Functional brain abnormalities in major depressive disorder using a multiscale community detection approach. (1st October 2022)

• Record Type: Journal Article
• Authors: Li, Na; Jin, Di; Wei, Jianguo; Huang, Yuxiao; Xu, Junhai
• Is Part Of: Neuroscience. Volume 501(2022)
• Page Start: 1
• Page End: 10
• Publication Date: 2022-10-01
• Subjects: Major depressive disorder -- Multiscale community detection -- Qcut -- Functional magnetic resonance imaging -- The Jaccard Index; DAN dorsal attention network -- DMN default mode network -- FPN frontoparietal network -- MDD Major depressive disorder -- NC normal controls -- NMI normalized mutual information -- SEN sensorimotor network -- VAN ventral attention network -- VI variation of information -- VIN visual network; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2022.08.007
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: Major depressive disorder (MDD) is associated with abnormal brain regions. A novel multiscale community detection method was proposed. Differences in brain regions between normal controls (NC) and MDD patients were compared. The proposed method is useful in detecting functional brain abnormalities in MDD. Abstract: Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.