چكيده به لاتين
Cognitive deficits are frequently found in the early phases of Multiple Sclerosis (MS) disease. Cognitive dysfunction is the result of structural lesions in the brains of patients with MS, which could impress the brain functional connectivity. Graph theoretical methods in combination with fMRI 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. In the present study, we aimed to identify the brain connectivity pattern alterations during a demanding cognitive task and detect the significant features for the distinction of MS patients from healthy controls (HC). In this regard, the importance of functional brain network construction for the better exhibition of changes inducing the improved reflection of functional organization structure should be precisely considered. In this study, the modified Paced Auditory Serial Addition Task (PASAT) was presented to the subjects in an fMRI study in a 3.0 T MRI scanner. At first, the adjacency matrices constructed by proportional thresholding of the Pearson correlation-based connectivity networks. Then, the network characteristics were studied in patients with relapsing-remitting MS (RRMS) in the early stages and matched HC through computing the different types of global and regional graph measures. We observed a link between modular structure, clustering, and small-world index with cognition in task-based brain state. We also detected sets of informative brain areas like superior temporo-polar gyrus, right putamen, fusiform gyrus, hippocampus, parahippocampal gyri, amygdala, and some parts of cerebellar which are affected by cognitive impairments in the early phases of MS disease. In the following, 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 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 of the two groups by considering the pairwise connectivity weights of regions and extracting graph-based measures. The experimental findings proved the high 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. Our findings also illustrated that integration of sparse representation and modular structure by applying pairwise connectivity strength and graph properties could lead to early diagnosis of cognitive alterations in the case of MS with the identification of reliable markers and informative brain regions.
