Cortical patterning of abnormal morphometricsimilarity in psychosis is associated with brainexpression of schizophrenia-related genesSarah E. Morgana,1, Jakob Seidlitza,b, Kirstie J. Whitakera,c, Rafael Romero-Garciaa, Nicholas E. Cliftond,e,Cristina Scarpazzaf,g, Therese van Amelsvoorth, Machteld Marcelish, Jim van Osf,h,i, Gary Donohoej, David Mothersillj,Aiden Corvink, Andrew Pocklingtone, Armin Raznahanb, Philip McGuiref, Petra E. Vértesa,c,l,2, and Edward T. Bullmorea,m,2

aDepartment of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom; bDevelopmental Neurogenomics Unit, National Institute ofMental Health, Bethesda, MD 20892; cThe Alan Turing Institute, London NW1 2DB, United Kingdom; dNeuroscience and Mental Health Research Institute,Cardiff University, Cardiff CF24 4HQ, United Kingdom; eMedical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute ofPsychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff CF24 4HQ, United Kingdom; fDepartment of Psychosis Studies, Institute ofPsychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, United Kingdom; gDepartment of General Psychology, University ofPadova, 35131 Padova, Italy; hDepartment of Psychiatry and Neuropsychology, Maastricht University, 616 6200, Maastricht, The Netherlands; iDepartmentof Psychiatry, University Medical Center Utrecht Brain Center, 3584 CG, Utrecht, The Netherlands; jSchool of Psychology, National University of IrelandGalway, Galway H91 TK33, Ireland; kDepartment of Psychiatry, Trinity College Dublin, Dublin 8, D08 W9RT, Ireland; lSchool of Mathematical Sciences,Queen Mary University of London, London E1 4NS, United Kingdom; and mImmunoPsychiatry, GlaxoSmithKline R&D, Stevenage SG1 2NY, United Kingdom

Edited by Genevieve Konopka, University of Texas Southwestern Medical Center, Dallas, TX, and accepted by Editorial Board Member Michael S. GazzanigaMarch 21, 2019 (received for review December 18, 2018)

Schizophrenia has been conceived as a disorder of brain connec-tivity, but it is unclear how this network phenotype is relatedto the underlying genetics. We used morphometric similarityanalysis of MRI data as a marker of interareal cortical connec-tivity in three prior case–control studies of psychosis: in total,n = 185 cases and n = 227 controls. Psychosis was associatedwith globally reduced morphometric similarity in all three studies.There was also a replicable pattern of case–control differences inregional morphometric similarity, which was significantly reducedin patients in frontal and temporal cortical areas but increased inparietal cortex. Using prior brain-wide gene expression data, wefound that the cortical map of case–control differences in morpho-metric similarity was spatially correlated with cortical expressionof a weighted combination of genes enriched for neurobiologi-cally relevant ontology terms and pathways. In addition, genesthat were normally overexpressed in cortical areas with reducedmorphometric similarity were significantly up-regulated in threeprior post mortem studies of schizophrenia. We propose that thiscombined analysis of neuroimaging and transcriptional data pro-vides insight into how previously implicated genes and proteinsas well as a number of unreported genes in their topological vicin-ity on the protein interaction network may drive structural brainnetwork changes mediating the genetic risk of schizophrenia.

dysconnectivity | psychosis | network neuroscience | morphometricsimilarity | Allen Human Brain Atlas

Psychotic disorders have a lifetime prevalence of 1–3% andcan be extremely debilitating. However, despite significantefforts, the brain architectural changes and biological mecha-nisms causing psychotic disorders are not yet well understood,and there has been correspondingly limited progress in thedevelopment of new therapeutics.

MRI studies of schizophrenia have robustly demonstratedlocal structural differences in multiple cortical areas, subcorti-cal nuclei, and white matter tracts (1). The most parsimoniousexplanation of this distributed, multicentric pattern of struc-tural change is that it reflects disruption or dysconnectivity oflarge-scale brain networks comprising anatomically connectedbrain areas. However, testing this dysconnectivity hypothesisof psychotic disorder has been constrained by the fundamen-tal challenges in measuring anatomical connectivity and brainanatomical networks in humans. The principal imaging methodsavailable for this purpose are tractographic analysis of diffusionweighted imaging (DWI) and structural covariance analysis of

conventional MRI. DWI-based tractography generally underes-timates the strength of long-distance anatomical connections:for example, between bilateral homologous areas of cortex.Structural covariance analysis is not applicable to single-subjectanalysis, and its biological interpretation is controversial (2).

We recently proposed a technique known as “morphometricsimilarity mapping” (3), which quantifies the similarity betweencortical areas in terms of multiple MRI parameters measured

Significance

Despite significant research, the biological mechanisms under-lying schizophrenia are still unclear. We shed light on struc-tural brain differences in psychosis using an approach calledmorphometric similarity mapping, which quantifies the struc-tural similarity between brain regions. Morphometric simi-larity was globally reduced in psychosis patients in threeindependent datasets, implying that patients’ brain regionswere more differentiated from each other and less intercon-nected. Similarity was especially decreased in frontal andtemporal regions. This anatomical pattern was correlated withexpression of genes enriched for nervous system develop-ment and synaptic signaling and genes previously associatedwith schizophrenia and antipsychotic treatments. Therefore,we begin to see how combining genomics and imagingcan give a more integrative understanding of schizophrenia,which might inform future treatments.

Author contributions: S.E.M., J.S., T.v.A., M.M., J.v.O., G.D., D.M., A.C., P.M., P.E.V., andE.T.B. designed research; S.E.M., J.S., C.S., P.E.V., and E.T.B. performed research; K.J.W.,R.R.-G., N.E.C., and A.P. contributed new reagents/analytic tools; S.E.M., J.S., N.E.C., andP.E.V. analyzed data; and S.E.M., J.S., A.R., P.E.V., and E.T.B. wrote the paper.y

Conflict of interest statement: E.T.B. is employed half-time by the University ofCambridge and half-time by GlaxoSmithKline; he holds stock in GlaxoSmithKline.y

This article is a PNAS Direct Submission. G.K. is a guest editor invited by the EditorialBoard.y

This open access article is distributed under Creative Commons Attribution License 4.0(CC BY).y

Data deposition: The data used in this paper have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.7908488.v1), and the code has been deposited in GitHub(https://github.com/SarahMorgan/Morphometric Similarity SZ).y1 To whom correspondence should be addressed. Email: sem91@cam.ac.uk.y2 P.E.V. and E.T.B. contributed equally to this work.y

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1820754116/-/DCSupplemental.y

Published online April 19, 2019.

9604–9609 | PNAS | May 7, 2019 | vol. 116 | no. 19 www.pnas.org/cgi/doi/10.1073/pnas.1820754116

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at each area and can be used to construct whole-brain anatomi-cal networks for individual subjects. In keeping with histologicalresults indicating that cytoarchitectonically similar areas of cor-tex are more likely to be anatomically connected (4), morphome-tric similarity in the macaque cortex was correlated with tract-tracing measurements of axonal connectivity. Compared withboth tractographic DWI-based networks and structural covari-ance networks, morphometric similarity networks included agreater proportion of connections between human cortical areasof the same cytoarchitectonic class. Individual differences inregional mean morphometric similarity or “hubness” of corticalnodes in morphometric similarity networks accounted for about40% of the individual differences in intelligence quotient (IQ) ina sample of 300 healthy young people. These results suggest thatmorphometric similarity mapping could provide a useful tool toanalyze psychologically relevant biological differences in brainstructure.

Here, we used morphometric similarity mapping to test thedysconnectivity hypothesis of psychosis in three independentcase–control MRI datasets: the Maastricht Genetic Risk andOutcome of Psychosis (GROUP) study (83 cases and 68 con-trols) and the Dublin study (33 cases and 82 controls), bothmade available as legacy datasets for the PSYSCAN project (5),as well as the publicly available Cobre dataset (69 cases and77 controls) (Materials and Methods). We mapped case–controlmorphometric similarity differences at global and nodal levelsof resolution individually in each dataset to assess replicability,and we tested for significant differences in network organiza-tion that were consistent across studies. We used partial leastsquares (PLS) regression to test the hypothesis that this MRI

network phenotype of psychosis was correlated with anatomicallypatterned gene expression using data from the Allen HumanBrain Atlas (AHBA). This analytical approach to combine imag-ing and genomic data has been methodologically established (6,7) and applied in the context of neuropsychiatric disorders (8,9). We used it to test the pathogenic hypothesis that the genesmost strongly associated with case–control differences in mor-phometric similarity were enriched: (i) for genes that have beenontologically linked to relevant neurobiological processes and(ii) for genes that are abnormally expressed in post mortemstudies of schizophrenia.

ResultsSamples. Sociodemographic and clinical data available on thesample are in SI Appendix, Table S1. There was considerableheterogeneity in clinical measures between studies (e.g., theMaastricht patients had relatively low mean scores on psychoticsymptom scales).

Case–Control Differences in Global Morphometric Similarity. Glob-ally, morphometric similarity was reduced in cases comparedwith controls in all three datasets (SI Appendix, Fig. S2).Regional morphometric similarity had an approximately nor-mal distribution over all 308 regions (after regressing age, sex,and age × sex) and, in all three datasets, there was a signifi-cant case–control difference in this distribution (P < 0.001,Kolmogorov–Smirnoff test). Modal values of regional morpho-metric similarity were more frequent and extreme values wereless frequent in cases compared with controls (Fig. 1A and SIAppendix, Fig. S2).

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Fig. 1. Case–control differences in regional morphometric similarity. (A) Case and control distributions of regional morphometric similarity strength, i.e., theaverage similarity of each region to all other regions, pooling data from all three primary studies. (B) Distributions of morphometric similarity strength for aregion with significantly reduced morphometric similarity in cases, namely left hemisphere caudal middle frontal part 1. (C) Regional morphometric similarityaveraged over controls from all three datasets. (D) t statistics and Hedge’s g effect sizes for the case–control differences in regional morphometric similarityin each dataset. (E) t statistics for regional case–control differences averaged across datasets in all regions and in the 18 cortical areas where the differencewas statistically significant across datasets (FDR = 0.05). (F) Scatterplot of mean control regional morphometric similarity (x axis) vs. case–control t statistic(y axis). Control morphometric similarity (from C) is strongly negatively correlated with case–control morphometric similarity differences (from D; Pearson’sr = −0.76, P < 0.001). Most cortical regions have positive morphometric similarity in controls, which decreases in cases (47% of regions), or negativemorphometric similarity in controls, which increases in cases (36% of regions). Statistically significant regions are circled in red or blue according to whethertheir mean t statistic increases or decreases, respectively, in patients. MS, morphometric similarity.

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Case–Control Differences in Regional Morphometric Similarity. Thecortical map of regional morphometric similarity in Fig. 1C sum-marizes the anatomical distribution of areas of positive and neg-ative similarity on average over controls from all three datasets.The results are similar to those reported in an independent sam-ple (3), with high positive morphometric similarity in frontal andtemporal cortical areas and high negative morphometric similar-ity in occipital, somatosensory, and motor cortices. This confirmsthe replicability of this pattern of regional morphometric similar-ity in healthy individuals and is consistent with prior knowledgethat primary cortex is more histologically differentiated thanassociation cortex.

We mapped the t statistics and corresponding Hedge’s g effectsizes for the case–control differences in regional morphometricsimilarity at each cortical area (Fig. 1D). A positive t statis-tic means that morphometric similarity increased in patients,whereas a negative t statistic means that morphometric similaritydecreased. We found somewhat similar patterns of case–controldifference across all three datasets, with increased regional mor-phometric similarity in occipital and parietal areas in patientsand decreased regional morphometric similarity in frontal andtemporal cortices. The case–control t map for the Dublin studywas significantly correlated with both the Maastricht and theCobre t maps (r =0.42, P < 0.001 and r =0.47, P < 0.001,respectively), although the Maastricht and Cobre t maps werenot significantly correlated (r =0.058, P =0.31) (SI Appendix,Fig. S4). However, a large number of patients in the Maastrichtdataset had very low symptom scores [below the threshold for“borderline mentally ill” (10)]. If those nonpsychotic patientswere excluded from the analysis, the Maastricht case–control tmap was correlated significantly with the Cobre map (r = 0.22,P < 0.001) (SI Appendix, section S6.2).

Combining the P values for case–control differences across allthree datasets, we identified 18 cortical regions where morpho-metric similarity was robustly and significantly different betweengroups (Fig. 1E). Morphometric similarity decreased in patientsin 15 regions located in the superior frontal, caudal middlefrontal, precentral, pars triangularis, and superior temporal areasand increased in 3 regions located in superior parietal andpostcentral areas (SI Appendix, Table S2).

To contextualize the regional morphometric similarity case–control differences, we referred them to two prior classificationsof cortical areas: the von Economo atlas of cortex classified bycytoarchitectonic criteria (6) and the Yeo atlas of cortex clas-sified according to resting-state networks derived from fMRI(11, 12). Morphometric similarity was significantly reduced invon Economo class 2 (association cortex) and in the ventralattention, frontoparietal, and default mode fMRI networks (allPFDR < 0.05) (SI Appendix, Tables S12 and S13).

There was a strong negative correlation between regional mor-phometric similarity in the control subjects and the case–controldifferences in regional morphometric similarity (both averagedover all three datasets; Pperm =0.002) (Fig. 1F). Hence, areaswith the highest positive morphometric similarity in controlstended to show the greatest decrease of morphometric similarityin patients, and conversely, areas with the highest negative mor-phometric similarity in healthy controls had the greatest increaseof morphometric similarity in psychosis. This result is analogousto the observation that highly connected “hub” regions are themost likely to show reduced connectivity in disease in fMRI anddiffusion tensor imaging (DTI) brain networks (13).

We tested for correlations between mean morphometric simi-larity and a range of clinical measures, including symptom scores,antipsychotic medication use, and cannabis use (SI Appendix,section S6.3). The only significant associations after false dis-covery rate (FDR) correction were with cannabis use, whichwas positively correlated with mean global morphometric simi-larity in the Maastricht study (PFDR =5× 10−4) as well as with

mean morphometric similarity averaged across the 15 regionswith significantly decreased morphometric similarity in Fig. 1E(PFDR = 0.0017).

Gene Expression Related to Morphometric Similarity. We used PLSregression to identify patterns of gene expression that were cor-related with the anatomical distribution of case–control morpho-metric similarity differences. The first PLS component explained13% of the variance in the case–control morphometric similaritydifferences combining data from all three studies, significantlymore than expected by chance (permutation test, P < 0.001).PLS1 gene expression weights were positively correlated withcase–control morphometric similarity differences in the Dublinstudy (r =0.49, P < 0.001) and the Cobre study (r =0.37, P <0.001) (Fig. 2A) but not in the Maastricht study (r =0.006,P =0.94). These positive correlations mean that genes positivelyweighted on PLS1 are overexpressed in regions where mor-phometric similarity was increased in patients, while negativelyweighted genes are overexpressed in regions where morphome-tric similarity was decreased in patients (Fig. 2D). Hence, genesthat are positively (or negatively) weighted on PLS1 were relatedto increased (or decreased) morphometric similarity in casescompared with controls.

Enrichment Analysis of Genes Transcriptionally Related to Morpho-metric Similarity. We found 1,110 genes with normalized PLS1weights Z 3, which we denote the PLS+ gene set. We firstconsider PLS− genes (the equivalent results for PLS+ genes arealso given below).

We mapped the network of known interactions between pro-teins coded by the PLS− gene set (14) (Fig. 3). The result-ing protein–protein interaction (PPI) network had 341 con-nected proteins and 1,022 edges, significantly more than the802 edges expected by chance (permutation test, P < 1−13).We also tested the PLS− gene set for significant gene ontol-ogy (GO) enrichment of biological processes and enrichmentof Kyoto Encyclopedia of Genes and Genomes (KEGG) path-ways. Enriched biological processes included “nervous systemdevelopment,” “synaptic signaling,” and “adenylate cyclase-modulating G-protein coupled receptor (GPCR) signaling path-way” (Dataset S1). There were two significantly enriched KEGGpathways: “neuroactive ligand-receptor interaction” and “ret-rograde endocannabinoid signaling” (SI Appendix, Fig. S13).The proteins coded by genes enriched for adenylate cyclase-modulating GPCR signaling pathway and the two KEGG path-ways formed the most strongly interconnected cluster of nodesin the PPI network (Fig. 3), compatible with them sharing aspecialized functional role for GPCR signaling.

Genes recently reported as overexpressed in post mortembrain tissue from patients with schizophrenia (15) were highlyenriched among genes that were negatively weighted on PLS1(permutation test, P < 0.001 after FDR correction). The rela-tionship between the sign of PLS1 weights of gene expressionrelated to the MRI case–control phenotype, and the sign ofcase–control differences in the histological measures of braingene expression, was highly nonrandom (Wilcoxon rank sum test,P < 10−26).

In other words, genes that were up-regulated in post mortembrain tissue from patients with schizophrenia are normallyoverexpressed in association cortical areas that have reducedmorphometric similarity in psychosis. This association betweengene expression in regions with reduced morphometric sim-ilarity and genes up-regulated in schizophrenia was repli-cated by analysis of two alternative datasets provided by thePsychENCODE consortium (16) and in ref. 17 (SI Appendix, sec-tion S8.5). We also observed enrichment by genes up-regulatedin other psychiatric disorders (e.g., autistic spectrum disorders),

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DiscussionMorphometric Similarity Network Phenotypes. Morphometric sim-ilarity mapping disclosed a robust and replicable cortical patternof differences in psychosis patients. Morphometric similarity wassignificantly reduced in frontal and temporal cortical areas and

significantly increased in parietal cortical areas. This patternwas consistent across three independent datasets with differentsamples, locations, scanners, and scanning parameters.

What does this MRI phenotype of psychosis represent?Morphometric similarity quantifies the correspondence or kin-ship of two cortical areas in terms of multiple macrostructuralfeatures (e.g., cortical thickness) and microstructural features[e.g., fractional anisotropy (FA)] that are measurable by MRI.We assume that high morphometric similarity between a pair ofcortical regions indicates that there is a high degree of correspon-dence between them in terms of cytoarchitectonic and myeloar-chitectonic features that we cannot directly observe given thelimited spatial resolution and cellular specificity of MRI. Priorwork also showed that morphometrically similar cortical regionsare more likely to be axonally connected to each other (i.e., mor-phometric similarity is a proxy marker for anatomical connec-tivity) (3). We, therefore, interpret the reduced morphometricsimilarity that we observe in frontal and temporal brain regionsin psychosis as indicating that there is reduced architectonicsimilarity or greater architectonic differentiation between theseareas and the rest of the cortex, which is probably indicative ofreduced anatomical connectivity to and from the less similar,more differentiated cortical areas.

There is a well-evidenced and articulated prior theoryof schizophrenia as a dysconnectivity syndrome; specifically,

)aA B

Fig. 3. Enrichment analysis of genes transcription-ally related to morphometric similarity. (A) PPI net-work for PLS− genes (Z

functional dysconnectivity of frontal and temporal cortical areashas been recognized as a marker of brain network disorga-nization in schizophrenia (19). Our results of reduced mor-phometric similarity in frontal and temporal cortices—implyingincreased architectonic differentiation and decreased axonalconnectivity—are descriptively consistent with this theory. Ourcomplementary finding of abnormally increased morphometricsimilarity in parietal cortex—implying increased architectonicsimilarity and axonal connectivity—is plausible but not so clearlyprecedented given the relatively limited prior data on the parietalcortex in studies of schizophrenia as a dysconnectivity syndrome(20, 21).

Encouragingly, this MRI network marker of psychosis washighly reliable across three independent and methodologicallyvarious case–control studies. This implies that the measure-ment is robust enough to be plausible as a candidate imag-ing biomarker of cortical network organization in large-scale,multicenter studies of psychosis.

Transcriptional Profiling of Morphometric Similarity Network Phe-notypes. In an effort to connect these MRI phenotypes to theemerging genetics and functional genomics of schizophrenia, wefirst used PLS to identify the weighted combination of genes inthe whole genome that has a cortical expression map most sim-ilar to the cortical map of case–control morphometric similaritydifferences. Then, we tested the mechanistic hypothesis that thegenes with greatest (positive or negative) weight on PLS1 wereenriched for genes previously implicated in the pathogenesis ofschizophrenia.

We found that the genes that are normally overexpressed infrontal and temporal areas of reduced morphometric similar-ity in psychosis were significantly enriched for genes that areup-regulated in post mortem brain tissue from patients withschizophrenia (15). Conversely, the genes that are normally over-expressed in parietal and other areas of increased morphometricsimilarity in psychosis were significantly enriched for genes thatare down-regulated in post mortem data (15). This tight couplingbetween MRI-derived transcriptional weights and gene tran-scription measured histologically was highly significant and theassociation with up-regulated genes was replicated across threeprior post mortem datasets.

Additional investigation showed that the proteins coded by thePLS− genes formed a dense, topologically clustered interactionnetwork that was significantly enriched for a number of relevantGO biological processes and KEGG pathways. The cluster ofinteractive proteins related to GPCR signaling included multi-ple proteins coded by genes previously linked to antipsychoticmechanisms of action [including DRD4 (22), HTR1 (23), NTSR1(24), and ADRA2C (25)], reported in transcriptional studies ofpost mortem brain tissue [e.g., PTGER3, S1PR1, ITPR2, andEDNRB (15, 26)], or associated with risk SNPs for schizophrenia[e.g., DRD5, OPRM1, and CNR1 (27–29)]. The remarkable den-sity of therapeutically relevant genes in the GPCR-related clustersuggests that other topologically neighboring genes may deserveadditional attention as targets for antipsychotic interventions.

Risk genes identified by the largest extant GWAS ofschizophrenia were not significantly enriched among PLS− orPLS+ genes. Nevertheless, the involvement of PLS− genes far-ther down the causal pathway is still mechanistically revealingand potentially useful.

Methodological Considerations. Some limitations of this studyshould be highlighted. The whole-brain data on “normal” braintissue expression of the genome were measured post mortem insix adult brains (mean age = 43 y) and not in age-matched sub-jects or patients with schizophrenia (such data are not currentlyavailable to our knowledge). Also, the transcriptional experi-ments that we use to label genes as up- or down-regulated in

schizophrenia were performed in regions of the parietal or pre-frontal cortex (15), whereas the neuroimaging results are forthe whole brain. We have used MRI data from three indepen-dent studies to measure morphometric similarity networks, butthe studies used different scanning protocols, leading to estima-tion of morphometric similarity between regions on the basisof seven MRI parameters that were measurable in all studies.Future work could usefully explore the opportunity to furtherimprove sensitivity and reliability of the morphometric similaritynetwork biomarker of schizophrenia by optimizing and standard-izing the MRI procedures to measure the most informative setof morphometric features. Finally, the datasets have varied, lim-ited clinical information available, making it difficult to assess theclinical significance of the morphometric similarity phenotype.

Materials and MethodsSamples. We used MRI data from three prior case–control studies: theMaastricht GROUP study (30) from The Netherlands; the Dublin dataset,which was acquired and scanned at the Trinity College Institute of Neu-roscience as part of a Science Foundation Ireland-funded neuroimaginggenetics study (“A structural and functional MRI investigation of genetics,cognition and emotion in schizophrenia”); and the publicly available Cobredataset (31). The Maastricht and Dublin datasets were PSYSCAN legacydatasets. The standing ethics committee of Maastricht University MedicalCenter approved the Maastricht GROUP study. The St. James Hospital andthe Adelaide and Meath Hospital Dublin Incorporating the National Chil-dren’s Hospital (AMNCH) joint ethics boards approved the Dublin study.All participants gave informed consent. All patients satisfied Diagnosticand Statistical Manual of Mental Disorders IV (DSM-IV) diagnostic criteriafor schizophrenia or other nonaffective psychotic disorders. MRI data werequality controlled for motion artifacts (SI Appendix, section S1). The Eulernumber, which quantifies image quality (32), was not significantly differentbetween groups in any of the studies, but it was different between stud-ies, indicating that the studies were ranked Dublin > Cobre > Maastricht interms of image quality (SI Appendix, Table S1).

Morphometric Similarity Mapping. The T1-weighted MRI data[magnetization-prepared rapid gradient-echo (MPRAGE) sequence]and the DWI data from all participants were preprocessed using apreviously defined computational pipeline (6). Briefly, we used therecon-all (33) and trac-all (34) commands from FreeSurfer (version 6.0).Following ref. 3, the surfaces were then parcellated using an atlas with 308cortical regions derived from the Desikan–Killiany atlas (6, 35). For eachregion, we estimated seven parameters from the MRI and DWI data: graymatter volume, surface area, cortical thickness, Gaussian curvature, meancurvature, FA, and mean diffusivity. Each parameter was normalized forsample mean and SD before estimation of Pearson’s correlation for eachpair of Z-scored morphometric feature vectors, which were compiled toform a 308× 308 morphometric similarity matrix Mi for each participant,i = 1, . . . N (3).

Case–Control Analysis of Morphometric Similarity Networks. The global meanmorphometric similarity for each participant is the average of Mi . Theregional mean MSi,j for the ith participant at each region j = 1, . . . , 308 isthe average of the jth row (or column) of Mi . Thus regional MS strengthis equivalent to the weighted degree or hubness of each regional node,connected by signed and weighted edges of pair-wise similarity to all othernodes in the whole brain connectome represented by the morphometricsimilarity matrix. For global and regional morphometric similarity statisticsalike, we fit linear models to estimate case–control difference, with age, sex,and age × sex as covariates. Our main results also replicated in subsets ofthe data balanced for age and sex (SI Appendix, section S5.6). P values forcase–control differences in regional morphometric similarity were combinedacross all three studies using Fisher’s method. The resulting P value for eachregion was thresholded for significance using FDR < 0.05, to control type 1error over multiple (308) tests.

Transcriptomic Analysis. We used the AHBA transcriptomic dataset withgene expression measurements in six post mortem adult brains (36)(human.brain-map.org) ages 24–57 y. Each tissue sample was assigned toan anatomical structure using the AHBA MRI data for each donor (37). Sam-ples were pooled between bilaterally homologous cortical areas. Regionalexpression levels for each gene were compiled to form a 308× 20, 647

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https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1820754116/-/DCSupplementalhttps://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1820754116/-/DCSupplementalhttps://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1820754116/-/DCSupplementalhttp://human.brain-map.orghttps://www.pnas.org/cgi/doi/10.1073/pnas.1820754116

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regional transcription matrix (37). Since the AHBA only includes datafor the right hemisphere for two subjects, in our analyses relating geneexpression to MRI data, we only consider intrahemispheric left hemisphereedges (38).

We used PLS to relate the regional morphometric similarity case–controldifferences (t statistics from the 152 cortical regions in the left hemispherecalculated from intrahemispheric edges only) to the post mortem geneexpression measurements for all 20, 647 genes. PLS uses the gene expressionmeasurements (the predictor variables) to predict the regional morphome-tric similarity case-control t statistics from all three datasets (the responsevariables). The first PLS component (PLS1) is the linear combination of theweighted gene expression scores that have a cortical expression map thatis most strongly correlated with the map of case–control morphometricsimilarity differences. The statistical significance of the variance explainedby PLS1 was tested by permuting the response variables 1,000 times. Theerror in estimating each gene’s PLS1 weight was assessed by bootstrap-ping (resampling with replacement of the 308 cortical regions), and theratio of the weight of each gene to its bootstrap SE was used to calculatethe Z scores and, hence, rank the genes according to their contribution toPLS1 (6).

We constructed PPI networks from the genes with PLS1 weights Z > 3 andZ 4 and Z 3 or Z