Dissecting autism and schizophrenia through neuroimaging genomics

Abstract

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Keywords: autism; copy number variants; neuroimaging; schizophrenia.

Figure 1

Genomic variants and neuroimaging alterations associated with ASD and schizophrenia. Top: Common and rare genetic variants (in green and blue, respectively) associated with ASD (left) or schizophrenia (SZ, right).Top middle: Genomic variants associated with both conditions and genetic correlation between ASD and schizophrenia.Bottom: Structural and resting-state functional MRI (in blue and green, respectively) intermediate brain phenotypes associated with ASD (right) or schizophrenia (left). Results were reported based on meta-analyses or from the largest study to date.Bottom middle: Shared anatomical and functional alterations associated with ASD and schizophrenia. BP = breakpoint; CT = cortical thickness; d = dorsal; Del = deletion; Dup = duplication; FC = functional connectivity; FPN = frontoparietal network; SA = surface area; SN = salience network; v = ventral; vol = volume.

Figure 2

Effect size across three psychiatric conditions and CNVs. Distributions of Cohen’s d are represented for case-control studies in ASD, schizophrenia (SZ), ADHD and CNVs using three modalities: cortical thickness (A, D and G, from Park et al. and Modenato et al.⁵¹); surface area (B, E and H, from Moreau et al. and Modenato et al.⁵¹) and Functional connectivity (C, F and I, from Moreau et al.^44,52). The same Cohen’s d distributions are presented for two large (22q11.2 and 16p11.2), one moderate (1q21.1) and one small effect size (15q11.2) deletion and duplication (D–I) from Modenato et al. and Moreau et al. For cortical thickness, surface area, and functional connectivity, CNVs show a much larger effect size at the global (mean shift) and regional level (spread of the Cohen’s d distribution) compared with psychiatric conditions.

Figure 3

Correspondence between brain regions and functional networks. What constitutes a core functional network is not clear, and no universal taxonomy has been adopted yet. Networks have been defined at several levels of resolution including the commonly used 7-network parcellation (top right⁵⁸) compared to the 12-network MIST parcellation (bottom right⁵⁹) (https://simexp.github.io/multiscale_dashboard/index.html). See also Table 1.

Figure 4

Top-down versus bottom-up approaches. The genetic-first, bottom-up approach (right) can build models/signatures from a lower level in the hierarchy (e.g. intermediate brain phenotype), and then asks questions about how such low-level models can explain observations higher up in the hierarchy (clinical manifestations).

Figure 5

Integrating top-down and bottom-up strategies in neuroimaging genomics. We propose a systematic investigation of a broad spectrum of neuropsychiatric variants to identify dimensions underlying idiopathic conditions. Multiscale and multimodal studies using multivariate approaches would allow for the identification of latent brain dimensions that best explain the relationships between genomic variants, biological processes, psychiatric conditions, and cognitive traits. Neuroimaging proxies of specific biological processes are identified through bottom-up approaches using individuals who carry mutations in genes involved in defined gene sets (akin to a polygenic score). Computing polygenic scores informed by biological and brain processes (e.g. genes highly expressed in sensory-motor regions) has considerable potential to parse out the contribution of specific pathways to alterations of brain architecture. Multivariate approaches such as canonical correlation analysis or structural equation modelling will allow investigating the relationship between genomic variants, neuroimaging features, psychiatric conditions, and behavioural traits. BIP = bipolar disorder; CCA = canonical correlation analysis; GE = gene expression components; IQ = intelligence quotient; pLI = probability of being loss-of-function intolerant; PLS = partial least square regression; PRS-SZ = polygenic risk score for schizophrenia; Pvalb = parvalbumin^,; SEM = structural equation modelling; SZ = schizophrenia. See also Table 1.

Figure 6

Historical timeline in neuroimaging genetics. Many advances in neuroimaging genomics have been made by large-scale initiatives and cohort studies, such as the Autism Brain Imaging Data Exchange (ABIDE), the Psychiatric Genomics Consortium (PGC), the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium and the UK Biobank. These collaborative initiatives, among others, facilitate advances in psychiatry by providing large brain imaging and genomics datasets to the research community worldwide. Human Genome Project (HGP); Neuroimaging Tools and Resources Collaboratory (NITRC platform); Psychiatric Genomics Consortium (PGC); Alzheimer’s Disease Neuroimaging Initiative (ADNI); 1000 Genomes; ADHD-200; Open fMRI; Human Brain Project (HBP); ENIGMA Consortium = Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA Consortium); = Autism Brain Imaging Data Exchange (ABIDE-1); NeuroVault; UK Biobank; HCP = Human Connectome Project (HCP); PING = Pediatric Imaging; Neurocognition; and Genetics (PING); SchizConnect; EU-Aims; Adolescent Brain Cognitive Development (ABCD).