Searching for a structural endophenotype in psychosis using computational morphometry

Psychiatry Research: Neuroimaging 122(2003) 153–167

0925-4927/03/$ - see front matter� 2002 Elsevier Science Ireland Ltd. All rights reserved.doi:10.1016/S0925-4927(02)00125-7

Searching for a structural endophenotype in psychosis usingcomputational morphometry

Machteld Marcelis , John Suckling , Peter Woodruff , Paul Hofman , Ed Bullmore ,a b,c,f d e f

Jim van Os *a,g,

Department of Psychiatry and Neuropsychology, European Graduate School of Neuroscience, Maastricht University,a

P.O. Box 616, 6200 MD Maastricht, The NetherlandsClinical Age Research Unit, Department of Health Care of the Elderly, Guy’s King’s and St. Thomas’ Medical School, London,b

UKDepartment of Biostatistics and Computing, Institute of Psychiatry, London, UKc

University of Sheffield, Sheffield, UKd

Department of Radiology, University Hospital Maastricht, Maastricht, The Netherlandse

Department of Psychiatry, University of Cambridge, UKf

Division of Psychological Medicine, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UKg

Received 26 November 2001; received in revised form 17 September 2002; accepted 14 November 2002

Abstract

Structural cerebral abnormalities are frequently observed in schizophrenia. These abnormalities may indicatevulnerability for the disorder, as evidenced by reports of familial clustering of measures identified through region-of-interest analyses using manual outlining procedures. We used computational morphometry to detect structuraldifferences within the entire brain to further examine possible structural endophenotypes. Magnetic resonance imagingscans were obtained in 31 psychotic patients, 32 non-psychotic first-degree relatives of psychotic patients and 27healthy controls. The images were processed using an automated procedure, yielding global grey matter, white matter,CSF and total brain volume. The relative distribution of grey matter was compared between groups on a clustered-voxel basis. Global grey matter and total brain volume did not differ between the groups. White matter volume wassignificantly higher and CSF volume significantly lower in relatives compared to both cases and controls. Theclustered-voxel based group comparison yielded evidence for significant grey matter deficits in fronto-thalamic-cerebellar regions, in psychotic patients, whereas the most prominent deficits in relatives involved the cerebellum.Patients with psychosis and first-degree healthy relatives of patients with psychosis show cerebellar abnormalities,which may constitute a marker of genetic transmission.� 2002 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Schizophrenia; Structural magnetic resonance imaging; Voxel-based; Cerebellum; Marker; Family study; Brain

*Corresponding author. Tel.:q31-43-3299-773; fax:q31-20-877-9249.E-mail address: [email protected](J. van Os).

154 M. Marcelis et al. / Psychiatry Research: Neuroimaging 122 (2003) 153–167

1. Introduction

Many questions remain regarding the origins ofthe structural cerebral abnormalities associatedwith schizophrenia. The structural alterations havebeen directly related to the clinical phenotype, buthave also been found to be indicators of(genetic)liability for the disorder(endophenotypic markersof liability). In addition, certain structural brainalterations may be the result of medication and theillness itself, thus not reflecting a possible causebut rather a consequence of the disorder.

Studies of first-degree relatives are useful in thesearch for endophenotypes. In addition to evidencefor familial aggregation of ventricular enlargementtypically derived from computed tomography scan-ning (reviewed in Cannon and Marco, 1994),recent magnetic resonance imaging(MRI) studiesshow more diverse patterns of cortical andyorsubcortical alterations in both patients with schiz-ophrenia and their first-degree relatives(Seidmanet al., 1997; Sharma et al., 1997; Cannon et al.,1998; Staal et al., 1998; Seidman et al., 1999;Staal et al., 2000; Wright et al., 2000). TraditionalMRI studies have been based on a priori definedregions of interest(ROI) and manual outliningprocedures to assess volumetric measurements.This method, however, may preclude the observa-tion of significant but unexpected findings, andmay have contributed to inconsistencies in theliterature and publication bias(Wolkin et al.,1998). The availability of computational mor-phometric techniques that permit the detection ofstructural differences within the entire brain(Andreasen et al., 1994a; Collins et al., 1994;Wright et al., 1995; Wolkin et al., 1998; Bullmoreet al., 1999) may possibly lead to more consistentresults regarding the origins of cerebral abnormal-ities in schizophrenia and their possible role in thepathophysiology. In addition, these techniques aregenerally more automated and faster than tradition-al ROI methods of analysis, providing the oppor-tunity to investigate larger samples. In the presentfamily study, such a computational morphometrictechnique was used, which comprised both globaland regional(clustered-voxel) comparisons of therelative distributions of the separate brain tissues.The present article focuses on grey matter, for

which we hypothesised that structural abnormali-ties would be present in psychotic patients, andnot necessarily in the same regions given thepossible effects of the illness and its treatment, innon-psychotic first-degree relatives of patients withpsychosis.

2. Methods

2.1. Study sample

MRI scans were acquired from 31 patients withpsychosis, 32 non-psychotic first-degree relativesof patients with psychosis and 27 healthy controls.The present subsample is part of a larger study,the Maastricht Psychosis Study(Krabbendam etal., 2001).

Patients with a lifetime history of clinical psy-chosis (of at least 2 weeks) according to theRearch Diagnostic Criteria(RDC) (Spitzer et al.,1978), who were not in need of in-patient treat-ment, were recruited at the community mentalhealth centre in Maastricht, the Netherlands. Non-psychotic first-degree relatives were recruitedthrough the participating patients, as well asthrough a local relative association. Relatives hadto be free from a lifetime history of psychosis.Unrelated healthy controls were sampled from thegeneral population, using a mailing procedure torandomly selected households in the local catch-ment area. Controls were excluded whenever theyhad a personal or family history of psychosis orother psychiatric disorder requiring hospitaladmission.

The present sample included 78 families, ofwhich 11 families contributed one patient and onediscordant sibling, and one family contributed twonon-psychotic relatives(parent and sib). From theremaining 66 families, 20 independent patients, 19independent first-degree relatives, and 27 controlswere included.

Other inclusion criteria for all participants(cas-es, relatives and controls) were being in the agerange of 18–55 years and being in good health asdetermined by a physical examination, electrocar-diography and routine laboratory investigations.Individuals with a history of severe head traumawith loss of consciousness, neurological disorders

155M. Marcelis et al. / Psychiatry Research: Neuroimaging 122 (2003) 153–167

andyor other medical disorders that might havesignificantly affected brain function or structurewere excluded, as well as individuals who usedalcohol in excess of five standard units per day orillicit drugs on a weekly basis.

2.2. Clinical and diagnostic procedures

Patients, relatives and controls were interviewedwith the Brief Psychiatric Rating Scale(Overalland Gorham, 1962; Lukoff et al., 1986) and thePositive and Negative Syndrome Scale(Kay et al.,1987). They were additionally screened for symp-toms listed in the OCCPI(McGuffin et al., 1991).Where necessary, additional information wasderived from case notes and interviews with theresponsible medical officer. Using the combinedinformation, the computerised programOPCRIT

(McGuffin et al., 1991) yielded the following RDCdiagnoses in the cases: schizophrenia(ns25) andschizo-affective disorder(ns6). Among the first-degree relatives, there were four lifetime diagnosesand in four first-degree relatives a(life-time) ofmajor depression. No other psychiatric disorderswere detected in the relatives, nor were any of thecontrols diagnosed with psychiatric illness. Hand-edness was assessed using the Annett HandednessScale(Annett, 1970). To determine lifetime historyof alcohol and drug use, the appropriate sectionsof the Composite International Diagnostic Inter-view (CIDI) (Smeets and Dingemans, 1993) wereused.

Demographic characteristics did not significant-ly differ among the three groups in terms of age,sex, handedness, height and paternal level of occu-pation (see Table 1). Significant differencesbetween the three groups were found for thenumber of years in education(Fs3.31, d.f.s2,87,Ps0.04) and for educational achievement(Fs4.65, d.f.s2,87,Ps0.01). Mean number of yearsof formal education was higher in the group ofrelatives compared to both cases and controls, whowere balanced in this respect. Mean level ofeducational achievement was lowest in the patientgroup with no difference between relatives andcontrols.

The mean duration of illness in the patients was8.5 years(S.D.s5.8). Twenty-eight patients were

receiving antipsychotic medication(14 patientsreceived atypical, 13 received typical and onepatient received a combination of atypical andtypical antipsychotic medication). The groups didnot differ on mean alcohol intake(in units perweek) over the last yearwpatients: 9.6(S.D.s12.5), relatives: 5.0 (S.D.s8.0), controls: 5.5(S.D.s5.8), Fs2.29, d.f.s2, 87,Ps0.11). Therewere four patients and one relative who had useddrugs within the past year(two patients and onerelative had used cannabis, one patient had usedboth cannabis and a stimulant drug, and one patienthad used cocaine). All of them stopped usingdrugs at least 1 month prior to study participation.

All the subjects gave written informed consentafter the procedures had been fully explained inconformity with the local ethics committeeguidelines.

2.3. Image acquisition

MRI scans were obtained at the Department ofRadiology, University Hospital Maastricht, TheNetherlands, with a Gyroscan NT T-I1(PhilipsMedical Systems) operating at 1.5 Tesla. Inter-leaved two-dimensional dual-echo fast spin-echoimages (60 slices, 3-mm thick, 0.3-mm gapbetween slices) were acquired and angled parallelto the clivus, covering the entire brain. Protondensity (PD) weighted and T2-weighted imageswere acquired simultaneouslywecho time(TE)1s20 ms, TE2s100 ms, repetition time(TR)s4000ms, echo train lengths6, total acquisition times10 min 12 sx. The matrix size and field of viewwere set at 256=205 and 22 cm, respectively. Thenumber of signal averages was one.

2.4. Image processing

Image processing and computations were doneon a SUN Ultra 10 (Sun MicroSystems Inc.,Mountain View, CA, USA) workstation with theBAMM software (Brain Activation and Morpho-logical Mapping, University of Cambridge, UK).Initially, a mask of parenchymal tissue was gen-erated from linear scale-space features derivedfrom the PD weighted images(Suckling et al.,1999a). Each voxel in the mask was then catego-


Table 1Demographic characteristics of the study sample

Cases Relatives Controls F or x2 PNs31 Ns32 Ns27 statisticMean(S.D.) Mean(S.D.) Mean(S.D.)

GenderMale 15 14 12 0.2 0.93Female 16 18 15

Age (years) 30.7 (7.5) 35.5 (10.0) 35.5 (9.8) 2.8 0.07

Handedness(total of 14-item 23.7(6.0) 23.1 (8.2) 23.5 (6.7) 0.1 0.93questionnaire score)

Years of education 13.4(2.9) 15.0 (3.0) 13.3 (2.6) 3.31 0.04

Level of educational 3.7(1.3) 4.8 (1.7) 4.3 (1.5) 4.65 0.01achievement

Paternal level of occupational 4.1(1.7) 3.8 (1.1) 3.4 (1.5) 1.64 0.20achievement

rised in terms of the proportion occupied by greymatter, white matter, CSF or durayblood vessels.This algorithm partitioned the feature space formedby the two MR echoes(PD and T2 weighting)using a four-class modified fuzzy clusteringscheme, and assigned continuous membership ofeach tissue class to every voxel(Suckling et al.,1999b). Axial non-uniformity of image contrastdue to the reduction in sensitivity at the edges ofthe transmittingyreceiving coil was corrected witha moving window scheme. Classifying data in thismanner allows for changes in the distribution ofvoxels in the feature space. For a detailed descrip-tion, see Suckling et al.(1999b). Total cerebraltissue volumes were obtained by summing overall proportions and multiplying by the voxelvolume.

The next step was the transformation of thethree maps obtained from each individual into astandard stereotactic space. To do this, a templateimage was first constructed by piecewise linearrescaling of a subset of five PD-weighted imagesfrom the control group. Using AFNI software(Cox, 1994), anatomical landmarks were identi-fied, including the anterior and posterior commis-sures and the lateral, superior and inferiorconvexities of the cerebral surface. The distancesbetween landmarks were linearly rescaled toapproximate each individual image to the reference

brain depicted in a standard stereotactic atlas(Talairach and Tournoux, 1988).

The five transformed PD images were thenaveraged to produce a single template image instandard space. The affine transformation, whichminimised the sum of grey level differencesbetween each individual’s PD weighted image andthe template image, was identified by the Fletcher–Davidson–Powell algorithm(Press et al., 1992;Brammer et al., 1997). This individually estimatedtransformation matrix was applied, in turn, to eachof that subject’s three tissue probability maps toregister them in standard space. This differs fromthe ‘optimised VBM’ method recently developedby Good et al.(2001a,b). In this method, an imageis segmented in native space, and these segmentedmaps are then separately registered to segmentedtemplate images in standard space. A novel featureis that it incorporates a step to adjust images forthe effect of the transformations necessary to mapimages into standard space. The linear registrationused in the present study applies a constant scalingfactor across the image, and does not produce localvolumetric changes.

2.5. Statistical analysis of global tissue

Both cases and relatives were compared sepa-rately to the control reference group, in line with


our expectation that differences between groupsmight arise in different parts of the brain. Groupdifferences in the three main brain tissue volumes(grey, white and CSF volume) and total correctedbrain volume in the native space of each individual(TCBVsgreyqwhiteqCSF volumes in cubiccentimetres) were analysed using multiple regres-sion techniques(STATA, 2001). Effect sizes wereexpressed as the regression coefficient(b), statis-tically evaluated by the Wald test. In order to takeinto account the fact that some individuals in thesample of relatives and patients were clustered inthe same families, compromising the statisticalindependence of the observations, theCLUSTER

and ROBUST options were used in theSTATA

regression analyses. TheCLUSTERoption combinedwith the ROBUST option allows for the use ofobservations that are not independent within clus-ters(in this case, within families) and obtains theHuberyWhiteySandwich estimator of varianceinstead of the traditional variance estimator. Theseprocedures result in standard errors that are adjust-ed for clustering within families. As describedabove, there were 12 families with a patient–relative(or relative–relative) pair, leaving 66 fam-ilies that contributed only one case, relative orcontrol.

The association between the group variables(comparing relatives and cases to the referencegroup of controls) on the one hand, and braintissue volumes(outcome variables) on the other,was investigated. Age, sex, handedness and TCBVwere used as covariates.

2.6. Statistical analysis of tissue probability maps

Before the calculation of between-group differ-ences, all images were smoothed with a two-dimensional Gaussian filter of 4.2-mm FWHM.An ANCOVA model was then fitted at each voxelin standard space where there wereN proportionalvolume (probability) estimates for each tissueclass. The model is written below with tissueproportional volume as the dependent variable:

T sb qaGroupqb Ageqb Handqb Genderj 0 j l j 2 j 3 j

qb Globalqe4 j j

Here, T denotes the proportional volume ofj

grey matter, white matter or CSF estimated at agiven voxel for thejth individual ande is randomj

variation. The independent variables Group , Age ,j j

Hand , Gender and Global denote the group mem-j j j

bership, age, handedness, gender and global tissuevolume, respectively, of thejth individual.

This model was fitted at each intra-cerebralvoxel of the observed data, with each class ofproportional volume taken in turn as the dependentvariable, to yield a set of three ‘effect maps’ ofcoefficienta standardised by its standard error: i.e.a*sayS.E.(a). This model was also fitted 10times at each voxel for each tissue class afterpermutation of the elements of the factor codinggroup membership. The order of permutation waseither entirely random or between pairs if the datawere considered repeated measures. This generated10 randomised or permuted effect maps for eachtissue class. Both observed and permuted effectmaps were then thresholded such that if the abso-lute value ofa* was less than 1.96(2 S.D.s fromthe mean of the normal distribution), the value ofthat voxel was set to zero, and if the absolutevalue of a* was greater than 1.96, the value ofthat voxel was set toa* equal to y1.96. Thisprocedure generates several clusters of suprathres-hold voxels that are spatially contiguous in threespatial dimensions. The sum of suprathresholdvoxel statistics, or ‘mass’, of each three-dimen-sional cluster was measured in each of the 10permuted effect maps generated for each tissueclass; and these measurements were ordered tosample the permutation distribution of cluster massunder the null hypothesis of zero differencebetween groups. The mass of each cluster in theobserved effect maps was then tested against two-tailed critical values obtained from the correspond-ing permutation distribution. This non-parametricor distribution-free hypothesis testing procedurewas adopted because there is considerable evi-dence from functional imaging that cluster levelstatistics, incorporating information about the spa-tial neighbourhood of each voxel, may be moresensitive than voxel test statistics(Poline andMazoyer, 1993; Rabe-Hesketh et al., 1997), buttheoretical distributions for cluster statistics maybe intractable or of limited generalisability(Friston


Table 2Mean volumes of grey matter, white matter, CSF and total brain volume in cubic centimetres after automated segmentation procedures

Cases Relatives ControlsMean(S.D.) Mean(S.D.) Mean(S.D.)

Grey matter 559.5(64.0) 572.3(56.4) 564 (45.1)White matter** 545.4(63.4) 588.3(71.2) 552.7(53.4)CSF* 169.2(32.0) 157.5(30.4) 166.3(38.3)TCBV 1274.1(127.2) 1317.9(133.4) 1283.7(116.9)

Results from the regression analyses were adjusted for age, sex, handedness and TCBV.Relatives vs. controls:bs21.4,Fs8.51, d.f.s1,77,Ps0.0046; relatives vs. patients:bs22.2,Fs7.19, d.f.s1,77,Ps0.0090.**

Relatives vs. controls:bsy15.0, Fs5.88, d.f.s1,77, Ps0.018; relatives vs. patients:bsy23.4, Fs13.26, d.f.s1,77,P-*

0.001.

et al., 1994; Poline, 1997). Cluster-level inferencealso mitigates the multiple comparisons problemassociated with voxel-level analysis, simply byreducing the total number of tests by one or twoorders of magnitude. For greater procedural detail,and a comparative validation of nominal Type Ierror control by this method, see Bullmore et al.(1999).

In order to deal with the fact that the sample ofrelatives and patients was partly dependent asdescribed above, paired statistical analyses wereconducted in the patient–relative comparison, thisrepresenting the more conservative approach(although results were similar when unpaired testswere used).

3. Results

3.1. Comparison of global volumes

There was no significant effect of group oneither TCBV (patients vs. controls:b equal toy8.8, Fs0.11, d.f.s1,77, Ps0.74; relatives vs.controls: bs35.0, Fs1.49, d.f.s1,77, Ps0.23)or grey matter volume, although the patients andrelatives tended to have lower grey matter volumesthan the control group(patients vs. controls:bequal toy7.8, Fs1.32, d.f.s1,77, Ps0.26; rel-atives vs. controls:bsy6.5, Fs1.26, d.f.s1,77,Ps0.27). In the relatives, white matter volumewas significantly increased compared to both thecontrol group(relatives vs. controls:bs21.4,Fs8.51, d.f.s1,77,Ps0.0046) and the patient group(relatives vs. patients:bs22.2, Fs7.19, d.f.s1,77, Ps0.0090), and total(intraventricular and

extracerebral) CSF volume was significantlydecreased compared to both the control and thepatient group(relatives vs. controls:bsy15.0,Fs5.88, d.f.s1,77, Ps0.018; relatives vs.patients: bsy23.4, Fs13.26, d.f.s1,77, P-0.001). (Table 2.)

3.2. Comparison of images

The results of using cluster mass to test(bynon-parametric inference) for differences betweenpsychotic patients and controls are shown in Fig.1A. Differences between non-psychotic relativesand controls are shown in Fig. 1B, and differencesbetween patients and relatives are shown in Fig.1C. The clustered voxels are superimposed on thePD-weighted grey matter template image. Clustersindicating deficits in tissue density are colouredyellow, whereas clusters indicating excesses arecoloured purple. Clusters were conservativelythresholded withPF0.005, the level at which thenumber of expected false-positive clusters perimage equals one(i.e. P-value times the numberof observed clusters).

The location in Talairach coordinates and thesize of each cluster’s centroid are summarised forall comparisons in Tables 3–5.

3.3. Case–control comparison

In the case–control comparison, the number ofobserved suprathreshold clusters by thresholdingwith PF0.005 (estimated number of false-posi-tivess1) was: excesss1, deficits6. Thus, a sig-nificant difference between controls and patients


Fig. 1. Significant differences using cluster mass to test(by randomisation) for grey matter differences between:(a) 31 psychoticpatients and 27 control subjects;(b) 32 non-psychotic first-degree relatives of psychotic patients and 27 control subjects; and(c)31 psychotic patients and 32 non-psychotic first-degree relatives. Clusters indicating deficits in tissue density are coloured yellow,whereas clusters indicating excess areas are coloured purple. The cluster-wise probability of Type I error was atPs0.005. Resultsare adjusted for total tissue volume, age, sex, and handedness. Numbers indicate approximate Talairachy-coordinates.

in grey matter density was identified at sevenspatially extensive three-dimensional voxel clus-ters. Six clusters indicated reduced grey matterdensity in the patients:(i) a cluster including thecaudate nucleus and the amygdala(left); (ii) acluster including the cingulate gyrus, central gyrusand medial frontal gyrus(left); (iii ) a clusterextending from the inferior frontal gyrus(opercularpart) to insula (right); (iv) a cluster in the rightthalamus(centroid in the dorsal medial nucleus);and (v) two clusters in the cerebellum(hemi-spheres, bilateral). Increased grey matter densityin patients vs. controls was found in one cluster

including putamen and globus pallidum(right)(Fig. 1A).

3.4. Relative–control comparison

In the relative–control comparison, the resultsof thresholding cluster mass withPF0.005 (esti-mated number of false positivess1) was 6(excesss1; deficitss5). One cluster indicatinggrey matterdeficit was found in the temporal lobe(fusiform gyrus) (right) and the other four deficitclusters were found bilateral in the cerebellarhemispheres. There was one cluster indicating grey


Table 3Summary of regional grey matter density differences—Case–control comparison

Cerebral region Side N x y z

Grey matter deficitCaudate nucleus, amygdala L 1051 4.8 18.6 6.6Cingulate gyrus, central gyrus, L 576 0.5 17.2 42.2

medial frontal gyrusInferior frontal gyrus R 745 y35.7 9.9 12.9(opercular part), insula

Thalamus: dorsal R 208 y7.3 y16.1 7.2medial nucleus

Cerebellum R 1605 y26.0 y53.6 y55.9Cerebellum L 1253 22.0 y55.9 y59.1

Grey matter excessPutamen, globus pallidum R 1649 y25.4 y1.8 14.8

The location of each cluster’s centroid is given in Talairach coordinateswx, y andz (mm)x, Nsnumber of voxels in each cluster.The cluster-wise probability threshold wasPs0.005.

Table 4Relative–control comparison


Grey matter deficitTemporal lobe R 269 y36.7 1.6 y22.6(fusiform gyrus)

Cerebellum R 2034 y25.0 y42.2 y62.2Cerebellum R 893 y19.4 y55.9 y48.8Cerebellum L 1519 20.9 y56.1 y55.3Cerebellum R 609 y2.2 y78.6 y32.3

Grey matter excessSuperior frontal gyrus L 447 1.3 y14.8 54.7


Table 5Caseyrelative comparison


Grey matter deficitFrontal limbic area, superior L 2256 1.6 3.1 54.3

frontal gyrusInsula, inferior frontal gyrus R 784 y38.8 3.9 13.0(opercular part)

Cingulate gyrus L 471 3.2 y41.1 45.5Paracentral lobule L 533 4.0 y59.5 21.9

Grey matter excessPutamen, globus pallidum R 1966 y25.5 y4.6 13.6



matter excess in the superior frontal gyrus(left).(Fig. 1B).

3.5. Case–relative comparison

When the group of patients were compared withthe group of relatives using paired statistical tests(threshold PF0.005, estimated number of falsepositivess1), five suprathreshold clusters wereobserved(four deficits; one excess). Thus, a sig-nificant difference between relatives and patientsin grey matter density was identified at five spa-tially extensive three-dimensional voxel clusters.The clusters representingdeficits included:(i) thefrontal limbic area and superior frontal gyrus(left);(ii) insula and inferior frontal gyrus;(iii ) cingulategyrus(left); and(iv) paracentral lobule(left). Onecluster indicatingincreased grey matter densitywas found which included the putamen and theglobus pallidum(Fig. 1C).

4. Discussion

Using computational morphometrics with struc-tural MRI to investigate global brain tissue volumeand regional group differences in tissue densitywith clustered-voxel statistics, the current familystudy provided evidence for detectable cortical andsubcortical grey matter deficits being present notonly in psychotic patients, but also in non-psy-chotic first-degree relatives of psychotic patients.Results showed substantial cerebellar grey matterdeficits in both groups. In addition, there wasevidence for grey matter deficits in the frontal andtemporal lobe, thalamus, insula, cingulate gyrusand caudate nucleus in patients, as well as fortemporal grey matter deficits in relatives.

4.1. Methodological considerations

Scans were segmented according to the movingwindow implementation(Suckling et al., 1999a),which reduces local misclassification by increasinggrey matter in areas where it is systematicallyunder-represented due to non-uniformity of imagecontrast. Nevertheless, it cannot be completelyruled out that none of this error remained, and ifso, this may have especially affected the cerebel-

lum as being located in the lower end of the coil.However, if existent, such an error is likely to benon-systematic, especially since the subjects of thethree groups were scanned in random orderthroughout the study period. This would make itdifficult to explain why grey matter alterationswere found in patients and relatives, but not incontrol subjects.

More generally, the reliance of computationalmorphometrics on image registration has broughtinto question its validity. As pointed out by Book-stein (2001), one important consequence of theaffine transformation used for this work is thatbetween-group differences at a given voxel mayrepresent a mismatch of cortical locations due tolocally imperfect registration rather than volumet-ric changes. For example, if there is pathologicaldeformation or displacement of an anatomicalstructure in patients, then affine transformationwill not generally correct this local deformitymatching only global size and shape. This will bemanifest as a discrepancy at the edges or bounda-ries of the structure due to its local misalignmentwith the corresponding structure in the templateimage. Bookstein(2001) is concerned that theseresidual misregistration signals cannot be disam-biguated from volumetric differences betweengroups in proportion of grey matter, say, at aperfectly registered voxel representing preciselythe same anatomical structure in all subjects.Although this argument is mathematically sound,we suggest that misregistration signals can oftenbe empirically recognised as such by the existenceof complementary changes in adjacent voxels rep-resenting different tissue classes. For example, afocus of cortical grey matter deficit immediatelyadjoining a focus of subcortical white matterexcess seems likely to be due to local misregistra-tion of the cortical boundary with subjacent whitematter. In any case, systematic differences betweengroups in proportion or probability of grey orwhite matter are relevant to a comprehensivelocalisation of pathological brain changes, whetherthey represent misregistration of a deformed struc-ture or volumetric differences in a perfectly regis-tered structure. We accept that affinetransformation does not always allow us to beconfident in making this distinction, but we main-


tain that computational morphometric methodssuch as ours remain invaluable for screening thewhole brain for evidence of distributed abnormal-ities (deformities or volumetric differences).

Structural abnormalities may be non-specific forschizophrenia and can, for example, be found inmood disorders(Elkis et al., 1995). Four relativeshad a lifetime diagnosis of major depressive dis-order according to the RDC. Although it is unlikelythat the similar findings in psychotic patients andnon-psychotic relatives would be fully explainedby this small subgroup of relatives having(had) adepressive illness, we tested this by excludingthem in the cluster analyses. This did not affectthe pattern or extent of brain changes.

Although groups were matched on age, sex andeducational achievement, patients were slightly,though not significantly, younger than both rela-tives and controls. In addition, educationalachievement in patients was lower than that inrelatives and controls. As higher age is known tobe associated with lower grey matter density andtherefore is a potential confounder, age was con-trolled for in the statistical analyses. Moreover, thefact that relatives exhibited more structural abnor-malities as compared to controls without beingdifferent on the variable age or educationalachievement, argues against age- or education-related findings.

Excessive alcohol consumption has been foundto be associated with cerebellar tissue deficits(Sullivan et al., 2000). In the present study, ahistory of alcohol abuse or dependence was partof the exclusion criteria. Moreover, there were nosignificant differences between the three studygroups in the amount of alcohol used per week,which makes differential alcohol consumption pat-terns an unlikely explanation for the cerebellardeficits that were seen in patients and relatives.

Similarly, current weekly drug use was one ofthe exclusary criteria. Four patients and one rela-tive did, however, use illicit drugs in the past yearprior to participation in the study. None of themused any drugs in at least the last month prior tostudy participation. The probability that the struc-tural brain alterations found in the patient and therelative groups are induced by drug use is assumedto be negligible as the tendency from the literature

to date is that there is hardly any suggestion forirreversible structural brain damage resulting from(chronic) cannabis and other substance abuse(Wert and Raulin, 1986; Cascella et al., 1991;Wiesbeck and Taeschner, 1991; Liu et al., 1995;Castle and Ames, 1996). Moreover, the presentresults replicate earlier structural MRI findings inpatients and relatives who had never used anyillicit drug (see Section 4.2).

4.2. Findings

Relatives had significantly higher white mattervolumes and lower CSF volumes than both casesand controls, whereas cases and controls did notsignificantly differ on these measures. As enlarge-ment of CSF spaces in schizophrenia is one of themost consistent findings in the neuroimaging lit-erature (Johnstone et al., 1976; Shelton et al.,1988; Raz and Raz, 1990), the only non-significanttendency of higher CSF volumes in cases com-pared to controls was perhaps unexpected. A likelyexplanation for this is chance, neuroimaging stud-ies sampling around a small effect size. The resultspertaining to white matter will be the subject offurther investigations using a cluster-statistic basedapproach. Global grey matter reduction in patientshas been demonstrated in several studies(Breieret al., 1992; Zipursky et al., 1992, 1998; Lim etal., 1996; Sullivan et al., 1996, 1998; Gur et al.,1999). In addition, Cannon et al.(1998) reportedgrey matter reduction in the relatives of patientswith schizophrenia, particularly in the frontal andtemporal lobes. In the present study, there were nosignificant differences in grey matter volumebetween patients and relatives on the one hand,and controls on the other. However, the directionof the effect was towards lower volume in thepatient and relative groups.

The current findings provided evidence for cer-ebellar grey matter deficits, in both psychoticpatients and non-psychotic first degree relatives.In patients, there was additional evidence forfrontal and thalamic deficits. The presence offronto-thalamic-cerebellar deficits suggests adysfunctional cortico-cerebellar-thalamic-cortical(CCTC) circuit, as proposed by Andreasen et al.(1998, 1999). A disruption in this circuitry may


possibly lead to ‘cognitive dysmetria’, or difficultyin coordinating the processing, prioritisation,retrieval and expression of information(Andreasenet al., 1998). Non-psychotic first-degree relativesin this study also exhibited grey matter deficits inthe cerebellum. As described above, the cerebellummay represent a fundamental element of the CCTCor any other cortico-(sub)cortical) circuit, andalterations therein may lead to dysconnectivity.Whatever the precise mechanism, however, thefinding of cerebellar abnormalities in patients andrelatives may, at least partly, reflect an associationwith the genetic factors that predispose for psy-chosis vulnerability.

The presence of structural deficits in elementsof cortico-(sub)cortical circuits leading to impairedinformation processing is compatible with evi-dence for generalised cognitive deficits in schizo-phrenia (Mohamed et al., 1999), and withneuropsychological findings in the present sampleshowing that both cases and relatives performdiffusely worse than controls on a broad range ofcognitive tasks(Krabbendam et al., 2001). Thelatter study replicates the findings of Cannon etal. (1994).

The observed regional deficits or excesses ingrey matter correspond well with previouslydetected regions in many neuroimaging studiesinvestigating schizophrenia. For instance, structuralfrontal lobe abnormalities have been found repeat-edly in patients with schizophrenia(Andreasen etal., 1986; Breier et al., 1992; Andreasen et al.,1994b; Buchanan et al., 1998; Goldstein et al.,1999; Gur et al., 2000a), and additional evidencesuggests similar abnormalities in non-psychoticsiblings(Cannon et al., 1998).

Temporal lobe abnormalities, particularly in thehippocampalyamygdala area, have been reportedin numerous studies investigating patients withschizophrenia(Breier et al., 1992; Woodruff et al.,1997; Nelson et al., 1998; Velakoulis et al., 1999;Gur et al., 2000b; Wright et al., 2000) and also infirst-degree relatives of psychotic patients(Cannonet al., 1998; Seidman et al., 1999). However, recentmeta-analyses on frontal lobe(Zakzanis and Hein-richs, 1999) and temporal lobe(Zakzanis et al.,2000) studies using structural and functional neu-roimaging techniques suggest a rather moderate

prevalence as well as moderate effect sizes offrontal and temporal lobe deficits in schizophrenia.

In the majority of MRI studies investigating thethalamus, an alteration in volume or shape of thisstructure was noted in patients(Andreasen et al.,1990, 1994a; Flaum et al., 1995; Buchsbaum etal., 1996; Hazlett et al., 1999; Konick and Fried-man, 2001) and in relatives of patients with schiz-ophrenia(Seidman et al., 1997, 1999; Staal et al.,1998).

The cerebellum has been less well investigated,but has received renewed interest due to therecognition of its role, besides motor functions, incognition (Rapoport et al., 2000), the disturbanceof which could be implicated in the pathophysiol-ogy and aetiology of schizophrenia(Andreasen etal., 1998; Wassink et al., 1999). Evidence forstructural and functional abnormalities of the cer-ebellum in schizophrenia has come from severalstudies(Jacobsen et al., 1997; Levitt et al., 1999;Loeber et al., 1999, 2001; Nopoulos et al., 1999;for reviews, see Martin and Albers, 1995; Katsetoset al., 1997). One group investigated cerebellarabnormalities in relatives using structural MRI,and found a tendency towards volume reductionin the group of all relatives, and significant volumereduction in siblings only(Seidman et al., 1999).To our knowledge, the present study is the first todemonstrate structural cerebellar grey matter defi-cits in non-psychotic first-degree relatives using avoxel based whole-brain analysis.

A recent structural imaging study by Volz et al.(2000) using an automatic whole-brain analysisdemonstrated reduced volumes simultaneously inthe frontal lobe, the temporal lobe, the thalamus,the left cerebellar hemisphere and the right cere-bellar vermis in patients with schizophrenia com-pared with controls. In contrast, two other recentvoxel-based studies investigating patients withschizophrenia showed cerebellar increases in greymatter (Wilke et al., 2001; Suzuki et al., 2002),whereas they found concurring evidence for greymatter deficits in the frontal and temporal lobe,anterior cingulate, and insula.

There is conflicting evidence with regard toalterations in the basal ganglia derived from neu-ropathological studies(Heckers, 1997), but severalMRI studies point towards increased volumes of


the striatum and globus pallidum(Jernigan et al.,1991; Breier et al., 1992; Swayze et al., 1992;Buchanan et al., 1993). Also, in the whole-brainanalysis study by Volz et al.(2000), a volumeincrease in the right putamen was found in patientswith schizophrenia. These increased volumes maybe positively associated with exposure to typicalantipsychotics(Chakos et al., 1994; Gur et al.,1998; Corson et al., 1999). Whether other mech-anisms, like defective synaptic pruning of subcor-tical brain structures(Feinberg, 1982) or increasedsynaptic density as a compensatory mechanism fordecreased input from other brain regions(Graybiel,1990), may additionally affect basal ganglia sizeneeds further investigation. A structural MRI studyof relatives of schizophrenia patients found a(marginally significant) enlargement in the palli-dum and a decreased volume of the putamen, butthe patient group was not investigated(Seidmanet al., 1999). The patients in the present study(butnot the relatives) displayed grey matter excessesin the globus pallidum and putamen, concurringwith the majority of studies.

In summary, structural cerebral alterations, par-ticularly in the cerebellum, can be identified innon-psychotic first-degree relatives. As these alter-ations resemble those in the patients, the sug-gestion is that they are not illness- ormedication-related and likely to be present beforeillness onset, thereby favouring a neurodevelop-mental origin(Murray and Lewis, 1987; Weinber-ger, 1987). Therefore, the grey matter deficitsfound in the present study may constitute markersof genetic transmission, and need further replica-tion both from structural and functional neuroim-aging studies in order to elucidate thepathophysiological mechanisms underlying psy-chosis vulnerability. In addition, structural endo-phenotypes may become useful candidates asquantitative measures in genetic studies.

Acknowledgments

We thank Truda Driesen for her assistance inseveral aspects of the study and Marc Geerlingsfor his technical assistance. This research wassupported by The Dutch Brain Society and TheDutch Prevention Fund.

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Searching for a structural endophenotype in psychosis using computational morphometry