Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Abstract

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

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 81–85 .…”

Multimodal, multiscale connectivity blueprints of the cerebral cortex

“…We next ask how connectivity modes shape the spatial patterning of brain disease. Emerging theories emphasize that the course and expression of multiple brain diseases is mediated by shared molecular vulnerability [57, 156]. We therefore compared connectivity modes with patterns of cortical abnormalities across thirteen different neurological, psychiatric, and neurodevelopmental diseases and disorders from the ENIGMA consortium ( N = 21 000 patients, N = 26 000 controls) [57, 79, 141].…”

“…We define the “exposure” that region i has to region j ’s pathology as the product between the ( i, j )-edge strength ( c ij ) and region j ’s abnormality ( d j ) (Fig. 4a) [31, 57, 127, 129]. Then the global disease exposure to region i is the mean exposure between region i and all other regions in the network with positive edge strength.…”

“…Combining connectivity modes has also been used to better resolve clusters of functional activation in BOLD data [84], and inform the application of deep brain stimulation to psychiatric and neurological diseases [11, 67]. Encouragingly, previous work has found that incorporating multiple perspectives of brain connectivity can result in novel discoveries, including improved generative models of brain connectivity [100], structure-function coupling [56, 105], epicentres of transdiagnostic alterations [57, 60], and the characterization of homophilic wiring principles [16].…”

“…This approach is already widely used on the haemodynamic BOLD signal but also exists for time-series measures from other imaging modalities such as magneto-/electroencephalography (MEG/EEG) and dynamic FDG-fPET (all called “functional connectivity”) [24, 32, 46, 52, 70]. In cases where multiple measures of a feature exist at each brain region, such as gene expression levels across many genes, connectivity can represent the similarity of brain regions with respect to a single local feature [56, 57, 60, 103, 111, 122, 125]. In each case, the ensuing region × region network represents a form of connectivity between brain regions.…”