Longitudinal fibre-specific white matter damage predicts cognitive decline in multiple sclerosis.

During the course of multiple sclerosis, many patients experience cognitive deficits which are not simply driven by lesion number or location. By considering the full complexity of white matter structure at macro- and microstructural levels, our understanding of cognitive impairment in multiple sclerosis may increase substantially. Accordingly, this study aimed to investigate specific patterns of white matter degeneration, the evolution over time, the manifestation across different stages of the disease and their role in cognitive impairment using a novel fixel-based approach. Neuropsychological test scores and MRI scans including 30-direction diffusion-weighted images were collected from 327 multiple sclerosis patients (mean age = 48.34 years, 221 female) and 95 healthy controls (mean age = 45.70 years, 55 female). Of those, 233 patients and 61 healthy controls had similar follow-up assessments 5 years after. Patients scoring 1.5 or 2 standard deviations below healthy controls on at least two out of seven cognitive domains (from the Brief Repeatable Battery of Neuropsychological Tests, BRB-N) were classified as mildly cognitively impaired or cognitively impaired, respectively, or otherwise cognitively preserved. Fixel-based analysis of diffusion data was used to calculate fibre-specific measures (fibre density, reflecting microstructural diffuse axonal damage; fibre cross-section, reflecting macrostructural tract atrophy) within atlas-based white matter tracts at each visit. At baseline, all fixel-based measures were significantly worse in multiple sclerosis compared with healthy controls (P < 0.05). For both fibre density and fibre cross-section, a similar pattern was observed, with secondary progressive multiple sclerosis patients having the most severe damage, followed by primary progressive and relapsing-remitting multiple sclerosis. Similarly, damage was least severe in cognitively preserved (n = 177), more severe in mildly cognitively impaired (n = 63) and worst in cognitively impaired (n = 87; P < 0.05). Microstructural damage was most pronounced in the cingulum, while macrostructural alterations were most pronounced in the corticospinal tract, cingulum and superior longitudinal fasciculus. Over time, white matter alterations worsened most severely in progressive multiple sclerosis (P < 0.05), with white matter atrophy progression mainly seen in the corticospinal tract and microstructural axonal damage worsening in cingulum and superior longitudinal fasciculus. Cognitive decline at follow-up could be predicted by baseline fixel-based measures (R2 = 0.45, P < 0.001). Fixel-based approaches are sensitive to white matter degeneration patterns in multiple sclerosis and can have strong predictive value for cognitive impairment. Longitudinal deterioration was most marked in progressive multiple sclerosis, indicating that degeneration in white matter remains important to characterize further in this phenotype.

A more unstable resting-state functional network in cognitively declining multiple sclerosis.

Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In a healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remain particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration: 15 years) and at 5-year follow-up. A sliding-window approach was used for functional MRI analyses, where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks were characterized using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥2/7 domains Z < -2) and 18% were mildly impaired (≥2/7 domains Z < -1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared with cognitively preserved patients and controls, i.e. showing higher promiscuity (P = 0.047), flexibility (P = 0.008) and cohesion (P = 0.008). Over time, cognitively declining patients showed a further increase in cohesion (P = 0.004), which was not seen in stable patients (P = 0.544). More cohesion was related to more severe structural damage (average r = 0.166, P = 0.015) and worse verbal memory (r = -0.156, P = 0.022), information processing speed (r = -0.202, P = 0.003) and working memory (r = -0.163, P = 0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that showed cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis.

OBJECTIVE: To characterize functional network changes related to conversion to cognitive impairment in a large sample of patients with multiple sclerosis (MS) over a period of 5 years. METHODS: Two hundred twenty-seven patients with MS and 59 healthy controls of the Amsterdam MS cohort underwent neuropsychological testing and resting-state fMRI at 2 time points (time interval 4.9 ± 0.9 years). At both baseline and follow-up, patients were categorized as cognitively preserved (CP; n = 123), mildly impaired (MCI; z < -1.5 on ≥2 cognitive tests, n = 32), or impaired (CI; z < -2 on ≥2 tests, n = 72), and longitudinal conversion between groups was determined. Network function was quantified with eigenvector centrality, a measure of regional network importance, which was computed for individual resting-state networks at both time points. RESULTS: Over time, 18.9% of patients converted to a worse phenotype; 22 of 123 patients who were CP (17.9%) converted from CP to MCI, 10 of 123 from CP to CI (8.1%), and 12 of 32 patients with MCI converted to CI (37.5%). At baseline, default-mode network (DMN) centrality was higher in CI individuals compared to controls (p = 0.05). Longitudinally, ventral attention network (VAN) importance increased in CP, driven by stable CP and CP-to-MCI converters (p < 0.05). CONCLUSIONS: Of all patients, 19% worsened in their cognitive status over 5 years. Conversion from intact cognition to impairment is related to an initial disturbed functioning of the VAN, then shifting toward DMN dysfunction in CI. Because the VAN normally relays information to the DMN, these results could indicate that in MS normal processes crucial for maintaining overall network stability are progressively disrupted as patients clinically progress.

The sequence of structural, functional and cognitive changes in multiple sclerosis.

BACKGROUND: As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. METHODS: We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. RESULTS: We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. CONCLUSION: Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.

Cortical atrophy accelerates as cognitive decline worsens in multiple sclerosis.

OBJECTIVE: To determine which pathologic process could be responsible for the acceleration of cognitive decline during the course of multiple sclerosis (MS), using longitudinal structural MRI, which was related to cognitive decline in relapsing-remitting MS (RRMS) and progressive MS (PMS). METHODS: A prospective cohort of 230 patients with MS (179 RRMS and 51 PMS) and 59 healthy controls was evaluated twice with 5-year (mean 4.9, SD 0.94) interval during which 22 patients with RRMS converted to PMS. Annual rates of cortical and deep gray matter atrophy as well as lesion volume increase were computed on longitudinal (3T) MRI data and correlated to the annual rate of cognitive decline as measured using an extensive cognitive evaluation at both time points. RESULTS: The deep gray matter atrophy rate did not differ between PMS and RRMS (-0.82%/year vs -0.71%/year, p = 0.11), while faster cortical atrophy was observed in PMS (-0.87%/year vs -0.48%/year, p < 0.01). Similarly, faster cognitive decline was observed in PMS compared to RRMS (p < 0.01). Annual cognitive decline was related to the rate of annual lesion volume increase in stable RRMS (r = -0.17, p = 0.03) to the rate of annual deep gray matter atrophy in converting RRMS (r = 0.50, p = 0.02) and annual cortical atrophy in PMS (r = 0.35, p = 0.01). CONCLUSIONS: These results indicate that cortical atrophy and cognitive decline accelerate together during the course of MS. Substrates of cognitive decline shifted from worsening lesional pathology in stable RRMS to deep gray matter atrophy in converting RRMS and to accelerated cortical atrophy in PMS only.

Reduced Network Dynamics on Functional MRI Signals Cognitive Impairment in Multiple Sclerosis.

Background Previous studies have demonstrated extensive functional network disturbances in patients with multiple sclerosis (MS), showing a less efficient brain network. Recent studies indicate that the dynamic properties of the brain network show a strong correlation with cognitive function. Purpose To investigate network dynamics on functional MRI in cognitively impaired patients with MS. Materials and Methods In secondary analysis of prospectively acquired data, with imaging performed between 2008 and 2012, differences in regional functional network dynamics (ie, eigenvector centrality dynamics) between cognitively impaired and cognitively preserved participants with MS were investigated. Functional network dynamics were computed on images from functional MRI (3 T) by using a sliding-window approach. Cognitively impaired and preserved groups were compared by using a clusterwise permutation-based method. Results The study included 96 healthy control subjects and 332 participants with MS (including 226 women and 106 men; median age, 48.1 years ± 11.0). Among the 332 participants with MS, 87 were cognitively impaired and 180 had preserved cognitive function; mildly impaired patients (n = 65) were excluded. The cognitively impaired group included a higher proportion of men compared with the cognitively preserved group (35 of 87 [40%] vs 48 of 180 [27%], respectively; P = .02) and had a higher mean age (51.1 years vs 46.3 years, respectively; P < .01). The clusterwise permutation-based comparison at P less than .05 showed reduced centrality dynamics in default-mode, frontoparietal, and visual network regions on functional MRI in cognitively impaired participants versus cognitively preserved participants. A subsequent correlation and hierarchical clustering analysis revealed that the default-mode and visual networks normally demonstrate negatively correlated fluctuations in functional importance (r = -0.23 in healthy control subjects), with an almost complete loss of this negative correlation in cognitively impaired participants compared with cognitively preserved participants (r = -0.04 vs r = -0.14; corrected P = .02). Conclusion As shown on functional MRI, cognitively impaired patients with multiple sclerosis not only demonstrate reduced dynamics in default-mode, frontoparietal, and visual networks, but also show a loss of interplay between default-mode and visual networks. © RSNA, 2019 Online supplemental material is available for this article. See also the article by Eijlers et al and the editorial by Zivadinov and Dwyer in this issue.

No Plasmatic Proteomic Signature at Clinical Disease Onset Associated With 11 Year Clinical, Cognitive and MRI Outcomes in Relapsing-Remitting Multiple Sclerosis Patients.

Background: The clinical course of relapsing-remitting multiple sclerosis (RRMS) is highly heterogeneous and prognostic biomarkers at time of diagnosis are lacking. Objective: We investigated the predictive value of the plasma proteome at time of diagnosis in RRMS patients. Methods: The plasma proteome was interrogated using a novel aptamer-based proteomics platform, which allows to measure the levels of a predefined set of 1310 proteins. Results: In 67 clinically and radiologically well characterized RRMS patients, we found no association between the plasma proteome at diagnosis and clinical, cognitive or MRI outcomes after 11 years. Conclusions: Proteomics studies on cerebrospinal fluid may be better suited to identify prognostic biomarkers in early RRMS.

Predicting cognitive decline in multiple sclerosis: a 5-year follow-up study.

Cognitive decline is common in multiple sclerosis and strongly affects overall quality of life. Despite the identification of cross-sectional MRI correlates of cognitive impairment, predictors of future cognitive decline remain unclear. The objective of this study was to identify which MRI measures of structural damage, demographic and/or clinical measures at baseline best predict cognitive decline, during a 5-year follow-up period. A total of 234 patients with clinically definite multiple sclerosis and 60 healthy control subjects were examined twice, with a 5-year interval (mean = 4.9 years, standard deviation = 0.9). An extensive neuropsychological evaluation was performed at both time points and the reliable change index was computed to evaluate cognitive decline. Both whole-brain and regional MRI (3 T) measures were assessed at baseline, including white matter lesion volume, diffusion-based white matter integrity, cortical and deep grey matter volume. Logistic regression analyses were performed to determine which baseline measures best predicted cognitive decline in the entire sample as well as in early relapsing-remitting (symptom duration <10 years), late relapsing-remitting (symptom duration ≥10 years) and progressive phenotypes. At baseline, patients with multiple sclerosis had a mean disease duration of 14.8 (standard deviation = 8.4) years and 96/234 patients (41%) were classified as cognitively impaired. A total of 66/234 patients (28%) demonstrated cognitive decline during follow-up, with higher frequencies in progressive compared to relapsing-remitting patients: 18/33 secondary progressive patients (55%), 10/19 primary progressive patients (53%) and 38/182 relapsing-remitting patients (21%). A prediction model that included only whole-brain MRI measures (Nagelkerke R2 = 0.22, P < 0.001) showed cortical grey matter volume as the only significant MRI predictor of cognitive decline, while a prediction model that assessed regional MRI measures (Nagelkerke R2 = 0.35, P < 0.001) indicated integrity loss of the anterior thalamic radiation, lesions in the superior longitudinal fasciculus and temporal atrophy as significant MRI predictors for cognitive decline. Disease stage specific regressions showed that cognitive decline in early relapsing-remitting multiple sclerosis was predicted by white matter integrity damage, while cognitive decline in late relapsing-remitting and progressive multiple sclerosis was predicted by cortical atrophy. These results indicate that patients with more severe structural damage at baseline, and especially cortical atrophy, are more prone to suffer from cognitive decline. New studies now need to further elucidate the underlying mechanisms leading to cortical atrophy, evaluate the value of including cortical atrophy as a possible outcome marker in clinical trials as well as study its potential use in individual patient management.

Determinants of Cognitive Impairment in Patients with Multiple Sclerosis with and without Atrophy.

Purpose To investigate the discrepancy between patients with multiple sclerosis (MS) without atrophy who have already developed cognitive impairment and patients with MS with atrophy who have preserved cognitive function. Materials and Methods This retrospective imaging study, with imaging acquired between 2008 and 2012, included 332 patients with MS (106 men and 226 women; mean age, 48.1 years; range, 23.0-72.5 years) and 96 healthy control participants. Cognitive impairment was defined as cognitive performance of z less than -1.5 compared with that in control participants in greater than or equal to two cognitive domains. Atrophy was defined as cortical and deep gray matter volumes of z less than -1.5 compared with that in control participants. White matter lesions were assessed with T2-imaging, tract fractional anisotropy (ie, integrity) with diffusion MRI, and regional centrality (ie, importance within network) with functional MRI. Within each atrophy group, patients with cognitive impairment and preserved cognitive function were compared and regression analyses were performed to predict cognitive impairment. Results A total of 132 of 328 patients with MS had no atrophy; of these, 42 of 132 (32%) had cognitive impairment. Cognitive impairment in patients without atrophy was predicted by level of education (Wald test, 11.63; P < .01) and posterior cingulate centrality (Wald test, 6.82; P < .01). A total of 65 of 328 patients with MS had atrophy; of these, 49 of 65 (75%) had cognitive impairment. Cognitive impairment in patients with atrophy was predicted by white matter tract fractional anisotropy (Wald test, 4.89; P = .03) and posterior cingulate centrality (Wald test, 7.19; P < .01). Conclusion Cognitive impairment was related to white matter damage, but only in patients with MS with atrophy. In patients without atrophy, a lower level of education was most important for cognitive impairment. Posterior cingulate cortex showed functional abnormalities in all MS groups with cognitive impairment, regardless of atrophy.

Staging of cortical and deep grey matter functional connectivity changes in multiple sclerosis.

OBJECTIVE: Functional connectivity is known to increase as well as decrease throughout the brain in multiple sclerosis (MS), which could represent different stages of the disease. In addition, functional connectivity changes could follow the atrophy pattern observed with disease progression, that is, moving from the deep grey matter towards the cortex. This study investigated when and where connectivity changes develop and explored their clinical and cognitive relevance across different MS stages. METHODS: A cohort of 121 patients with early relapsing-remitting MS (RRMS), 122 with late RRMS and 53 with secondary progressive MS (SPMS) as well as 96 healthy controls underwent MRI and neuropsychological testing. Functional connectivity changes were investigated for (1) within deep grey matter connectivity, (2) connectivity between the deep grey matter and cortex and (3) within-cortex connectivity. A post hoc regional analysis was performed to identify which regions were driving the connectivity changes. RESULTS: Patients with late RRMS and SPMS showed increased connectivity of the deep grey matter, especially of the putamen and palladium, with other deep grey matter structures and with the cortex. Within-cortex connectivity was decreased, especially for temporal, occipital and frontal regions, but only in SPMS relative to early RRMS. Deep grey matter connectivity alterations were related to cognition and disability, whereas within-cortex connectivity was only related to disability. CONCLUSION: Increased connectivity of the deep grey matter became apparent in late RRMS and further increased in SPMS. The additive effect of cortical network degeneration, which was only seen in SPMS, may explain the sudden clinical deterioration characteristic to this phase of the disease.

Increased connectivity of hub networks and cognitive impairment in multiple sclerosis.

OBJECTIVE: To investigate default-mode network (DMN) and frontoparietal network (FPN) dysfunction in cognitively impaired (CI) patients with multiple sclerosis (MS) because these networks strongly relate to cognition and contain most of the hubs of the brain. METHODS: Resting-state fMRI and neuropsychological assessments were performed in 322 patients with MS and 96 healthy controls (HCs). Patients with MS were classified as CI (z score < -2.0 on at least 2 tests; n = 87), mildly cognitively impaired (z score < -1.5 on at least 2 tests and not CI; n = 65), and cognitively preserved (CP; n = 180). Within-network connectivity, connectivity with the rest of the brain, and between-network connectivity were calculated and compared between groups. Connectivity values were normalized for individual means and SDs. RESULTS: Only in CI, both the DMN and FPN showed increased connectivity with the rest of the brain compared to HCs and CP, with no change in within- or between-network connectivity. Regionally, this increased connectivity was driven by the inferior parietal, posterior cingulate, and angular gyri. Increased connectivity with the rest of the brain correlated with worse cognitive performance, namely attention for the FPN as well as information processing speed and working memory for both networks. CONCLUSIONS: In CI patients with MS, the DMN and FPN showed increased connectivity with the rest of the brain, while normal within- and between-network connectivity levels were maintained. These findings indicate that cognitive impairment in MS features disturbed communication of hub-rich networks, but only with the more peripheral (i.e., nonhub) regions of the brain.

Increased default-mode network centrality in cognitively impaired multiple sclerosis patients.

OBJECTIVE: To investigate how changes in functional network hierarchy determine cognitive impairment in multiple sclerosis (MS). METHODS: A cohort consisting of 332 patients with MS (age 48.1 ± 11.0 years, symptom duration 14.6 ± 8.4 years) and 96 healthy controls (HCs; age 45.9 ± 10.4 years) underwent structural MRI, fMRI, and extensive neuropsychological testing. Patients were divided into 3 groups: cognitively impaired (CI; n = 87), mildly cognitively impaired (MCI; n = 65), and cognitively preserved (CP; n = 180). The functional importance of brain regions was quantified with degree centrality, the average strength of the functional connections of a brain region with the rest of the brain, and eigenvector centrality, which adds to this concept by adding additional weight to connections with brain hubs because these are known to be especially important. Centrality values were calculated for each gray matter voxel based on resting-state fMRI data, registered to standard space. Group differences were assessed with a cluster-wise permutation-based method corrected for age, sex, and education. RESULTS: CI patients demonstrated widespread centrality increases compared to both HCs and CP patients, mainly in regions making up the default-mode network. Centrality decreases were similar in all patient groups compared to HCs, mainly in occipital and sensorimotor areas. Results were robust across centrality measures. CONCLUSIONS: Patients with MS with cognitive impairment show hallmark alterations in functional network hierarchy with increased relative importance (centrality) of the default-mode network.