Background

Brain tissue damage is closely linked to disability in multiple sclerosis (MS). The localization of white matter (WM) lesions influences the course of the disease.

Objectives

However, the interrelation between lesions topography and cortical atrophy distribution for predicting the clinical disability remains unclear. Use a deep learning neural network framework with the purpose to identify critical co-varying patterns for individualized disease prediction.

Methods

Clinical disability was measured using the Expanded Disability Status Score at baseline and at a one-year follow-up in a cohort of 119 patients with early relapsing-remitting MS and in a replication cohort of 81 patients. Co-varying patterns of cortical atrophy and baseline lesion distribution were extracted by parallel ICA and used as features for constructing a deep learning convolutional neural network. The prediction was conducted for each identified lesion pattern separately using 50% as training cohort and 50% as testing cohort.

Results

In the study cohort, we identified three distinct distribution types of WM lesions (“cerebellar”, “bihemispheric” and “left-lateralized”). The “cerebellar” and “left-lateralized” patterns were reproducibly detected in the second cohort. Each of the patterns predicted to different extents, short-term disability progression, while the “cerebellar” pattern predicting individual disability progression with an 10-fold cross-validation accuracy of above 90% for the Study cohort (95% CI: 88%-94%) and above 85% for the replication cohort (95% CI: 81%-88%) respectively.

Conclusions

These findings highlight that role of distinct spatial distribution of cortical atrophy and WM lesions predicting disability. The cerebellar involvement is shown as a key feature in the CNN framework for prediction of rapid clinical deterioration.

Background

Up to date prognostic estimation in newly diagnosed patients is hardly possible while the differentiation between disabling versus more benign courses is of utmost relevance. Reliable blood-based biomarkers that are associated with diagnosis and prognosis of multiple sclerosis (MS) have not been established.

Objectives

Can serum neurofilament light chain measurements serve as a reliable biomarker for diagnostic accuracy and prognosis for multiple sclerosis patients at the time point of diagnosis?

Methods

In a multicenter prospective longitudinal observational cohort, patients with a first diagnosis of multiple sclerosis (MS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers and assessed yearly with a standardized protocol. Patients were offered standard immunotherapies according to national treatment guidelines. Serum NfL concentrations were measured using an ultrasensitive single-molecule array (Simoa).

Results

A possible association between sNfL levels and clinical diagnosis, relapses, MRI parameters and treatment decisions was tested in 814 patients classified according to current (2017) and older (2010) McDonald criteria at time point of diagnosis and two years after study inclusion sNfL levels correlated with number of T2 and Gd+ lesions and clinical relapses. After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2-13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4-20.1 pg/ml, n = 213; p = 0.036) and increased accuracy of distinction between CIS and RRMS, when including ≥ 90th percentile of sNfL values. Patients receiving disease-modifying treatment (DMT) during the first two years had higher sNfl baseline levels (11.8 pg/ml, 7.5-20.9 pg/ml, n = 727) than patients never receiving DMT (9.5 pg/ml, IQR 6.4-14.1 pg/ml, n = 87, p = 0.002). Longitudinal sNfL levels reflected treatment decisions within the first four years.

Conclusions

sNfL is associated with diagnosis and prognosis of MS patients at the time point of first diagnosis and may be of use for initial treatment stratification.

Background

People with MS present disruption of structural brain networks, but the differential characteristics of such changes among MS phenotypes and their clinical impact are not well elucidated.

Objectives

To characterize diffusion-based brain connectivity abnormalities in different MS phenotypes and their relation with disability in a large cohort of patients.

Methods

In this multicenter, retrospective, cross-sectional study, we collected clinical and brain MRI data from 344 patients with MS [median Expanded Disability Status Scale, EDSS 2.0 (range 0-7.0)] and 91 healthy volunteers (HV) from four MAGNIMS centers. Cognition was assessed with the Paced Auditory Serial Addition Test (PASAT) and Symbol Digits Modalities Test (SDMT) in 298 patients. We collected 3D-T1, FLAIR, diffusion-weighted images (DWI) and T2 or field maps acquisitions. FSL and ANTs packages were used to carry out DWI preprocessing and MRtrix software to generate connectivity matrices based on fractional anisotropy values. We computed six network measures (strength, global and local efficiency, clustering coefficient, assortativity and transitivity), and applied the ComBat tool to reduce inter-site variability. We calculated age-adjusted differences in graphs between groups using Mann-Whitney with FDR correction or Kruskal-Wallis with Dunn’s Test when necessary. Associations with clinical features were explored with Spearman’s rank correlation.

Results

Thirty-eight (11%) patients presented a clinically isolated syndrome (CIS), 262 (76%) had relapsing-remitting (RR) and 44 (13%) secondary progressive (SP) MS. CIS patients showed reduced global and local efficiency, clustering coefficient and transitivity compared to HV (corrected p<0.001), whilst RRMS did not differ from CIS patients. Compared with CIS and RRMS, patients with SPMS showed larger changes for the same previous graphs measures (corrected p<0.05), and lower strength than RRMS (corrected p=0.019).

In patients, reduced measures of strength, global and local efficiency, clustering and transitivity correlated with higher EDSS (rho:-0.12–-0.16, corrected p<0.034), lower PASAT (rho:0.26–0.30, corrected p<0.001) and worse SDMT scores (rho:0.28–0.32, corrected p<0.001).

Conclusions

Structural network integrity at the whole brain level is already widely reduced in people with MS from the earliest phases of the disease and becomes more abnormal in SPMS. Network modifications may contribute to the clinical manifestations of the disease.

Background

Motor skills are commonly impaired in patients with multiple sclerosis (MS) as a consequence of gray (GM) and white matter (WM) pathology and cortical excitability abnormalities.

Objectives

We hypothesized that microstructural characteristics of motor regions as assessed with the neurite orientation dispersion and density imaging (NODDI) model predict motor cortical excitability that is frequently altered in MS. Further, we evaluated pathological microstructure alterations in motor WM tracts of MS patients compared to healthy controls (HC) using NODDI in comparison to the diffusion tensor imaging (DTI) parameter fractional anisotropy (FA).

Methods

We applied advanced diffusion imaging in 50 MS patients and 49 age-matched HC. As excitability maker, we assessed resting motor thresholds using non-invasive transcranial magnetic stimulation. For quantification of microstructural integrity of the motor system, neurite density index (NDI), orientation dispersion index (ODI), isotropic volume fraction (IVF) and FA averaged within left primary motor cortex as the stimulation site were considered. We applied hierarchical regression modeling to evaluate the prediction of the resting motor threshold by NDI, ODI, IVF and FA in MS patients and HC. Cognitive-motor performance quantified by the Nine Hole Peg Test and Trail Making Test part A (TMT-A) and part B (TMT-B) was regressed on the diffusion parameters in a subsample of 44 MS patients. In the WM, we applied tract-based spatial statistics with the threshold-free cluster enhancement (TFCE) method within motor tracts comparing MS patients and HC. We tracked contributions of NDI and ODI to FA and evaluated if the NODDI model detects additional pathological alterations.

Results

A hierarchical regression revealed that lower NDI suggestive for axonal loss in the GM significantly predicted higher motor thresholds, i.e. reduced excitability in MS patients (F(1,48) = 7.493, p = .009). Lower NDI was indicative for decreased performance in TMT-A (F(1,42) = 8.102; p = .007) and TMT-B (F(1,42) = 7.390; p = .009). Microstructural abnormalities of the interconnected WM tracts were characterized by lowered FA, decreased NDI and increased ODI in MS (all TFCE-corrected p < .05). NDI exclusively (56%) and in overlap with FA (19%) accounted for the largest amount of differences, followed by ODI alone (9%).

Conclusions

Our work shows that lower neurite density in primary motor cortex is linked to decreased motor cortical excitability and decreased cognitive-motor performance in MS patients. Lower neurite density and higher orientation dispersion are characteristic in the WM of MS patients compared to HC. Our results suggest that these markers are more sensitive to pathological alterations than the classical DTI measure FA. This work outlines the potential of microstructure imaging using advanced biophysical models to forecast neurodegeneration and excitability alterations in neuroinflammation.

Background

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), one of its pathophysiological hallmarks is demyelination, which is known to be involved in neurodegenerative mechanisms.

Objectives

Modular architecture and its dynamic adaptation could play a critical role in achieving flexible alterations of cerebral network architecture during de- and remyelination which is still not fully elucidated.

Methods

We address dynamic adaptation to cuprizone model of general de- and remyelination and ask if network community organization can relate to the longitudinal time events. To start with baseline and then by introducing cuprizone into the diet of mice we induced full CNS demyelination by targeting oligodendrocytes, over a period of 5 weeks (two time points). A subsequent myelin synthesis was allowed over reintroduction of normal food (two time points). To identify the modular organization the resting state fMRI within the graph theory framework was analyzed from each of the five time points. The dynamic network reconfiguration was estimated by flexibility as parameter of modularity allegiance and effective connectivity analyses were applied to test the causality of network dynamics between the identified modules.

Results

We found six modules namely default mode network (DMN), hippocampus, thalamus, lateral cortical network, basal forebrain and ventral mid brain. Interestingly the dynamics of de- and remyelination was mirrored by an initial significant increase in flexibility values and a return to baseline in the hippocampus (F_{(4, 80)} = 22.8, p < 0.001), DMN (F_{(4, 80)} = 36.5, p < 0.001) and thalamus (F_{(4, 80)} = 24.5, p < 0.001). The other three networks showed a reversed pattern. The strength of connections from the hippocampus to DMN was associated with the behavioral indicators of memory novel object recognition (NOR) (r² = 0.3854, p < 0.001) and thalamus to hippocampus to locomotor activity (r² = 0.3144, p < 0.001).

Conclusions

Taken together, our fMRI modular analyses showed that global modularity and flexibility partially compensate for demyelination. Dynamics of compensation could be identified as modular specific (i.e. hippocampus, thalamus and DMN) at different intermediate time points, supporting the hypothesis that altered thalamocortical connectivity is an early pathological hallmark of the disease. Causality dynamics also provide biomarkers for evaluating the course of MS and disease dynamics.

Background

Clinical and imaging features of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) may overlap with those of aquaporin 4-neuromyelitis optica spectrum disorder (AQP4-NMOSD) and relapsing remitting multiple sclerosis (RRMS). There is an unmet need for MRI biomarkers which reflect biological mechanisms involved in MOGAD and can help in the differential diagnosis.

Objectives

We aim to identify imaging features able to differentiate between non-acute MOG-antibody disease, AQP4-NMOSD and RRMS.

Methods

In this ongoing retrospective, cross-sectional MAGNIMS study, we analyzed data collected from 8 centers. All subjects (n=352) had brain and cervical cord 3T MRI. Quantification of MRI biomarkers included brain white matter lesions (WMLs), cortical lesions (CL), brain parenchymal fraction (BPF), white matter fraction (WMF), cortical and deep grey matter fractions (GMF) and cross-sectional cervical cord area (CSA) at C1-C2. Linear regression models were used to compare MRI measures between groups, corrected for age, sex, and centre. Statistical significance was considered when p was <0.05.

Results

91 patients with MOGAD (50F, mean age: 41yrs [±15]), 85 with AQP4-NMOSD (68F, 49yrs [±14]), 90 with RRMS (56F, 41yrs [±11]) and 87 healthy controls (HCs) (54F, 36yrs [±11.6]) were collected. The most common phenotypes at onset were optic neuritis and transverse myelitis in MOGAD (93%) and AQP4-NMOSD (87%). WMLs were detected in 57% MOGAD, 79% AQP4-NMOSD, all RRMS (100%) patients, and in 15% HCs. The mean lesion load and number of lesions were higher in RRMS than both MOGAD (p=0.007, p<0.001) and AQP4-NMOSD (p=0.001, p<0.001). At least one CL was seen in 8% patients with MOGAD (total n=8), 10% patients with AQP4-NMOSD (n=7), and in 69% patients with RRMS (n=150). All patient groups showed lower BPF than HCs, with lower WMF in MOGAD and RRMS than HCs (all p<0.01). Between groups, deep GMF was lower in RRMS than MOGAD (p<0.001) and AQP4-NMOSD (p=0.001). CSA was reduced in all disease groups when compared to HCs (all p<0.01) and lower in AQP4-NMOSD than RRMS (p=0.01).

Conclusions

This ongoing study indicates that MOGAD and AQP4-NMOSD share similar MRI features, and no specific MRI biomarker can distinguish between them. Patients with AQP4-NMOSD showed greater spinal cord atrophy than RRMS, and RRMS patients had a higher number of cortical lesions, and greater deep GM atrophy than AQP4-NMOSD and MOGAD. The next step is to investigate whether lesion distribution differs between the two antibody-mediated disease.

Background

The hippocampus is a complex anatomical structure with a fine-tuned intrinsic network architecture, shaped by functional and structural compartmentalization. The hippocampus is affected early in multiple sclerosis (MS) and besides focal neuroinflammatory damage, network disruption is thought to account for cognitive deficits in MS. Given the sex-related vulnerability to cognitive decline in MS, sex-driven differences in hippocampal networks and regional integrity can be hypothesized.

Objectives

To characterize sex effects on hippocampal network organization and subfield integrity, and their relation to cognitive performance.

Methods

In a cohort of 476 MS patients (age 35±10 years), 337 females and 139 males with a disease duration of 16±14 months were imaged on a 3T MRI scanner at baseline and after 2 years. A control group of healthy subjects (HS, n=110, age 34±15 years, 54 females) was included. Volumes of 12 hippocampal subfields were quantified and fed into the reconstruction of the single-subject morphometric networks and analyzed within the graph theoretical framework. Sex-related differences in network and subfield properties were evaluated with linear mixed-effects models, adjusted for age, center and total hippocampal volume; p-values are reported after Bonferroni correction for multiple comparisons.

Results

At baseline, both female and male patients displayed higher clustering (p<0.05) compared to HS. Female patients had higher clustering (p<0.05) but equally efficient network organization (local and global efficiency, p>0.05) compared to male patients. At follow-ups, independently of sex, patients had increased modularity, clustering and global efficiency, however, with higher values in female patients (all p<0.05). Both female and male patients had lower volumes in almost all subfields compared to HS. Female patients had smaller parasubiculum and presubiculum but larger molecular layer as compared to male patients. Over time, female patients had more widespread regional volumetric reduction compared to male patients. Cognitive performance was positively associated with clustering (r=0.27, p<0.01), local (r=0.25, p<0.01) and global efficiency (r=0.24, p<0.01) only in female but not in male patients.

Conclusions

Our findings suggest a more clustered and modular network architecture in female patients despite a more extensive local atrophy over time. The stronger association of cognitive performance with intrinsic hippocampal connectivity may explain cognitive reserve in female patients. These results may serve for sex-targeted neuropsychological interventions.