Targeting Cognitive Impairment in Multiple Sclerosis—The Road toward an Imaging-based Biomarker

Multiple Sclerosis (MS) is a neuro-degenerative and -inflammatory disease leading to physical and cognitive impairment, pathological fatigue and depression, and affecting patients' quality of life and employment status. The combination of inflammation, demyelination, and neurodegeneration leads to the emergence of MS lesions, reduced white and gray matter brain volumes, a reduced conduction velocity and microstructural changes in the so-called Normal Appearing White Matter (NAWM). Currently, there are very limited options to treat cognitive impairment and its origin is only poorly understood. Therefore, several studies have attempted to relate clinical scores with features calculated either using T1- and/or FLAIR weighted MR images or using neurophysiology. The aim of those studies is not only to provide an improved understanding of the processes that underlie the different symptoms, but also to develop a biomarker—sensitive to therapy induced change—that could be used to speed up therapeutic developments (e.g., cognitive training/drug discovery/…). Here, we provide an overview of studies that have established relationships between either neuro-anatomical or neurophysiological measures and cognitive outcome scores. We discuss different avenues that may help to improve the prediction of cognitive impairment, and how well we can expect them to predict cognitive scores

On Statistical Analysis of Brain Variability.

We discuss what we believe could be an improvement in future discussions of the ever-changing brain. We do so by distinguishing different types of brain variability and outlining methods suitable to analyse them. We argue that, when studying brain and behaviour data, classical methods such as regression analysis and more advanced approaches both aim to decompose the total variance into sensible variance components. In parallel, we argue that a distinction needs to be made between innate and acquired brain variability. For varying high-dimensional brain data, we present methods useful to extract their low-dimensional representations. Finally, to trace potential causes and predict plausible consequences of brain variability, we discuss how to combine statistical principles and neurobiological insights to make associative, explanatory, predictive, and causal enquires; but cautions are needed to raise association- or prediction-based neurobiological findings to causal claims

Gut microbiome variation is associated to Multiple Sclerosis phenotypic subtypes

Objective Multiple sclerosis (MS) is a heterogenous, inflammatory disease of the central nervous system. Microbiota alterations in MS versus healthy controls (HC) are observed, but results are inconsistent. We studied diversity, enterotypes, and specific gut microbial taxa variation between MS and HC, and between MS subgroups. Methods Amplicon sequencing of the 16S ribosomal RNA V4 region (Illumina MiSeq) was used to evaluate alpha and beta diversity, enterotypes, and relative taxa abundances on stool samples. MS subgroups were based on phenotype, disease course modifiers, and treatment status. Results were controlled for recently identified confounders of microbiota composition. Results Ninety-eight MS patients and 120 HC were included. Microbial richness was lower in interferon-treated (RRMS_I, N = 24) and untreated relapsing-remitting MS during relapse (RRMS_R, N = 4) when compared to benign (BMS, N = 20; Z = -3.07, Pcorr = 0.032 and Z = -2.68, Pcorr = 0.055) and primary progressive MS (PPMS, N = 26; Z = -2.39, Pcorr = 0.062 and Z = -2.26, Pcorr = 0.071). HC (N = 120) and active untreated MS (RRMS_U, N = 24) showed intermediate microbial richness. Enterotypes were associated with clinical subgroups (N = 218, chi(2) = 36.10, P = 0.002), with Bacteroides 2 enterotype being more prevalent in RRMS_I. Butyricicoccus abundance was lower in PPMS than in RRMS_U (Z = -3.00, Pcorr = 0.014) and BMS (Z = -2.56, Pcorr = 0.031), lower in RRMS_I than in BMS (Z = -2.50, Pcorr = 0.034) and RRMS_U (Z = -2.91, Pcorr = 0.013), and inversely correlated with self-reported physical symptoms (rho = -0.400, Pcorr = 0.001) and disease severity (rho = -0.223, P = 0.027). Interpretation These results emphasize the importance of phenotypic subcategorization in MS-microbiome research, possibly explaining previous result heterogeneity, while showing the potential for specific microbiome-based biomarkers for disease activity and severity

Definition, prevalence and predictive factors of benign multiple sclerosis

Background: Multiple sclerosis (MS) is characterized by a great inter-individual variability in disease course and severity. Some patients experience a rather mild course, controversially called ‘benign MS’ (BMS). The usefulness of this entity in clinical practice remains unclear. Methods: We performed a literature search in PubMed, Web of Science and Cochrane Library databases from November 1980 to December 2015, using the following key words: benign multiple sclerosis, diagnosis, imaging, prognosis, predictive, natural history and predefined inclusion criteria. Results: Our search yielded 26 publications. Most definitions were based on the Expanded Disease Status Scale (EDSS), which is heavily weighted towards physical disability. Between 30 and 80% of relapsing-remitting MS patients have EDSS <3 or 4 at 10 years after onset. Having only one relapse in the first 5 years and EDSS ≤2 at 5 years or EDSS ≤3 at 10 years appears to be predictive for a prolonged benign disease course, without protecting against disease progression at a later stage. Evidence on the predictive value of MRI parameters remains limited. Conclusions: Current BMS definitions have some predictive value for future physical disability, but do not take into account the age at EDSS and the potentially disrupting effects of non-EDSS symptoms and cognitive impairment. It appears to correspond to mild RRMS in the first decades and its prevalence varies. Since early and accurate prediction of BMS is not yet possible, the clinical relevance is limited. Research approaches are suggested

Correlations of health status indicators with perceived neuropsychological impairment and cognitive processing speed in multiple sclerosis

Background: Comorbidity and health behaviours may explain heterogeneity regarding cognitive performance in multiple sclerosis. Patient-reported cognitive difficulties have impact but do not consistently correlate with objective cognitive performance. Our study aims to investigate whether health status indicators including comorbidities, body mass index, physical activity, smoking, sleeping behaviour and consumption patterns for fish, alcohol and caffeinated drinks are associated with measures of subjective and objective cognitive performance. Methods: Survey data on self-reported cognitive performance, assessed with the MS Neuropsychological Screening Questionnaire (MSNQ), were related to the presence of arterial hypertension, diabetes mellitus, cardiovascular and chronic renal diseases, hypercholesterolemia, depression based on 2-question screening tool, health and consumption behaviors. We included the Symbol Digit Modalities Test when available within 6 months as an objective, performance-based metric of cognitive processing speed. We investigated the interrelation between all variables with a Spearman correlation matrix and corrected for multiple testing. Regression models were built and controlled for age, sex and phenotype. Results: We used available data from 751 patients with definite MS, including 290 SDMT scores within a time window of 6 months, to study relations between variables. MSNQ and SDMT scores were not significantly correlated. Correlation patterns for subjective and objective performance differed. Age, disease duration and physical disability correlated with SDMT scores only. Regression analyses could be performed for MSNQ scores in 595/751 (79.2%) and for SDMT scores in 234/751 (31.2%) participants. After restricting variables to avoid collinearity and adjusting for the number of variables, regression models explained 15% of the variance for subjective and 14% of the variance for objective cognitive performance. A higher number of physical comorbidities, reporting depressive symptoms, sleeping 9 h or more and daily use of sleeping medication were associated with lower subjective cognitive performance, whereas increasing age was associated with reduced processing speed. These associations persisted after correction for multiple testing. Conclusion: Increasing age is associated with reduced cognitive processing speed whereas comorbidities and sleep behaviors contribute to subjective cognitive performance

The influence of nociceptive and neuropathic pain states on the processing of acute electrical nociceptive stimulation : a dynamic causal modeling study

Introduction: Despite the worldwide increase in prevalence of chronic pain and the subsequent scientific interest, researchers studying the brain and brain mechanisms in pain patients have not yet clearly identified the exact underlying mechanisms. Quantifying the neuronal interactions in electrophysiological data could help us gain insight into the complexity of chronic pain. Therefore, the aim of this study is to examine how different underlying pain states affect the processing of nociceptive information. Methods: Twenty healthy participants, 20 patients with non-neuropathic low back-related leg pain and 20 patients with neuropathic failed back surgery syndrome received nociceptive electrical stimulation at the right sural nerve with simultaneous electroencephalographic recordings. Dynamic Causal Modeling (DCM) was used to infer hidden neuronal states within a Bayesian framework. Results: Pain intensity ratings and stimulus intensity of the nociceptive stimuli did not differ between groups. Compared to healthy participants, both patient groups had the same winning DCM model, with an additional forward and backward connection between the somatosensory cortex and right dorsolateral prefrontal cortex. Discussion: The additional neuronal connection with the prefrontal cortex as seen in both pain patient groups could be a reflection of the higher attention towards pain in pain patients and might be explained by the higher levels of pain catastrophizing in these patients. Conclusion: In contrast to the similar pain intensity ratings of an acute nociceptive electrical stimulus between pain patients and healthy participants, the brain is processing these stimuli in a different way

Fluoxetine in Progressive Multiple Sclerosis (FLUOX-PMS) : study protocol for a randomized controlled trial

Background: Currently available disease-modifying treatments acting by modifying the immune response are ineffective in progressive multiple sclerosis (MS), which is caused by a widespread axonal degeneration. Mechanisms suspected to be involved in this widespread axonal degeneration are reduced axonal energy metabolism, axonal glutamate toxicity, and reduced cerebral blood flow. Fluoxetine might theoretically reduce axonal degeneration in MS because it stimulates energy metabolism through enhancing glycogenolysis, stimulates the production of brain-derived neurotrophic factor, and dilates cerebral arterioles. The current document presents the protocol of a clinical trial to test the hypothesis that fluoxetine slows down the progressive phase of MS. Methods/Design: The FLUOX-PMS trial is a multi-center, randomized, controlled and double-blind clinical study. A total of 120 patients with the diagnosis of either secondary or primary progressive MS will be treated either by fluoxetine (40 mg daily) or placebo for a total period of 108 weeks. The primary endpoint is the time to confirmed disease progression defined as either at least a 20% increase in the timed 25-Foot Walk or at least a 20% increase in the 9-Hole Peg Test. Secondary endpoints include the Hauser ambulation index, cognitive changes, fatigue, magnetic resonance imaging of the brain, and in a small subgroup optical coherence tomography. Discussion: The FLUOX-PMS trial will gives us information as to whether fluoxetine has neuroprotective effects in patients with progressive MS

Transfer Learning on Structural Brain Age Models to Decode Cognition in MS: A Federated Learning Approach.

Introduction. Classical deep learning research requires lots of centralised data. However, data sets are often stored at different clinical centers, and sharing sensitive patient data such as brain images is difficult. In this manuscript, we investigated the feasibility of federated learning, sending models to the data instead of the other way round, for research on brain magnetic resonant images of people with multiple sclerosis (MS). Methods. Using transfer learning on a previously published brain age model, we trained a model to decode performance on the symbol digit modalities test (SDMT) of patients with MS from structural T1 weighted MRI. Three international centers in Brussels, Greifswald and Prague participated in the project. In Brussels, one computer served as the server coordinating the FL project, while the other served as client for model training on local data (n=97). The other two clients were Greifswald (n=104) and Prague (n=100). Each FL round, the server sent a global model to the clients, where its fully connected layer was updated on the local data. After collecting the local models, the server applied a weighted average of two randomly picked clients, yielding a new global model. Results. After 22 federated learning rounds, the average validation loss across clients reached a minimum. The model appeared to have learned to assign SDMT values close to the mean with a mean absolute error of 9.04, 10.59 and 10.71 points between true and predicted SDMT on the test data sets of Brussels, Greifswald and Prague respectively. The overall test MAE across all clients was 10.13 points. Conclusion. Federated learning is feasible for machine learning research on brain MRI of persons with MS, setting the stage for larger transfer learning studies to investigate the utility of brain age latent representations in cognitive decoding tasks

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health