Can routine MRI help forecast cognition in multiple sclerosis? A 171-person study
AI-derived brain-network estimates predicted four-year processing-speed scores moderately well, but follow-up data were available for only 111 participants and there was no external cohort.
The short version
- What happened
- The study followed 171 people with multiple sclerosis or clinically isolated syndrome and used lesion masks from structural MRI to estimate structural and functional brain connectivity.
- Who it affects
- People with MS could eventually receive more informative monitoring from scans already collected in care, but this model cannot yet support individual treatment decisions.
- What remains unproven
- Single research cohort with no external validation
Keep in mind
Single research cohort with no external validation
Only 111 participants had four-year processing-speed data
Prediction correlation does not establish calibration or clinical benefit
Source analysis · 1 linked source. Primary analysed source date: 23 October 2026.
Read the full report · 2 min The evidence, methods, implications and limitations
Routine scans supplied a proxy for costlier connectome imaging
Researchers at Cornell and Weill Cornell studied 171 people: 158 with relapsing-remitting multiple sclerosis, five with secondary-progressive disease and eight with clinically isolated syndrome. They took lesion masks from ordinary structural scans, estimated structural connections with the Network Modification tool, then used the Krakencoder model to estimate functional connectivity.
Ridge-regression models predicted three cognitive-test scores at baseline and after four years. The strongest result was for follow-up processing speed: median Spearman correlation between observed and predicted scores was 0.58 using regional estimated structural connectivity and 0.56 using estimated functional connectivity. Nested five-fold cross-validation separated tuning from performance testing.[1]
Promising for research selection, not a clinical prognosis yet
Lower estimated connectivity in the cerebellum and higher estimated functional connectivity in the default-mode network were associated with poorer later scores. That may help researchers choose circuits to study or stratify trials while avoiding scans uncommon in routine care.
It is not evidence that the system improves treatment or gives one patient a dependable forecast. Only 111 participants had four-year processing-speed data, follow-up loss was linked partly to the pandemic, and evaluation stayed within one cohort. Prospective multi-centre validation with calibration and decision-impact testing is needed.[1]
The distinction that matters
From a routine scan to a research prediction
The study estimated networks rather than directly measuring them with diffusion or functional MRI.
Lesion mask
Extract MS lesions from structural MRI
Routine clinical input
Estimated networks
Infer structural and functional connectivity
Regional connection features
Cognitive model
Use nested cross-validated regression
Moderate within-cohort prediction