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Grey matter network measures predict disability and cognition

Conference
MS Virtual 2020
Trial
Phase 3, ASCEND
Data-driven, MRI network-based measures of co-varying grey matter volumes predict disability progression better than volumetric measures of grey and white matter lesion loads, a new study found [1]. Independent component analysis (ICA) of MRI can help select clinical MS study participants who are more likely to respond to treatment. Baseline MRI and longitudinal clinical data were used from 988 participants of the randomised, double-blind, placebo-controlled ASCEND trial, which evaluated the effect of natalizumab on disease progression in secondary progressive MS. Spatial ICA was applied to baseline structural grey matter probability maps to identify co-varying grey matter regions. Correlations between ICA components and EDSS, 9-Hole Peg Test (9HPT), and Symbol Digit Modalities Test (SDMT) scores were computed. A total of 15 clinically relevant ne...


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