Kun Bi1, Mohammad Ridwan Chattun2, Xiaoxue Liu2, Qiang Wang3, Shui Tian1, Siqi Zhang1, Qing Lu4, Zhijian Yao5. 1. School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing 210096, China. 2. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China. 3. Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China. 4. School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing 210096, China. Electronic address: luq@seu.edu.cn. 5. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China. Electronic address: zjyao@njmu.edu.cn.
Abstract
BACKGROUND: The functional networks are associated with emotional processing in depression. The mapping of dynamic spatio-temporal brain networks is used to explore individual performance during early negative emotional processing. However, the dysfunctions of functional networks in low gamma band and their discriminative potentialities during early period of emotional face processing remain to be explored. METHODS: Functional brain networks were constructed from the MEG recordings of 54 depressed patients and 54 controls in low gamma band (30-48 Hz). Dynamic connectivity regression (DCR) algorithm analyzed the individual change points of time series in response to emotional stimuli and constructed individualized spatio-temporal patterns. The nodal characteristics of patterns were calculated and fed into support vector machine (SVM). Performance of the classification algorithm in low gamma band was validated by dynamic topological characteristics of individual patterns in comparison to alpha and beta band. RESULTS: The best discrimination accuracy of individual spatio-temporal patterns was 91.01% in low gamma band. Individual temporal patterns had better results compared to group-averaged temporal patterns in all bands. The most important discriminative networks included affective network (AN) and fronto-parietal network (FPN) in low gamma band. LIMITATIONS: The sample size is relatively small. High gamma band was not considered. CONCLUSIONS: The abnormal dynamic functional networks in low gamma band during early emotion processing enabled depression recognition. The individual information processing is crucial in the discovery of abnormal spatio-temporal patterns in depression during early negative emotional processing. Individual spatio-temporal patterns may reflect the real dynamic function of subjects while group-averaged data may neglect some individual information.
BACKGROUND: The functional networks are associated with emotional processing in depression. The mapping of dynamic spatio-temporal brain networks is used to explore individual performance during early negative emotional processing. However, the dysfunctions of functional networks in low gamma band and their discriminative potentialities during early period of emotional face processing remain to be explored. METHODS: Functional brain networks were constructed from the MEG recordings of 54 depressedpatients and 54 controls in low gamma band (30-48 Hz). Dynamic connectivity regression (DCR) algorithm analyzed the individual change points of time series in response to emotional stimuli and constructed individualized spatio-temporal patterns. The nodal characteristics of patterns were calculated and fed into support vector machine (SVM). Performance of the classification algorithm in low gamma band was validated by dynamic topological characteristics of individual patterns in comparison to alpha and beta band. RESULTS: The best discrimination accuracy of individual spatio-temporal patterns was 91.01% in low gamma band. Individual temporal patterns had better results compared to group-averaged temporal patterns in all bands. The most important discriminative networks included affective network (AN) and fronto-parietal network (FPN) in low gamma band. LIMITATIONS: The sample size is relatively small. High gamma band was not considered. CONCLUSIONS: The abnormal dynamic functional networks in low gamma band during early emotion processing enabled depression recognition. The individual information processing is crucial in the discovery of abnormal spatio-temporal patterns in depression during early negative emotional processing. Individual spatio-temporal patterns may reflect the real dynamic function of subjects while group-averaged data may neglect some individual information.
Authors: Abdullah A Mosabbir; Thenile Braun Janzen; Maryam Al Shirawi; Susan Rotzinger; Sidney H Kennedy; Faranak Farzan; Jed Meltzer; Lee Bartel Journal: Cureus Date: 2022-02-24
Authors: Bashkim Kadriu; Laura Musazzi; Jenessa N Johnston; Lisa E Kalynchuk; Hector J Caruncho; Maurizio Popoli; Carlos A Zarate Journal: Drug Discov Today Date: 2021-08-03 Impact factor: 8.369