Imaging Oral Presentation

PS07.03 - Predicting disability progression and cognitive worsening in multiple sclerosis with gray matter network measures 

Speakers
  • E. Colato
Authors
  • E. Colato
  • J. Stutters
  • C. Tur
  • S. Narayanan
  • D. Arnold
  • O. Ciccarelli
  • D. Chard
  • A. Eshaghi
Presentation Number
PS07.03
Presentation Topic
Imaging
Lecture Time
13:15 - 13:27

Abstract

Background

In multiple sclerosis (MS), MRI measures at a whole and regional brain level have proven able to predict future disability, albeit to a limited degree. Their modest prognostic ability may reflect how cognitive and neurological functions are served by distributed networks rather than by single brain regions.

Objectives

We aimed to identify data-driven MRI network-based measures of covarying gray matter (GM) volumes that can predict disability progression.

Methods

We used baseline MRI and longitudinal clinical data from 988 patients with secondary progressive MS (SPMS) from a randomized, double-blind, placebo-controlled, multicenter trial (ASCEND). We applied spatial-ICA to baseline structural GM probability maps to identify co-varying GM regions. We computed correlations between the loading of our ICA components and expanded disability status scale (EDSS), 9 hole peg test (9HPT), and symbol digit modalities test (SDMT) scores. We estimated the progression of the EDSS confirmed at 3 months, 6 months, and 1 year, and respectively the 20% and 10% worsening of 9HPT and SDMT. We used Cox proportional hazard models to determine the prognostic value of our ICA-components and conventional MRI measures (whole and deep GM volumes, and white matter lesion load).

Results

We identified 15 networks of co-varying GM patterns that were clinically relevant. At baseline, SDMT and 9HPT scores correlated more strongly with ICA-components than the conventional MRI measures. The highest correlations were with a mainly basal ganglia component (encompassing the thalamus, caudate, putamen, frontal and temporal lobe). EDSS correlated more closely with an ICA-component involving cerebellum, brainstem, temporal and parietal lobes (r= -0.11, p<0.001). Prognostically, the baseline volume of caudate predicted EDSS progression confirmed at 3 months (HR= 0.81, 95%CI [0.68: 0.98], p<0.05), while some GM network-based measures outperformed conventional MRI measures in predicting SDMT and 9HPT worsening. SDMT progression was predicted by 6 ICA-components (component 8 (HR= 1.26, 95% CI [1.08-1.48], p< 0.005, and component 13 (HR= 1.25, 95% CI [1.07:1.46], p<0.005)). Two ICA-components were predictors of 9HPT worsening (HR=1.30, 95% CI [1.06:1.60], p<0.01; and HR= 1.21, 95%CI [1.01:1.45], p<0.05).

Conclusions

Data-driven MRI network-based measures of covarying GM volumes predict disability progression better than volumetric measures of GM and white matter lesion loads. ICA of MRI shows promise as a method that could enrich clinical MS studies with patients more likely to show a treatment response.

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