Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications.
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ABSTRACT:
SUBMITTER: Carotenuto A
PROVIDER: S-EPMC7320961 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications.
Carotenuto Antonio A Wilson Heather H Giordano Beniamino B Caminiti Silvia P SP Chappell Zachary Z Williams Steven C R SCR Hammers Alexander A Silber Eli E Brex Peter P Politis Marios M
Journal of neurology 20200326 7
There is mounting evidence regarding the role of impairment in neuromodulatory networks for neurodegenerative diseases, such as Parkinson's and Alzheimer's disease. However, the role of neuromodulatory networks in multiple sclerosis (MS) has not been assessed. We applied resting-state functional connectivity and graph theory to investigate the changes in the functional connectivity within neuromodulatory networks including the serotonergic, noradrenergic, cholinergic, and dopaminergic systems in ...[more]
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Project description:Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.
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Project description:Objective: Multiple sclerosis (MS) is characterized by impairments in basic cognitive functions such as information processing speed as well as in more complex, higher-order domains such as social cognition. However, as these deficits often co-occur, it has remained challenging to determine whether they have a specific pathological basis or are driven by shared biology. Methods: To identify neural signatures of social cognition deficits in MS, data were analyzed from n = 29 patients with relapsing-remitting MS and n = 29 healthy controls matched for age, sex, and education. We used neuropsychological assessments of information processing speed, attention, learning, working memory, and relevant aspects of social cognition (theory of mind, emotion recognition (ER), empathy) and employed neuroimaging of CNS networks using resting-state functional connectivity. Results: MS patients showed significant deficits in verbal learning and memory, as well as implicit ER. Performance in these domains was uncorrelated. Functional connectivity analysis identified a distinct network characterized by significant associations between poorer ER and lower connectivity of the fusiform gyrus (FFG) with the right lateral occipital cortex, which also showed lower connectivity in patients compared to controls. Moreover, while ER was correlated with MS symptoms such as fatigue and motor/sensory functioning on a behavioral level, FFG connectivity signatures of social cognition deficits showed no overlap with these symptoms. Conclusions: Our analyses identify distinct functional connectivity signatures of social cognition deficits in MS, indicating that these alterations may occur independently from those in other neuropsychological functions.
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Project description:Multiple sclerosis (MS) is an inflammatory-demyelinating and neurodegenerative disease of the central nervous system (CNS) and the most frequent cause of non-traumatic disability in young and middle-age adults. Although conventional MRI (including T2-weighted, pre- and post-contrast T1-weighted scans) has had a huge impact on MS by enabling an earlier diagnosis, and by providing surrogate markers for monitoring treatment response, it is limited by the low pathological specificity and the low sensitivity to diffuse damage in normal-appearing white matter and gray matter. Diffusion weighted MRI is a quantitative technique able to overcome these limitations by providing markers more specific to the underlying pathologic substrates and more sensitive to the full extent of 'occult' brain tissue damage. This review describes diffusion studies in MS, discusses their pathophysiological implications and emphasizes their clinical relevance.
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Project description:Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.
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Project description:BackgroundImpaired eye movements in multiple sclerosis (MS) are common and could represent a non-invasive and accurate measure of (dys)functioning of interconnected areas within the complex brain network. The aim of this study was to test whether altered saccadic eye movements are related to changes in functional connectivity (FC) in patients with MS.MethodsCross-sectional eye movement (pro-saccades and anti-saccades) and magnetoencephalography (MEG) data from the Amsterdam MS cohort were included from 176 MS patients and 33 healthy controls. FC was calculated between all regions of the Brainnetome atlas in six conventional frequency bands. Cognitive function and disability were evaluated by previously validated measures. The relationships between saccadic parameters and both FC and clinical scores in MS patients were analysed using multivariate linear regression models.ResultsIn MS pro- and anti-saccades were abnormal compared to healthy controls A relationship of saccadic eye movements was found with FC of the oculomotor network, which was stronger for regional than global FC. In general, abnormal eye movements were related to higher delta and theta FC but lower beta FC. Strongest associations were found for pro-saccadic latency and FC of the precuneus (beta band β = -0.23, p = .006), peak velocity and FC of the parietal eye field (theta band β = -0.25, p = .005) and gain and FC of the inferior frontal eye field (theta band β = -0.25, p = .003). Pro-saccadic latency was also strongly associated with disability scores and cognitive dysfunction.ConclusionsImpaired saccadic eye movements were related to functional connectivity of the oculomotor network and clinical performance in MS. This study also showed that, in addition to global network connectivity, studying regional changes in MEG studies could yield stronger correlations.
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