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Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using

Jidan Zhong1,2,3, David Qixiang Chen4,5, Julia C Nantes6,7

  • 1Research Institute of the McGill University Health Centre, Montreal, QC, Canada. jidanz@gmail.com.

Brain Imaging and Behavior
|May 6, 2016
PubMed
Summary

Machine learning accurately classified multiple sclerosis (MS) patients based on brain structure and function, identifying neural patterns linked to preserved or impaired motor performance. Combining grey matter measures and functional connectivity achieved 85.61% accuracy.

Keywords:
Cortical thicknessDeep grey matter volumeFunctional connectivityMotor disabilityMultiple sclerosisMultivariate analysisSupport vector machine

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Understanding neuroplasticity in multiple sclerosis (MS) is crucial for differentiating motor impairment.
  • Identifying brain networks involved in motor function preservation or compensation in MS is a key challenge.

Purpose of the Study:

  • To classify MS patients into motor function preserved (MP) and motor function impaired (MI) groups using machine learning.
  • To determine the most accurate features (grey matter measures, functional connectivity, or both) for classification.
  • To identify specific structural and functional brain patterns distinguishing MP, MI, and healthy controls (HCs).

Main Methods:

  • Applied support vector machine (SVM) classification to T1-weighted and resting-state functional MRI data.
  • Utilized regional grey matter measures (GMM) and inter-regional functional connectivity (FC).
  • Employed leave-one-out cross-validation for accuracy assessment.

Main Results:

  • A combined GMM and FC feature set achieved the highest classification accuracy of 85.61% (p < 0.001) between MS groups.
  • Demonstrated that structural and functional brain patterns are sufficient for classifying upper limb motor ability in MS.
  • Identified specific multivariate patterns of GMM and FCs discriminative between groups.

Conclusions:

  • Machine learning effectively classifies MS motor function using neuroimaging data.
  • Combining structural and functional brain features enhances the accuracy of motor function classification in MS.
  • This approach aids in identifying neural substrates critical for preserving motor function in MS.