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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
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Functional gradients reveal cortical hierarchy changes in multiple sclerosis.

Alessandro Pasquale De Rosa1, Alessandro d'Ambrosio1, Alvino Bisecco1

  • 1Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.

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Multiple sclerosis (MS) alters brain network hierarchy, impacting sensory and cognitive functions. Functional gradient analysis reveals disrupted cortical organization and predicts cognitive decline in MS patients.

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functional connectivityfunctional gradientsmachine learningmultiple sclerosisresting‐state fMRI

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Systems Neuroscience

Background:

  • Functional gradient (FG) analysis is a key method for understanding brain hierarchical organization.
  • The impact of multiple sclerosis (MS) on brain network hierarchy and functional connectivity remains unclear.

Purpose of the Study:

  • To investigate alterations in cortical hierarchy in MS using FG analysis.
  • To explore the relationship between FG scores, cognitive function, and prediction of processing speed deficits in MS.

Main Methods:

  • Resting-state functional MRI (rs-fMRI) data from 122 MS patients and 97 healthy controls (HC).
  • Functional gradient (FG) scores derived from rs-fMRI connectivity matrices.
  • Analysis of primary (visual-to-sensorimotor) and secondary (sensory-to-transmodal) FG components.
  • Correlation analysis with the Symbol Digit Modality Test (SDMT) and machine learning for predictive modeling.

Main Results:

  • MS patients exhibited altered cortical hierarchy, particularly in the sensorimotor network.
  • A compression of the sensory-to-transmodal gradient axis was observed in MS, indicating disrupted sensory and cognitive segregation.
  • FG scores in limbic and default mode networks correlated with SDMT scores.
  • FG scores within the default mode network accurately predicted SDMT scores in MS patients.

Conclusions:

  • Functional gradient analysis reveals significant alterations in brain hierarchy in MS.
  • Disrupted cortical organization in MS impacts sensory and cognitive processing.
  • FG analysis is a valuable tool for understanding MS pathophysiology and predicting cognitive outcomes.