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Graph theory allows us to gain better insight concerning changes in brain functional architecture associated with cognitive impairments in the early stages of multiple sclerosis (MS). In the present study, we employed a machine-learning system based on graph measures from functional networks constructed by cognitive task-related functional magnetic resonance imaging (fMRI) data. We used a predefined atlas to define the brain regions and Pearson’s correlation to describe the connectivity strength between the regions. Then, several graph metrics were extracted for each subject. After that, the most efficient subsets of features were selected through the Wilcoxon rank-sum test, and the linear support vector machine (SVM) classifier was employed to distinguish between MS and healthy subjects. The node degree, subgraph centrality, K-coreness, and PageRank centralities measured in the left fusiform, hippocampus, and parahippocampal gyri regions demonstrated an accuracy of 85% through the combination of all local measures. Two optimal global measures, modularity and small-worldness index, and individual betweenness centrality feature improved the identification of MS patients with a sensitivity of 81.25%. Our results indicated the potential of the proposed system to identify cognitive changes in early MS for diagnostic purposes.

Cognitive dysfunction or physical impairment is the result of structural lesions in the brains of patients with Multiple Sclerosis (MS), which could impress the brain functional connectivity. Cognitive deficits are frequently found in the early phases of MS disease. The changes in brain functional connectivity associated with cognitive tasks can be detected through blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). In the present study, we evaluated a set of task-related fMRI data in combination with graph theory analysis. The modified Paced Auditory Serial Addition Task (PASAT) was presented to the subjects in an fMRI study in a 3.0 T MRI scanner. Graph theoretical methods allow us to model the brain networks for the identification of functional connectivity patterns in various conditions and to assess the topological properties of brain networks. The adjacency matrices constructed by proportional thresholding of the Pearson correlation-based connectivity networks were studied in patients with relapsing-remitting MS (RRMS) in the early stages and matched healthy controls (HC) through computing the different types of global and regional graph measures. We compared the extracted graph properties to investigate significant cognitive-related alterations in network characteristics between the early MS patients and the controls. We observed a link between functional modularity and clustering with cognition in task-based brain state. We also detected sets of informative brain areas involved in cognitive dysfunction that could distinguish MS patients from the healthy controls in most of local graph measures. It seems that the regions of superior temporo-polar gyrus, right putamen, fusiform gyrus, and some parts of limbic system such as hippocampus, parahippocampal gyri, and amygdala are the brain areas which are affected by cognitive impairment in early phases of MS disease. Our findings demonstrated the potential of applying graph analysis on task-related fMRI data to reflect the cognitive disorders in the early stages of MS.

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.

Cognitive dysfunction in multiple sclerosis (MS) seems to be the result of neural disconnections, leading to a wide range of brain functional network alterations. It is assumed that the analysis of the topological structure of brain connectivity network can be used to assess cognitive impairments in MS disease. We aimed to identify these brain connectivity pattern alterations and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain networks construction for better exhibition of changes, inducing the improved reflection of functional organization structure should be precisely considered. In this paper, we strove to introduce a framework for modeling the functional connectivity network by considering the two most important intrinsic sparse and modular structures of brain. For the proposed approach, we first derived group-wise sparse representation via learning a common over-complete dictionary matrix from the aggregated cognitive task-based functional magnetic resonance imaging (fMRI) data of all subjects of the two groups to be able to investigate between-group differences. We then applied the modularity concept on achieved sparse coefficients to compute the connectivity strength between the two brain regions. We examined the changes in network topological properties between relapsing-remitting MS (RRMS) and matched HC groups by considering the pairwise connections of regions of the resulted weighted networks and extracting graph-based measures. We found that the informative brain regions were related to their important connectivity weights, which could distinguish MS patients from the healthy controls. The experimental findings also proved the discrimination ability of the modularity measure among all the global features. In addition, we identified such local feature subsets as eigenvector centrality, eccentricity, node strength, and within-module degree, which significantly differed between the two groups. Moreover, these nodal graph measures have been served as the detectors of brain regions, affected by different cognitive deficits. In general, our findings illustrated that integration of sparse representation, modular structure, and pairwise connectivity strength in combination with the graph properties could help us with the early diagnosis of cognitive alterations in the case of MS.