ArticleBMC neurology2026
The use of routinely collected structural neuroimaging to identify cognitive impairment in multiple sclerosis.
Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
backgroundCognitive impairment is common in multiple sclerosis (MS), yet comprehensive cognitive assessment is not universally accessible, and patients need to be triaged for referral. Given the links between brain atrophy and cognition, this study investigated whether routinely collected neuroimaging markers could identify MS patients at risk of cognitive impairment.
methodsData were retrospectively analysed from adult MS patients assessed in a specialist cognitive neuroimmunology clinic, who had undergone MRI within 12 months prior to cognitive testing. Normalised brain volume (NBV), normalised grey matter volume (NGMV), normalised white matter volume (NWMV), and corpus callosum index (CCI) were measured using the Siemens MorphoBox automated software. GLMs were estimated to investigate group differences in brain volume metrics between cognitively impaired and non-impaired patients. ROC curves were estimated to investigate screening performance for neuroimaging metrics (Youden's J). Results are expressed as parameter estimates with 95% bootstrapped confidence intervals (CI).
results120 patients were included (35% cognitively impaired). Cognitively impaired patients had lower NBV (b = - 2.06, 95% CI [- 3.35, - 0.81]) and NWMV (b = - 1.65, 95% CI [- 2.60, - 0.77]). NWMV (area under the curve [AUC] = 0.67, 95% CI [0.57, 0.76]) and CCI (AUC = 0.62, 95% CI [0.51, 0.72]) classified impairment, although sensitivity was low (< 0.70). No clear associations or sufficient classification performance were observed for NGMV. Diagnostic performance improved when neuroimaging markers were statistically combined with relevant demographic information.
conclusionThe present study did not find strong evidence supporting routinely collected neuroimaging as standalone cognitive screening tools. Classification performance improved when combined with demographic factors, but remained below thresholds for clinical utility. These findings highlight a gap between group-level associations reported in the literature and their translation to individual-level clinical application.
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