C. Tur
UCL Queen Square Institute of Neurology, Faculty of Brain Sciences Department of NeuroinflammationAuthor Of 2 Presentations
LB01.04 - Brain microstructural and metabolic alterations detected in vivo at the onset of the first demyelinating event.
Abstract
Background
In early multiple sclerosis, a clearer understanding of normal-brain tissue microstructural and metabolic abnormalities will provide valuable insights into its pathophysiology. Here, we studied the brain of patients with their first demyelinating episode using neurite orientation dispersion and density imaging (NODDI), for information about neuro-axonal density and spatial distribution, and 23Na MRI, for total sodium concentration reflecting neuro-axonal metabolic dysfunction and loss.
Objectives
To detect, using a multi-parametric quantitative MRI approach, clinically relevant alterations in the brain of early patients not captured by conventional MRI.
Methods
We enrolled 42 patients with clinically isolated syndrome or multiple sclerosis within 3 months from the onset and 16 healthy controls. We assessed physical and cognitive scales. On a 3T scanner, we acquired brain and spinal cord structural scans, and brain NODDI. Thirty-two patients and 13 healthy controls also underwent brain 23Na MRI. In the brain normal-appearing white matter, white matter lesions, and grey matter, we measured, from NODDI, the neurite density index (NDI), a marker of neuro-axonal density, and the orientation dispersion index (ODI), reflecting the fanning and crossing of neurites, and, from 23Na MRI, the TSC. We used linear regression models, adjusted for brain parenchymal fraction and lesion load, and Spearman correlation tests. For robust regression estimates, we used a p≤0.01.
Results
Patients showed higher ODI in normal-appearing white matter, including the corpus callosum, where they also showed lower NDI and higher TSC, compared with controls. In grey matter, compared with controls, patients had lower ODI in frontal, parietal and temporal cortex; lower NDI in parietal, temporal and occipital cortex; and higher TSC in limbic and frontal cortex. Brain volumes did not differ between patients and controls. In patients, higher ODI in corpus callosum was associated with worse performance on timed walk test (p=0.009, B=0.01, 99% Confidence Interval=0.0001-0.02), independent of brain and lesion volumes. Higher TSC in left frontal middle gyrus was associated with higher disability on Expanded Disability Status Scale (rs=0.5, p=0.005).
Conclusions
We found increased axonal dispersion in normal-appearing white matter, particularly corpus callosum, where we found also reduced axonal density and total sodium accumulation suggesting that this structure can be early affected by neurodegeneration. The association between increased axonal dispersion in the corpus callosum and worse walking performance implies that morphological and metabolic alterations in this structure may contribute to disability in multiple sclerosis. Brain volumes were neither altered nor related to disability in patients, so these two advanced MRI techniques can be more sensitive at detecting clinically relevant pathology in very early multiple sclerosis.
PS07.03 - Predicting disability progression and cognitive worsening in multiple sclerosis with gray matter network measuresÂ
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.