Network alterations underlying anxiety symptoms in early multiple sclerosis

Background: Anxiety, often seen as comorbidity in multiple sclerosis (MS), is a frequent neuropsychiatric symptom and essentially afects the overall disease burden. Here, we aimed to decipher anxiety-related networks functionally connected to atrophied areas in patients sufering from MS. Methods: Using 3-T MRI, anxiety-related atrophy maps were generated by correlating longitudinal cortical thinning with the severity of anxiety symptoms in MS patients. To determine brain regions functionally connected to these maps, we applied a technique termed “atrophy network mapping”. Thereby, the anxiety-related atrophy maps were projected onto a large normative connectome (n=1000) performing seed‐based functional connectivity. Finally, an instructed threat paradigm was conducted with regard to neural excitability and efective connectivity, using transcranial magnetic stimulation combined with high-density electroencephalography. Results: Thinning of the left dorsal prefrontal cortex was the only region that was associated with higher anxiety levels. Atrophy network mapping identifed functional involvement of bilateral prefrontal cortex as well as amygdala and hippocampus. Structural equation modeling confrmed that the volumes of these brain regions were signifcant determinants that infuence anxiety symptoms in MS. We additionally identifed reduced information fow between the prefrontal cortex and the amygdala at rest, and pathologically increased excitability in the prefrontal cortex in MS patients as compared to controls. Conclusion: Anxiety-related prefrontal cortical atrophy in MS leads to a specifc network alteration involving structures that resemble known neurobiological anxiety circuits. These fndings elucidate the emergence of anxiety as part of the disease pathology and might ultimately enable targeted treatment approaches modulating brain networks in MS. Keywords: Multiple sclerosis, Anxiety, Atrophy, Functional connectivity, Excitabilit

Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers

Disability in multiple sclerosis is generally classified by sensory and motor symptoms, yet cognitive impairment has been identified as a frequent manifestation already in the early disease stages. Imaging- and more recently blood-based biomarkers have become increasingly important for understanding cognitive decline associated with multiple sclerosis. Thus, we sought to determine the prognostic utility of serum neurofilament light chain levels alone and in combination with MRI markers by examining their ability to predict cognitive impairment in early multiple sclerosis. A comprehensive and detailed assessment of 152 early multiple sclerosis patients (Expanded Disability Status Scale: 1.3 ± 1.2, mean age: 33.0 ± 10.0 years) was performed, which included serum neurofilament light chain measurement, MRI markers (i.e. T2-hyperintense lesion volume and grey matter volume) acquisition and completion of a set of cognitive tests (Symbol Digits Modalities Test, Paced Auditory Serial Addition Test, Verbal Learning and Memory Test) and mood questionnaires (Hospital Anxiety and Depression scale, Fatigue Scale for Motor and Cognitive Functions). Support vector regression, a branch of unsupervised machine learning, was applied to test serum neurofilament light chain and combination models of biomarkers for the prediction of neuropsychological test performance. The support vector regression results were validated in a replication cohort of 101 early multiple sclerosis patients (Expanded Disability Status Scale: 1.1 ± 1.2, mean age: 34.4 ± 10.6 years). Higher serum neurofilament light chain levels were associated with worse Symbol Digits Modalities Test scores after adjusting for age, sex Expanded Disability Status Scale, disease duration and disease-modifying therapy (B = −0.561; SE = 0.192; P = 0.004; 95% CI = −0.940 to −0.182). Besides this association, serum neurofilament light chain levels were not linked to any other cognitive or mood measures (all P-values > 0.05). The tripartite combination of serum neurofilament light chain levels, lesion volume and grey matter volume showed a cross-validated accuracy of 88.7% (90.8% in the replication cohort) in predicting Symbol Digits Modalities Test performance in the support vector regression approach, and outperformed each single biomarker (accuracy range: 68.6–75.6% and 68.9–77.8% in the replication cohort), as well as the dual biomarker combinations (accuracy range: 71.8–82.3% and 72.6–85.6% in the replication cohort). Taken together, early neuro-axonal loss reflects worse information processing speed, the key deficit underlying cognitive dysfunction in multiple sclerosis. Our findings demonstrate that combining blood and imaging measures improves the accuracy of predicting cognitive impairment, highlighting the clinical utility of cross-modal biomarkers in multiple sclerosis

Serum neurofilament levels reflect outer retinal layer changes in multiple sclerosis

Background:Serum neurofilament light chain (sNfL) and distinct intra-retinal layers are both promising biomarkers of neuro-axonal injury in multiple sclerosis (MS). We aimed to unravel the association of both markers in early MS, having identified that neurofilament has a distinct immunohistochemical expression pattern among intra-retinal layers. Methods:Three-dimensional (3D) spectral domain macular optical coherence tomography scans and sNfL levels were investigated in 156 early MS patients (female/male: 109/47, mean age: 33.3 ± 9.5 years, mean disease duration: 2.0 ± 3.3 years). Out of the whole cohort, 110 patients had no history of optic neuritis (NHON) and 46 patients had a previous history of optic neuritis (HON). In addition, a subgroup of patients (n = 38) was studied longitudinally over 2 years. Support vector machine analysis was applied to test a regression model for significant changes. Results:In our cohort, HON patients had a thinner outer plexiform layer (OPL) volume compared to NHON patients (B = −0.016, SE = 0.006, p = 0.013). Higher sNfL levels were significantly associated with thinner OPL volumes in HON patients (B = −6.734, SE = 2.514, p = 0.011). This finding was corroborated in the longitudinal subanalysis by the association of higher sNfL levels with OPL atrophy (B = 5.974, SE = 2.420, p = 0.019). sNfL levels were 75.7% accurate at predicting OPL volume in the supervised machine learning. Conclusions:In summary, sNfL levels were a good predictor of future outer retinal thinning in MS. Changes within the neurofilament-rich OPL could be considered as an additional retinal marker linked to MS neurodegeneration

Selective brain network and cellular responses upon dimethyl fumarate immunomodulation in multiple sclerosis

Background Efficient personalized therapy paradigms are needed to modify the disease course and halt gray (GM) and white matter (WM) damage in patients with multiple sclerosis (MS). Presently, promising disease-modifying drugs show impressive efficiency, however, tailored markers of therapy responses are required. Here, we aimed to detect in a real-world setting patients with a more favorable brain network response and immune cell dynamics upon dimethyl fumarate (DMF) treatment. Methods In a cohort of 78 MS patients we identified two thoroughly matched groups, based on age, disease duration, disability status and lesion volume, receiving DMF (n = 42) and NAT (n = 36) and followed them over 16 months. The rate of cortical atrophy and deep GM volumes were quantified. GM and WM network responses were characterized by brain modularization as a marker of regional and global structural alterations. In the DMF group, lymphocyte subsets were analyzed by flow cytometry and related to clinical and MRI parameters. Results Sixty percent (25 patients) of the DMF and 36% (13 patients) of the NAT group had disease activity during the study period. The rate of cortical atrophy was higher in the DMF group (−2.4%) compared to NAT (−2.1%, p < 0.05) group. GM and WM network dynamics presented increased modularization in both groups. When dividing the DMF-treated cohort into patients free of disease activity (n = 17, DMFR) and patients with disease activity (n = 25, DMFNR) these groups differed significantly in CD8+ cell depletion counts (DMFR: 197.7 ± 97.1/μl; DMFNR: 298.4 ± 190.6/μl, p = 0.03) and also in cortical atrophy (DMFR: −1.7%; DMFNR: −3.2%, p = 0.01). DMFR presented reduced longitudinal GM and WM modularization and less atrophy as markers of preserved structural global network integrity in comparison to DMFNR and even NAT patients. Conclusions NAT treatment contributes to a reduced rate of cortical atrophy compared to DMF therapy. However, patients under DMF treatment with a stronger CD8+ T cell depletion present a more favorable response in terms of cortical integrity and GM and WM network responses. Our findings may serve as basis for the development of personalized treatment paradigms

Increased cerebrospinal fluid albumin and immunoglobulin a fractions forecast cortical atrophy and longitudinal functional deterioration in relapsing-remitting multiple sclerosis

Background Currently, no unequivocal predictors of disease evolution exist in patients with multiple sclerosis (MS). Cortical atrophy measurements are, however, closely associated with cumulative disability. Objective Here, we aim to forecast longitudinal magnetic resonance imaging (MRI)-driven cortical atrophy and clinical disability from cerebrospinal fluid (CSF) markers. Methods We analyzed CSF fractions of albumin and immunoglobulins (Ig) A, G, and M and their CSF to serum quotients. Results Widespread atrophy was highly associated with increased baseline CSF concentrations and quotients of albumin and IgA. Patients with increased CSFIgA and CSFIgM showed higher functional disability at follow-up. Conclusion CSF markers of blood–brain barrier integrity and specific immune response forecast emerging gray matter pathology and disease progression in MS

Detection of Plant Volatiles after Leaf Wounding and Darkening by Proton Transfer Reaction ‘‘Time-of-Flight’’ Mass Spectrometry (PTR-TOF)

Peer reviewe

Continuous reorganization of cortical information flow in multiple sclerosis : a longitudinal fMRI effective connectivity study

Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS

NfL predicts relapse-free progression in a longitudinal multiple sclerosis cohort study

Background Easily accessible biomarkers enabling the identification of those patients with multiple sclerosis (MS) who will accumulate irreversible disability in the long term are essential to guide early therapeutic decisions. We here examine the utility of serum neurofilament light chain (sNfL) for forecasting relapse-free disability progression and conversion to secondary progressive MS (SPMS) in the prospective Neurofilament and longterm outcome in MS (NaloMS) cohort. Methods The predictive ability of sNfL at Baseline and sNfL follow-up (FU)/ Baseline (BL) ratio with regard to disability progression was assessed within a development cohort (NaloMS, n=196 patients with relapsing-remitting MS (RRMS) or clinically isolated syndrome) and validated with an external independent cohort (Düsseldorf, Essen, n=204). Both relapse-free EDSS-progression (RFP: inflammatory-independent EDSS-increase 12 months prior to FU) and SPMS-transition (minimum EDSS-score of 3.0) were investigated. Findings During the study period, 17% (n=34) of NaloMS patients suffered from RFP and 14% (n=27) converted to SPMS at FU (validation cohort RFP n=42, SPMS-conversion n=24). sNfL at BL was increased in patients with RFP (10.8 pg/ml (interquartile range (IQR) 7.7-15.0) vs. 7.2 pg/ml (4.5-12.5), p<0.017). In a multivariable logistic regression model, increased sNfL levels at BL (Odds Ratio (OR) 1.02, 95% confidence interval (CI) 1.01-1.04, p=0.012) remained an independent risk factor for RFP and predicted individual RFP risk with an accuracy of 82% (NaloMS) and 83% (validation cohort) as revealed by support vector machine. In addition, the sNfL FU/BL ratio was increased in SPMS-converters (1.16 (0.89-1.70) vs. 0.96 (0.75-1.23), p=0.011). This was confirmed by a multivariable logistic regression model, as sNfL FU/BL ratio remained in the model (OR 1.476, 95%CI 1.078-2,019, p=0.015) and individual sNfL FU/BL ratios showed a predictive accuracy of 72% in NaloMS (63% in the validation cohort) as revealed by machine learning. Interpretation sNfL levels at baseline predict relapse-free disability progression in a prospective longitudinal cohort study 6 years later. While prediction was confirmed in an independent cohort, sNfL further discriminates patients with SPMS at follow-up and supports early identification of patients at risk for later SPMS conversion. Funding This work was supported by the German Research Council (CRC-TR-128), Else Kröner Fresenius Foundation and Hertie-Stiftung

Linking microstructural integrity and motor cortex excitability in multiple sclerosis

Motor skills are frequently impaired in multiple sclerosis (MS) patients following grey and white matter damage with cortical excitability abnormalities. We applied advanced diffusion imaging with 3T magnetic resonance tomography for neurite orientation dispersion and density imaging (NODDI), as well as diffusion tensor imaging (DTI) in 50 MS patients and 49 age-matched healthy controls to quantify microstructural integrity of the motor system. To assess excitability, we determined resting motor thresholds using non-invasive transcranial magnetic stimulation. As measures of cognitive-motor performance, we conducted neuropsychological assessments including the Nine-Hole Peg Test, Trail Making Test part A and B (TMT-A and TMT-B) and the Symbol Digit Modalities Test (SDMT). Patients were evaluated clinically including assessments with the Expanded Disability Status Scale. A hierarchical regression model revealed that lower neurite density index (NDI) in primary motor cortex, suggestive for axonal loss in the grey matter, predicted higher motor thresholds, i.e. reduced excitability in MS patients (p = .009, adjusted r² = 0.117). Furthermore, lower NDI was indicative of decreased cognitive-motor performance (p = .007, adjusted r² = .142 for TMT-A; p = .009, adjusted r² = .129 for TMT-B; p = .006, adjusted r² = .142 for SDMT). Motor WM tracts of patients were characterized by overlapping clusters of lowered NDI (p <.05, Cohen’s d = 0.367) and DTI-based fractional anisotropy (FA) (p <.05, Cohen’s d = 0.300), with NDI exclusively detecting a higher amount of abnormally appearing voxels. Further, orientation dispersion index of motor tracts was increased in patients compared to controls, suggesting a decreased fiber coherence (p <.05, Cohen’s d = 0.232). This study establishes a link between microstructural characteristics and excitability of neural tissue, as well as cognitive-motor performance in multiple sclerosis. We further demonstrate that the NODDI parameters neurite density index and orientation dispersion index detect a larger amount of abnormally appearing voxels in patients compared to healthy controls, as opposed to the classical DTI parameter FA. Our work outlines the potential for microstructure imaging using advanced biophysical models to forecast excitability alterations in neuroinflammation

Altered grey matter integrity and network vulnerability relate to epilepsy occurrence in patients with multiple sclerosis

Background and purpose: The aim of this study was to investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in multiple sclerosis (MS) patients with concomitant epilepsy. Methods: From 3-T magnetic resonance imaging scans of 30 MS patients with epilepsy (MSE group; age 41 ± 15 years, 21 females, disease duration 8 ± 6 years, median Expanded Disability Status Scale [EDSS] score 3), 60 MS patients without epilepsy (MS group; age 41 ± 12 years, 35 females, disease duration 6 ± 4 years, EDSS score 2), and 60 healthy subjects (HS group; age 40 ± 13 years, 27 females) the regional volumes of GM lesions and of cortical, subcortical and hippocampal structures were quantified. Network topology and vulnerability were modelled within the graph theoretical framework. Receiver-operating characteristic (ROC) curve analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. Results: Higher lesion volumes within the hippocampus, mesiotemporal cortex and amygdala were detected in the MSE compared to the MS group (all p < 0.05). The MSE group had lower cortical volumes mainly in temporal and parietal areas compared to the MS and HS groups (all p < 0.05). Lower hippocampal tail and presubiculum volumes were identified in both the MSE and MS groups compared to the HS group (all p < 0.05). Network topology in the MSE group was characterized by higher transitivity and assortativity, and higher vulnerability compared to the MS and HS groups (all p < 0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67–0.91]) in discriminating between MSE and MS patients. Conclusions: High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity for epilepsy occurrence in people with MS