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Characterizing multiple sclerosis disease progression using a combined structural and functional connectivity metric.

P K Bhattacharyya1, R J Fox2, K E Sakaie1

  • 1Imaging Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.

Magnetic Resonance Imaging
|August 3, 2023
PubMed
Summary
This summary is machine-generated.

The structural and functional connectivity index (SFCI) effectively tracks multiple sclerosis (MS) progression. This combined MRI metric showed greater sensitivity to treatment-induced changes than individual imaging measures, supporting its use in MS management.

Keywords:
Clinical trialDiffusion tensor imagingFunctional connectivityMultiple sclerosisStructural and functional connectivity index

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Area of Science:

  • Neuroimaging
  • Neurology
  • Biomedical Engineering

Background:

  • Multiple Sclerosis (MS) is a chronic neurological disease.
  • Tracking disease progression and treatment response in MS is crucial.
  • Current imaging metrics may not fully capture the complexity of MS pathology.

Purpose of the Study:

  • To evaluate the structural and functional connectivity index (SFCI) as a metric for tracking MS disease status and progression.
  • To compare the sensitivity of SFCI to treatment-induced changes versus its individual components (fcMRI and DTI).
  • To investigate the relationship between SFCI and neurological measures in MS patients.

Main Methods:

  • A longitudinal study involving 25 MS patients undergoing 3T MRI scans (DTI and fcMRI) at multiple time points over 24 months.
  • Calculation of SFCI by combining functional connectivity MRI (fcMRI) and transverse diffusivity (TD) along transcallosal (TC) motor and frontoparietal (FP) cognitive pathways.
  • Correlation analysis between SFCI, its components, and individual/composite neurological scores; comparison of imaging metric efficacies in tracking network integrity.

Main Results:

  • Individual TD along the TC pathway significantly correlated with neurological scores.
  • Pathway-combined SFCI significantly correlated with all neurological scores.
  • SFCI demonstrated a significant decrease in the first year of follow-up, followed by a significant increase in the second year, indicating treatment effects.

Conclusions:

  • The SFCI metric is more effective in tracking network integrity and disease progression in MS compared to individual imaging components.
  • SFCI shows promise as a sensitive imaging biomarker for monitoring MS disease status and response to therapy.
  • The findings support the utility of SFCI in clinical trials and patient management for MS.