Structural covariance analysis for neurodegenerative and neuroinflammatory brain disorders

Abstract

Structural MRI can robustly assess brain tissue alterations related to neurological diseases and ageing. Traditional morphological MRI metrics, such as cortical volume and thickness, only partially relate to functional impairment and disease trajectories at the individual level. Emerging research has increasingly focused on reconstructing interregional meso- and macro-structural relationships in the brain by analysing covarying morphometric patterns. These patterns suggest that structural variations in specific brain regions tend to covary with deviations in other regions across individuals, a phenomenon termed structural covariance. This concept reflects the idea that physiological and pathological processes follow an anatomically defined spreading pattern. Advanced computational strategies, particularly those within the graph-theoretical framework, yield quantifiable properties at both the whole-brain and regional levels, which correlate more closely with the clinical state or cognitive performance than classical atrophy patterns. This review highlights cutting-edge methods for evaluating morphometric covariance networks on an individual basis, with a focus on their utility in characterizing ageing, central nervous system inflammation and neurodegeneration. Specifically, these methods hold significant potential for quantifying structural alterations in patients with Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia and multiple sclerosis. By capturing the distinctive morphometric organization of each individual’s brain, structural covariance network analyses allow the tracking and prediction of pathology progression and clinical outcomes, information that can be integrated into clinical decision-making and used as variables in clinical trials. Furthermore, by investigating distinct and cross-diagnostic patterns of structural covariance, these approaches offer insights into shared mechanistic processes critical to understanding severe neurological disorders and their therapeutic implications. Such advancements pave the way for more precise diagnostic tools and targeted therapeutic strategies.