Cortical regions are ordered by the difference in the rate of change in thickness. From top to bottom are those regions with the most negative difference (ie, old thinning more than young thinning) to the most positive difference (ie, young thinning more than old thinning).
Deeper red shading indicates greater thinning, and deeper blue shading indicates greater thickening.
A, Shaded areas indicate CIs with 1 SE; points with white centers, significant (P < .05) correlations; and points with black centers, correlations that exceeded the significance threshold correcting for comparisons by age. B, Highlights negative expression-thinning correlation. C, Highlights positive expression-thinning correlation. The pattern is representative of the change in mean expression-thinning correlation with age for the spine panel.
eMethods. Cortical Thinning, Gene Expression, Analytical Approach, and Statistical Analysis
eTable 1. Cohort Imaging Characteristics
eTable 2. Corrections for Multiple Comparisons
eTable 3. GO Terms for Gene Panels
eTable 4. Psychiatric Disorder Gene Set Size
eTable 5. Sex and Age of Participants With Missing Values, Imaging Complications, and Loss to Follow-up
eTable 6. Panel Correlations and Uncorrected P Values
eTable 7. Variance in Cortical Thinning Explained by Gene Panels
eTable 8. Number of Genes from Gene Panels Correlated With Thinning Profiles (Seed Genes)
eTable 9. Enrichment of Longitudinal Thinning Coexpression Networks for Psychiatric Disorders
eFigure 1. Study Flowchart
eFigure 2. Regional Thickness by Age
eFigure 3. Regional Change in Thickness by Age
eFigure 4. Expression-Thinning Profile Correlations for All Panel Genes
eFigure 5. Neurobiological Correlates of Longitudinal Thinning Profiles
eFigure 6. SYS Sample Gene Panel Analysis
eFigure 7. Psychiatric Disorder Enrichment
eFigure 8. Change in Cortical Thickness by Age in Females and Males
eFigure 9. Correlations of Female and Male Age-Specific Cortical Thinning Profiles
eFigure 10. Age-Specific Thinning Profiles and Their Neurobiological Correlates in Females and Males
eFigure 11. Psychiatric Disorder Enrichment in Females and Males
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Parker N, Patel Y, Jackowski AP, et al. Assessment of Neurobiological Mechanisms of Cortical Thinning During Childhood and Adolescence and Their Implications for Psychiatric Disorders. JAMA Psychiatry. 2020;77(11):1127–1136. doi:10.1001/jamapsychiatry.2020.1495
What are the neurobiological mechanisms of cortical thinning during childhood and adolescence, and do these mechanisms have implications for psychiatric disorders?
In this cohort study of 3596 individuals aged 9 to 21 years, interregional profiles of age-associated cortical thinning correlated with transcriptomic profiles of genes involved in myelin and dendritic architecture. These genes (and their coexpression networks) were enriched among genes associated with a number of psychiatric disorders that emerge during the first 2 decades of life.
This study found genetic similarity between interregional variation in cortical thinning during maturation and multiple psychiatric disorders, which suggests specific cellular and molecular pathways that may modify susceptibility to these disorders.
Many psychiatric disorders can be conceptualized as disorders of brain maturation during childhood and adolescence. Discovering the neurobiological underpinnings of brain maturation may elucidate molecular pathways of vulnerability and resilience to such disorders.
To investigate the underlying neurobiological mechanisms of age-associated cortical thinning during maturation and their implications for psychiatric disorders.
Design, Setting, and Participants
This multicohort analysis used data from 3 community-based studies. The Saguenay Youth Study provided data from 1024 adolescents who were recruited at a single site in Quebec, Canada. The IMAGEN cohort provided data from 1823 participants who were recruited in 8 European cities. The Brazil High Risk Cohort Study for the Development of Childhood Psychiatric Disorders provided data from 815 participants who were recruited in 2 Brazilian cities. Cortical thickness was estimated from the results of magnetic resonance imaging (MRI) scans, and age-associated cortical thinning was estimated in 34 cortical regions. Gene expression from the Allen Human Brain Atlas was aligned with the same regions. Similarities in the interregional profiles of gene expression and the profiles of age-associated cortical thinning were measured. The involvement of dendrites, dendritic spines, and myelin was tested using 3 gene panels. Enrichment for genes associated with psychiatric disorders was tested among the genes associated with thinning and their coexpression networks. Data analysis was conducted between March and October 2019.
Main Outcomes and Measures
MRI-derived estimates of age-associated cortical thinning and gene expression in 34 cortical regions.
A total of 3596 individuals aged 9 to 21 years were included in this study. Of those, 1803 participants (50.1%) were female, and the mean (SD) age was 15.2 (2.6) years. Interregional profiles of age-associated cortical thinning were associated with interregional gradients in the expression of genes associated with dendrites, dendritic spines, and myelin; the variance in thinning explained by the gene panels across different points ranged from 0.45% to 10.55% for the dendrite panel, 0.00% to 9.98% for the spine panel, and 0.19% to 26.39% for the myelin panel. These genes and their coexpression networks were enriched for genes associated with several psychiatric disorders.
Conclusions and Relevance
In this study, genetic similarity between interregional variation in cortical thinning during maturation and multiple psychiatric disorders suggests overlapping molecular underpinnings. This finding adds to the understanding of the neurodevelopmental mechanisms of psychiatric disorders.
The human cerebral cortex undergoes changes in its thickness from childhood through early adulthood; the prevalent change is an age-associated decrease in thickness that is both global and regional.1,2 The first 2 decades of life are also marked by the onset of many psychiatric disorders.3 It is likely that maturational changes in cortical thickness and their cellular underpinnings during this developmental period are associated in some manner with factors modulating vulnerability or resilience to mental illness.
Variations in the cellular composition of the cerebral cortex, which are reflected in its cortical cytoarchitecture and myeloarchitecture, may be associated with changes in cortical thickness observed with magnetic resonance imaging (MRI). Early histological studies of postmortem tissue indicated a reduction in synaptic density in childhood and adolescence, with some regional differences in the extent of reduction.4,5 Research from 2011 suggested that decreases in synaptic spine density continue into adulthood.6 Postmortem histological results have also indicated that the amount of intracortical myelin increases from birth and continues to increase well into adulthood; these changes are predominantly localized to deep cortical layers.7 Taken together, cortical thinning observed in neuroimaging studies may be the result of age-associated changes in molecular processes that have implications for dendritic and myelin architecture.
As we increasingly recognize psychiatric disorders as disorders of brain development, it is important to improve our understanding of the possible cellular underpinnings of MRI-derived brain measurement. Collaborative studies performed by large international consortia, such as the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) Consortium,8 have identified abnormalities in cortical thickness associated with many psychiatric and neurodevelopmental disorders. For example, a study of nearly 10 000 individuals found that 4474 patients with schizophrenia exhibited widespread reductions in cortical thickness compared with those without schizophrenia.9 Moreover, genomic and transcriptomic studies of psychiatric disorders have implicated genes involved in the cellular composition of the cerebral cortex.10,11 For example, a number of genes differentially expressed in autism spectrum disorder, schizophrenia, and bipolar disorder are implicated in the development of neuron projections and synaptic function.12 Myelin-associated genes have also been implicated in the pathophysiologic processes of schizophrenia, as indicated by differential expression analyses of postmortem tissue from the dorsal lateral prefrontal cortex.10 To some extent, molecular pathways underlying the development and maturation of the cerebral cortex may overlap with those involved in the pathophysiologic processes of psychiatric disorders.
As revealed in a 2018 study, spatial and temporal gradients in the expression of genes in the human cerebral cortex are associated with dendritic and myelin-associated processes; these gradients begin during embryogenesis and continue into adulthood.13 Previous studies have used spatial gradients of gene expression in the human cerebral cortex to identify molecular14,15 and cellular16-19 processes involved in cortical maturation during adolescence and across the life span.
In the present study, we aimed to investigate the neurobiological underpinnings of cortical thinning during childhood and adolescence, focusing on dendritic architecture and myelin owing to their previous implications in both cortical maturation and psychiatric disorders. We used spatial gradients of transcriptomic data derived from the human cerebral cortex to investigate spatiotemporal gradients in cortical thinning derived from MRI data in 3 developmental cohorts. We tested the involvement of dendrites and myelin by using 3 functional gene groups: dendrite, spine, and myelin. We also investigated the overlap in genes associated with cortical thinning and genes associated with psychiatric disorders.
The study sample comprised participants from 3 community-based cohorts in which the assessment protocol included at least one T1-weighted MRI scan of the brain. Cross-sectional data from the Saguenay Youth Study (SYS; 2003-2012) cohort provided data from 1024 adolescents (484 male [49%]; mean [SD] age, 15.0 [1.8] years at baseline) who were recruited at a single site in the Saguenay Lac St Jean region of Quebec, Canada.20 The IMAGEN cohort (2008-2015) provided data from 1823 participants (813 male [48%]; mean [SD] age, 14.4 [0.4] years at baseline and 19.0 [0.7] years at follow-up; 3026 MRI scans) who were recruited in 8 European cities (London and Nottingham, United Kingdom; Dublin, Ireland; Paris, France; and Berlin, Dresden, Hamburg, and Mannheim, Germany).21 The Brazil High Risk Cohort Study (BHRCS; 2009-2015) for the Development of Childhood Psychiatric Disorders provided data from 815 participants (319 male [57%]; mean [SD] age, 11.3 [1.5] years at baseline and 14.3 [1.9] years at follow-up; 1209 MRI scans) who were recruited in Porto Alegre and Sao Paolo, Brazil22 (Table). Details of scanner protocols can be found in eTable 1 in the Supplement.
All cohorts received approval from the local ethics committees in Canada, the United Kingdom, France, Germany, Ireland, and Brazil. Informed written consent was received from a parent or guardian in addition to the participant’s assent. The analytical design used in this study (data acquisition [MRI and gene expression], measures of interest [cortical thinning and filtered expression list], main analyses [with 3 gene panels], and analysis of psychiatric disorder enrichment) is outlined in eFigure 1 in the Supplement. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.
All T1-weighted images were processed using FreeSurfer, version 5.3 (Laboratory for Computational Neuroimaging) and parcellated based on the Desikan-Killiany atlas.23 The gene expression values used in our analyses were restricted to the left hemisphere of the brain; therefore, we used thickness values from 34 cortical regions of the left hemisphere. Cortical thinning (measured in mm/y) was estimated in an age-specific manner. Within each of the 34 regions, thickness-by-age associations were modeled using generalized additive mixed models. Thickness values from all 3 cohorts and all points (in the case of the BHRCS and IMAGEN cohorts) were included in the models. To achieve robust estimation of cortical thinning, we restricted the participant age range to 9 to 21 years; this restriction ensured that for every 1-year shift in age, at least 100 participants were included. Nonparametric local smoothing splines were used to model the thickness-by-age associations adjusted for sex as a fixed effect, and participant identifiers nested within MRI scanning-site identifiers were used as random intercepts (eFigure 2 in the Supplement). Local smoothing splines are robust measures of nonlinear age trajectories for brain phenotypes.24,25 Using regional derivatives (slopes) of the generalized additive mixed models, a trajectory of the rate of change in thickness (mm/y) was modeled across the study age range (eFigure 3 in the Supplement). To capture the variation in thinning during maturation, we sampled this trajectory in 1-year increments from age 9 to 21 years (eFigure 3 in the Supplement). This approach resulted in 13 age-specific interregional profiles of cortical thinning across the 34 cortical regions (Figure 1).
To test the reliability of the age-specific thinning results, we also estimated longitudinal thinning for participants with 2 MRI scans (ie, those from the IMAGEN and BHRCS cohorts). The procedure for calculating longitudinal thinning profiles is reported in the eMethods in the Supplement.
The primary source of gene expression data was the Allen Human Brain Atlas. Using the technique described by French and Paus,26 gene expression from the Allen Human Brain Atlas was aligned with each of the 34 cortical regions (eMethods in the Supplement). As described by Shin et al,16 a total of 2511 genes had consistent interregional profiles of their expression during childhood and adolescence as indicated by a 2-stage filtering process (eMethods in the Supplement). We restricted panel analyses to these consistent genes.
Three gene panels were created using genetic ontologic terms (eMethods in the Supplement). Two panels captured dendritic architecture: (1) the spine panel, which included genes specifically associated with the structure and function of spines, and (2) the dendrite panel, which included genes generally associated with the structure and function of the entire dendritic arbor. The myelin panel was created to capture genes associated with myelin structure and function. All of the genetic ontologic terms included are listed in eTable 3 in the Supplement. The 3 panels included 71 genes for the spine panel, 266 genes for the dendrite panel, and 53 genes for the myelin panel.
We harmonized gene expression data from the human cerebral cortex included in the Allen Human Brain Atlas, BrainCloud,27,28 the Brain eQTL Almanac,29 the Genotype-Tissue Expression Project,30 and BrainSpan17,31 (eMethods in the Supplement), and we used these data to create coexpression networks. The resultant harmonized data set included 16 245 genes from 534 donors aged 0 to 102 years.
We aimed to investigate the association between interregional profiles of cortical thinning and interregional profiles of gene expression. For example, the interregional profile of cortical thinning at age 9 years represented the spatial variation in cortical thinning across the 34 regions of the cerebral cortex at age 9 years (Figure 1). In the same manner, the interregional profile of gene expression represented the spatial variation in the expression of a given gene across the same 34 regions of the cerebral cortex. We tested the association between interregional profiles of cortical thinning (both age-specific and longitudinal) and interregional profiles of expression in the a priori–defined gene panels for spines, dendrites, and myelin. For each gene within a panel, the expression profile was correlated with a thinning profile (expression-thinning correlation). The average correlation coefficient served as the test statistic in which significance was assessed through a resampling approach (eMethods in the Supplement). This procedure was repeated for each of the 13 age-specific thinning profiles (with resampling-based correction for 13 comparisons) (eTable 2 in the Supplement) and for the BHRCS and IMAGEN longitudinal profiles.
Coexpression networks were generated for all seed genes, which included the genes from each of the 3 panels that indicated significant expression-thinning correlations (after correction for false discovery rate for the number of genes in a panel). To generate these networks, we used linear mixed-effect models that iteratively assessed the association between seed genes and all of the other 16 245 genes in the harmonized expression data set (eMethods in the Supplement). The top 0.1% of positively coexpressed genes (ie, those with the lowest P value) were combined with the original seed gene.
Genes associated with 6 psychiatric disorders, which included autism spectrum disorder, attention-deficit/hyperactivity disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and schizophrenia, were extracted from the DisGeNET database. The number of genes associated with each disorder is included in eTable 4 in the Supplement. Gene enrichment for psychiatric disorders was tested using hypergeometric tests, and P values were corrected within age (false discovery rate for the 6 disorders) and across age (resampling method for the 13 sets of age-specific coexpression networks) (eTable 2 in the Supplement).
All statistical analyses were conducted using R, version 3.5.3 (R Project for Statistical Computing). To measure association, we used generalized additive mixed models, correlation analyses, linear mixed-effect models, and hypergeometric tests. Each technique and its associated methodology are described in the eMethods and eTable 2 in the Supplement. Data analysis was conducted between March and October 2019.
This study included 3596 individuals aged 9 to 21 years. Of those, 1803 participants (50.1%) were female, and the mean (SD) age was 15.2 (2.6) years. The Table contains additional demographic information for the participants in each cohort. Demographic information for participants who were excluded owing to missing values, complications with imaging (eg, pipeline failure), and loss to follow-up is provided in eTable 5 in the Supplement.
At most periods of development, reductions in cortical thickness were observed; across the 34 cortical regions and 13 ages, 88.5% of the change in thickness values were less than 0 (ie, thinning) (eFigure 3 in the Supplement). For this reason, in this study, the general process of changes in cortical thickness was defined as cortical thinning. To facilitate a global evaluation of the spatiotemporal gradients in cortical thinning, cortical regions were ordered by the difference in the rate of thinning between ages 9 and 21 years from the most negative difference (ie, more old thinning than young thinning) to the most positive difference (ie, more young thinning than old thinning) (Figure 1).
Regions of the temporal and frontal lobes exhibited the greatest variations in thinning across the maturation period. In late childhood, thinning in the temporal lobe was most evident (−0.071 mm/y at age 9 years in the inferior temporal gyrus); by late adolescence, however, some of these regions experienced decelerated thinning or even thickening (−5.63e-3 mm/y at age 19 years in the inferior temporal gyrus). In the late-adolescence stage, regions of the frontal lobe displayed cortical thinning (−0.038 mm/y at age 19 years in the pars triangularis). The maturational shift in rates of thinning (temporal to frontal lobe) (Figure 2) resulted in a plateau in interregional variation during the midadolescent period (Figure 1). During this time, moderate rates of thinning occurred across most of the cerebral cortex (−0.044 to 0.016 mm/y at age 15 years of age compared with −0.071 to 0.025 mm/y at age 9 years and −0.068 to 0.055 mm/y at age 21 years). Several regions, such as the postcentral gyrus, indicated little variation in thinning (ie, linear rates of thinning throughout development) across the entire period examined (−5.810e-4 to −5.811e-4 mm/y across the 13 points).
Expression-thinning correlations for the 3 gene panels varied across age (Figure 3A; eTable 6 in the Supplement). The spine and dendrite gene panels were associated with cortical thinning both in late childhood (aged 9-12 years) and late adolescence (aged 17-19 years). The myelin panel was associated with thinning in midadolescence (aged 12-15 years). The variance in thinning explained by the gene panels across different points ranged from 0.45% to 10.55% for the dendrite panel, 0.00% to 9.98% for the spine panel, and 0.19% to 26.39% for the myelin panel (eTable 7 in the Supplement).
The average correlation coefficients for all gene panels shifted with increasing age from negative (more expression and more thinning) to positive (more expression and less thinning) (Figure 3A). We illustrate this inversion of association at age 9 years with the paralemmin gene (PALM; OMIM 608134) and at age 17 years with the leucine-rich repeat-containing protein 7 gene (LRRC7; OMIM 614453); these 2 genes belong to the spine panel (Figure 3B; eFigure 4 in the Supplement). This pattern in association resembled the age-associated shift in interregional thinning patterns from early thinning in the temporal lobe to later thinning in the frontal lobe.
In the SYS cohort, MRI images of all participants were acquired using a single scanner. Therefore, to test the reliability of these gene panel associations, we repeated the analysis (ie, estimating age-specific thinning and associations with gene panels) in the SYS sample only. Of the 8 age-specific thinning profiles (aged 12-19 years), positive expression-thinning correlations with the spine and dendrite panels were apparent at age 15 to 17 years (for example, r = 0.093 [spine] and r = 0.051 [dendrite] at age 16 years) (eFigure 6 in the Supplement).
For each age and gene panel, genes with the most substantial expression-thinning correlations (seed genes) (eTable 8 in the Supplement) and their coexpression networks were identified and tested for enrichment among genes associated with psychiatric disorders. Enrichment for autism spectrum disorder, bipolar disorder, and schizophrenia was apparent among seed genes and coexpression networks from the spine and dendrite panels during midadolescence (eFigure 7 in the Supplement). Myelin-associated seed genes and their coexpression networks were only enriched for genes associated with schizophrenia at age 15 years. Genes associated with attention-deficit/hyperactivity disorder and major depressive disorder indicated modest enrichment for dendrite-associated seed genes (and their coexpression networks). Enrichment for genes associated with obsessive-compulsive disorder was not observed.
For each disorder, genes among the coexpression networks that exceeded the significance threshold of P = .006 (eFigure 7A in the Supplement) were quantified and displayed in word clouds (eFigure 7B in the Supplement).
For the BHRCS and IMAGEN cohorts, longitudinal thinning profiles were derived, and all analyses were repeated. Similar to the results of the main analyses, the younger BHRCS cohort displayed substantial thinning in the temporal lobe (eFigure 5A in the Supplement). In contrast, the older IMAGEN cohort displayed a flatter thinning profile. Similar to the results of the main analysis, expression-thinning correlations in the younger cohort were associated with the myelin panel (eFigure 5B in the Supplement), while expression-thinning correlations in the older cohort were associated with the spine and dendrite panels. Consistent with the results of the main analyses, enrichment for psychiatric disorders was more apparent in the older cohort (eTable 9 in the Supplement).
All analyses were repeated for each sex separately (eFigure 8, eFigure 9, eFigure 10, and eFigure 11 in the Supplement). Similar to the results of the main analyses, both female and male participants had greater cortical thinning in the temporal cortex early in maturation and greater cortical thinning in the frontal cortex later in maturation. These age-specific profiles of cortical thinning were similar between female and male participants. Moreover, for both sexes, the average expression-thinning correlations indicated a similar inversion of association with increasing age (ie, from greater gene expression and more thinning early in maturation to greater gene expression and less thinning late in maturation). In male participants, we observed a wider range in expression-thinning profile correlations (male participants, R = –0.14 to 0.15; female participants, R = –0.10 to 0.08) (eFigure 10 in the Supplement). Male participants exhibited the most similar enrichment pattern for psychiatric disorders observed in the main analyses.
This study used a multicohort sample of participants aged 9 to 21 years to investigate the neurobiological underpinnings of interregional variation in cortical thinning and the implication of these findings for psychiatric disorders. The patterned changes in cortical thickness exhibited a spatiotemporal gradient, with a shift from more cortical thinning in the temporal lobe just before adolescence to more thinning in the frontal lobe during late adolescence. Our results suggest that modifications to dendritic architecture and myelin may explain these variations in the interregional profiles of thinning during brain maturation, with directionality of the association being opposite before and during late adolescence. The same genes (and their coexpression networks) associated with thinning also indicated enrichment for genes associated with several psychiatric disorders.
Developmental changes in cortical structure exhibited spatial and temporal variation. Early in maturation, we observed the greatest cortical thinning in the temporal lobe. In contrast, later in maturation (ie, late adolescence), the greatest cortical thinning was observed in the frontal lobe. Previous studies have reported interregional variations in trajectories of cortical thinning.1,2,32,33 The interregional patterning of cortical thinning during maturation may reflect microstructural changes associated with spatial variations in the transcriptome.
Neuroimaging studies often propose that variations in cytoarchitecture and myeloarchitecture may underlie observed cortical thinning during maturation.34 We found that interregional transcriptomic gradients of genes associated with spines, dendrites, and myelin were associated with interregional cortical thinning. The average expression-thinning correlations for each panel were modest. Nonetheless, the variance in thinning explained by the gene panels ranged (across points) from 0.45% to 10.55% for the dendrite panel, 0.00% to 9.98% for the spine panel, and 0.19% to 26.39% for the myelin panel. Moreover, we found that cortical regions with higher expression of genes associated with the 3 panels had greater cortical thinning early in maturation. Later in maturation, the association switched such that regions with less expression of these genes exhibited greater cortical thinning. These maturational patterns in the expression-thinning association likely reflect the spatiotemporal gradients in cortical thinning (ie, early temporal lobe and late frontal lobe). Genetic variations and environmental experiences may be involved in this spatial and temporal variability in cortical microstructure. A twin study found that thickness of the temporal and ventral-frontal cortex exhibited shared genetic associations, which clustered separately from more dorsal regions of the cerebral cortex.35 This genetic similarity may imply that similar molecular mechanisms are implicated in the structural changes during maturation. At the same time, large portions of both the frontal cortex and the temporal cortex are involved in integrating multisensory information. Therefore, experience-dependent cortical activity in these regions may result in microstructural variability during maturation. In other words, higher-order temporal and frontal cortices are likely to experience greater plasticity during maturation that, in turn, leads to greater variability (ie, nonlinearity) in the rates of cortical thinning. Whether it be genes or environment that result in changes to cortical structure, these factors likely act through molecular mechanisms to alter neuronal microstructure.
Genes associated with cortical microstructure are associated with not only interregional cortical thinning but with many psychiatric disorders. Histological analysis of postmortem tissue has revealed lower dendrite length and number of spines per dendrite in patients with bipolar disorder and schizophrenia compared with individuals without psychiatric disorders.36 Moreover, greater spine density in the cerebral cortex has been associated with autism.37 We observed that dendritic genes associated with interregional cortical thinning (and their coexpression networks) were also enriched for genes associated with these same disorders. Therefore, not only is modification to dendritic architecture associated with cortical thinning during maturation, dysregulation of this cellular remodeling is implicated in the pathophysiologic processes of psychiatric disorders. Moreover, studies investigating structural brain correlates of autism spectrum disorder,38,39 attention-deficit/hyperactivity disorder,40,41 bipolar disorder,42 major depressive disorder,43 and schizophrenia44,45 often report abnormalities in the frontal and temporal cortices. These 2 sets of similarities (namely, shared genes and shared cortical substrate) are consistent with the neurodevelopmental origin for these disorders. Although obsessive-compulsive disorder has previously been associated with thinner cortices, we did not observe enrichment of genes associated with this disorder among genes associated with thinning. Overall, we speculate that perturbations (whether owing to genes or experience) to the typical maturation of the cortical microstructure may be associated with susceptibility for psychiatric disorders. In this study, we provide a molecular association between the structural maturation of the cerebral cortex and psychiatric disorders.
To our knowledge, the Allen Human Brain Atlas provides the most complete spatial analysis of gene expression across the human brain to date. It is, however, limited by its sampling of only 6 adult donors. In the present study, we used only genes with a reliable spatial gradient in expression during both adulthood and childhood/adolescence by applying 2 filters for consistency in interregional expression profiles.16 Moreover, the absolute expression levels and differential expression across brain regions appears to be conserved across the donor brains in the Allen Human Brain Atlas.46,47 Various methods are used to align raw probe data with brain regions and to summarize gene expression (as reviewed by Arnatkevičiūtė et al48), and our approach is consistent with many of the guidelines suggested.
Similarities in cortical gradients of gene expression and interregional variation in cortical thinning during maturation suggest the involvement of modifications to dendritic architecture and myelin. Those genes, which are likely involved in cortical maturation, are also enriched for several psychiatric disorders. This genetic similarity suggests overlapping molecular pathways that modify susceptibility to psychiatric disorders during maturation.
Accepted for Publication: April 13, 2020.
Published Online: June 17, 2020. doi:10.1001/jamapsychiatry.2020.1495
Correction: This article was corrected on July 1, 2020, to fix errors in Figure 3.
Corresponding Author: Tomáš Paus, MD, PhD, Bloorview Research Institute, 150 Kilgour Rd, East York, Toronto, ON M4G 1R8, Canada (firstname.lastname@example.org).
Author Contributions: Dr Paus and Ms Parker had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Parker, Patel, Pan, Paus.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Parker, Patel.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Parker, Patel.
Obtained funding: Salum, Paus.
Administrative, technical, or material support: Pan, Salum, Paus.
Supervision: Jackowski, Pausova.
Conflict of Interest Disclosures: None reported.
Funding/Support: The Saguenay Youth Study was supported by the Canadian Institutes of Health Research (Drs Pausova and Paus), the Heart and Stroke Foundation of Quebec (Dr Pausova), and the Canadian Foundation for Innovation (Dr Pausova). The Brazil High Risk Cohort Study was supported by grants Fapesp 2014/50917-0 and Fapesp 2013/08531-5 from the Sao Paulo Research Foundation funded by the National Institute of Developmental Psychiatry and grant CNPq 465550/2014-2 from the Brazilian National Council for Scientific and Technological Development. The IMAGEN study was supported by grant LSHM-CT- 2007-037286 from the FP6 Integrated Project IMAGEN funded by the European Union; grant 695313 from the ERC Advanced Grant STRATIFY funded by Horizon 2020; grant PR-ST-0416-10004 from ERANID; grant MR/N027558/1 from BRIDGET; grant HBP SGA 2, 785907 from the Human Brain Project; grant 603016 from the FP7 MATRICS project; grant MR/N000390/1 from the Medical Research Council; grants 01GS08152, 01EV0711, Forschungsnetz AERIAL 01EE1406A, and Forschungsnetz AERIAL 01EE1406B from the Bundesministeriumfur Bildung und Forschung; grants SM 80/7-2, SFB 940, TRR 265, and NE 1383/14-1 from the Deutsche Forschungsgemeinschaft; grants MR/R00465X/1, and MR/S020306/1 from the Medical Research Foundation and Medical Research Council; grants 5U54EB020403-05 and 1R56AG058854-01 from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) Consortium funded by the National Institutes of Health; and funding from the National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS (National Health Services) Foundation Trust and King’s College London. Further support was provided by grants AF12-NEUR0008-01-WM2NA and the French National Research Agency (ANR)-12-SAMA-0004 from the ANR; grant ANR-18-NEUR00002-01 from Eranet Neuron; grant 00081242 from the Fondation de France; grant DPA20140629802 from the Fondation pour la Recherche Medicale; grant AP-RM-17-013 from the Fondation de l’Avenir; grant 16/ERCD/3797 from the National Institutes of Health (Science Foundation Ireland); grants RO1 MH085772-01A1, and U54 EB020403 from the National Institutes of Health; a grant from the Assistance-Publique-Hopitaux-de-Paris and INSERM; and funding from the Mission Interministerielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives, Paris Sud University IDEX 2012, and the Federation pour la Recherche sur le Cerveau.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Group Information: The IMAGEN Consortium members include the following: Tobias Banaschewski, MD, PhD, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Gareth Barker, PhD, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; Arun L. W. Bokde, PhD, Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland; Uli Bromberg, Dipl-Psych, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany; Christian Buchel, MD, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany; Erin Burke Quinlan, PhD, Medical Research Council, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; Sylvane Desrivieres, PhD, Medical Research Council, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; Herta Flor, PhD, Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, and Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany; Vincent Frouin, PhD, NeuroSpin, CEA, Universite Paris-Saclay, Gif-sur-Yvette, France; Hugh Garavan, PhD, Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont; Penny Gowland, PhD, Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom; Andreas Heinz, MD, PhD, Department of Psychiatry and Psychotherapy, Charite, Universitatsmedizin Berlin, Berlin, Germany; Bernd Ittermann, PhD, Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany; Jean-Luc Martinot, MD, PhD, Institut National de la Santé et de la Recherche Medicale, INSERM Unit 1000 Neuroimaging and Psychiatry, University Paris Sud, University Paris Descartes–Sorbonne Paris Cité and Maison de Solenn, Paris, France; Marie-Laure Paillere Martinot, MD, PhD, Maison de Solenn, Cochin Hospital, Paris, France; Eric Artiges, MD, PhD, Institut National de la Sante et de la Recherche Medicale, INSERM Unit 1000 Neuroimaging and Psychiatry, University Paris Sud, University Paris Descartes–Sorbonne Paris Cite and Psychiatry Department, Orsay Hospital, Orsay, France; Herve Lemaitre, PhD, Institut National de la Sante et de la Recherche Medicale, INSERM Unit 1000 Neuroimaging and Psychiatry, Faculte de Medecine, Universite Paris-Sud, Le Kremlin-Bicetre, and Universite Paris Descartes, Sorbonne Paris Cite, Paris, France; Frauke Nees, PhD, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, and Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Dimitri Papadopoulos Orfanos, PhD, NeuroSpin, CEA, Universite Paris-Saclay, Gif-sur-Yvette, France; Tomáš Paus, MD, PhD, Rotman Research Institute, Baycrest and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada; Luise Poustka, MD, Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany, and Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria; Sarah Hohmann, MD, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Sabina Millenet, Dipl-Psych, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Juliane H. Frohner, Dipl-Psych, Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany; Michael N. Smolka, MD, Department of Psychiatry and Neuroimaging Center, Technische Universitat Dresden, Dresden, Germany; Henrik Walter, MD, PhD, Department of Psychiatry and Psychotherapy, Charite, Universitatsmedizin Berlin, Berlin, Germany; Robert Whelan, PhD, School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; and Gunter Schumann, MD, Medical Research Council–Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom.
The Saguenay Youth Study members include the following: Michal Abrahamowicz, PhD, Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada; Manon Bernard, Hospital for Sick Children, Toronto, Ontario, Canada; Daniel Gaudet, MD, University of Montreal, Montreal, Quebec, Canada; Gabriel Leonard, PhD, McGill University, Montreal, Quebec, Canada; Tomáš Paus, MD, PhD, Bloorview Research Institute, Toronto, Ontario, Canada; Zdenka Pausova, MD, Hospital for Sick Children, Toronto, Ontario, Canada; Michel Perron, PhD, CEGEP do Jonquere, Jonquere, Quebec, Canada; Bruce Pike, PhD, University of Calgary, Calgary, Alberta, Canada; Louis Richer, PhD, University of Quebec at Chicoutimi, Chicoutimi, Quebec, Canada; Jean Seguin, PhD, University of Montreal, Montreal, Quebec, Canada; and Suzanne Veillette, PhD, CEGEP do Jonquere, Jonquere, Quebec, Canada. Jean Shin, MD, of the Hospital for Sick Children, Toronto, Ontario, Canada, provided statistical advice. Con compensation was received.