Neurocognitive endophenotypes of
Lara Menzies,1^3Sophie Achard,1Samuel R.Chamberlain,2^4Naomi Fineberg,2Chi-Hua Chen,1Natalia del
Campo,3,4Barbara J. Sahakian,3,4Trevor W. Robbins3and Ed Bullmore1,3,5
1Brain Mapping Unit,University of Cambridge,2Department of Psychiatry,Queen Elizabeth II Hospital,Welwyn Garden City,
3Department of Experimental Psychology, Behavioural and Clinical Neurosciences Institute,University of Cambridge,
Cambridge,CB2 3EB,4Department of Psychiatry, Addenbrooke’s Hospital,Cambridge,CB2 2QQ and5Clinical
Unit Cambridge, Addenbrooke’s Centre for Clinical Investigations,Clinical Pharmacology & Discovery Medicine,
GlaxoSmithKline,Cambridge CB2 2QQ,UK
Correspondence to: Professor Ed Bullmore, Brain Mapping Unit, Department of Psychiatry,University of Cambridge,
Addenbrooke’s Hospital,Cambridge,CB2 2QQ,UK
Endophenotypes (intermediate phenotypes) are objective, heritable, quantitative traits hypothesized to repre-
sent genetic risk for polygenic disorders at more biologically tractable levels than distal behavioural and clinical
phenotypes. It is theorized that endophenotype models of disease will help to clarify both diagnostic classifica-
tion and aetiological understanding of complex brain disorders such as obsessive-compulsive disorder (OCD).
T o investigate endophenotypes in OCD, we measured brain structure using magnetic resonance imaging
(MRI), and behavioural performance on a response inhibition task (Stop-Signal) in 31 OCD patients, 31 of their
unaffected first-degree relatives, and 31 unrelated matched controls. Both patients and relatives had delayed
response inhibition on the Stop-Signal task compared with healthy controls. We used a multivoxel analysis
method (partial least squares) to identify large-scale brain systems in which anatomical variation was asso-
ciated with variation in performance on the response inhibition task. Behavioural impairment on the Stop-
Signal task, occurring predominantly in patients and relatives, was significantly associated with reduced grey
matter in orbitofrontal and right inferior frontal regions and increased grey matter in cingulate, parietal and
striatal regions. A novel permutation test indicated significant familial effects on variation of the MRI markers
of inhibitoryprocessing, supporting the candidacyof these brain structuralsystems as endophenotypes of OCD.
In summary, structural variation in large-scale brain systems related to motor inhibitory control may mediate
genetic risk for OCD, representing the first evidence for a neurocognitive endophenotype of OCD.
Keywords: neuroimaging; inhibition; obsessive-compulsive; multivoxel; familial
Abbreviations: ANOVA=analysis of variance; DLPFC=dorsolateral prefrontal cortex; LSD=Least Significant Difference;
MADRS=Montgomery Asberg Depression Rating Scale; MNI=Montreal Neurological Institute; MRI=magnetic resonance
imaging; NART=National Adult ReadingTest; OCD=obsessive-compulsive disorder; OCI-R=Obsessive Compulsive
Inventory - Revised; OFC=orbitofrontal cortex; PLS=partial least squares; QTL=quantitative trait locus; RIFG=right
inferior frontal gyrus; SPECT=Single Photon Emission Computed Tomography; SSRT=stop-signal reaction time;
YBOCS=Yale Brown Obsessive Compulsive Scale
Received April16, 2007 . Revised July 30, 2007 . Accepted July 31 , 2007
An endophenotype was originally defined in the 1960s as
a ‘measurable component unseen by the unaided eye on the
pathway between disease (phenotype) and distal genotype’
(John and Lewis, 1966; Gottesman and Shields, 1973). It is
a heritable quantitative trait associated with increased
genetic risk for a disorder and therefore present in both
patients and their clinically unaffected relatives (Gottesman
and Gould, 2003; Bearden and Freimer, 2006). Interest in
endophenotypes (or intermediate phenotypes) has been
stimulated by the difficulties encountered in establishing
specific genetic causes for complex disorders by classical
linkage or association designs. Although most major psychia-
tricsyndromes are highlyheritable, several decadesofeffort to
discover causative genes has yielded disappointing results.
It is argued that traditional clinical phenotypes, such as a
doi:10.1093/brain/awm205Brain (2007) Page1of14
? 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which
permits unrestricted non-commercialuse, distribution, andreproductionin anymedium, provided the original work is properlycited.
Brain Advance Access published September 13, 2007
diagnosis of schizophrenia or obsessive-compulsive disorder
(OCD), likely subtend considerable genetic heterogeneity in
pathogenetic mechanisms and are too far ‘downstream’
from sites of gene action to support statistically powerful
and replicable linkage to chromosomal loci or association
with specific allelic variation (Leboyer et al., 1998; Bearden
et al., 2004). In this context, endophenotypes offer an
attractive strategy for discovering susceptibility genes since
they represent deconstruction of the clinical phenotype
into biological variables hypothetically more proximal to
Neuroimaging measurements have obvious potential
interest as endophenotypes for neuropsychiatric disorders.
Healthy twin studies have shown that brain structure is
highly heritable (Thompson et al., 2001; Wright et al.,
2002) andbrain structural
described in case-control studies of most major psychiatric
syndromes. Several experimental designs have been adopted
to identify structural magnetic resonance imaging (MRI)
endophenotypes of psychiatric syndromes. A common
approach has been to identify MRI markers that are
abnormal in clinically unaffected relatives of patients with
the index disorder, compared to unrelated healthy volun-
teers. More complex designs have included studies of
probands and relatives drawn from multiplex families,
where each individual could be assigned an estimate of
genetic risk for a disorder and MRI endophenotypes were
then defined as grey or white matter systems covarying
significantly with this risk (McDonald et al., 2004). Sib-pair
designs, where endophenotypes are identified by reduced
variability within genetically related pairs (one proband and
one unaffected relative), have not yet been widely used in
MRI studies of neuropsychiatric syndromes, but see
Marcelis et al. (2003).
Obsessive-compulsive disorder (OCD) is a heritable
neuropsychiatric disorder with a lifetime prevalence of
2–3% (Karno et al., 1988; Weissman et al., 1994). Evidence
for a genetic contribution to aetiology is provided by
twin studies (Inouye, 1965; Carey and Gottesman, 1981),
and family studies showing that the disorder is 5–7 times
more frequent in first-degree relatives of patients than
controls (Pauls et al., 1995; Nestadt et al., 2000). OCD is a
major cause of social and occupational disability with
considerable associated economic costs; ?$8.4 billion/year
in the US (DuPont et al., 1995). It is clinically characterized
by two symptom dimensions:
unwanted, intrusive, recurrent thoughts often concerned
with contamination, checking or symmetry; and compul-
sions, which are repetitive behaviours carried out in relation
to obsessions e.g. washing, household safety checking and
Association, 1994). It is increasingly clear that such classic
obsessive-compulsive symptoms are associated with a
pattern of cognitive impairments, suggesting that the
perseverative thoughts and behaviours that are symptomatic
abnormalities have been
of the disorder may reflect a loss of normal inhibitory
processes (Chamberlain et al., 2005).
According to theoretical models of OCD, symptoms and
associated cognitive impairments emerge from disordered
structure and functionof
(OFC) (Graybiel and Rauch, 2000; Saxena et al., 2001).
These circuits, first identified by anatomical studies in
primates (Alexander et al., 1986; Lawrence et al., 1998),
have been implicated in the pathophysiology of OCD by
human imaging and lesion-based studies (Rapoport and
Wise, 1988; Saxena, 2003). The most consistent finding
from structural MRI measurements of selected regions-of-
interest has been reduced grey matter volume of OFC in
patients with OCD; there is less consistent evidence in
support of volume changes in caudate nucleus, medial
temporal lobe structures, and anterior cingulate cortex
(Scarone et al., 1992; Robinson et al., 1995; Aylward et al.,
1996; Jenike et al., 1996; Rosenberg and Keshavan, 1998;
Szeszko et al., 1999; Gilbert et al., 2000; Kwon et al., 2003;
Choi et al., 2004, 2006; Kang et al., 2004; Szeszko et al.,
2004; Atmaca et al., 2007); see Supplementary Fig. 1 for
More recently, studies have used computational tech-
niques, such as voxel-based morphometry (Ashburner and
Friston, 2000), to map local structural differences in case-
control designs without restriction a priori to selected
regions (Kim et al., 2001; Pujol et al., 2004; Valente et al.,
2005). However, the few studies adopting this approach to
date have produced inconsistent results (see Supplementary
Fig. 2). There are various possible reasons for the limited
replicability of imaging studies of OCD, including clinical
heterogeneity or co-morbidity of patient samples, medica-
tion effects and small sample sizes in the context of
conservative significance thresholds mandated by the large
number of voxels to be tested in a whole brain approach.
Another possibility is that regional or voxel level analysis,
focused on local changes in brain structure, may be less
than optimally powerful to detect case-control differences
in brain structure which are theoretically expected at the
more distributed level of large-scale neurocognitive systems.
Two prior imaging studies have used theoretically more
abnormalities associated with OCD at a systems level.
Soriano-Mas et al. (2007) described a whole brain profile of
anatomical abnormality in patients with OCD compared to
healthy controls by testing the sum of t-statistics over all
voxels against a chi-square distribution (Worsley et al.,
1995, 1997). Harrison et al. (2006) used a non-parametric
[implemented using NPAIRS software (Strother et al.,
2002)] to identify a cortico-striatal system of abnormal
brain activation in patients with OCD performing the
Stroop task during PET scanning. This study also demon-
strated relatively greater power of this multivariate method
Page 2 of14 Brain (2007) L. Menzies et al.
to detect brain functional abnormalities when compared to
the results of a more traditional mass or multiple univariate
approach to analysis, entailing a significance test at each
individual voxel (Harrison et al., 2006). However, no prior
MRI studies have provided evidence for heritability or
familiality of brain structural abnormalities in OCD, as is
required to support the candidacy of neurocognitive
systems as endophenotypes of OCD.
In this context, we were motivated to address four key
hypotheses: (i) that motor response inhibition is abnormal
in patients with OCD and their first-degree relatives;
(ii) that variation in motor inhibition is associated with
structural variation in large-scale brain systems identified by
a multivoxel analysis of MRI data; (iii) that patients
with OCD and their first-degree relatives have abnormal
grey matter density in these motor inhibition systems,
likely including orbitofronto-striatal regions previously
implicated in OCD and (iv) that there are familial effects
on variation in inhibitory function and associated brain
Fig.1 Motor inhibitory behaviour (stop-signal reaction time; SSRT) and associated brain scores (summary measures of grey matter
correlation with SSRTover the whole brain). (A) Scatter plot showing the relationship between brain score (x-axis) and log-transformed
SSRT (y-axis); Pearson’sr=0.82, N=93, P<0.001. (B) Boxplots of brain score by group showing significantly larger scores for both patients
and relatives compared to controls.For each boxplot, thick bar indicates median; box and whiskers represent interquartile range and
Fig. 2 Brain maps illustrating regions where grey matter density was most strongly correlated with latency of motor inhibitory response
(SSRT). Red/yellow regions indicate areas in which increased grey matter density is associated with prolonged SSRT (impaired response
inhibition); blue regions indicate areas where decreased grey matter density is associated with prolonged SSRT .Colour bar indicates
strength of correlation between SSRTand grey matter density for each voxel; R and L markers indicate side of the brain, numbers denote
the z dimension of each slice in MNI space.
Neurocognitive endophenotypes of OCDBrain (2007) Page 3 of14
To test these predictions, we estimated the stop-signal
reaction time (SSRT) using a well-validated stop-signal task
(Logan et al., 1984), and acquired structural MRI data, in
patients with OCD, their unaffected first-degree relatives,
and healthy volunteers. The available evidence suggests
structural abnormalities at a systems level in OCD, and
prior functional imaging studies of motor inhibition
indicate this processes is subserved by an ‘inhibitory
neurocognitive network’ (Rubia et al., 2001b). Therefore,
we used the statistical method of partial least squares (PLS)
(McIntosh et al., 1996; McIntosh and Lobaugh, 2004), in an
innovative application to structural neuroimaging data,
to identify grey matter systems (comprising multiple
voxels) maximally correlated with variation in SSRT.
Finally, we assessed the familiality of cognitive and MRI
markers by a permutation test of their variation within
proband–relative pairs, each pair comprising a patient with
OCD and their clinically unaffected first-degree relative.
Materials and Methods
Participants and clinical assessments
The sample comprised 31 patients with a diagnosis of OCD,
31 unaffected first-degree relatives of a patient with OCD, and 31
unrelated healthy volunteers (including 30 complete pairs of
a proband and their first-degree relative). Patients were recruited
from an outpatient service by a consultant psychiatrist (NF) and
satisfied DSM-IV criteria (American Psychiatric Association, 1994)
for a diagnosis of OCD. Patients with symptoms of excessive
washing or checking, in the absence of hoarding or motor tics,
were selectedby clinicalinterview,
screening instrument; the YBOCS Symptom Checklist (Goodman
et al., 1989). This careful clinical screening process was adopted
to minimize co-morbidity and maximize symptomatic homo-
geneity in the patient sample selected for subsequent assessment
by cognitive testing and MRI, since there is evidence that
OCD subgroups with different symptom profiles may have
different underlying profiles of brain abnormality (Mataix-Cols
et al., 2004).
Eligible patients gave consent for a first-degree relative to be
contacted (preferably a similarly aged sibling; alternatively a parent
or child). Unrelated healthy volunteers were recruited by local
Participants were excluded if they had an axis I psychiatric
disorder (apart from OCD in the patients), serious head injury,
substance abuse, epilepsy or MRI contraindications.
The Mini International Neuropsychiatric Inventory (MINI)
(Sheehan et al., 1998) was used to screen for axis I psychiatric
disorders; the Montgomery–Asberg
(MADRS) (Montgomery and Asberg, 1979) to measure current
depressive symptoms; and two instruments, the clinician-rated
yale-brown obsessive compulsive scale (YBOCS) (Goodman et al.,
1989) and the self-rated obsessive compulsive inventory-revised
(OCI-R) (Foa et al., 2002), were used to measure obsessive-
compulsive symptom severity. Verbal IQ was estimated using the
National Adult Reading Test (NART) (Nelson, 1982).
Patients were clinically medicated as follows: 21 were prescribed
selective serotonin reuptake inhibitors; 1 was prescribed clomi-
pramine and 1 was prescribed quetiapine. Eight patients were
unmedicated. Relatives and healthy volunteers were not taking
All participants gave written informed consent and the study
was approved by the Addenbrooke’s NHS Trust Local Research
Ethics Committee (Cambridge, UK). Behavioural performance
on this motor inhibition task was previously reported for a subset
of the individuals in this sample (Chamberlain et al., 2007).
Here we report for the first time both behavioural and MRI data
on the full sample.
We used a computerized version of the stop-signal task to assess
inhibition of prepotent motor responses (Logan et al., 1984); for
a full description see Aron et al. (2003a). Briefly, participants
watched a computer screen on which a series of five blocks of 64
arrows per block were visually presented. Arrows pointed either to
the right (50%) or the left (50%) and subjects responded
accordingly by pressing the appropriate button; the order of
presentation of right- and left-pointing arrows was randomized.
In a randomly assigned proportion (25%) of trials, an audible
stop-signal was heard after presentation of the arrow and
subjects were instructed to inhibit their motor response to these
trials. The interstimulus interval (ISI) and the stop-signal delay
were varied according to the subject’s performance such that
subjects were able successfully to inhibit their responses to 50% of
the stop trials. From these behavioural data, the stop-signal
reaction time (SSRT, ms), i.e. the processing time required to
inhibit a prepotent motor response, was calculated for each
subject. SSRT data were normalized by log transformation before
statistical analysis in SPSS v11 for Windows.
MRI data acquisition
Structural MRI data were obtained using a GE Signa system
(General Electric, Milwaukee, USA) operating at 1.5T in the
Magnetic Resonance Imaging
Addenbrooke’s Hospital, Cambridge, UK. Axial 3D T1-weighted
images were acquired using a spoiled gradient recall (SPGR)
sequence and the following parameters: number of slices=124,
addition, axial dual-echo, fast spin echo (T2- and PD-weighted)
images were acquired with number of slices=40, slice thick-
ness=4mm, TR=5625ms, TE=20 and 102ms with an 8-echo
train length, field of view=24cm, matrix size=256?256, voxel
dimensions=0.94mm?0.94mm; scanning time=10min. Total
scanning time (including a localizer scan and a diffusion tensor
imaging sequence not reported here) amounted to 40min.
MRI data analysis: pre-processing
First, non-brain tissue
brain extraction procedure (Smith, 2002). The T1-, T2- and
PD-weighted images were then segmented using a multichannel
tissue classification algorithm and probabilistic maps of grey
matter, white matter, CSF and dural tissues were created by
estimating the partial volume coefficient for each voxel, which
represents the probability of each voxel belonging to one of four
tissue classes (Zhang et al., 2001). The segmented grey matter
partial-volume maps were registered in Montreal Neurological
was removedusing an automated
Page 4 of14Brain (2007) L. Menzies et al.
Institute (MNI) standard space by an affine transformation using
a segmented grey matter template in FSL (Sheehan et al., 1998;
Jenkinson and Smith, 2001; Jenkinson et al., 2002). The registered
data were spatially smoothed by a 2D Gaussian kernel with full
width at half maximum (FWHM)=3mm. All preprocessing was
performed using FSL software (http://www.fmrib.ox.ac.uk/fsl).
MRI data analysis: partial least squares
To identify grey matter systems optimally correlated with SSRT,
we used the statistical technique of partial least squares; PLS
(McIntosh et al., 1996). For implementation, we used PLSGUI
Briefly, over all participants, we estimated the correlations
between log-transformed SSRT scores and normalized grey matter
density at each voxel in the registered images where the
probabilityof grey matterP(GM)
(thus excluding fromconsideration
predominantly white matter or CSF). The normalization of grey
matter density involved dividing each voxel’s density estimate
by the mean density of grey matter over all voxels in the brain
(thus correcting individual voxel values for between-subject
differences in global grey matter volume). The overall strength
of correlation between grey matter density and SSRT was
summarized by the scalar d ¼
at the ith voxel and the sum is over all i=1,2,3,...V voxels with
P(GM)>0.1. The brain score for each participant was calculated
as the sum of grey matter probabilities multiplied by the local
weighted correlations with SSRT: i.e. B j ð Þ ¼PP GM
probability of grey matter at the ith voxel for the jth participant,
and the sum is over all V voxels for each participant.
The association between grey matter probability and SSRT was
tested for statistical significance by a permutation test of d. The
orderingof SSRT scoreswas
recalculation of the correlations with grey matter at each voxel,
leading to an estimate of d under the null hypothesis. This process
was repeated 1000 times to sample the permutation distribution of
d and the observed value was compared to the 950th value of the
ranked permutation distribution for a test with one-tailed
probability of type 1 error, P=0.05. Brain scores were also
compared between groups by analysis of variance (ANOVA) and
post hoc testing (SPSS v11 for Windows).
associated with SSRT was visualized by thresholding the correla-
tions at each voxel with an arbitrary threshold, |ri|>0.14 and a
minimum cluster size of 400 voxels. This cluster size threshold was
chosen for illustrative purposes, to best demonstrate the large-
scale anatomical covariation with SSRT scores. The choice of
visualization thresholds makes no difference to the statistical
significance of d (the overall strength of correlation between grey
matter density and SSRT) or the calculation of brain scores for
We also used this thresholded set of correlated voxels as
a ‘mask’, applied to the preprocessed maps of grey matter
probability, to estimate the grey matter density represented by the
thresholded system and its component regions in each participant.
These measures of regional grey matter density were also
compared between groups by ANOVA and post hoc testing.
, where riis the correlation
where B(j) is the brain score for the jth participant, P(GM)i,jis the
ofbrain systems strongly
Assessment of familiality
We used two complementary techniques to assess the familiality
of SSRT scores, or related grey matter systems (brain score and
grey matter density), in the OCD patients and their first-degree
relatives. First, we calculated the variance of the within-pair
difference in SSRT (or brain scores or grey matter density):
? proband ? relative pair
observed within-pair difference of the measure for the jth pair of
participants, u ¯ is the mean within-pair difference and N is the total
number of pairs (N=30). Then we randomly reassigned the
observations to new pairs, so that each patient was now paired
with a clinically unaffected individual to whom they were not
personally related. We recalculated the variance of the within-pair
difference in phenotype after each random permutation and
repeated this process 100000 times to sample the permutation
distribution of ?[proband–relative pair] under the null hypothesis
that the observed variance in within-pair differences was not
determined by the familial relatedness of the observed pairs. On the
alternative hypothesis that the observed variance would be small,
we compared it to the 5000th value of the permutation distribution
for a test with one-tailed P<0.05.
Second, as another exploration of the similarity between
patients and their relatives on behavioural and brain-based
measures, we examined the strength and significance of the
within-pair correlation of SSRT, brain score and grey matter
densities between patients and their own relatives. We used
within-pair correlation rather than intraclass correlation because
there was a natural ordering (patient or relative) within each pair.
½ ? ¼Pui? ? u
Demographic and clinical data
The three groups were well matched for age, verbal IQ,
gender and handedness (Table 1). As expected, there were
significant differences between groups on both measures of
obsessive-compulsive symptom severity (YBOCS: ANOVA,
P<0.001). Post hoc least significant difference (LSD) tests
confirmed that patients scored significantly higher on both
instruments than either healthy volunteers (YBOCS: df=60,
P<0.001; OCIR: df=57, P<0.001) or relatives (YBOCS:
df=60, P<0.001; OCIR: df=56, P<0.001); whereas
relatives did not differ significantly from healthy volunteers
on either instrument (YBOCS: df=60, P=0.08; OCIR:
df=59, P=0.54). Although mean depressive symptom
severity scores measured using MADRS were below the
threshold for a diagnosis of depressive disorder in all three
groups, patients with OCD had higher scores than relatives
and healthy volunteers (ANOVA, F2,90=14.2, P<0.001;
LSD tests, patients compared with healthy volunteers:
df=60, P<0.001). Relatives did not differ from healthy
volunteers (df=60, P=0.36) (Table 1).
Stop-signal task performance
There was a significant difference between groups in mean
SSRT (ANOVA, F2,90=9.07, P<0.001) (Table 1). Post hoc
analysis demonstrated that both patients (df=60, P=0.001)
Neurocognitive endophenotypes of OCDBrain (2007) Page 5 of14
and relatives (df=60, P<0.001) had significantly greater
SSRT than healthy volunteers. There was no significant
difference in SSRT between patients and relatives (df=60,
P=0.50). Of note, these between-group differences in
response inhibition were not accompanied by significant
non-specific latency differences in responding to uninhib-
ited trials (median ‘go’ reaction time: ANOVA, F2,90=2.76,
P=0.07). Additionally, there was no significant correlation
between age and SSRT in relatives (r=0.18, N=31,
P=0.33), excluding the possibility that younger relatives
perform worse on the task and might therefore represent
individuals with an OCD prodrome.
Grey matter systems correlated with SSRT
There was a significant correlation between SSRT and grey
matter probability (d=44.9, permutation test, P=0.05) and
individual brain scores
(Fig. 1A). As expected, brain scores were significantly
different between groups (ANOVA, F2,90=4.18, P=0.018)
(Fig. 1B) and post hoc analysis demonstrated that this was
due to significantly greater brain scores in patients
compared to healthy volunteers (df=60, P=0.021) and in
P=0.010); there was no significant difference in brain
scores between patients and relatives (df=60, P=0.78).
An anatomical map of voxels strongly correlated with
SSRT over all participants highlighted two extensive
systems which were, respectively, positively and negatively
correlated with latency of inhibitory processing (Fig. 2).
In the positively correlated system, longer SSRT was
associated with increased grey matter probability. This
predominantly parieto-cingulo-striatal system comprised
middle and posterior cingulate cortices (approximate
Brodmann areas [BA] 23, 24, 31), bilateral putamen/
caudate and amygdala, bilateral parietal cortical areas (BA
39, 40) and bilateral cerebellum. In the negatively correlated
system, longer SSRT was associated with decreased grey
matter probability. This predominantly frontal system
comprised bilateral middle
cortex (BA 11, 47), inferior frontal gyri (BA 44, 45),
superior frontal and premotor cortices (BA 6, 8, 9), anterior
cingulate cortex (BA 32) and bilateral temporal cortical
areas (BA 21, 22, 37, 42) (Table 2).
To explore these results further, we extracted grey matter
values for the systems in Fig. 2 that were correlated with
SSRT. We confirmed that grey matter density in the
parieto-cingulo-striatal system was positively correlated
with SSRT (r=0.70, N=93, P<0.001), grey matter density
in the frontal system was negatively correlated with SSRT
(r=?0.78, N=93, P<0.001), and grey matter density in
the two systems was negatively correlated with each other
(r=?0.78, N=93, P<0.001), indicating that individuals
with prolonged SSRT and larger positive brain scores
(typically patients and relatives) tended to have both
system and reduced grey matter in the frontal system.
There were significant between-group differences in grey
matter probability measured in both the parieto-cingulo-
striatal system (ANOVA, F2,90=4.27, P=0.017) and frontal
system (ANOVA, F2,90=3.36, P=0.039). When we explored
and medial orbitofrontal
T able1 Demographic, clinical and behavioural data for patients with OCD, their first-degree relatives and healthy,
Mean SD MeanSD MeanSDF (df=2,90)a
Age of onset of symptoms (years)
Duration of illness (years)
SSRT (log transformed)
Median‘go’reaction time (ms)
0.417 .0 8.76.4 0.91
5.360.345.42 0.43 5.030.38
0.07 457103 406 124 403
Abbreviations; OCD; obsessive-compulsive disorder, SD; standard deviation.
adf=2, 89 for NARTand df=2, 86 for OCI-R due to data unavailability.
bFisher’s exact test, total N=91due to data unavailability.
Page 6 of14 Brain (2007) L. Menzies et al.
group differences separately for each anatomically distinct
region in each system, we found that left inferior parietal and
dorsal occipital regions demonstrated the greatest between-
group difference in grey matter probability among all regions
comprising the system positively correlated with SRRT
(ANOVA, F2,90=7.48, P=0.001); whereas bilateral orbito-
frontal cortex demonstrated the greatest between-group
difference among all regions comprising the system negatively
correlated with SSRT (ANOVA, F2,90=4.92, P=0.009)
(Table 2, Fig. 3). There were no regions in which there were
significant differences in grey matter between patients and
relatives (LSD; df=60, P>0.1).
Familiality of cognitive and neuroimaging
By a permutation test of the variance of within-pair
differences in brain scores, we were able to show that the
observed within-pair variance was small (?[proband–relative
pair]=127) compared to the distribution of variances in a
T able 2 Anatomical details for brain regions where grey matter density was positively or negatively correlated with
stop^signal reaction time (SSRT)
Cluster numberSize (voxels) Peak correlation (r) MNI coordinates (mm)Region
Red regions (positive correlation between grey matter density per voxel and SSRT)
1 4172 0.4080
Mid cingulate gyrus (BA 23, 24)
Posterior cingulate gyrus (BA 24, 31)
Precuneus (BA 7)
R Lingual gyrus (BA18)
L Lingual gyrus (BA 30)
L superior occipital gyrus (BA19)
L inferior lateral parietal lobe (BA 40, 7)
R globus pallidus
R superior temporal gyrus (BA 38)
R mid occipital gyrus (BA18,19)
R angular gyrus (BA 39)
L cerebellar hemisphere
R cerebellar hemisphere
R lingual gyrus (BA19)
L lingual gyrus (BA19)
4 11760.344 128
6 3882 0.340
Blue regions (negative correlation between grey matter density per voxel and SSRT)
R cerebellar hemisphere
R postcentral gyrus (BA 40, 43)
R precentral gyrus (BA 4, 6)
R supramarginal gyrus (BA 40)
L mid/medial orbitofrontal cortex (BA11, 47)
R mid/medial orbitofrontal cortex (BA11, 47)
L inferior frontal gyrus pars triangularis (BA 44, 45)
R inferior frontal gyrus pars opercularis (BA 44, 45)
R mid temporal gyrus (BA 20, 21)
L superior frontal gyrus (BA 8, 6)
Bilateral supplementary motor area (BA 8, 6)
Medial frontal gyrus/anterior cingulate (BA 32)
R mid frontal gyrus (BA 6, 8,9)
L superior temporal gyrus (BA 22, 42)
L inferior temporal gyrus (BA 21, 37)
Abbreviations: BA=Brodmann’s area; L=left; R=right.
Neurocognitive endophenotypes of OCDBrain (2007)Page 7 of14
sample of 100000 randomly permuted pairs of patients and
first-degree relatives (permutation test, P=0.014) (Fig. 4A).
This observation is compatible with the (alternative) hypoth-
esis that variance of true proband–relative pair differences is
smaller because the phenotype in question, e.g. brain scores,
is determined by familial factors in common to both the
patient and the first-degree relative in each pair.
We applied the same permutation test to analyses of the
variance of within-pair differences in grey matter density of
the parieto-cingulo-striatal system positively correlated with
SSRT and in grey matter density of the frontal system
negatively correlated with SSRT. In both cases, we found
that the observed within-pair variance was significantly
small compared to the appropriate permutation distribu-
tions (permutation tests, P=0.009 and P=0.015, respec-
tively), again implying that familial factors shared between
patients and relatives determined grey matter volume in
these systems. Interestingly, applying the same approach to
analysis of the SSRT scores, provided no evidence for
significantly reduced variance of within-pair differences on
this cognitive measure (permutation test, P=0.25).
A broadly consistent pattern of results was obtained by
analysis of correlation between patients and their relatives:
these were significantly different from zero for brain score
(r=0.41, N=30, P=0.023), mean grey matter probability
in the parieto-cingulo-striatal system (r=0.46, N=30,
P=0.012) and mean grey matter probability in the frontal
system (r=0.42, N=30, P=0.022). In contrast, there was
no significant within-pair correlation for the SSRT score
Fig. 4 Estimating familiality effects on MRI and behavioural variation in patients and their own first-degree relatives (N=30 per group)
(A) Histogram showing distribution of variance of within-pair difference in brain scores for randomly permuted pairs of patients and
relatives, compared with the observed variance of within-pair difference in brain score for patients and their own relative (arrow).
(B) Scatter plots exploring (within-pair) correlation between patients and their relatives for SSRT (top panel) and brain score
Fig. 3 Brain maps highlighting regions of most significant group
difference in grey matter density between OCD patients and
first-degree relatives compared to healthy volunteers. (A) Most
significant red regions were in left occipital and inferior parietal
areas (BA19, 40) (Table 2; cluster 3); one way ANOVA (F2,90=7 .48,
P=0.001), post hoc tests; patients versus healthy volunteers;
P=0.002, relatives versus healthy volunteers; P=0.001, patients
versus relatives; P=0.85. (B) Most significant blue regions were in
bilateral orbitofrontal and left inferior frontal gyral regions
(Table 2; cluster 9) (BA11, 44, 45, 47); one way ANOVA
(F2,90=4.92, P=0.009), post hoc tests; patients versus healthy
volunteers; P=0.013, relatives versus healthy volunteers; P=0.005,
patients versus relatives; P=0.76. R marker indicates right side of
the brain; x, y, and z indicate planes of brain maps; cross-hairs
indicate point of peak correlation with the behavioural
Page 8 of14Brain (2007)L. Menzies et al.
(r=0.16, N=30, P=0.41) (Fig. 4B). Taken together with
the results on variance of within-pair differences in MRI
and cognitive phenotypes, these data suggest that compared
with a behavioural measure of response inhibition, the MRI
systems correlated with response inhibitory processing are
more strongly determined by familial factors shared
between true proband–relative pairs.
These data provide empirical support for each of the four
hypotheses motivating this study. We have confirmed that
response inhibition, indexed by SSRT, is abnormal in
patients with OCD and their first-degree relatives. We have
identified extensive brain systems where grey matter
variability in stop-signal task performance; and we have
shown that patients with OCD and their relatives have
structural abnormalities in these systems compared to
healthy volunteers. Finally, we have exploited our pro-
band–relative pair design to assess the familiality of
variation in cognitive and associated MRI phenotypes and
shown that variation in brain systems correlated with
inhibitory function is likely determined by familial factors
in common between patients and their first-degree relative.
In short, we have combined structural neuroimaging and
cognitive testing to identify for the first time a neurocog-
nitive endophenotype of OCD.
Inhibition and OCD
The classical clinical symptoms of OCD are persistent,
obsessional thoughts attended by an inability to inhibit
compulsive behaviour repetition. It therefore seems almost
self-evident that inhibitory processes might be abnormal in
OCD and there is empirical evidence in support of this
hypothesis. Patients with OCD are impaired across a range
of tests of inhibitory function including motor inhibition
tasks, e.g. go/no-go and stop-signal tasks (Bannon et al.,
2002; Chamberlain et al., 2005, 2006; Penades et al., 2006);
attentional set shifting tasks, such as the object alternation
task (Abbruzzese et al., 1997; Aycicegi et al., 2003); the
1996; Watkins et al., 2005; Chamberlain et al., 2006)
response strategy in response to changing criteria for task
performance; and the Stroop task, a putative test of
cognitive inhibition (van den Heuvel et al., 2005b;
Penades et al., 2006). Our data, indicating that patients
are impaired on a motor inhibition task, are consistent with
Brain systems associated with
Prior data on human brain systems underlying motor
inhibition have been provided by lesion studies and
neuroimaging. In a structural MRI study of patients
Aron et al. (2003b) found that grey matter volume deficit
in the right inferior frontal gyrus (RIFG) was specifically
predictive of prolonged SSRT; for a review of further
evidence implicating the RIFG in motor inhibition see Aron
et al. (2004). Functional neuroimaging studies of motor
inhibition have generally identified a more extensive but
predominantly right-sided system of regions including
orbitofrontal, dorsolateral and medial frontal, temporal
and parietal cortices, the
ganglia (Godefroy et al., 1996; Humberstone et al., 1997;
Garavan et al., 1999; Rubia et al., 1999, 2000, 2001a, b
and c; Horn et al., 2003).
In keeping with the focus on the RIFG in previous
literature, we also found evidence that reduced grey matter
density in this region was associated with prolonged SSRT.
However, consistent with the functional neuroimaging data
inhibition, we found that brain areas negatively correlated
with latency of inhibitory processing in our data were not
restricted to the RIFG but included regions such as bilateral
orbitofrontal cortex, right premotor and anterior cingulate
cortex, left dorsolateral prefrontal cortex and bilateral
temporal cortex. We also found a number of regions in
cingulate cortex, parietal and dorsal occipital cortex, and
basal ganglia where SSRT was positively correlated with
grey matter density, i.e. impaired inhibitory function was
predicted by increased grey matter density.
a network ofregionsin
Brain systems implicated in OCD
‘Affective’ fronto-striatal circuits including the orbitofrontal
cortex, the striatum and anterior cingulate have been invoked
theoretically to account for OCD. As already discussed
(Introduction, Supplementary Figs 1 and 2), there is some-
what inconsistent evidence in support of this hypothesis
from structural MRI studies published to date. However,
there is additional evidence for orbitofrontal dysfunction in
OCD from positron emission tomography (PET) studies
reporting abnormal resting or task-related orbitofrontal
metabolism in OCD (Baxter et al., 1987, 1988; Nordahl
et al., 1989; Swedo et al., 1989; McGuire et al., 1994; Rauch
et al., 1994). Functional MRI studies investigating executive
function in OCD have also identified fronto-striatal
abnormalities in patients (Maltby et al., 2005; van den
Heuvel et al., 2005a; Remijnse et al., 2006; Rauch et al.,
2007); and there is evidence that affective fronto-striatal
symptom provocation (Breiter et al., 1996; Adler et al.,
2000; Phillips et al., (2000); Mataix-Cols et al., 2004; Nakao
et al., 2005; Schienle et al., 2005). Moreover, fMRI studies
have often also shown changes in activation of theoretically
unanticipated regions such as the dorsolateral prefrontal
cortex (DLPFC) and parietal cortex. For example, van den
Heuvel et al. (2005a) found decreased DLPFC activation in
Neurocognitive endophenotypes of OCD Brain (2007)Page 9 of14
OCD patients compared with controls, Maltby et al. (2005)
found hyperactivity in the anterior and posterior cingulate
and lateral prefrontal cortex during unsuccessful stopping
in a go/no-go task, and Schienle et al. (2005) found
increased activation in DLPFC and parietal areas. There is
corroborative evidence for parietal cortical abnormalities in
OCD from PET (Nordahl et al., 1989; McGuire et al., 1994;
Rauch et al., 1994) and SPECT (Lucey et al., 1995)
Thus our findings of extensive grey matter abnormality
in orbitofrontal cortex, ventral and dorsal prefrontal cortex,
cingulate cortex, parietal cortex, striatum and cerebellum
include many of the regions anticipated by an orbitofronto-
striatal model; but also include other regions (such as
parietal cortex or cerebellum), which have been reported in
the functional neuroimaging literature and by some of the
prior structural MRI studies (Pujol et al., 2004; Valente
et al., 2005; Soriano-Mas et al., 2007), yet are not so readily
accommodatedby an exclusively
model. As the neuroimaging evidence base grows and
becomes more replicable, we predict that this will drive
development of systems-level theory beyond the model of
abnormality in a single cortico-striatal circuit.
Candidacy of cognitive and MRI
endophenotypes of OCD
There is no universally accepted set of criteria to judge
the validity of a candidate endophenotype. However,
Gottesman proposed that endophenotypes are quantitative
heritable traits that are abnormal in both probands
and their relatives (Gottesman and Gould, 2003). How
well do our data on behavioural and MRI markers of
inhibitory processing satisfy these criteria?
Both behavioural and MRI markers were quantitatively
abnormal on average in both patients and relatives
compared to healthy volunteers. However, the demonstra-
tion of strict sense heritability is impossible in the absence
of a twin design controlling for shared environmental
influences on trait variation in genetically related indivi-
duals. We have therefore adopted the logistically more
feasible approach of assessing familial (rather than strictly
heritable) effects on trait variation in a proband–relative
pair design. We have used an innovative permutation test
of the within-pair variance in trait differences, and within-
pair correlations, to quantify familiality of variation in
discordant proband–relative pairs, finding evidence for
significant familial effects on variation in the MRI systems
associated with inhibitory processing, but not on the
behaviourally derived SSRT measure. We conclude that
the MRI markers of inhibitory processing more completely
satisfy Gottesman’s criteria (by virtue of their greater
familiality), perhaps reflecting the fact that structural
variation in brain systems is more proximal to genetic
effects than variation in task performance. This result
strengthens the rationale for searching for neurocognitive
endophenotypes in other complex behavioural disorders
such as schizophrenia and bipolar disorder.
Utility and specificity of endophenotypes
Using probands and first-degree relatives to identify
of discounting any non-familial explanations (such as
exposure to psychotropic medication in the probands) for
abnormal patterns of brain structure. Endophenotypes
could also be used to refine diagnostic subclassification of
patients (based on the extent of their expression of
endophenotypic abnormality), or to highlight additional
abnormalities occurring only in patients (not relatives),
although these were not objectives of the current study.
However, it is interesting also to consider how our results
could be exploited in future to identify specific genes
determining variation in brain systems important for motor
inhibition. In principle, the grey matter density of the
motor inhibitory system could be used as a quantitative
trait in a genome-wide search for associated polymorphisms
by quantitative trait locus (QTL) analysis. Although we
currently lack sufficient experience of genome-wide QTL
mapping based on human imaging, this has been success-
fully used to identify genetic markers associated with
imaging measurements of cortical and subcortical grey
matter volumes in inbred strains of mice (Beatty and
Another question concerns the diagnostic specificity of
an inhibitory endophenotype for OCD. Since the present
study focused on patients with predominantly classical
washing/checking symptoms, these results may not general-
ize to other OCD subgroups. On the other hand, recent
evidence has accumulated to suggest that a deficit in
response inhibition may also be an endophenotype for
attention-deficit/hyperactivity disorder (ADHD) (Aron and
Poldrack, 2005; Crosbie and Schachar, 2001). Behavioural
deficits in response inhibition have been identified in
ADHD (Casey et al., 1997; Vaidya et al., 1998; Chamberlain
and Sahakian, 2007) and previously related to abnormal
activation of right inferior prefrontal cortex and left caudate
during the stop-signal task (Rubia et al., 1999). Clinically
unaffected first-degree relatives
have also shown deficits on motor response inhibition in
the go/no-go task (Slaats-Willemse et al., 2003). Future
studies of ADHD patients and their relatives would
establish if impairments in response inhibition are under-
pinned by anatomical variation in the same brain systems
that we have identified in OCD; and test that behavioural
and/or imaging markers of impaired inhibitory function
satisfy the Gottesman criteria as endophenotypes of ADHD.
Without such data, it is speculative but intriguing to
consider that the same neurocognitive endophenotype
might be related to important dimensions of these two
traditionally distinct clinical syndromes.
Page10 of14Brain (2007) L. Menzies et al.
An innovative aspect of this study was the use of the
PLS method, a technique previously employed mainly in
the analysis of functional neuroimaging data (McIntosh and
Lobaugh, 2004), to find structural brain systems optimally
correlated with a behavioural variable. PLS was attractive
for our purpose because there were prior theoretical
reasons to expect that inhibitory deficits in OCD might
be related to structural abnormalities at a systems level,
rather than in a discrete brain region, and PLS is designed
to optimize correlation between one or more exogenous
(behavioural) variables and a set of correlated image
voxels, without specifying a priori which voxels are likely
to be components of the behaviourally correlated system.
systems level has the considerable merit of minimizing
the number of significance tests required. To search for
significant structure–function associations at voxel level
would entail approximately 150000 tests, with concomi-
tantly severe thresholds for significance to mitigate the
multiple comparisons problem; whereas testing for a
systems level associationin PLS required
significance test, which could be thresholded conventionally
In principle, partial least squares can be used as a
multivariate analysis method to explore the relationships
imaging variables. Here we have used it to test for
association between a single behavioural variable and grey
matter density at multiple voxels. To distinguish this
application from the more general multivariate case,
where PLS is used to find associations between multiple
behavioural variables and imaging measures at multiple
voxels, we have referred to our application as a multivoxel
analysis because the (non-parametric) test for significant
association is based on behavioural correlations summed
across all voxels in the brain. Thus PLS with a single
behavioural variable is conceptually close to the analysis of
OCD imaging data by Soriano-Mas et al. (2007), based on
the method proposed by Worsley et al. (1995, 1997). The
main differences are that Soriano-Mas et al. (2007) tested
a multivoxel measure of between-group difference, whereas
we have tested a multivoxel measure of continuous
groups. More technically, the PLS approach has the relative
merit of an entirely non-parametric (resampling-based)
approach to significance testing which confers greater
flexibility in choice of test statistics and greater robustness
against violation of the conditions required for validity of
parametric tests (Worsley et al., 1995, 1997).
To the best of our knowledge, this is the first example of
a potentially powerful experimental and data analytic
strategy to identify cognitive and related brain structural
neuropsychiatric disorders. In a sample of OCD patients,
their first-degree relatives and unrelated healthy volunteers,
we have found substantial evidence that variation in motor
inhibitory control is correlated with grey matter density
changes in an extensive system comprising orbitofrontal,
cingulate and parietal cortical areas as well as striatal
and other subcorticalregions.
rigorously by Gottesman’s criteria the candidacy of these
inhibition-related brain systems as the first neurocognitive
endophenotype for obsessive-compulsive disorder.
of heritablebut geneticallycomplex
We have also tested
Supplementary material is available at Brain online.
This work was funded by the National Alliance for
Research on Schizophrenia and Depression (Distinguished
Investigator Award to E.B.), the Wellcome Trust (T.W.R.,
B.J.S.), the Harnett Fund (University of Cambridge;
MB/PhD studentship to L.M.), the Medical Research
Council (MB/PhD studentship
National Institutes of Mental Health and Biomedical
Imaging & Bioengineering (Human Brain Project; E.B.).
The Behavioural and Clinical Neuroscience Institute is
supported by a joint award from the Medical Research
Council and WellcomeTrust.
Dr Robbins and Dr Sahakian consult for Cambridge
Cognition. All other authors report no competing interests.
We thank Drs Randy McIntosh and Nancy Lobaugh for
expert support in using their PLSGUI software. Funding to
pay the Open Access publication charges for this article was
provided by the Wellcome Trust.
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