Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

Abstract

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.

Multimodal neural correlates of childhood psychopathology

“…, greater segregation), suggesting that connectivity between the two anchors of the principal gradient might be affected by the p factor. This finding is in line with recent studies showing that the sensory-to-transmodal axis is impacted across several disorders (Hettwer et al, 2022; Opel et al, 2020; Park et al, 2022a). As the p factor represents a general liability to all common forms of psychopathology, this points towards a disorder-general biomarker of dysconnectivity between lower-order and higher-order systems in the cortical hierarchy (Elliott et al, 2018; Kebets et al, 2019), which might be due to abnormal differentiation between higher-order and lower-order brain networks, possibly due to atypical maturation of higher-order networks.…”

Three components of human brain gene expression reflect normative developmental programmes with specific links to neurodevelopmental disorders

“…If so, the lack of an association between G1 and SCZ may be due to limited signal-to-noise, and remains a question for future research. Overall, our results complement recent findings pointing to shared molecular and structural underpinnings across psychiatric disorders [81][82][83][84][85] .…”

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

“…Neuropsychiatric disorders including schizophrenia (SCZ), bipolar (BD) and major depressive disorders (MDD), are increasingly recognized as network disorders (1-4) characterized by widespread cortical and subcortical alterations (5)(6)(7)(8)(9)(10). Among these, SCZ is associated with the most severe and distributed structural alterations (6,11,12) appearing in all disease stages (13), with temporal-paralimbic and frontal regions being the most affected (5,(13)(14)(15). However, the pattern is not uniformly distributed across the entire brain (15) raising the question how the brain's connectome architecture guides the spatial distribution of subcortical and cortical alterations in SCZ (5,6).…”

“…To assess generalizability and specificity of these network associations, we contextualized the influence of site, disease stage of SCZ, and individual clinical profiles. Finally, we examined whether our network models would reflect SCZ-specific features or rather represent a shared signature across other major psychiatric conditions (33) such as has been shown for subcortical volume, cortical thickness, surface area measures (34–36), structural covariance (37), and connectome properties (3). Therefore, we extended our analysis to meta-analytic case-control alterations in BD and MDD.…”