Brain structural alterations are distributed following functional, anatomic and genetic connectivity

Abstract

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxelbased morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

“…In neurodegenerative diseases, transneuronal transport of toxic misfolded proteins is associated with cell death and atrophy [40,43,74]. As a result, patterns of atrophy in neurodegenerative diseases often resemble structural and functional network patterns [17], a result that has been reported in Alzheimer's disease [35,53,60], Parkinson's disease [77,80,81] and amyotrophic lateral sclerosis [58].…”

Longitudinal increases in structural connectome segregation and functional connectome integration are associated with better recovery after mild TBI

“…This model is linear, has a simple, closed-form solution and only one tuning parameter, making it computationally more tractable and less prone to overfitting than, for example, high-dimensional, nonlinear neural mass models. The ND model has been applied to predicting patterns of atrophy in dementia (Raj, Kuceyeski, & Weiner, 2012), epilepsy (Abdelnour, Mueller, & Raj, 2015) and a range of neurological disorders (Cauda et al, 2018). The ND model's one tuning parameter, called ND model propagation time, allows quantification of the FC-SC relationship.…”

Hubs of long-distance co-alteration in brain pathology

“…However, the network-like account of coalterations seems to provide insights also in clinical conditions that do not have a neurodegenerative origin, such as schizophrenia, autism, obsessive-compulsive disorder, depression, and chronic pain (Cauda et al, , 2018a. Furthermore, transdiagnostic meta-analyses merging data of studies about psychiatric and neurologic diseases support the following ideas: i) the most affected areas of the brain correspond to the hubs of the functional and structural connectomes , and ii) the distribution and development of co-alterations are mainly explained by functional and structural connectivity constraints (Cauda et al, 2018b).…”

“…Higher-order associative brain regions, which are more prone to be targeted by diseases , are characterized by a long physical distance and a high centrality . So, if connectivity influences the development of pathology , Cauda et al, 2018b, the spatial distribution of the physical distance of coalterations might provide an insightful indication of how pathology is distributed across the brain. It would be also interesting to compare such information with a measure of centrality from a normative connectome, so as to test if there is a correlation between abnormal distance and connectivity.…”