Functional gradients reveal cortical hierarchy changes in multiple sclerosis

• Published in: Human brain mapping Vol. 45; no. 6; pp. e26678 - n/a
• Main Authors: De Rosa, Alessandro Pasquale, d'Ambrosio, Alessandro, Bisecco, Alvino, Altieri, Manuela, Cirillo, Mario, Gallo, Antonio, Esposito, Fabrizio
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.04.2024
• Subjects: Adult; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiopathology; Connectome - methods; Default Mode Network - diagnostic imaging; Default Mode Network - physiopathology; Female; functional connectivity; functional gradients; Humans; machine learning; Magnetic Resonance Imaging; Male; Middle Aged; multiple sclerosis; Multiple Sclerosis - diagnostic imaging; Multiple Sclerosis - pathology; Multiple Sclerosis - physiopathology; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; resting‐state fMRI; functional connectivity; machine learning; multiple sclerosis; functional gradients; resting‐state fMRI
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26678

Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting‐state functional MRI (rs‐fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs‐fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual‐to‐sensorimotor) and a secondary (sensory‐to‐transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory‐transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ=0.30$$ \rho =0.30 $$, p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network‐level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold‐out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs‐fMRI studies across different MS populations. Functional connectivity gradients reveal significant alterations in the sensorimotor network hierarchy and a compression of the sensory‐transmodal gradient, indicating disrupted segregation between sensory and cognitive processing, in multiple sclerosis patients. Functional connectivity gradients within limbic and default mode networks are associated with information processing speed in multiple sclerosis patients.