Dissecting Autism and Schizophrenia Through Neuroimaging Genomics

Clara A. Moreau; Armin Raznahan; Pierre Bellec; Mallar Chakravarty; Paul M. Thompson; Sebastien Jacquemont;


Brain. 2021;144(7):1943-1957. 

In This Article

Lessons Learned From Top-down Studies

Reproducible Neuroimaging Findings in Autism Spectrum Disorder and Schizophrenia are Limited

The most consistent structural MRI finding in ASD is, on average, a higher total brain volume (Figure 1). This is mainly reported before 24 months,[33,38,46,47] but is also observed in older individuals with autism (+0.25 Cohen's d).[39] Although debated,[48] lower volumes of the cerebellum and corpus callosum and increased CSF volume were also recurrently reported in ASD compared to controls.[38,49] Inconsistent findings have been reported for the hippocampus, amygdala, thalamus and basal ganglia.[38] Such heterogeneity and small effect sizes (Cohen's d < 0.3; Figure 2) underscore the necessity for large samples allowing subtyping strategies.[53]

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).25–30, Top middle: Genomic variants associated with both conditions and genetic correlation between ASD and schizophrenia.26, 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.31–42, Bottom middle: Shared anatomical and functional alterations associated with ASD and schizophrenia.43–45 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.50 and Modenato et al.51); surface area (B, E and H, from Moreau et al.44 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 (DI) from Modenato et al.51 and Moreau et al.52 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.

To improve reproducibility, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium increased sample size by aggregating data from 49 scanning sites. This effort identified smaller volumes of the pallidum, putamen, amygdala, and nucleus accumbens with small effect sizes (0.13 Cohen's d). Cortical thickness was higher in the frontal cortex and smaller in the temporal cortex (0.21 Cohen's d).[39] Subsequent studies of cortical morphometry in ASD[40] reported higher mean cortical thickness (Cohen's d = 0.22) compared to controls, in particular in the inferior frontal and prefrontal cortex, in the superior temporal, postcentral and posterior cingulate gyri, and the precuneus (Cohen's d < 0.32). Superior temporal gyrus and inferior frontal sulcus cortical thickness were negatively correlated with age and full-scale intelligence quotient (FSIQ) in the ASD group. These two large studies provided convergent results, but authors also noted inconsistencies (cortical thickness decreases in the ENIGMA study[39]), which in part have been reconciled by adjusting the stringency of quality checking (e.g. motion) across both studies. Asymmetry in ASD has also been under scrutiny. An ENIGMA study of 54 datasets reported cortical thickness asymmetries involving mainly the superior frontal gyrus (Cohen's d = 0.13), the medial frontal, orbitofrontal, inferior temporal, and cingulate regions, that were reduced in ASD compared to controls.[54]

Likewise, functional connectivity has been investigated in ASD. Resting-state functional MRI is particularly appropriate to study psychiatric paediatric population because it enables data acquisition on functional connectivity without patient participation (contrary to task-based functional MRI) and limits excessive motion during scanning. Several analytical methods applied to a large aggregate dataset showed a widespread decrease of connectivity in ASD compared to controls across all datasets.[55,56] Underconnectivity was predominantly observed in the default mode network (DMN; Figure 3 and Table 1), the salience, the visual, and the auditory networks. Thalamocortical overconnectivity (in particular, between the thalamus and the sensorimotor network) is also a finding replicated in most studies.[36,55,60] Many other findings are inconsistent across sites[41] and may reflect differences in ascertainment and mechanistic heterogeneity in ASD.[61] These include reduced long-range connectivity, increased short-range connectivity, and decreased homotopic connectivity.[41] Furthermore, functional connectivity is a field that lacks standardization and many analytical strategies are used by different groups (e.g. whether to perform global signal regression is an ongoing debate in the field and creates discrepancies across publications).[62,63] As an example, the largest resting-state functional MRI study in ASD identified across four datasets reproducible patterns of underconnectivity within sensorimotor networks and overconnectivity within the frontoparietal networks (Figure 3 and Table 1) across datasets (0.2–0.6 Cohen's d).[64] However, these results were not found by previous studies, likely due to different analytical strategies.

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.57 Networks have been defined at several levels of resolution including the commonly used 7-network parcellation (top right 58) compared to the 12-network MIST parcellation (bottom right 59) (https://simexp.github.io/multiscale_dashboard/index.html). See also Table 1.

The 'gradient' analysis of human functional networks provides an additional coordinate system.[65] It has been studied in the general population, and more recently in ASD. In normative/typically developing studies, this framework identifies a smooth transition along a gradient from unimodal areas of function (sensory, auditory, motor, visual) to higher-order transmodal areas (e.g. DMN). Studies showed that both extremes of the rostrocaudal gradient were decreased in ASD.[66] Further analyses revealed cortical surface area decreases in ASD specifically within transmodal medial prefrontal and posterior cingulate regions.[66]

Results have been less conflicted in schizophrenia. Although both conditions are associated with small effects, those detected in schizophrenia are typically 2–3-fold larger than in ASD (Figure 2). This difference, which is puzzling as ASD and schizophrenia have similar severities and prevalence, may suggest a lower level of neuroanatomical heterogeneity in schizophrenia compared to autism. A large meta-analysis reported a global grey matter reduction that was mainly driven by the dorsomedial and orbitofrontal cortex, as well as the medial temporal, insula, thalamic, and striatal area.[31] The ENIGMA consortium (2028 schizophrenia and 2540 controls) reported smaller hippocampus (Cohen's d = −0.46), amygdala (Cohen's d = −0.31), thalamus (Cohen's d = −0.31), nucleus accumbens (Cohen's d = −0.25), and larger pallidum (Cohen's d = 0.21) and lateral ventricle volumes (Cohen's d = 0.37).[34] A follow-up study (4474 schizophrenia and 5098 controls) examined cortical thickness and surface area[35] showing a decrease in the total surface area driven by frontal and temporal lobe regions (Cohen's d = −0.25). A widespread decrease in cortical thickness (Cohen's d = −0.52) was also reported. Adjusting for mean cortical thickness showed thinner cortex in fusiform, parahippocampal, and inferior temporal gyri, and thicker cortex in the precuneus, and superior parietal cortex (Cohen's d = 0.25). CT differences were greater in the group of individuals treated with antipsychotic medication and were correlated with illness duration.[35] Of note, treatment may play a larger role in neuroimaging studies in schizophrenia compared to ASD due to the lower frequency of medication in the latter group.

Functional imaging studies in schizophrenia show reduced mean connectivity but in the absence of large functional MRI datasets in schizophrenia, results should be interpreted with caution.[32,67] This is predominantly observed within the DMN, ventral attention, frontoparietal, and somatomotor networks (Figure 3 and Table 1).[68] In contrast, the thalamus has been reported as overconnected with the somatomotor network and the middle temporal gyrus (correlated with positive symptoms) and underconnected with cerebellar regions (correlated with delusions and bizarre behaviour).[37,69] Cerebellar (Crus-I, lobule IX and lobule X) overconnectivity has been also reported with the salience and sensorimotor networks.[42]

An ongoing debate is whether to consider resting-state as a collection of individual states that may be captured using dynamic connectivity. Studies showed that functional networks are dominated by contributions from common organizational principles and conjunction of individual features.[70] Therefore, disease-related effects that are state-dependent might appear as highly heterogeneous because of limited temporal sampling.

Earlier top-down studies were vastly underpowered to report effects in ASD and schizophrenia (e.g. analyses of the corpus callosum volume in ASD[48]), but larger studies are now yielding more reproducible findings. Small effect sizes reported in both schizophrenia and ASD might be an indicator of significant heterogeneity (Figure 2). Several factors such as medication exposure (e.g. antipsychotic medications might modulate the functional MRI signal[71,72]) and the stage of the disease could confound these findings. There are likely subgroups associated with different patterns of brain alterations, possibly cancelling each other out in idiopathic cohorts. Examples of such effects are 16p11.2 deletions and duplications that equally increase autism risk but are associated with mirror effects on neuroimaging traits such as the insula volume.[73] The subgroups and dimensions nested within conditions have however remained elusive. Furthermore, many of the alterations described above have been observed across several conditions. A core set of vulnerable brain regions and networks may be present across psychiatric diagnoses.

The Polygenic Architecture of Autism Spectrum Disorder and Schizophrenia

Twin studies estimate the genetic contribution to ASD and schizophrenia around 73–93% and 79%, respectively.[74–76] Heritability estimates are, however, based on models [phenotype (P) = G(genetic) + E(environment)] that do not take into account the interaction between G and E. These estimated values may, therefore, be inflated by mechanisms such as assortative matting or dynastic effects.[77]

Most of the genetic contribution is due to common variants. Although the contribution of rare mutations to the total population liability is modest (5%), they contribute substantially to individual risk[78] and occur mostly de novo. They are identified in 20% of individuals with ASD[79,80] and have important implications for carriers. Among these rare variants, copy number variants (CNVs) are routinely screened in the clinic using chromosomal microarray analysis. Sixteen recurrent CNVs have been associated with ASD (Figure 1).[30,81] However, studies of non-recurrent CNVs estimate that any 1 megabase deletion or duplication including coding elements increases autism risk (albeit mildly) with a mean odds ratio (OR) of 1.6 and 1.2, respectively.[82]

Large effect size CNVs, such as the 16p11.2 deletion, are identified in 7–14% of patients with ASD.[21,81] Rare large effect-size SNVs are identified in 13–15% of individuals with ASD.[83] Exome sequencing studies have identified 102 genes conferring high risk for ASD, intellectual disability, and related neurodevelopmental conditions.[84,85] These large risk ASD genes were enriched in the genome-wide association study (GWAS) signal of schizophrenia and educational attainment, as well as gene ontology terms including gene neuronal regulation and neuronal communication.[85]

For schizophrenia, large risk variants have been harder to identify in comparison with ASD.[86] Early candidate gene studies identified rare putative large risk schizophrenia genes (e.g. COMT, DISC1, DTNBP1, and NRG1), but they were not subsequently replicated.[87] Burden analyses show that de novo variants distributed across many coding genes are over-represented in schizophrenia.[88] However, few genes have been robustly identified as large effect-size risk factors for schizophrenia (i.e. SETD1A, NRXN1).[89,90] Eight CNVs have been formally associated with schizophrenia with OR ranging from 2 to 30[9,91] and eight additional CNVs met criteria for suggestive association (Figure 1).[27,92] However, burden analyses have demonstrated that many more CNVs increase risk for schizophrenia.[27]

The common-allele model posits that the psychiatric condition results from the cumulative effect of multiple common alleles with small effects. The yield of GWAS studies has significantly increased with sample size. The latest studies in ASD and schizophrenia identified five single nucleotide polymorphisms (SNPs)[25] and 145 SNPs,[29] respectively. However, predictive models suggest that as sample sizes increase, the rate of future common variant discoveries for ASD will be between those for schizophrenia and major depression.[93]

Neuroimaging and Genomic Dimensions Across Diagnostic Boundaries

Neuroimaging traits and genetic factors specific to a psychiatric diagnosis have yet to be identified. The field has, however, progressed in characterizing neural substrates and genomic variants common across disorders.

In one of the first large transdiagnostic efforts, anterior cingulate area and anterior insula were among the top regions to demonstrate shared anatomical alterations across schizophrenia, bipolar disorder, major depression, addiction, OCD, and anxiety.[43,94] Shared alterations were the highest between psychotic disorders and minimum with anxiety and OCD. A neuroanatomical investigation of ASD, schizophrenia, and ADHD has suggested that shared dimensions may arise through alterations in functional networks responsible for processing complex cognitive traits. Patterns associated with ASD and ADHD were distributed within the DMN, while ADHD and schizophrenia patterns were preferentially observed in the ventral attentional network (Figure 3 and Table 1).[50] The remaining components of the ASD and schizophrenia alteration profiles were distributed within the frontoparietal and limbic networks. Interestingly, thickness and surface alterations were observed within the same network, but not necessarily with the same directionality across conditions. Identifying overlap between these three conditions was difficult possibly because of the small neuroimaging effect size in ASD and ADHD, and the lower correlation between schizophrenia and these two earlier onset conditions.

Deficits in the social communication questionnaire measured in individuals with ASD, ADHD, and OCD were associated with a decrease in the right insula cortical thickness and the ventral striatum volume.[95] Larger amygdala and hippocampus volumes were associated with higher scores on the 'Reading the Mind in the Eyes Test'.[95]

At the functional level, studies showed that underconnectivity in the medial prefrontal cortex, anterior and posterior cingulate cortex, as well as the precuneus, were altered along a psychosis spectrum (i.e. bipolar disorder and schizophrenia).[67,96,97] A large meta-analysis[45] across eight psychiatric disorders identified shared alterations in network connectivity predominantly in the ventral salience and the frontoparietal networks, and the DMN. An underconnectivity pattern was identified between the DMN and the ventral salience network and between the frontoparietal and the salience networks. An overconnectivity pattern was found between the DMN and frontoparietal network and between the DMN and salience network.

Overall, these studies suggest that neuropsychiatric disorders may be related to similar hubs of vulnerability including the anterior cingulate cortex, the DMN, the frontoparietal network (especially prefrontal regions), and the insular cortex. Although these findings should be interpreted with caution, recurrent involvement of these brain areas could be due to their complex functions such as social cognition and executive functions,[94] in line with the RDoC and p-factor. Neuroimaging dimensional reduction such as the gradient approach[98] may help position psychiatric conditions along general dimensions.

Genetic correlations between psychiatric conditions are well-replicated findings and are much higher than what has been observed for neurological conditions.[25,26,99,100] A recent study showed that among 146 genome-wide significant SNPs reported in ASD, ADHD, schizophrenia, bipolar disorder, major depression, anorexia nervosa, OCD, and Tourette syndrome, 109 (75%) showed association with two or more conditions and 23 with four or more neuropsychiatric disorders. These 23 SNPs were located within genes expressed in the brain from the second trimester. Modelling the joint genetic architecture of these eight conditions identified three groups of neuropsychiatric disorders: compulsive, mood, and psychotic, as well as early-onset conditions. These results suggest pleiotropic mechanisms as well as genetic dimensions spanning diagnoses.[26,101]

Similar observations have been reported for rare variants. Twenty nine pathogenic CNVs were shared across ASD and schizophrenia, including recurrent CNVs at 12 loci (such as 1q21.1, 3q29, 15q11.2, 16p11.2, 16p13.11, 17p12, 22q11.2).[102] Gene set analyses pointed towards a substantial overlap of biological pathways involved in both disorders. Identified mechanisms included synapse/neuron projection, cell adhesion/junction, MAPK signalling, transcription/gene expression regulation, and the actin cytoskeleton. Shared mechanisms have been also investigated using gene expression data. Analyses of post-mortem cortex samples revealed shared gene-expression profiles between ASD and schizophrenia, as well as bipolar disorder and schizophrenia. Shared differential expression profiles involved downregulation of neuronal and synaptic signalling pathways with a gradient of transcriptomic severity showing the largest changes in ASD compared with schizophrenia or bipolar disorder.[103]

Overall, genetic factors appear to converge early on at the transcriptional level, which may in part explain phenotypic and neuroimaging traits shared across psychiatric conditions.