more patientshad to be excluded due to missing HRDS-21 scores, 30 patients re-mained to be studied with age and gender matched HCs.

2.1.2. Healthy controlsThirty-three age and gender matched HCs were chosen from the

database of the Department of Psychiatry of the University Leipzig.Controls were excluded if they met one of the following criteria:(1) a clinical significant neurological or other medical disorder,(2) use of centrally active medications in the last 4 weeks, (3)any current DSM-IV axis-I disorder including OCD and affectivedisorder as assessed via the extended screening questionnaire ofthe Structured Clinical Interview for DSM-IV Axis I Disorders(SCID-I; First et al., 1997) and (4) lifetime history of affective disor-ders or OCD.

The study was approved by the local ethics committee. Writtenand informed consent was obtained prior to investigation accord-ing to the declaration of Helsinki.

2.2. EEG acquisition

Fifteen minutes of resting state EEG in OCD patients and HCswere recorded between 9:00 a.m. and 3:00 p.m. Participants wereseated comfortably in half-reclined position in a dimly lit roomwith approximately 40 lux and temperature between 20 and23 �C, environment noise was attenuated and eyes were closed.Participants were instructed to relax and not to fight a possiblyoccurring urge to fall asleep. The EEG was recorded with a 40 chan-nel QuickAmp amplifier (Brain Products GmbH, Gilching, Ger-many) from 31 electrode sites according to an extended versionof the international 10–20 system at a sampling rate of 1 kHz, ref-erenced against common average using a low-pass filter at 280 Hz.Impedances were usually kept below 10 kO. EEG data were pro-cessed using BrainVision Analyzer 2.0 (Brain Products GmbH, Gil-ching, Germany). Electrooculogram (EOG) electrodes were placedabove the upper left eye and under the lower right eye. Standard-ized auditory instructions were given using Presentation software(Neurobehavioral Systems Inc., Albany, USA). EEG data was prepro-cessed using BrainVision Analyzer 2.0 software (Brain ProductsGmbH, Gilching, Germany). EEG raw data was filtered at 70 Hz(low-pass), 0.5 Hz (high-pass) and 50 Hz (notch-filter, range5 Hz). EOG channels were screened for periods of open eyes (simul-taneous video recording). Eye movement artefacts were removedby extracting 1–3 independent components that clearly repre-sented vertical and horizontal eye movements and had been iden-tified by visual topographic inspection and comparison with theEOG channel (Delorme et al., 2007) before EEG-time series weresegmented into consecutive one-second intervals. Segments wereagain screened for remaining muscle, movement, eye and sweatingartefacts in EEG and EOG channels. Artefacts were marked forexclusion from further analysis.

2.3. EEG-vigilance stages and EEG-vigilance regulation

All consecutive 900 one-second segments of each participantwere classified into seven different EEG-vigilance stages (Table 1)

Table 1EEG vigilance stages and their introspective and electrophysiological correlates.

Vigilancestage

Introspection EEG/EOG

0 Concentration Low amplitude EEG without SEMsA1 Relaxed wakefulness Occipital alpha activityA2 Relaxed wakefulness Parietal/temporal alpha activityA3 Relaxed wakefulness Frontal alpha activityB1 Drowsiness Low amplitude EEG with SEMsB2/3 Loss of

consciousnessTheta and delta activity/vertexwaves

C Sleep Sleep spindles/K-complexes

SEM = slow eye movements.

S. Olbrich et al. / Clinical Neurophysiology 124 (2013) 497–502 499

using the Vigilance Algorithm Leipzig (VIGALL). EEG-vigilancestages of all consecutive segments of each proband were trans-formed into a measure ranging from the value 7 (highest vigilancestage 0) to 1 (sleep onset stage C). Average vigilance values werecomputed for 3-min blocks for ANOVA analysis. Percentages of vig-ilance stages were computed by dividing the amount of segmentsof a certain stage by the amount of non-artefact segments. Transi-tion rates between stages were calculated by dividing the amountof changes between consecutive different vigilance stages with thetotal amount of non-artefact segments.

2.4. Assessment of sleepiness, vigilance altering substances and foodintake

Sleep quality and quantity of the preceding night as well as thefeeling of being well-rested after sleeping were evaluated in pa-tients and healthy controls using a German sleep questionnaire(Görtelmeyer, 1981). The questionnaire consists of 25 questionssummarized into 5 items: SQ for Sleep quality; GES for the feelingof being well-rested after sleeping; PSYA for being psychologicallywell balanced before sleeping; PSYE for being psychologically ex-hausted after sleeping; PSS for psychosomatic symptoms duringthe sleep phases.

Sleepiness at the beginning of the EEG-recording was assessedusing the Stanford Sleepiness Scale (SSS; Hoddes et al., 1973) inboth groups. Additionally, times of going to bed, waking up themorning, time of the last food intake before EEG-recording tookplace and information about caffeine and nicotine consumptionwere acquired.

2.5. Statistics

All statistical analysis was performed using STATA softwareVersion 10.1 (StataCorp LP, TX) and Microsoft Excel 2007.

The amounts of EEG-vigilance stages in OCD and HCs were com-pared using Whitney-U Test since Shapiro–Wilk test showed thatthey were not distributed normally (Z = 4.057–6.521; p < 0.001).Correlations between symptom severity as assessed via the YBOCSand amounts of EEG-vigilance stages were computed using theSpearmans rank correlation coefficient. Transition rates, sociode-mographic data as well as the scores from the SF-A and drug con-sumption questionnaire and the timespans from last food intakewere compared using two-sided T-tests.

After transformation of all seven vigilance stages into one semiquantitative measure, Shapiro–Wilk test showed that the resultingEEG-vigilance variable was distributed normally (Z = �0.853;p = 0.80). Thus, for analysis of the EEG-vigilance regulation, arepeated-measures ANOVA with ‘‘group’’ (OCD vs. HC) as be-tween-subject factor and ‘‘time’’ (averaged over 3 min blocks) aswithin-subject factor and EEG-vigilance stage as dependentvariable was computed (Gleason, 1999). Corrections of degrees of

freedom due to violation against independence of observations ina repeated measures design were applied according to Greenhouseand Geisser (Greenhouse and Geisser, 1959). Post-hoc tests foreach 3 min block were performed using Scheffé-test.

3. Results

3.1. Sociodemography

The 30 included OCD patients were age and gender matchedwith 30 healthy controls (Table 2a). Patients showed medium to se-vere OCD-symptoms with a YBOCS mean of 23.5 (±5.6; obsession:11.2 ± 3.3; compulsion 12.4 ± 2.8) and a subclinical depressivesymptom severity with a HDRS mean of 9.1 (±4.5; see Table 2a).

3.2. EEG-vigilance stages and transitions

In average patients with OCD spent more time at the highestvigilance stages 0 and A1 and also at stage B1 while HC showedin average more low vigilance stages B2/3 and C. When usingWhitney-U Test, none of these differences was significant(Table 2b). Also, Spearmans rank correlation coefficients were notsignificant for associations between amounts of EEG-vigilancestages and YBOCS scores. OCD patients showed significantly lesstransitions between vigilance stages, (p < 0.03) (Table 2c).

3.3. ANOVA and EEG-vigilance regulation

The repeated-measures ANOVA revealed a significant effect ofthe dependent variable EEG-vigilance for the factor ‘‘group’’ and asignificant effect for the factor ‘‘time’’ (Table 2d). Also the interac-tion of ‘‘group � time’’ showed a significant difference (Table 2d).Both, OCD patients and HC started at a similar level of EEG vigilancebut HCs showed a more pronounced decline to lower EEG-vigilancestages over the course of time (Fig. 1). Post-hoc Scheffé-test re-vealed a significantly higher EEG-vigilance for OCD patients in com-parison to HCs for minutes 9–12 (p < 0.003, Table 2d).

3.4. Sleep and substance consumption

Although total sleep time was longer in HC than in patients withOCD (Table 2e), this difference was not significant. Still, OCD pa-tients were recorded in average about 2 h earlier during daytimethan HC, a difference that was found to be significant (Table 2e).

Concerning results of the sleep questionnaire, HC in comparisonto OCD patients showed a tendency for higher scores of sleep qual-ity (SQ) and significantly increased scores for recreation after sleep(GES) and mental balance before going to sleep (PSYA; Table 2e).

There was no significant difference between OCD patients andHC for the timespan between EEG recording and the last food in-take or the last consumption of vigilance enhancing drugs like caf-feine and nicotine. Also the amount of consumed cups of coffee andcigarettes before EEG-recording did not differ between groups(Table 2f).

4. Discussion

This study revealed an altered vigilance regulation during a15 min resting state recording in unmedicated patients sufferingfrom OCD in comparison to age and gender matched healthy con-trols. OCD patients showed a significantly higher and more stablevigilance in the repeated measures ANOVA with fewer transitionsbetween vigilance stages. These differences could not be explainedby differences of nicotine/caffeine usage or time of the last mealbefore EEG-recording.

Table 2All main findings are listed with Table 2a presenting the sociodemographic data from patients suffering from obsessive compulsive disorder (OCD) and healthy controls (HC) andsymptom severity from patients as assessed via questionnaires. Table 2b–d depicts the results from EEG-vigilance analysis with patients showing a significant higher EEG-vigilance during the course of time as revealed by ANOVA and post-hoc analysis (Table 2d). Patients also revealed lower subjective sleep quality and were recorded about 2 hearlier (Table 2e). No differences were found concerning the amount and time of last consumption of cigarettes and caffeine as well as the time of the last meal before recording(Table 2f).

OCD HC Statistics pValue (±SD) Value (±SD)

(a) Sociodemography/questionnaire T-valuesAge [years] 34.6 (11.9) 34.5 (11.1) 0.022 0.98Gender 17 Female 17 Female 0 1Y-BOCS [score] 23.5 (±5.6) – – –HDRS [score] 9.1(±4.5) – – –

(b) EEG-vigilance stages Z-valuesStage 0 [%] 17.89% (±24.68%) 5.81% (±6.85%) �1.24 0.21Stage A1 [%] 31.91% (±29.96%) 26.81% (±22.73%) �0.01 0.99Stage A2 [%] 14.16% (±19.06%) 21.77% (±22.47%) 1.58 0.11Stage A3 [%] 4.24% (±8.13%) 10.59% (±14.13%) 1.71 0.09Stage B1 [%] 21.91% (±26.07%) 15.95% (±20.07%) 0.27 0.79Stage B2/3 [%] 7.46% (±8.19%) 15.69% (±19.12%) 1.48 0.14Stage C [%] 2.45% (±8.61%) 3.8% (±8.42%) 0.71 0.48

(c) Transitions between stages T-valuesTransitions [amount] 289.2(±120.9) 369.1 (±154.9) �1.96 0.03

(d) Repeated-measures ANOVA F-valuesGroup 4.27 0.04*

Time 13.6 0.001*

Group � time 4.33 0.002*

Post-hoc (Scheffé-test) minutes 1–3 0.35 0.555Post-hoc (Scheffé-test) minutes 3–6 0.82 0.369Post-hoc (Scheffé-test) minutes 6–9 3.58 0.064Post-hoc (Scheffé-test) minutes 9–12 9.90 0.003*

Post-hoc (Scheffé-test) minutes 12–15 3.80 0.056

(e) Sleep parameters and recording time T-valuesSleep quality (SQ) 3.462 (1.017) 4.116 (0.701) �2.799 0.07Recreation after Sleep (GES) 2.609 (0.851) 3.603 (0.827) �4.432 0.0004*

Mental balance in the evening (PSYA) 2.554 (0.986) 3.843 (0.619) �5.861 0.0004*

Mental tiredness in the evening (PSYE) 3.193 (0.936) 2.793 (0.610) 1.895 0.063Psychosomatic symptoms at sleep (PSS) 1.417 (0.596) 1.179 (0.311) 1.875 0.068Sleep duration [h] 06:24 (01:37) 06:55 (01:20) �1.299 0.199Recording time [hh:mm] 11:55 a.m. 01.52 p.m. �3.316 0.002*

(f) Substances and meals T-valuesNicotine [Cigarettes] 3.5 (2.121) 5.14 (4.100) �0.528 0.614Caffeine [cups of coffee] 1.4 (0.516) 1.5 (0.607) �0.446 0.659Timespan from last coffee [h] 05:33 (03:46) 05:23 (02:24) 0.143 0.887Timespan from last cigarette [h] 01:02 (0:25) 02:15 (0:56) �2.094 0.7Timespan from last meal [h] 03:38 (04:07) 03:28 (01:15) �0.184 0.86

* Significant findings with p < 0.05.

Fig. 1. EEG-vigilance regulation time series of consecutive three minute blocks forpatients with obsessive compulsive disorder (OCD) and healthy controls (HCs).Vertical lines indicate standard errors. Initially both groups showed a similar EEG-vigilance level. While OCD patients stayed more stable at higher EEG-vigilancestages, HC dropped more rapidly to lower EEG-vigilance stages after 6 min.Repeated measures ANOVA showed a significant effect for factors ‘‘group’’, ‘‘time’’and ‘‘group � time’’ interaction. Post-hoc Scheffé-Tests showed significantly higherEEG-vigilance stages during minutes 9–12 for OCD patients in comparison to HCs(p < 0.003).

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Patients suffering from OCD and HCs did not differ concerningthe mean percentages of EEG-vigilance stages, a result that is inline with findings of Hegerl et al. (2008). In contrast to this earlierstudy, the ANOVA analysis of the presented study was able to re-veal a difference for factor ‘‘group’’, ‘‘time’’ and the interaction be-tween ‘‘group � time’’ with increased vigilance stages for OCDpatients in comparison to HCs. These differences may be due to alonger recording period of 15 min in the presented work insteadof 5 min in the study of Hegerl et al. the higher number of includedsubjects (n = 20 vs. n = 30), and the improved algorithm for EEG-vigilance classification provided by VIGALL with its peculiar advan-tage of classifying seven different stages instead of four used in theHegerl et al. study. This study therefore confirmed and extendedthe results of the Hegerl et al. work by showing a significant differ-ence of EEG-vigilance regulation between OCD patients and HC.

Disturbances and impairments of sleep regulation in OCD haveoften been argued in the light of comorbid depression (Stein andMellmann, 2005). However, OCD patients in our study revealedan altered EEG-vigilance regulation, although patients with clini-cally relevant depressive symptoms were excluded prior to analy-sis. While Hegerl et al. (2012) report a significantly increased

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amount of A1 stages in patients suffering from major depression(MD), no such statistically significant differences were found inour study for the comparison between OCD patients and HCs. Still,OCD patients within this study showed a more stable EEG vigilanceregulation over time than HCs. This increased tonic arousal duringrest might be a neurophysiological correlate of ongoing obsessivethoughts and ruminations that prevent the patients from relaxingand declining toward sleep, although more evidence for thishypothesis is needed from further studies.

EEG-vigilance regulation not only affects daytime sleepinessbut also has impact on sleep behaviour. Therefore, the impairedability to drop to low vigilance stages can be put in a line with find-ings of shifted sleep phase (Turner et al., 2007; Mukhopadhyayet al., 2008) and problems of falling asleep (Storch et al., 2008).Although not significant within the presented study, the elsewherereported decreased total sleep time in OCD (Alfano and Kerri, 2011;Voderholzer et al., 2007) would imply a compensatory decreasedvigilance in OCD during daytime. Thus the contrary finding of in-creased EEG-vigilance in OCD in comparison to HC points towarda disease inherent pathomechanism with a steadily increased tonicbrain arousal. Further evidence for such increased arousal levelsstems from studies reporting increased cortisol and adrenocortico-tropic hormone (Kluge et al., 2007) and decreased levels of plasmamelatonin (Monteleone et al., 1995), which presumably reflect theneuroendocrinological analogy of altered EEG-vigilance regulationin OCD. The impaired vigilance regulation and sleep problemsmight explain the significant findings from the sleep questionnaireSF-A with decreased mental balance before sleep and the missingrecreation after sleep in OCD found in this study.

The findings of an altered cortical arousal with a more rigid reg-ulation of vigilance during the resting state in OCD raise the ques-tion, whether findings from other neuroimaging studies with e.g.altered BOLD signals or cerebral glucose metabolism might be inparts explainable as metabolic correlates of unspecific phenomenasuch as altered vigilance rather than reflecting mere pathogno-monic neuronal activity of OCD patients e.g. during expositiontasks. This is supported by findings of Guenther et al. (2011) whoshowed in a simultaneous EEG-PET recording that the glucosemetabolism is negatively associated with the EEG-vigilance level.Further, our research group was able to highlight a positive associ-ation between EEG-vigilance level and subcortical BOLD signalsand a negative association between EEG-vigilance levels and corti-cal BOLD signals (Olbrich et al., 2009). These studies suggest anenormous impact of EEG-vigilance on results of other neuroimag-ing modalities. Since the presented study revealed altered EEG-vig-ilance levels in OCD in comparison to HCs, future studies should tryto control vigilance fluctuations or at least report the exact dura-tion of the used paradigms.

The main limitation of this study is the statistically significantdifference of time of recording in OCD patients and HC. The pa-tients group was recorded about 2 h earlier at noon than the con-trol group. If an assumed increased drowsiness in the afternoonwould have impacted the results, EEG-vigilance would be expectedto be lower even at the beginning of the recording. Still, bothgroups showed a similar EEG-vigilance at the beginning of therecording, remarkable differences appeared only after 6 min ofrecording. Not the initial vigilance but its regulation over timeseemed to be different in both groups. Further, research on cogni-tive performance throughout the habitual wakefulness periodshowed no significant differences in alertness or cognitive perfor-mance, suggesting a stable intraindividual vigilance regulationduring daytime (Dijk et al., 1992). Also measures of the multiplesleep latency during daytime showed no pronounced differencesbetween measurements at 11.30 a.m. and 01.30 p.m. (Carskadonand Dement, 1987), times that reassemble the two different timesfor recording of our groups. Further, analysis of the timespans

between last food intake and EEG-recording did not differ betweengroups. Therefore, the differences of recording time around noon(and lunch time) appear to have only a minor influence on vigi-lance regulation.

5. Conclusion

OCD is associated with a more stable regulation of vigilancesimilar to what has been found in patients with major depression.An overactivity of wakefulness promoting neurochemical systemscould explain this finding as well as the decreased total sleep timefrequently reported from these patients. This study demonstratedthe possible usage of the VIGALL tool to assess the impaired vigi-lance regulation in OCD. Further studies should focus on the usageof the EEG-vigilance biomarker to delineate clinically meaningfulsubgroups of patients with OCD for e.g. prediction of treatment re-sponse and to control for vigilance related variance in data ob-tained with functional brain imaging methods.

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