Author Of 3 Presentations
P0004 - Convolutional neural network framework for predicting progression in early MS (ID 1679)
Abstract
Background
Brain tissue damage is closely linked to disability in multiple sclerosis (MS). The localization of white matter (WM) lesions influences the course of the disease.
Objectives
However, the interrelation between lesions topography and cortical atrophy distribution for predicting the clinical disability remains unclear. Use a deep learning neural network framework with the purpose to identify critical co-varying patterns for individualized disease prediction.
Methods
Clinical disability was measured using the Expanded Disability Status Score at baseline and at a one-year follow-up in a cohort of 119 patients with early relapsing-remitting MS and in a replication cohort of 81 patients. Co-varying patterns of cortical atrophy and baseline lesion distribution were extracted by parallel ICA and used as features for constructing a deep learning convolutional neural network. The prediction was conducted for each identified lesion pattern separately using 50% as training cohort and 50% as testing cohort.
Results
In the study cohort, we identified three distinct distribution types of WM lesions (“cerebellar”, “bihemispheric” and “left-lateralized”). The “cerebellar” and “left-lateralized” patterns were reproducibly detected in the second cohort. Each of the patterns predicted to different extents, short-term disability progression, while the “cerebellar” pattern predicting individual disability progression with an 10-fold cross-validation accuracy of above 90% for the Study cohort (95% CI: 88%-94%) and above 85% for the replication cohort (95% CI: 81%-88%) respectively.
Conclusions
These findings highlight that role of distinct spatial distribution of cortical atrophy and WM lesions predicting disability. The cerebellar involvement is shown as a key feature in the CNN framework for prediction of rapid clinical deterioration.
P0601 - Longitudinal functional modularisation and causality dynamics during de- and remyelination (ID 1715)
Abstract
Background
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), one of its pathophysiological hallmarks is demyelination, which is known to be involved in neurodegenerative mechanisms.
Objectives
Modular architecture and its dynamic adaptation could play a critical role in achieving flexible alterations of cerebral network architecture during de- and remyelination which is still not fully elucidated.
Methods
We address dynamic adaptation to cuprizone model of general de- and remyelination and ask if network community organization can relate to the longitudinal time events. To start with baseline and then by introducing cuprizone into the diet of mice we induced full CNS demyelination by targeting oligodendrocytes, over a period of 5 weeks (two time points). A subsequent myelin synthesis was allowed over reintroduction of normal food (two time points). To identify the modular organization the resting state fMRI within the graph theory framework was analyzed from each of the five time points. The dynamic network reconfiguration was estimated by flexibility as parameter of modularity allegiance and effective connectivity analyses were applied to test the causality of network dynamics between the identified modules.
Results
We found six modules namely default mode network (DMN), hippocampus, thalamus, lateral cortical network, basal forebrain and ventral mid brain. Interestingly the dynamics of de- and remyelination was mirrored by an initial significant increase in flexibility values and a return to baseline in the hippocampus (F(4, 80) = 22.8, p < 0.001), DMN (F(4, 80) = 36.5, p < 0.001) and thalamus (F(4, 80) = 24.5, p < 0.001). The other three networks showed a reversed pattern. The strength of connections from the hippocampus to DMN was associated with the behavioral indicators of memory novel object recognition (NOR) (r2 = 0.3854, p < 0.001) and thalamus to hippocampus to locomotor activity (r2 = 0.3144, p < 0.001).
Conclusions
Taken together, our fMRI modular analyses showed that global modularity and flexibility partially compensate for demyelination. Dynamics of compensation could be identified as modular specific (i.e. hippocampus, thalamus and DMN) at different intermediate time points, supporting the hypothesis that altered thalamocortical connectivity is an early pathological hallmark of the disease. Causality dynamics also provide biomarkers for evaluating the course of MS and disease dynamics.
P0837 - Assessing the real-world effectiveness of ocrelizumab in patients with multiple sclerosis – CONFIDENCE one-year interim analysis (ID 1133)
Abstract
Background
Multiple sclerosis (MS) is a chronic inflammatory neurological disease that requires life-long treatment, and new therapies must be safe and effective over a long treatment duration. Ocrelizumab is a humanized antibody that selectively targets CD20+ B cells and has been shown to be efficacious for the treatment of both relapsing MS (RMS) and primary progressive MS (PPMS). Effectiveness data in large, real-world populations are needed for better informed clinical treatment.
Objectives
CONFIDENCE (ML39632, EUPAS22951) evaluates the safety and effectiveness of ocrelizumab in patients with RMS & PPMS in a real-world setting. Here, we present the first analysis of one-year effectiveness data from patients newly treated with ocrelizumab.
Methods
CONFIDENCE is a non-interventional study in patients with RMS or PPMS newly treated (up to 30 days prior or 60 days after enrolment) with ocrelizumab or other selected disease modifying therapies (DMTs) during the course of their disease. Data will be collected for 3000 ocrelizumab-treated patients and 1500 patients treated with other DMTs according to label at ~250 centers in Germany for up to 10 years. Here, we analyze effectiveness outcomes for patients treated with ocrelizumab for the first year, including treatment success (the proportion of patients with no relapse, progression or treatment discontinuation due to an adverse event) and change in Expanded Disability Status Scale (EDSS) from baseline. In addition, we will present patient-reported outcomes. Safety assessments are presented separately.
Results
As of 30 June 2020, 2,129 patients have been recruited for ocrelizumab treatment. The interim analysis is expected to include data from approximately 559 patients newly treated with ocrelizumab that had one year of follow up. Of these patients, ~82% had RMS and ~18% had PPMS. Mean (standard deviation [SD]) baseline EDSS was 3.3 (1.9) for patients with RMS and 4.5 (1.7) for patients with PPMS. Preliminary data show that 64% of patients were female (66% female RMS; 55% female PPMS). Over an observational period of one year, 83.6% of RMS and 93.2% of PPMS patients experienced treatment success. About 85.3% of patients with RMS experienced no relapses. The mean (SD) change in EDSS from baseline after one year of treatment was 0.0 [0.6] for patients with RMS and 0.1 [0.6] for patients with PPMS.
Conclusions
This analysis of one-year interim data in the CONFIDENCE study shows the effectiveness of ocrelizumab in a real-world setting.