Background

Treatment response in multiple sclerosis (MS) is frequently suboptimal and, in many cases, different disease-modifying therapies need to be tested. The expanded disability status scale (EDSS) score and other markers of disease activity (such as relapses, new lesions, brain atrophy, etc.) are crucial for treatment decision making. However, EDSS suffers from poor reliability, repeatability, and high inter-rater variability. Therefore, automatic and objective disability scoring using MRI information could help to monitor disease progression reliably and optimize treatment.

Objectives

Develop a machine-learning model that learns relations between MRI-based brain volumes and clinical disability measured by EDSS.

Methods

Multi-center FLAIR and T1 MRI data from 325 MS patients were used. Individuals were rated in each center using EDSS within 0-89 days before or after the MRI scan. Automated image analysis was performed using icobrain, providing volumetric quantification of gray matter, white matter, whole brain, lateral ventricles, T1 hypointense and FLAIR hyperintense lesions. Moreover, other features, such as age, sex, and center, were available. A machine-learning model based on random forest regression was built for estimating EDSS automatically from these features. The model’s performance was assessed by means of mean squared error (MSE) and mean absolute error (MAE) evaluated overall, and on two EDSS subgroups, <=4 and >4,in a 100 repetition 10-fold-cross-validation fashion. Subsequently, the percentage of cases for which the automatic EDSS was within 1.5, 1 or 0.5 points, respectively, from the clinically reported EDSS was computed.

Results

The proposed automatic EDSS estimation model obtained MSE=2.36±0.03, MAE=1.24±0.89 for the overall interval, with MSE=1.83±0.04, MAE=1.11±0.76 for EDSS<=4 (N=200) and MSE=3.26±0.06, MAE=1.46±1.05 for EDSS>4 (N=118). The percentage of cases with absolute error strictly below 1.5, 1 and 0.5 EDSS points was 67%, 46% and 24%, respectively.

Conclusions

A good match between the automatic EDSS and the measured EDSS is only possible up to a certain extent, suggesting that the MRI-based EDSS score might also capture complementary information on disease activity compared to the clinically measured EDSS. Understanding such differences is a prerequisite for predicting future disability progression in MS.

Background

In multiple sclerosis, the interplay of neurodegeneration, demyelination and inflammation leads to changes in neurophysiological functioning.

Objectives

This study aims to characterise the relation between reduced brain volumes and spectral power in multiple sclerosis patients and matched healthy subjects.

Methods

During resting-state eyes closed, we collected magnetoencephalographic data in 67 multiple sclerosis patients and 47 healthy subjects, matched for age and gender. Additionally, we quantified different brain volumes (white matter, cortical and deep grey matter, FLAIR lesion load and volume of black holes) and calculated the power spectral density. Instead of using the traditionally used frequency bands, we calculated the source reconstructed power spectral density in frequency bins of 0.25 Hz (range: 0-40 Hz) and corrected for multiple comparisons through permutation testing.

Results

First, a principal component analysis (PCA) of brain volumes demonstrates that atrophy can be largely described by two components: one overall degenerative component that is indicative of brain integrity and correlates strongly with different cognitive tests, and one component that mainly captures degeneration of the cortical grey matter that strongly correlates with age. As the first PC was observed both when performing the PCA on the full cohort and on the two subcohorts, we denote this component as an index of brain integrity. Logically, this component was more strongly expressed in the MS cohort.

Next, a multimodal correlation analysis indicates that reduced brain integrity is accompanied by increased alpha1 power in the temporoparietal junction (TPJ). Patients showing this local increase in alpha-peak also scored significantly worse on different cognitive tests and reduced thalamic volumes. The increase in alpha1-power comes from both a slowing of the main alpha-peak and an increase in power.

Conclusions

MS patients with reduced brain integrity demonstrated increased alpha1 power in the TPJ and impaired cognitive functioning.

Background

Real-world evidence can be used to better characterize the course of multiple sclerosis (MS), care provision and outcomes in clinical practice. Magnetic resonance imaging (MRI) that occurs in the context of usual care is an important source of information that can inform clinical decision-making. Guidelines exist to enhance the clinical impact of routine MRI in MS, but it is unclear whether MRIs acquired as part of routine care in the United States adhere to these guidelines.

Objectives

To describe the clinical routine brain MRIs from patients with MS across different US imaging sites.

Methods

FlywheelMS is a novel patient-centered study that aims to extract and digitize health information not readily available in existing electronic health records of patients with MS. Up to 5,000 consenting adults with a confirmed MS diagnosis will be enrolled. Brain MRI data were retrieved, and summary statistics were computed to describe the sessions, imaging sites, scanner field strengths and slice thickness of T1-weighted and FLAIR (fluid-attenuated inversion recovery) images. Longitudinal acquisition consistency (i.e. MRIs acquired from the same center with the same scanner) was also assessed.

Results

Out of 2,389 patients enrolled, 1555 brain MRI data were retrieved from the first 492 patients (female, 81%; mean age at consent, 49±11 years). The mean number of MRI sessions per patient was 3.2±2.4, and data were captured between 1999 and 2018. Sessions were acquired at 598 different imaging sites, using mainly 1.5T scanners (61.3%), followed by 3T (32.7%) and lower field-strength magnets (3.4%; not available, 2.6%). The mean slice thickness of T1-weighted (3.1±1.7 mm) and FLAIR images (3.1±1.3 mm) was similar. Of the 352 patients (72%) that had more than one MRI session, 85 (24.1%) had consistent acquisition (i.e. same site/scanner), 153 (43.5%) had one site or scanner change, and 114 (32.4%) had more than one site and/or scanner change.

Conclusions

The novel, patient-centered approach of FlywheelMS can successfully extract imaging data from medical records of patients with MS across US imaging sites. These data will help us in describing the clinical routine MRI, determining the compliance to guidelines and understanding which measure (e.g. lesion volume and/or atrophy) could be potentially extracted from MRI data.