Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.

Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington's Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.

Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

OBJECTIVE: To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. METHODS: A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. RESULTS: We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. INTERPRETATION: GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024.

Predictors of atrial fibrillation after embolic stroke of undetermined source in patients with implantable loop recorders.

BACKGROUND: In patients with embolic stroke of undetermined source (ESUS), underlying subclinical atrial fibrillation (AF) is often suspected. Previous studies identifying predictors of AF have been limited in their ability to diagnose episodes of AF. Implantable loop recorders enable prolonged, continuous, and therefore more reliable detection of AF. The aim of this study was to identify clinical and ECG parameters as predictors of AF in ESUS patients with implantable loop recorders. METHODS: 101 ESUS patients who received an implantable loop recorder between 2012 and 2020 were included in this study. Patients were followed up regularly on a three-monthly outpatient interval. RESULTS: During a mean follow-up of 647 ± 385 days, AF was detected in 26 patients (26%). Independent risk factors of AF were age ≥ 60 years (HR 2.753, CI 1.129-6.713, p = 0.026), P-wave amplitude in lead II ≤ 0.075 mV (HR 3.751, CI 1.606-8.761, p = 0.002), and P-wave duration ≥ 125 ms (HR 4.299, CI 1.844-10.021, p < 0.001). In patients without risk factors, the risk of developing AF was 16%. In the presence of one risk factor, the probability increased only slightly to 18%. With two or three risk factors, the risk of AF increased to 70%. CONCLUSION: AF was detected in about one in four patients after ESUS in this study. A comprehensive evaluation involving multiple parameters and the existence of multiple risk factors yields the highest predictive accuracy for detecting AF in patients with ESUS.

Digital assessment of speech in Huntington disease.

Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System.

Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.

Minor Phenomena in Parkinson's Disease-Prevalence, Associations, and Risk of Developing Psychosis.

BACKGROUND: Minor phenomena, including passage phenomena, feeling of presence, and illusions, are common and may represent a prodromal form of psychosis in Parkinson's disease (PD). We examined the prevalence and clinical correlates of minor phenomena, and their potential role as a risk factor for PD psychosis. METHODS: A novel questionnaire, the Psychosis and Mild Perceptual Disturbances Questionnaire for PD (PMPDQ), was completed by Fox Insight cohort participants with and without PD. Additional assessments included the Non-Motor Symptoms Questionnaire (NMSQuest), REM Sleep Behavior Disorder Single Question Screen (RBD1Q), Movement Disorder Society-Unified Parkinson Disease Rating Scale Part II, demographic features, and medication usage. For participants with PD, we used regression models to identify clinical associations and predictors of incident psychosis over one year of follow-up. RESULTS: Among participants with PD (n = 5950) and without PD (n = 1879), the prevalence of minor phenomena was 43.1% and 31.7% (P < .001). Of the 3760 participants with PD and no baseline psychosis, independent correlates of minor phenomena included positive responses on the NMSQuest apathy/attention/memory (OR 1.7, 95% CI 1.3-2.1, P < .001) or sexual function domain (OR 1.3, 95% CI 1.1-1.6, P = .01) and positive RBD1Q (OR 1.3, 95% CI 1.05-1.5, P = .01). Independent risk factors for incident PD psychosis included the presence of minor phenomena (HR 3.0, 95% CI 2.4-3.9, P < .001), positive response on the NMSQuest apathy/attention/memory domain (HR 1.8, 95% CI 1.3-2.6, P < .001), and positive RBD1Q (HR 1.5, 95% CI 1.1-1.9, P = .004). CONCLUSIONS: Minor phenomena are common, associated with specific non-motor symptoms, and an independent predictor of incident psychosis in PD.

Automatic estimation of brain parenchymal fraction in patients with multple sclerosis: a comparison between synthetic MRI and an established automated brain segmentation software based on FSL.

PURPOSE: We aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes. METHODS: MRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI. RESULTS: We observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland-Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA. CONCLUSION: In summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

Factors associated with depressive mood at the onset of multiple sclerosis - an analysis of 781 patients of the German NationMS cohort.

Background: Depression has a major impact on the disease burden of multiple sclerosis (MS). Analyses of overlapping MS and depression risk factors [smoking, vitamin D (25-OH-VD) and Epstein-Barr virus (EBV) infection] and sex, age, disease characteristics and neuroimaging features associated with depressive symptoms in early MS are scarce. Objectives: To assess an association of MS risk factors with depressive symptoms within the German NationMS cohort. Design: Cross-sectional analysis within a multicenter observational study. Methods: Baseline data of n = 781 adults with newly diagnosed clinically isolated syndrome or relapsing-remitting MS qualified for analysis. Global and region-specific magnetic resonance imaging (MRI)-volumetry parameters were available for n = 327 patients. Association of demographic factors, MS characteristics and risk factors [sex, age, smoking, disease course, presence of current relapse, expanded disability status scale (EDSS) score, fatigue (fatigue scale motor cognition), 25-OH-VD serum concentration, EBV nuclear antigen-1 IgG (EBNA1-IgG) serum levels] and depressive symptoms (Beck Depression Inventory-II, BDI-II) was tested as a primary outcome by multivariable linear regression. Non-parametric correlation and group comparison were performed for associations of MRI parameters and depressive symptoms. Results: Mean age was 34.3 years (95% confidence interval: 33.6-35.0). The female-to-male ratio was 2.3:1. At least minimal depressive symptoms (BDI-II > 8) were present in n = 256 (32.8%), 25-OH-VD deficiency (<20 ng/ml) in n = 398 (51.0%), n = 246 (31.5%) participants were smokers. Presence of current relapse [coefficient (c) = 1.48, p = 0.016], more severe fatigue (c = 0.26, p < 0.0001), lower 25-OH-VD (c = -0.03, p = 0.034) and smoking (c = 0.35, p = 0.008) were associated with higher BDI-II scores. Sex, age, disease course, EDSS, month of visit, EBNA1-IgG levels and brain volumes at baseline were not. Conclusion: Depressive symptoms need to be assessed in early MS. Patients during relapse seem especially vulnerable to depressive symptoms. Contributing factors such as fatigue, vitamin D deficiency and smoking, could specifically be targeted in future interventions and should be investigated in prospective studies.

Using AI to measure Parkinson's disease severity at home.

We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson's disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0-4, following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters' average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.

Immunoglobulin A Antibodies Against Myelin Oligodendrocyte Glycoprotein in a Subgroup of Patients With Central Nervous System Demyelination.

Importance: Differential diagnosis of patients with seronegative demyelinating central nervous system (CNS) disease is challenging. In this regard, evidence suggests that immunoglobulin (Ig) A plays a role in the pathogenesis of different autoimmune diseases. Yet little is known about the presence and clinical relevance of IgA antibodies against myelin oligodendrocyte glycoprotein (MOG) in CNS demyelination. Objective: To investigate the frequency of MOG-IgA and associated clinical features in patients with demyelinating CNS disease and healthy controls. Design, Setting, and Participants: This longitudinal study comprised 1 discovery and 1 confirmation cohort derived from 5 centers. Participants included patients with suspected or confirmed demyelinating diseases and healthy controls. MOG-IgA, MOG-IgG, and MOG-IgM were measured in serum samples and cerebrospinal fluid (CSF) of patients, who were assessed from September 2012 to April 2022. Main Outcomes and Measures: Frequency and clinical features of patients who were seropositive for MOG-IgA and double-seronegative for aquaporin 4 (AQP4) IgG and MOG-IgG. Results: After the exclusion of 5 participants with coexisting AQP4-IgG and MOG-IgA, MOG-IgG, and/or MOG-IgM, 1339 patients and 110 healthy controls were included; the median follow-up time was 39 months (range, 0-227 months). Of included patients with isolated MOG-IgA, 11 of 18 were female (61%), and the median age was 31.5 years (range, 3-76 years). Among patients double-seronegative for AQP4-IgG and MOG-IgG (1126/1339; 84%), isolated MOG-IgA was identified in 3 of 50 patients (6%) with neuromyelitis optica spectrum disorder, 5 of 228 patients (2%) with other CNS demyelinating diseases, and 10 of 848 patients (1%) with multiple sclerosis but in none of the healthy controls (0/110). The most common disease manifestation in patients seropositive for isolated MOG-IgA was myelitis (11/17 [65%]), followed by more frequent brainstem syndrome (7/16 [44%] vs 14/75 [19%], respectively; P = .048), and infrequent manifestation of optic neuritis (4/15 [27%] vs 46/73 [63%], respectively; P = .02) vs patients with MOG-IgG. Among patients fulfilling 2017 McDonald criteria for multiple sclerosis, MOG-IgA was associated with less frequent CSF-specific oligoclonal bands (4/9 [44%] vs 325/351 [93%], respectively; P < .001) vs patients with multiple sclerosis who were MOG-IgG/IgA seronegative. Further, most patients with isolated MOG-IgA presented clinical attacks after recent infection or vaccination (7/11 [64%]). Conclusion and Relevance: In this study, MOG-specific IgA was identified in a subgroup of patients who were double-seronegative for AQP4-/MOG-IgG, suggesting that MOG-IgA may be a novel diagnostic biomarker for patients with CNS demyelination.

Trichloroethylene: An Invisible Cause of Parkinson's Disease?

The etiologies of Parkinson's disease (PD) remain unclear. Some, such as certain genetic mutations and head trauma, are widely known or easily identified. However, these causes or risk factors do not account for the majority of cases. Other, less visible factors must be at play. Among these is a widely used industrial solvent and common environmental contaminant little recognized for its likely role in PD: trichloroethylene (TCE). TCE is a simple, six-atom molecule that can decaffeinate coffee, degrease metal parts, and dry clean clothes. The colorless chemical was first linked to parkinsonism in 1969. Since then, four case studies involving eight individuals have linked occupational exposure to TCE to PD. In addition, a small epidemiological study found that occupational or hobby exposure to the solvent was associated with a 500% increased risk of developing PD. In multiple animal studies, the chemical reproduces the pathological features of PD.Exposure is not confined to those who work with the chemical. TCE pollutes outdoor air, taints groundwater, and contaminates indoor air. The molecule, like radon, evaporates from underlying soil and groundwater and enters homes, workplaces, or schools, often undetected. Despite widespread contamination and increasing industrial, commercial, and military use, clinical investigations of TCE and PD have been limited. Here, through a literature review and seven illustrative cases, we postulate that this ubiquitous chemical is contributing to the global rise of PD and that TCE is one of its invisible and highly preventable causes. Further research is now necessary to examine this hypothesis.

Early spinal cord pseudoatrophy in interferon-beta-treated multiple sclerosis.

BACKGROUND AND PURPOSE: Brain pseudoatrophy has been shown to play a pivotal role in the interpretation of brain atrophy measures during the first year of disease-modifying therapy in multiple sclerosis. Whether pseudoatrophy also affects the spinal cord remains unclear. The aim of this study was to analyze the extent of pseudoatrophy in the upper spinal cord during the first 2 years after therapy initiation and compare this to the brain. METHODS: A total of 129 patients from a prospective longitudinal multicentric national cohort study for whom magnetic resonance imaging scans at baseline, 12 months, and 24 months were available were selected for brain and spinal cord volume quantification. Annual percentage brain volume and cord area change were calculated using SIENA (Structural Image Evaluation of Normalized Atrophy) and NeuroQLab, respectively. Linear mixed model analyses were performed to compare patients on interferon-beta therapy (n = 84) and untreated patients (n = 45). RESULTS: Patients treated with interferon-beta demonstrated accelerated annual percentage brain volume and cervical cord area change in the first year after treatment initiation, whereas atrophy rates stabilized to a similar and not significantly different level compared to untreated patients during the second year. CONCLUSIONS: These results suggest that pseudoatrophy occurs not only in the brain, but also in the spinal cord during the first year of interferon-beta treatment.

Clinical and MRI measures to identify non-acute MOG-antibody disease in adults.

MRI and clinical features of myelin oligodendrocyte glycoprotein (MOG)-antibody disease may overlap with those of other inflammatory demyelinating conditions posing diagnostic challenges, especially in non-acute phases and when serologic testing for MOG antibodies is unavailable or shows uncertain results. We aimed to identify MRI and clinical markers that differentiate non-acute MOG-antibody disease from aquaporin 4 (AQP4)-antibody neuromyelitis optica spectrum disorder and relapsing remitting multiple sclerosis, guiding in the identification of patients with MOG-antibody disease in clinical practice. In this cross-sectional retrospective study, data from 16 MAGNIMS centres were included. Data collection and analyses were conducted from 2019 to 2021. Inclusion criteria were: diagnosis of MOG-antibody disease; AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis; brain and cord MRI at least 6 months from relapse; and Expanded Disability Status Scale (EDSS) score on the day of MRI. Brain white matter T2 lesions, T1-hypointense lesions, cortical and cord lesions were identified. Random forest models were constructed to classify patients as MOG-antibody disease/AQP4-neuromyelitis optica spectrum disorder/multiple sclerosis; a leave one out cross-validation procedure assessed the performance of the models. Based on the best discriminators between diseases, we proposed a guide to target investigations for MOG-antibody disease. One hundred and sixty-two patients with MOG-antibody disease [99 females, mean age: 41 (±14) years, median EDSS: 2 (0-7.5)], 162 with AQP4-neuromyelitis optica spectrum disorder [132 females, mean age: 51 (±14) years, median EDSS: 3.5 (0-8)], 189 with multiple sclerosis (132 females, mean age: 40 (±10) years, median EDSS: 2 (0-8)] and 152 healthy controls (91 females) were studied. In young patients (<34 years), with low disability (EDSS < 3), the absence of Dawson's fingers, temporal lobe lesions and longitudinally extensive lesions in the cervical cord pointed towards a diagnosis of MOG-antibody disease instead of the other two diseases (accuracy: 76%, sensitivity: 81%, specificity: 84%, P < 0.001). In these non-acute patients, the number of brain lesions < 6 predicted MOG-antibody disease versus multiple sclerosis (accuracy: 83%, sensitivity: 82%, specificity: 83%, P < 0.001). An EDSS < 3 and the absence of longitudinally extensive lesions in the cervical cord predicted MOG-antibody disease versus AQP4-neuromyelitis optica spectrum disorder (accuracy: 76%, sensitivity: 89%, specificity: 62%, P < 0.001). A workflow with sequential tests and supporting features is proposed to guide better identification of patients with MOG-antibody disease. Adult patients with non-acute MOG-antibody disease showed distinctive clinical and MRI features when compared to AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis. A careful inspection of the morphology of brain and cord lesions together with clinical information can guide further analyses towards the diagnosis of MOG-antibody disease in clinical practice.

Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy.

Immune-mediated demyelination and neurodegeneration are pathophysiological hallmarks of Multiple Sclerosis (MS) and main drivers of disease related disability. The principal method for evaluating qualitatively demyelinating events in the clinical context is contrast-weighted magnetic resonance imaging (MRI). Moreover, advanced MRI sequences provide reliable quantification of brain myelin offering new opportunities to study tissue pathology in vivo. Towards neurodegenerative aspects of the disease, spinal cord atrophy - besides brain atrophy - is a powerful and validated predictor of disease progression. The etiology of spinal cord volume loss is still a matter of research, as it remains unclear whether the impact of local lesion pathology or the interaction with supra- and infratentorial axonal degeneration and demyelination of the long descending and ascending fiber tracts are the determining factors. Quantitative synthetic MR using a multiecho acquisition of saturation recovery pulse sequence provides fast automatic brain tissue and myelin volumetry based on R1 and R2 relaxation rates and proton density quantification, making it a promising modality for application in the clinical routine. In this cross sectional study a total of 91 MS patients and 31 control subjects were included to investigate group differences of global and regional measures of brain myelin and relaxation rates, in different MS subtypes, using QRAPMASTER sequence and SyMRI postprocessing software. Furthermore, we examined associations between these quantitative brain parameters and spinal cord atrophy to draw conclusions about possible pathophysiological relationships. Intracranial myelin volume fraction of the global brain exhibited statistically significant differences between control subjects (10.4%) and MS patients (RRMS 9.4%, PMS 8.1%). In a LASSO regression analysis with total brain lesion load, intracranial myelin volume fraction and brain parenchymal fraction, the intracranial myelin volume fraction was the variable with the highest impact on spinal cord atrophy (standardized coefficient 4.52). Regional supratentorial MRI metrics showed altered average myelin volume fraction, R1, R2 and proton density in MS patients compared to controls most pronounced in PMS. Interestingly, quantitative MRI parameters in supratentorial regions showed strong associations with upper cord atrophy, suggesting an important role of brain diffuse demyelination on spinal cord pathology possibly in the context of global disease activity. R1, R2 or proton density of the thalamus, cerebellum and brainstem correlated with upper cervical cord atrophy, probably reflecting the direct functional connection between these brain structures and the spinal cord as well as the effects of retrograde and anterograde axonal degeneration. By using Synthetic MR-derived myelin volume fraction, we were able to effectively detect significant differences of myelination in relapsing and progressive MS subtypes. Total intracranial brain myelin volume fraction seemed to predict spinal cord volume loss better than brain atrophy or total lesion load. Furthermore, demyelination in highly myelinated supratentorial regions, as an indicator of diffuse disease activity, as well as alterations of relaxation parameters in adjacent infratentorial and midbrain areas were strongly associated with upper cervical cord atrophy.