S. Narayanan
McGill University McConnell Brain Imaging Centre, Montreal Neurological InstituteAuthor Of 2 Presentations
PS07.03 - Predicting disability progression and cognitive worsening in multiple sclerosis with gray matter network measures
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.
PS07.04 - Fibre-specific white matter differences in children with pediatric acquired demyelinating syndromes compared to healthy children
Abstract
Background
White matter (WM) microstructural changes occur in youth with multiple sclerosis (MS) and myelin oligodendrocyte glyoprotein (MOG)-associated disorders. While diffusion tensor imaging has been extensively used to characterize white matter, this method lacks microstructural and pathological specificity. ‘Fixel Based Analysis’ (FBA) statistically estimates changes in diffusion MRI connectivity that is specific to micro and macro-structure. WM damage that leads to less densely packed axons in a fiber bundle causes a decrease in fibre density (FD). If the number of axons is not reduced but occupies less area, then fibre cross-section (FC) will decrease. Last, if the density of axons within a fibre bundle and the area the bundle occupies are reduced, then fibre density and cross-section (FDC) will decrease.
Objectives
To use whole-brain FBA to measure differences in FD, FC, FDC in youth with demyelinating syndromes compared to healthy controls.
Methods
We evaluated group differences in the FBA metrics between 28 typically developing children (17F; age 15.0±2.6y), 19 children with MS (13F; 16.9±1.1y; disease duration (DD)=0.1-11.7y; expanded disability status scale(EDSS):median=1.5,range=0-4.5), and 11 children with MOG (8F;12.1±2.8y; DD=0.5-6.4y;EDSS:m=1.0,r=0-3). Multi-shell diffusion-weighted imaging of the brain was acquired with echo planar imaging on a 3T MRI scanner and was pre-processed to correct for distortions and movement. Whole-brain group FBA was performed on FD, FC and FDC to test differences between groups adjusting for age, sex, total intracranial volume, EDSS and DD (p<0.05, family-wise error (FWE) corrected).
Results
Participants with MS and MOG showed reduced FD, FC and FDC relative to typically developing children (FWE corrected p<0.05). Differences in FD were found within splenium, superior longitudinal fasciculus and optic radiations. MS patients had reduced FDC within the corticospinal tract and cerebellar peduncle compared to MOG patients. In participants with MS and MOG, decreased FD within the brain stem, cerebellar peduncles and corona radiata was associated with increased DD and EDSS.
Conclusions
Our preliminary findings showed that patients with demyelinating disorders display decreased axonal density and fibre bundle size in multiple WM tracts relative to typically developing children, which were related to clinical outcomes (EDSS, DD). These changes were more pronounced in MS compared to MOG participants in selected WM tracts.