Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome Doron Gothelf a, b, 1 , Fumiko Hoeft c,1 , Takefumi Ueno c , Lisa Sugiura c , Agatha D. Lee d , Paul Thompson d , Allan L. Reiss c, * a The Child Psychiatry Department, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel b Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel c Center for Interdisciplinary Brain Sciences Research (CIBSR), Dept. of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA 94305-5795, USA d Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA article info Article history: Received 27 April 2010 Received in revised form 8 July 2010 Accepted 22 July 2010 Keywords: Velocardiofacial syndrome Psychosis COMT Prefrontal cortex abstract The primary objective of the current prospective study was to examine developmental patterns of voxel- by-voxel gray and white matter volumes (GMV, WMV, respectively) that would predict psychosis in adolescents with 22q11.2 deletion syndrome (22q11.2DS), the most common known genetic risk factor for schizophrenia. We performed a longitudinal voxel-based morphometry analysis using structural T1 MRI scans from 19 individuals with 22q11.2DS and 18 typically developing individuals. In 22q11.2DS, univariate analysis showed that greater reduction in left dorsal prefrontal cortical (dPFC) GMV over time predicted greater psychotic symptoms at Time2. This dPFC region also showed significantly reduced volumes in 22q11.2DS compared to typically developing individuals at Time1 and 2, greater reduction over time in 22q11.2DS COMT Met compared to COMT Val , and greater reduction in those with greater decline in verbal IQ over time. Leave-one-out Multivariate pattern analysis results (MVPA) on the other hand, showed that patterns of GM and WM morphometric changes over time in regions including but not limited to the dPFC predicted risk for psychotic symptoms (94.7e100% accuracy) significantly better than using univariate analysis (63.1%). Additional predictive brain regions included medial PFC and dorsal cingulum. This longitudinal prospective study shows novel evidence of morphometric spatial patterns predicting the development of psychotic symptoms in 22q11.2DS, and further elucidates the abnormal maturational processes in 22q11.2DS. The use of neuroimaging using MVPA may hold promise to predict outcome in a variety of neuropsychiatric disorders. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction The 22q11.2DS, also known as velocardiofacial syndrome (Shprintzen et al., 1978), is the most common microdeletion syndrome in humans occurring in at least 1e5000 live births (Botto et al., 2003). It has been shown that at least 25% of individuals with 22q11.2DS develop a schizophrenia-like psychosis by young adulthood (Murphy et al., 1999). Being the most common identifi- able genetic risk factor for schizophrenia, 22q11.2DS serves as an important model from which to elucidate the path leading from a well defined genetic defect to variation in brain development and eventually to the evolution of psychotic symptoms. Research has shown links between the development of psychotic symptoms and VIQ decline or catechol-O-methyl- transferase (COMT) hemizygosity (Gothelf et al., 2005), but no studies have demonstrated whether neuroanatomical patterns can predict the development of psychotic symptoms in 22q11.2DS. This may be due to the fact that past longitudinal studies have used univariate analysis of more crude volumetric or lobar volume measures (Gothelf et al., 2005) rather than multivariate analysis of voxel-based measures, which could be a more sensitive and powerful measure in detecting subtle regional changes. Indeed, studies have begun to elucidate neuroanatomical patterns that predict disease transition in at-risk mental states of psychosis (Koutsouleris et al., 2009). Therefore, the main purpose of the current study was to identify neuroanatomical patterns that pre- dicted risk of psychotic symptoms with high accuracy using cross- validation support vector machine (SVM) algorithms and to compare that with univariate methods. * Corresponding author. Tel.: þ1 650 498 4538; fax: þ1 650 724 4794. E-mail address: [email protected](A.L. Reiss). 1 Shared first authors. Authors contributed equally. Contents lists available at ScienceDirect Journal of Psychiatric Research journal homepage: www.elsevier.com/locate/psychires 0022-3956/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jpsychires.2010.07.008 Journal of Psychiatric Research xxx (2010) 1e10 Please cite this article in press as: Gothelf D, et al., Developmental changes in multivariate neuroanatomical patterns that predict risk for..., Journal of Psychiatric Research (2010), doi:10.1016/j.jpsychires.2010.07.008
Developmental changes in multivariate neuroanatomical patterns that predictrisk for psychosis in 22q11.2 deletion syndrome
Doron Gothelf a,b,1, Fumiko Hoeft c,1, Takefumi Ueno c, Lisa Sugiura c, Agatha D. Lee d, Paul Thompson d,Allan L. Reiss c,*
a The Child Psychiatry Department, The Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israelb Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, IsraelcCenter for Interdisciplinary Brain Sciences Research (CIBSR), Dept. of Psychiatry & Behavioral Sciences,Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA 94305-5795, USAd Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
a r t i c l e i n f o
Article history:Received 27 April 2010Received in revised form8 July 2010Accepted 22 July 2010
1 Shared first authors. Authors contributed equally.
0022-3956/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.jpsychires.2010.07.008
Please cite this article in press as: Gothelf DJournal of Psychiatric Research (2010), doi:1
a b s t r a c t
The primary objective of the current prospective study was to examine developmental patterns of voxel-by-voxel gray and white matter volumes (GMV, WMV, respectively) that would predict psychosis inadolescents with 22q11.2 deletion syndrome (22q11.2DS), the most common known genetic risk factorfor schizophrenia. We performed a longitudinal voxel-based morphometry analysis using structural T1MRI scans from 19 individuals with 22q11.2DS and 18 typically developing individuals. In 22q11.2DS,univariate analysis showed that greater reduction in left dorsal prefrontal cortical (dPFC) GMV over timepredicted greater psychotic symptoms at Time2. This dPFC region also showed significantly reducedvolumes in 22q11.2DS compared to typically developing individuals at Time1 and 2, greater reductionover time in 22q11.2DS COMTMet compared to COMTVal, and greater reduction in those with greaterdecline in verbal IQ over time. Leave-one-out Multivariate pattern analysis results (MVPA) on the otherhand, showed that patterns of GM and WM morphometric changes over time in regions including butnot limited to the dPFC predicted risk for psychotic symptoms (94.7e100% accuracy) significantly betterthan using univariate analysis (63.1%). Additional predictive brain regions included medial PFC and dorsalcingulum. This longitudinal prospective study shows novel evidence of morphometric spatial patternspredicting the development of psychotic symptoms in 22q11.2DS, and further elucidates the abnormalmaturational processes in 22q11.2DS. The use of neuroimaging using MVPA may hold promise to predictoutcome in a variety of neuropsychiatric disorders.
� 2010 Elsevier Ltd. All rights reserved.
1. Introduction
The 22q11.2DS, also known as velocardiofacial syndrome(Shprintzen et al., 1978), is the most common microdeletionsyndrome in humans occurring in at least 1e5000 live births (Bottoet al., 2003). It has been shown that at least 25% of individuals with22q11.2DS develop a schizophrenia-like psychosis by youngadulthood (Murphy et al., 1999). Being the most common identifi-able genetic risk factor for schizophrenia, 22q11.2DS serves as animportant model from which to elucidate the path leading fromawell defined genetic defect to variation in brain development andeventually to the evolution of psychotic symptoms.
: þ1 650 724 4794.
All rights reserved.
, et al., Developmental chan0.1016/j.jpsychires.2010.07.00
Research has shown links between the development ofpsychotic symptoms and VIQ decline or catechol-O-methyl-transferase (COMT) hemizygosity (Gothelf et al., 2005), but nostudies have demonstrated whether neuroanatomical patterns canpredict the development of psychotic symptoms in 22q11.2DS. Thismay be due to the fact that past longitudinal studies have usedunivariate analysis of more crude volumetric or lobar volumemeasures (Gothelf et al., 2005) rather than multivariate analysis ofvoxel-based measures, which could be a more sensitive andpowerful measure in detecting subtle regional changes. Indeed,studies have begun to elucidate neuroanatomical patterns thatpredict disease transition in at-risk mental states of psychosis(Koutsouleris et al., 2009). Therefore, the main purpose of thecurrent study was to identify neuroanatomical patterns that pre-dicted risk of psychotic symptoms with high accuracy using cross-validation support vector machine (SVM) algorithms and tocompare that with univariate methods.
ges in multivariate neuroanatomical patterns that predict risk for...,8
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e102
2. Methods
2.1. Subjects
Time1 and Time2 data included 19 children with 22q11.2DSand 18 typically developing (TD) controls. The presence of the22q11.2 microdeletion was confirmed in all subjects with22q11.2DS by fluorescence in situ hybridization (FISH). All controlswere screened and were not included in the study if they hada history of major psychiatric disorder or neurological or cognitiveimpairment. The follow-up interval was 4.9 � 0.7 for the22q11.2DS group and 4.9 � 0.9 years for the controls. The demo-graphic and clinical characteristics of the sample are presented inTable 1. None of the subjects had history of substance abuse andthe sample was well matched across diagnostic groups in meanage, parents’ years of education, male to female ratio, ethnicity,and handedness (Gothelf et al., 2007). None of the subjects hada psychotic disorder at Time1. In general, the 22q11.2DS group hadsignificantly lower IQ scores compared to the TD group. Therewere also significant IQ interactions such that the TD groupshowed general increase while the 22q11.2DS group showeda general decrease in IQ over time.
By the time of the Time2 scan, 10 participants with 22q11.2DShad received atypical antipsychotics (6 subjects) or mood stabi-lizers (10 subjects) for more than six months. All 6 subjectsreceiving antipsychotics had a psychotic disorder. After providinga complete description of the study to the subjects and theirparents, written informed consent was obtained at both timepoints, according to protocols approved by the institutional reviewboard at Stanford University School of Medicine.
2.2. Genotyping
Blood samples were drawn from the 22q11.2DS group todetermine genotype. The COMT Val108/158Met polymorphism(rs165688) was genotyped using a standard method (Lachmanet al., 1996). Eleven individuals had COMTMet and eight hadCOMTVal genotypes. The demographic and clinical characteristics ofthe sample are presented in Tables 1 and 2.
2.3. Cognitive and psychiatric measures
Cognitive and psychiatric assessments were conducted at bothtime points. For the cognitive assessment, the Wechsler Intelli-gence Scale for Children, 3rd edition (WISC III) was used for
Table 1Demographic information for the 22q11.2DS and TD groups.
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subjects 17 years and younger and the Wechsler Adult Intelli-gence Scale, 3rd edition (WAIS III) was used for subjects olderthan 17 years. For screening of psychotic disorders, the ScreeningQuestion portion of the Schedule for Affective Disorders andSchizophrenia for School Age Children-Present and LifetimeVersion (K-SAD-PL) was used. In addition, subjects above the ageof 18 years were also evaluated with the Structured ClinicalInterview for DSM-IV Diagnoses (SCID). At Time2, all 22q11.DSindividuals were tested by a child and adolescent psychiatrist,who completed the Brief Psychiatric Rating Scale (BPRS) tomeasure psychotic symptoms.
2.4. Magnetic resonance imaging (MRI) acquisition
All imaging datawere acquired at the RichardM. Lucas ResearchCenter (Stanford University, Palo Alto, CA USA) using the sameSigna 1.5 T scanner (General Electric, Milwaukee, WI). Data wereacquired at two time points with a slow spoiled gradient echo(SPGR) sequence: flip angle ¼ 45�, repetition time (TR) ¼ 6 s, echotime (TE) ¼ 1 s, matrix size ¼ 256 � 256, field of view(FOV) ¼ 240 � 240 mm, pixel size ¼ 0.9375 � 0.9375 mm, slicenumber ¼ 124, thickness ¼ 1.5 mm.
VBM analyses of T1 MR images were performed using SPM5(http://www.fil.ion.ucl.ac.uk/spm) and VBM5.1 (http://dbm.neuro.uni-jena.de/vbm). T1 images were bias corrected, segmented toGM, WM and cerebrospinal fluid (CSF), spatially normalized andmodulated, followed by smoothing with an isotropic Gaussiankernel with full-width at half-maximum (FWHM) of 12 mm. Sincethe results from standard and customized templates were essen-tially unchanged, the results from the standard template arereported here.
2.6. Statistical analysis
2.6.1. Analyses of GM and WM volumesWe examined total GMV, WMV and total tissue volume (TTV,
GMV þ WMV) obtained from VBM analyses using repeatedmeasures analyses of variance (ANOVA).
2.6.2. VBM analysisWe examined regional GM and WM volume differences
between 22q11.2DS and TD controls using whole-brain analysis of
Notes: Not applicable.Italicized small font: Standard deviation.
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e10 3
covariance (ANCOVA) covarying out age and total GMV/WMV (forregional GM and WM analyses, respectively). As supplementaryanalyses, VIQ was also included as a nuisance variable. Compari-sons between 22q11.2DS and TD groups were performed alsoexamining Time1 and Time2 data separately. Similarly, compari-sons between Time1 and Time2 data were performed examining22q11.2DS and TD groups separately.
2.6.3. Covariation between GM and WM volumes and BPRS, VIQand COMT genotypes
Whole-brain multiple regression analyses were performed withTime2 BPRS score as the outcome variable and change in GMV orWMV as the predictor. The effects of total GMV or WMV at Time1and age were regressed out of these models. Within the brainregions that showed a significant effect, small-volume correction(SVC) was performed to examine whether there were significantdifferences between 22q11.2DS and TD groups at both Time1 andTime2.
Mean values from significant brain regions in the whole-brainVBM multiple regression analyses described above were extractedfor each subject (significant effects were only found for GMV).These values were then adjusted for age and total GMV by per-forming linear multiple regression analysis with age and total GMVas independent variables, and obtaining the residuals. Theseadjusted brain volumes were used to examine whether there weredifferences between 22q11.2DS individuals with COMTMet andCOMTVal. In addition, decrease in VIQ as a function of time (i.e., VIQslope ¼ [Time2 VIQ � Time1 VIQ]/duration [yr]) was evaluated forcorrelations with adjusted brain volumes.
A statistical threshold with a joint-expected probability ofp ¼ 0.01 with a correction for non-stationary cluster extentthreshold (to correct for non-isotropic smoothness) was used in theVBM analyses P ¼ 0.05 corrected for family-wise-error (FWE) wasused for SVC analysis.
2.6.4. Multivariate pattern analysis results (MVPA)We performed leave-one-out linear SVM analysis (regulari-
zation parameter C ¼ 1) using in-house tools based on Matlab.First, we constructed a class vector constituting either ‘þ1’s and‘�1’s depending on whether the individual with 22q11.2DS hadmore or less psychotic symptoms (median split, where medianvalue was a BPRS score of 34). As expected, there was a signifi-cant difference in Time2 BPRS score (group with more symp-toms: n ¼ 10, mean ¼ 45.6, SD ¼ 9.75; group with less
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symptoms: n ¼ 9, mean ¼ 26.0, SD ¼ 4.72; t(17) ¼ 5.47,p ¼ 0.00004).
Next, we converted contrast images into an S-by-N matrixwhere S is the number of subjects (19) and N is the number offeatures/voxels (4 � 4 � 4 mm) and normalized the matrix so thatmean ¼ 1 and SD ¼ 1. The number of features was reduced byrecursive feature elimination iteratively, removing 30% of worst-discriminating voxels at a time until the performance starteddeteriorating. This was compared with classification performancebased on the results from univariate GLM analyses, i.e., meanaverage of left dPFC GM volume, which showed significant negativecorrelation with Time2 BPRS score and change in regional GMvolume, as the only feature. All procedures were performed bykeeping training data, which were used to construct the classifier,and test data independent using leave-one-out cross-validation.Significance was determined using permutation analysis byrandomly reassigning class labels 2000 times (p < 0.05). Resultswere similar when classes were determined based on the existenceof a full-blown psychotic disorder (cut-off: BPRS ¼ 35, psychosis:n ¼ 7).
3. Results
3.1. Between-group differences using univariate analysis
Summariesofbaseline (Time1)and follow-up (Time2)brainTTV ispresented in Table 1, and results of regional brain volume are pre-sented in Table 3. When repeatedmeasures ANCOVAwas performed(total GMV and age as covariates), there was a main effect of diag-nostic group, a main effect of time, but no significant interaction(p ¼ 0.01 corrected, Fig. 1a and b). Differences in regional GMVbetween 22q11.2DS and TD groups were very similar at Time1 andTime2with the 22q11.2DS group showing significantly reducedGMVinposteriormedial parieto-occipital and cerebellar regions (posteriorvermis, inferior semi-lunar lobule, uvula and pyramis, and anteriorculmen), inferior/middle occipital, lingual, parahippocampal, poste-rior cingulate gyri, (pre)cuneus, and midbrain. In contrast, the22q11.2DS group showed significantly greater GMV compared to TDin anterior medial cortical and sub-cortical regions: bilateral rectal,orbital, inferior/middle/superior/medial frontal, subcallosal, inferior/middle/superior/transverse temporal, supramarginal (and inferiorparietal lobule), fusiform, parahippocampal gyri, anterior cingulate,insula, claustrum, thalamus, putamen, lateralglobalpallidus, caudate,uncus, hippocampus and midbrain red nucleus. Most GM regions,
ges in multivariate neuroanatomical patterns that predict risk for...,8
Table 3VBM Results (Note: Only peaks are reported here. See text for details.).
Region BA Talairach coordinates T P (corr) Cluster
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e104
except for some midline cerebellar and medial cortical and sub-cortical structures, showedsignificant reductionover time inboth the22q11.2DS and TD groups. The results did not change when thestatistical effects of VIQ were controlled in the model.
Spatial distribution (anterioreposterior, medial-lateral gradi-ents) of regional WMV results was similar to that of the GMVresults. When repeated measures ANCOVA was performed (totalWMV and age as covariates), there was a main effect of diagnosticgroup (p ¼ 0.01 corrected, Fig. 2a). Both at Time1 and Time2, the22q11.2DS group showed significantly lower regional WMV in theparieto-occipital and midline cerebellar regions. This was nolonger significant when VIQ was included in the model. On theother hand, the 22q11.2DS group had significantly greater regionalWMV in frontal and sub-cortical regions (results unchanged whenVIQ was regressed out). There was also a main effect of time(p¼ 0.01 corrected, Fig. 2b) such that WMV increased over time inboth groups (results unchanged when VIQ included). The22q11.2DS group however, had many more brain regions thatshowed increase in WMV over time. Reflecting this qualitativeobservation and unlike GMV, there was a significant interaction;
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regions mainly in the fronto-temporal regions exhibited signifi-cantly greater increase in WMV in the 22q11.2DS compared to theTD group (p ¼ 0.01 corrected, Fig. 2c). The interaction results didnot change when VIQ was included as a covariate of uninterest inthe model.
3.2. Associations between longitudinal changes in VBM and Time2psychotic symptoms in the 22q11.2DS group
Whole-brain regression analysis revealed significant associationbetween greater decrease in regional GMV of the left dorsal prefrontalcortex (dPFC) over time and greater psychotic symptoms as measuredby higher BPRS scores (Fig. 3a). In addition, this prefrontal GM regionwas significantly reduced in the 22q11.2DS compared to the TD groupat both Time1 and Time2 (Time1: t ¼ 4.09, p ¼ 0.017 SVC, extentthreshold (ET)¼ 272; Time2: t¼ 4.02, p¼ 0.021, SVC, ET¼ 229).Whenindividualswith22q11.2DSweregroupedbasedonCOMTstatus, thosewith theMet genotypehad significantlygreater reduction inGMVovertime in the same left dPFC region (t(17) ¼ 2.75, p ¼ 0.014, Fig. 3a). Asexpected, decrease in VIQ as a function of time (i.e., VIQ slope) was
ges in multivariate neuroanatomical patterns that predict risk for...,8
Fig. 1. Regional GMV results. a. Group differences between 22q11.2DS and TD groups are displayed by examining the main effect of group, and for Time1 and Time2 separately.b. Differences between Time1 and Time2 data are displayed by examining the main effect of time, and for 22q11.2DS and TD groups separately.
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e10 5
correlated with decrease in left dPFC GMV over time (i.e., those withgreaterdecrease inGMVshowedgreaterdecline inVIQover timewere;r¼ 0.46,p¼ 0.05). Therewereno significant clusters inWMorCSF thatpredicted the severity of psychotic symptoms at Time2.
3.3. Associations between longitudinal changes in VBM and COMTgenotype
Besides the left dPFC region found to be significantly differentwith genotype status, there were significant differences in regionalWMV in a large region along the cingulum to the superior longi-tudinal fasciculus/arcuate fasciculus (Fig. 3b). The Met groupshowed significantly greater increase in this WM region comparedto the Val group.
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3.4. Multivariate pattern analyses classifying those by severity ofpsychotic symptoms
Regional GM volumes of left dPFC (cluster shown in Fig. 3a) asthe only feature, accuracy of classifying 22q11.2DS individualswith more or less psychotic symptoms was 63.1% and wassignificantly better than chance (p < 0.05) (Fig. 4a). Results fromwhole-brain pattern classification using voxel-by-voxel GMvolumes (accuracy: 94.7%), WM volumes (accuracy: 100%) anda combination of GM and WM volumes (accuracy: 94.7%) weresignificantly better than the results of univariate analysis (allp’s < 0.05), but not significantly different from each other (allp’s > 0.1). Distance from the hyperplane of the classifiers for eachsubject showed significant correlation with Time2 BPRS scoresfor whole-brain pattern classification analyses (GM: r ¼ 0.49,
ges in multivariate neuroanatomical patterns that predict risk for...,8
Fig. 2. Regional WMV results. a. Group differences between 22q11.2DS and TD groups are displayed by examining the main effect of group, and for Time1 and Time2 separately.b. Differences between Time1 and Time2 data are displayed by examining the main effect of time, and for 22q11.2DS and TD groups separately. C. Interaction effects between-group(22q11.2DS and TD groups) and time (Time1 and Time2).
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Fig. 3. Covariation between brain regional volumes and Time2 psychotic symptoms and COMT genotypes. a. Brain regions that show significant correlation with GMV and Time2BPRS scores (left). Extracted and adjusted brain volumes (for age and total GMV) are plotted against Time2 BPRS scores (right) as well as for COMTMet and COMTVal groups (below).b. Brain regions that show significant differences in WMV between COMTMet and COMTVal groups.
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e10 7
p ¼ 0.035; WM: r ¼ 0.73, p < 0.001; GM and WM combined:r ¼ 0.70, p < 0.001) but only a trend for significant effect forunivariate analysis (r ¼ 0.42, p ¼ 0.072) (Fig. 4b). Patterns ofvoxels that contributed to the classifier included not only the leftdPFC but also other regions such as the medial PFC (mPFC), rightamygdala, orbitofrontal and dorsal cingulum (Fig. 4c).
4. Discussion
In this longitudinal study of 22q11.2DS adolescents, we show thatlater psychotic symptoms can be predicted by developmental changesin morphometric spatial GMV and WMV patterns with very highaccuracy using cross-validated MVPA (94.7 and 100%, respectively),and significantly better than using univariate analysis of the PFC GMV(63.1%).We further show that longitudinal VBManalysis replicates andextends previous findings regarding the developmental neuroana-tomical characteristics of 22q11.2DS and its association with theemergence of psychotic symptoms and COMT genotype.
In line with previous cross-sectional studies (Bearden et al.,2009, 2007; Campbell et al., 2006; Eliez et al., 2000; Kates et al.,2001), we found reduced brain volumes in extensive parieto-occipital and cerebellar regions and the midbrain. Conversely, we
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found significantly increased GMV in anterior medial cortical andsub-cortical regions. As can be seen in Fig. 1a the antero-posteriorbetween-group gradient of cortical development was observed atboth time point measures. An even more dramatic dissociationbetween the anterior and posterior cortical poles occurs in anotherintriguing neurogenetic condition e Williams syndrome (Gothelfet al., 2008; Reiss et al., 2004). It is likely that haploinsufficiencyof genes in these syndromes is responsible for this antero-posteriorneuroanatomical developmental abnormality.
The cortical pattern of volume increase in frontal regions anddecrease in posterior regions occurred for both GMV and WMV.While we did not find a group by time interaction for GMV therewas a group by time interaction for WMV. There was a morerobust increase in WMV in 22q11.2DS than in controls inextensive fronto-temporal brain regions (Fig. 2c). Severalprevious studies have demonstrated that WMV deficits, espe-cially in posterior cortical regions, are common in 22q11.2DS(Campbell et al., 2006; Eliez et al., 2000; Kates et al., 2001; Simonet al., 2005; van Amelsvoort et al., 2004). In a previous longitu-dinal analysis of the same sample, using a coarse lobar dissectionof the brain, we also observed decreased cranial WMV inadolescents with 22q11.2DS compared to controls (Gothelf et al.,
ges in multivariate neuroanatomical patterns that predict risk for...,8
Fig. 4. Multivariate pattern analysis results. a. Classification accuracy using left dPFC GM Pattern volume as single feature (left dPFC), patterns of whole-brain GM Pattern volume(GM), patterns of whole-brain WM Pattern volume (WM), and combination of GM Pattern and WM Pattern (GM, WM Comb). b. Association between distance from hyperplane forthe whole-brain GM Pattern and WM pattern classifier and BPRS scores. r ¼ 0.70, p < 0.001. c. Morphometric patterns that discriminate between 22q11.2DS individuals with andwithout psychotic symptoms. Voxels that remained during the recursive feature elimination with positive weights are plotted in red (GM) and violet (WM), and with negativeweights in blue (GM) and cyan (WM). Yellow solid circle indicates overlap with univariate analysis showing associations between GMV changes and Time2 psychotic symptoms.(Forinterpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
D. Gothelf et al. / Journal of Psychiatric Research xxx (2010) 1e108
2007). Our VBM results replicate these findings and further showthat the accelerated growth in WMV is localized to fronto-temporal cortical regions. It is yet to be determined if theaccelerated growth of fronto-temporal WM in adolescents with22q11.2DS represents aberrant brain maturation or alternativelya functional “catch-up”.
In terms of the association between brain developmentaltrajectories and the emergence of psychotic symptoms, we foundthat decrease in the left dPFC over time correlated with the severityof psychotic symptoms at Time2. The same prefrontal cluster wasreduced in size in 22q11.2DS individuals at both time points andwas also more reduced in size in the 22q11.2DS subgroup carryingthe COMTMet allele. Subjects with 22q11.2DS carrying the COMTMet
allele putatively have very high levels of prefrontal cortical dopa-mine and this is likely to interfere with prefrontal cortical matu-rational processes especially during adolescence a time of dramaticincrease in cortical dopamine levels (Lambe et al., 2000). A fewstudies have shown that the COMT genotype affects prefrontalneuroanatomy (Gothelf et al., 2005; Kates et al., 2006; vanAmelsvoort et al., 2008). In line with our results, van Amelsvoortet al. (2008) found that 22q11.2DS adult COMTMet adult carriershad significantly smaller frontal lobe volumes. Kates et al. (2006)found that dPFC volumes of COMTMet 22q11.2DS male childrenwere decreased but were increased in COMTMet 22q11.2DS femalecarriers. In a previous analysis of the same cohort using a simplified(delimiting plane based) cortical parcellation approach, we foundthat prefrontal volumes declined significantly more in COMTMet
compared to COMTVal 22q11.2DS carriers when followed fromchildhood to late adolescence-young adulthood (Gothelf et al.,2005). However, limitations of this and previous analyses investi-gating prefrontal neuroanatomy in 22q11.2DS includes the use of
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a simplified ROI based measure of the PFC (Gothelf et al., 2005;Kates et al., 2006) or cross-sectional samples only (Kates et al.,2006; Vorstman et al., 2008). The simplified PFC measure used inour previous studies may also explain why wewere not able to findan association between the severity of psychotic symptoms andprefrontal neuroanatomy in these prior analyses (Gothelf et al.,2005, 2007). With the application of an MVPA approach, we wereable to identify developmental changes in neuroanatomicalpatterns of GMV and WMV such as lesser left dPFC and dorsalcingulum, and greater mPFC, right amygdala and orbitofrontalcortex that predicted which 22q11.2DS adolescent would havegreater or lesser psychotic symptoms with very high accuracy. ThisMVPA approach was significantly better than using univariateapproaches. Further, how likely one were to be in one group versusthe other based on neuroanatomical patterns (i.e., distance fromthe hyperplane) was associated with later psychotic symptoms,which further supports the validity of this approach. MVPA isconsidered as a more sensitive method than the traditionalunivariate analysis and therefore identified additional regions,besides the dPFC, as contributing to the prediction of psychosis.MVPA is based on the hypothesis that multiple brain regionscontribute to a disease progression. The results of our study suggestthat dPFC is a very significant region in contributing to theprediction of psychosis as it shows up in univariate as well asmultivariate analyses. The MVPA additionally shows the impor-tance of these other regions that on their own may seeminglycontribute very little, and hence are nonsignificant in the univariateanalysis, but are detected by MVPA.
Several studies with healthy subjects and with subjects withschizophrenia found that the mPFC is strongly and consistentlyactivated while individuals perform mentalizing tasks or perceive
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emotions (Brunet-Gouet and Decety, 2006). The mPFC was alsofound to be abnormally activated in patients with schizophreniawhile performing tasks involving theory of mind and socialcognition (Brunet-Gouet and Decety, 2006). Recent studies suggestthat in addition to psychotic disorders, autism spectrum disordersare also common in 22q11.2DS (Kates et al., 2007). Hence, theabnormal development of the mPFC and yet to be identified otherbrain regions, possibly contribute to the psychotic and autisticphenotypes of 22q11.2DS. In a recent longitudinal analysis ofEuropean cohort of individuals with 22q11.2DS and measuringcortical thickness, Schaer et al. (2009) also showed abnormaldevelopment of the prefrontal cortex in individuals with22q11.2DS.
The finding of greater increase in WMV along the dorsalcingulum bundle and superior longitudinal fasciculus/arcuatefasciculus in 22q11.2DS COMTMet versus COMTVal subgroups isinteresting in light of the literature on abnormalities of these fibertracts in schizophrenia in relation to working memory and otherexecutive functions (Buchsbaum et al., 2007; Green et al., 2009;Takei et al., 2009). Future work elucidating the relationshipsbetween the regions observed to predict outcome is warranted.
There are several limitations to the current study. The influenceof antipsychotic medications on the neuroanatomical findings isdifficult to disentangle in the current study. In addition, althoughthe age of onset of psychosis in 22q11.2DS is earlier than the age ofonset of schizophrenia in the general population (Green et al.,2009), some of the participants currently not psychotic mightdevelop psychotic symptoms in the future.
In conclusion, there are probably a large number of interactingfactors affecting brain development in subjects with 22q11.2DS.However, it seems that some of the brain maturational processesthat are consistently emerging as hallmarks of 22q11.2DS are theantero-posterior dissociation in cortical development and abnormalmaturation of WM. Other processes, such as decline in dPFC GMVvolume occur in at-risk 22q11.2DS subgroups, e.g. COMTMet carriersand 22q11.2DS subjects who later develop psychotic disorders. Theinteraction between these processes and developmental changes inregions suchas themPFCanddorsal cingulumare likely tobe thekeyrisk factors of psychosis. If replicated in a larger independent sample,multivariate machine learning methods may be useful in thefuture in identifying neuroanatomical patterns that predict clinicaloutcome in 22q11.2DS.
Financial disclosure
None.
Role of funding sources
This study was funded by grants NIMH MH50047-15 (Dr. Reiss),NARSAD Young Investigator Award (Drs. Gothelf and Hoeft), theChild Health Research Program from the Stanford University Schoolof Medicine and NICHD 1K23HD054720-01 (Dr Hoeft). The NIMH,NARSAD and NICHD had no further role in the study design;collection, analysis, and interpretation of data; writing of thereport; and decision to submit the paper for publication.
Contributors
Doron Gothelf: author, subject recruitment, data collection, dataanalysis.
Fumiko Hoeft: author, method development, data analysis.Takefumi Ueno: author, data analysis.Lisa Sugiura: data management, data analysis.
Please cite this article in press as: Gothelf D, et al., Developmental chanJournal of Psychiatric Research (2010), doi:10.1016/j.jpsychires.2010.07.00
Agatha D. Lee & Paul Thompson: method development, dataanalysis.
Allan Reiss: author, data analysis and oversees the researchproject.
Conflict of interestNo authors have any conflicts of interest. No authors have any
financial ties to any people or organizations that could have influ-enced this research study.
Acknowledgement
None.
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