Improving explanation of motor disability with diffusion-based graph metrics at onset of the first demyelinating event

TL;DR

This study shows that graph metrics from brain scans can better explain motor disability in early multiple sclerosis than traditional MRI methods.

Contribution

The study introduces diffusion-based graph metrics as a novel way to assess motor disability in early multiple sclerosis.

Findings

Graph metrics like local efficiency, clustering, and transitivity were reduced in patients compared to controls.

Higher assortativity in brain networks was linked to higher disability scores and faster walking times.

Adding graph metrics to conventional MRI improved prediction of disability outcomes.

Abstract

Conventional magnetic resonance imaging (MRI) does not account for all disability in multiple sclerosis.

The objective was to assess the ability of graph metrics from diffusion-based structural connectomes to explain motor function beyond conventional MRI in early demyelinating clinically isolated syndrome (CIS).

A total of 73 people with CIS underwent conventional MRI, diffusion-weighted imaging and clinical assessment within 3 months from onset. A total of 28 healthy controls underwent MRI. Structural connectomes were produced. Differences between patients and controls were explored; clinical associations were assessed in patients. Linear regression models were compared to establish relevance of graph metrics over conventional MRI.

Local efficiency (p = 0.045), clustering (p = 0.034) and transitivity (p = 0.036) were reduced in patients. Higher assortativity was associated with higher Expanded Disability Status Scale (EDSS) (β = 74.9, p = 0.026) scores. Faster timed 25-foot walk (T25FW) was associated with higher assortativity (β = 5.39, p = 0.026), local efficiency (β = 27.1, p = 0.041) and clustering (β = 36.1, p = 0.032) and lower small-worldness (β = −3.27, p = 0.015). Adding graph metrics to conventional MRI improved EDSS (p = 0.045, ΔR2 = 4) and T25FW (p < 0.001, ΔR2 = 13.6) prediction.

Graph metrics are relevant early in demyelination. They show differences between patients and controls and have relationships with clinical outcomes. Segregation (local efficiency, clustering, transitivity) was particularly relevant. Combining graph metrics with conventional MRI better explained disability.

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