Brain pathology recapitulates physiology: A network meta-analysis

Abstract

Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic ‘cost’ significantly differs along this transdiagnostic/multimodal gradient.

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas

“… 18 Pairs of brain regions which are both consistently affected by a neuropathology could have this relationship for a number of informative reasons, such as a shared biological vulnerability, or a common pathway along which the disease spreads. 19 The latter possibility is supported by studies of Parkinson’s and Alzheimer’s disease, which have unveiled networks of brain atrophy and tau accumulation that follow intrinsic functional connectivity networks. 20 , 21 The notion that patterns of collateral damage can reveal insights into the aetiology of brain disease is also supported by the phenomenon of lesion covariance in stroke, which stems from the vascular origins of the injury.…”

Submaximal Exercise Provokes Increased Activation of the Anterior Default Mode Network During the Resting State as a Biomarker of Postexertional Malaise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

“…Mechanisms leading to atrophy or hypertrophy in one region can spread along axons in long white matter tracts to transmit these pathological alterations to other nodes in the interconnected network. The network denegation hypothesis ( Vanasse et al, 2021 ) contends that pathological injury in one localized region can be transmitted to connected brain regions via networks, and suggests that dysfunction in one node will predict that other regions in the associated network will become preferentially affected. The concept of network dysfunction may also predict that diseases with different pathologies may have superficial similarities that can be resolved by objective fMRI or other investigations of effects on networks.…”

Modeling Neurodegeneration in silico With Deep Learning

“…Prior work in modeling neurological diseases have primarily explored computational models of cellular processes focusing on pathological changes such as excitotoxicity or abnormal bioenergetics (Le Masson et al, 2014;Muddapu et al, 2019), or connectome models based on structural or functional connectivity in the brain (Hof et al, 1997;Raj et al, 2012;Zhou et al, 2012;Ortiz et al, 2015;Peraza-Goicolea et al, 2020;Vanasse et al, 2021). Early work investigating structural or functional connectomes primarily focused on modeling specific aspects of disease progression, such as diffusive spread of misfolded tau and beta amyloids (Raj et al, 2012) or changes in network connectivity contributing to disease vulnerability or diagnosis (Zhou et al, 2012;Ortiz et al, 2015).…”

“…More recent work has begun using the connectome to simulate disease states in silico, for example by modifying connection weights in a simulated functional connectome to predict changes in functional activation and connectivity in the brain (Peraza-Goicolea et al, 2020). A meta-analysis of the relationship between structural pathology and behavioral pathology supported the notion that network degeneration is a contributing factor to disease pathology (Vanasse et al, 2021). However, none of the aforementioned approaches used models that can perform tasks at near-human levels (LeCun et al, 2015;Vercio et al, 2020).…”