Structural cortical network reorganization associated with early conversion to multiple sclerosis

Abstract

Brain structural covariance networks (SCNs) based on pairwise statistical associations of cortical thickness data across brain areas reflect underlying physical and functional connections between them. SCNs capture the complexity of human brain cortex structure and are disrupted in neurodegenerative conditions. However, the longitudinal assessment of SCN dynamics has not yet been explored, despite its potential to unveil mechanisms underlying neurodegeneration. Here, we evaluated the changes of SCNs over 12 months in patients with a first inflammatory-demyelinating attack of the Central Nervous System and assessed their clinical relevance by comparing SCN dynamics of patients with and without conversion to multiple sclerosis (MS) over one year. All subjects underwent clinical and brain MRI assessments over one year. Brain cortical thicknesses for each subject and time point were used to obtain group-level between-area correlation matrices from which nodal connectivity metrics were obtained. Robust bootstrap-based statistical approaches (allowing sampling with replacement) assessed the significance of longitudinal changes. Patients who converted to MS exhibited significantly greater network connectivity at baseline than non-converters (p = 0.02) and a subsequent connectivity loss over time (p = 0.001–0.02), not observed in non-converters’ network. These findings suggest SCN analysis is sensitive to brain tissue changes in early MS, reflecting clinically relevant aspects of the condition. However, this is preliminary work, indicated by the low sample sizes, and its results and conclusions should be treated with caution and confirmed with larger cohorts.

Advances in brain imaging in multiple sclerosis

“…These findings suggest that SCNs may reveal important features of GM dynamics, otherwise hidden. 97 Finally, functional MRI (fMRI) studies have investigated functional connectivity abnormalities within the principal brain networks in patients with MS, to provide clinically relevant information about MS pathology and to define the trajectory of changes over disease stages. 98 Resting-state fMRI (rsfMRI) has proved to be a powerful tool for studying whole brain neural connectivity by assessing the correlations of spontaneous fluctuations of blood oxygen level-dependent signals between different regions of the brain.…”

Cognitive Impairment and Brain Reorganization in MS: Underlying Mechanisms and the Role of Neurorehabilitation

“…Impaired cognition is associated with early increases in FC, which then decreases due to the exhaustion of compensating mechanisms, forming the "inverted U" rs-FC curve (89). Indeed, patients who converted to MS exhibited "significantly greater network connectivity at baseline than nonconverters" and a "subsequent connectivity loss over time, not observed in the non-converters' network" (30,90). Therefore, despite methodological difficulties (91), widely available imaging markers could soon offer more (92).…”

Gray matter network reorganization in multiple sclerosis from 7‐Tesla and 3‐Tesla MRI data

“…Consequently, a growing body of evidence offered by network neuroscience highlights the relevance of network‐based approaches in understanding the fundamental principles and pathophysiological mechanisms of network responses elicited by GM neurodegeneration. Modeling brain networks based on 3T‐MRI data has become a widely accepted tool to describe tissue reorganization occurring beyond the MS pathology recognizable by common MRI morphometric measures 20,39,40 . Here, morphometric networks reconstructed on CT and GWc from 7T‐ and 3T‐MRI data were characterized by the same directionality of network metrics: higher modularity and normalized clustering coefficient in MS patients, and preserved small‐world characteristics.…”