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Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning.

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Summary
This summary is machine-generated.

A new model accurately predicts cognitive status in early multiple sclerosis (MS) using neuroimaging. This approach identifies brain patterns linked to cognitive decline, offering a potential biomarker for subtle changes.

Keywords:
Multiple sclerosiscognitive impairmentmachine learningmultimodal neuroimagingrandom forest

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Area of Science:

  • Neuroimaging
  • Neurology
  • Medical Diagnostics

Background:

  • Early multiple sclerosis (MS) often involves subtle cognitive changes impacting daily life.
  • Identifying reliable biomarkers for cognitive status in early MS is crucial for timely intervention.

Purpose of the Study:

  • To develop a predictive model for cognitive status in early MS.
  • To identify neuroimaging features reflecting structural, functional, and white matter integrity changes associated with cognitive performance.

Main Methods:

  • 183 early MS patients were categorized into lower and higher cognitive performance groups based on the Symbol Digit Modalities Test (SDMT).
  • Structural, diffusion-weighted, and resting-state functional MRI data were acquired and analyzed using Random Forest models.
  • Models were built using demographic/clinical data, lesion characteristics, tissue volumes, diffusion tensor imaging metrics, and functional connectivity.

Main Results:

  • A combined model integrating key features from all neuroimaging modalities achieved the highest predictive accuracy (AUC = 0.90).
  • Key predictive features included volumes of the nucleus accumbens and thalamus, white matter integrity (mean diffusivity) in the cingulum-angular bundle, and functional connectivity patterns.

Conclusions:

  • A distinct pattern of brain changes, particularly in areas related to attention, is associated with lower cognitive performance in early MS.
  • The high accuracy of the developed model suggests its potential as a neuroimaging biomarker for detecting subtle cognitive impairments in early MS.