Altered topological properties of brain networks in the early MS patients revealed by cognitive task-related fMRI and graph theory. (February 2018)

• Record Type: Journal Article
• Authors: Miri Ashtiani, Seyedeh Naghmeh; Daliri, Mohammad Reza; Behnam, Hamid; Hossein-Zadeh, Gholam-Ali; Mehrpour, Masoud; Motamed, Mohammad Reza; Fadaie, Fatemeh
• Is Part Of: Biomedical signal processing and control. Volume 40(2018)
• Page Start: 385
• Page End: 395
• Publication Date: 2018-02
• Subjects: Cognitive deficits -- Early phase of multiple sclerosis (MS) disease -- Cognitive task-related functional MRI -- Graph theory -- Informative brain regions -- Discriminative graph measures; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.10.006
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A cognitive task-related functional connectivity changes in MS has been evaluated. A task-based fMRI data in combination with graph theory analysis has been used. A link between modularity and clustering with cognition has been observed. Sets of informative brain areas involved in cognitive disorders in MS was detected. The potential of applying graph analysis on task data to reflect cognitive deficit was shown. Abstract: 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.