Understanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders

Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.

Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis

Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.

Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis

Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy.

In vivo quantitative imaging of hippocampal inflammation in autoimmune neuroinflammatory conditions: a systematic review

The hippocampus is a morphologically complex region of the brain limbic system centrally involved in important cognitive, affective, and behavioural regulatory roles. It has exquisite vulnerability to neuroinflammatory processes, with some of its subregions found to be specific sites of neuroinflammatory pathology in ex-vivo studies. Optimizing neuroimaging correlates of hippocampal neuroinflammation would enable the direct study of functional consequences of hippocampal neuroinflammatory pathology, as well as the definition of therapeutic end-points for treatments targeting neuroinflammation, and their related affective or cognitive sequelae. However, in vivo traditional imaging of the hippocampus and its subregions is fraught with difficulties, due to methodological challenges deriving from its unique anatomical characteristics. The main objective of this review is to provide a current update on the characterization of quantitative neuroimaging correlates of hippocampal neuroinflammation by focusing on three prototypical autoimmune neuro-inflammatory conditions [multiple sclerosis (MS), systemic lupus erythematosus (SLE), and autoimmune encephalitis (AE)]. We focused on studies employing TSPO-targeting positron emission tomography (PET), quantitative magnetic resonance imaging (MRI), and spectroscopy techniques assumed to be sensitive to neuroinflammatory tissue changes. We found 18 eligible studies (14, 2, and 2 studies in MS, AE, and SLE, respectively). Across conditions, the largest effect was seen in TSPO PET and diffusion-weighted MRI studies. No study examined neuroinflammation-related changes at the hippocampal subfield level. Overall, results were largely inconsistent due to heterogeneous imaging methods, small sample sizes, and different population studies. We discuss how these data could inform future study design and conclude by suggesting further methodological directions aimed at improving the precision and sensitivity of neuroimaging techniques to characterize hippocampal neuroinflammatory pathology in the human brain.

Effect of BDNF Val66Met polymorphism on hippocampal subfields in multiple sclerosis patients

Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism was shown to strongly affect BDNF function, but its role in modulating gray matter damage in multiple sclerosis (MS) patients is still not clear. Given BDNF relevance on the hippocampus, we aimed to explore BDNF Val66Met polymorphism effect on hippocampal subfield volumes and its role in cognitive functioning in MS patients. Using a 3T scanner, we obtained dual-echo and 3DT1-weighted sequences from 50 MS patients and 15 healthy controls (HC) consecutively enrolled. MS patients also underwent genotype analysis of BDNF, neurological and neuropsychological evaluation. Hippocampal subfields were segmented by using Freesurfer. The BDNF Val66Met polymorphism was found in 22 MS patients (44%). Compared to HC, MS patients had lower volume in: bilateral hippocampus-amygdala transition area (HATA); cornus ammonis (CA)1, granule cell layer of dentate gyrus (GCL-DG), CA4 and CA3 of the left hippocampal head; molecular layer (ML) of the left hippocampal body; presubiculum of right hippocampal body and right fimbria. Compared to BDNF Val66Val, Val66Met MS patients had higher volume in bilateral hippocampal tail; CA1, ML, CA3, CA4, and GCL-DG of left hippocampal head; CA1, ML, and CA3 of the left hippocampal body; left HATA and presubiculum of the right hippocampal head. In MS patients, higher lesion burden was associated with lower volume of presubiculum of right hippocampal body; lower volume of left hippocampal tail was associated with worse visuospatial memory performance; lower volume of left hippocampal head with worse performance in semantic fluency. Our findings suggest the BNDF Val66Met polymorphism may have a protective role in MS patients against both hippocampal atrophy and cognitive impairment. BDNF genotype might be a potential biomarker for predicting cognitive prognosis, and an interesting target to study for neuroprotective strategies.

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.

Effects of walking exercise training on learning and memory and hippocampal neuroimaging outcomes in MS: A targeted, pilot randomized controlled trial

PURPOSE

The current pilot study involved a single-blind, randomized controlled trial (RCT) on the effects of treadmill walking exercise training compared with an active control condition on learning and memory (L/M) and hippocampal neuroimaging outcomes in 11 fully-ambulatory persons with multiple sclerosis (MS) who demonstrated impairments in new learning.

METHODS

The study protocol is registered at clinicaltrials.gov: NCT03319771 (February 2018). Eleven fully-ambulatory persons with MS-related impairments in new learning were randomly assigned into either 12-weeks of supervised, treadmill walking exercise training or 12-weeks of low-intensity resistive exercise (active control condition). Participants underwent neuropsychological tests of L/M and hippocampal neuroimaging before and after the 12-week study period; outcomes were administered by treatment-blinded assessors.

RESULTS

There were moderate-to-large intervention effects on measures of verbal L/M (η_p² = 0.11, d = 0.63, 95% CI: -0.61, 1.83), whereby those in the intervention condition demonstrated improvement in California Verbal Learning Test-II (CVLT-II) scores compared with the control condition. There were smaller effects on a composite L/M measure (η_p² = 0.02, d = 0.28, 95% CI: -0.93, 1.46). There were large intervention effects on normalized hippocampal volume (η_p² = 0.36, d = 1.13, 95% CI: 0.09, 2.82), whereby hippocampal volume was preserved in the intervention condition, compared with hippocampal atrophy in the control condition. By comparison, there were no intervention effects on hippocampal resting-state functional connectivity.

CONCLUSIONS

Collectively, this study provides initial proof-of-concept data for further examining treadmill walking exercise training as a possible behavioral approach for managing L/M impairment and preserving hippocampal volume as common and debilitating manifestations of MS.

Structural and functional hippocampal alterations in Multiple sclerosis and neuromyelitis optica spectrum disorder

BACKGROUND

Hippocampal involvement may differ between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).

OBJECTIVE

To investigate the morphometric, diffusion and functional alterations in hippocampus in MS and NMOSD and the clinical significance.

METHODS

A total of 752 participants including 236 MS, 236 NMOSD and 280 healthy controls (HC) were included in this retrospective multi-center study. The hippocampus and subfield volumes, fractional anisotropy (FA) and mean diffusivity (MD), amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) were analyzed, and their associations with clinical variables were investigated.

RESULTS

The hippocampus showed significantly lower volume, FA and greater MD in MS compared to NMOSD and HC (p < 0.05), while no abnormal ALFF or DC was identified in any group. Hippocampal subfields were affected in both diseases, though subiculum, presubiculum and fimbria showed significantly lower volume only in MS (p < 0.05). Significant correlations between diffusion alterations, several subfield volumes and clinical variables were observed in both diseases, especially in MS (R = -0.444 to 0.498, p < 0.05). FA and MD showed fair discriminative power between MS and HC, NMOSD and HC (AUC > 0.7).

CONCLUSIONS

Hippocampal atrophy and diffusion abnormalities were identified in MS and NMOSD, partly explaining how clinical disability and cognitive impairment are differentially affected.

FreeSurfer ‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized.