Kun Bi1, Lingling Hua2, Maobin Wei1, Jiaolong Qin1, Qing Lu3, Zhijian Yao4. 1. Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China. 2. Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China. 3. Key Laboratory of Child Development and Learning Science, Research Centre for Learning Science, Southeast University, Nanjing 210096, China; Suzhou Research Institute of Southeast University, 399 Linquan Street, Suzhou 215123, China. Electronic address: luq@seu.edu.cn. 4. Medical School, Nanjing University, 22 Hankou Road, Nanjing 210093, China; Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China. Electronic address: zjyao@njmu.edu.cn.
Abstract
BACKGROUND: Dynamic functional-structural connectivity (FC-SC) coupling might reflect the flexibility by which SC relates to functional connectivity (FC). However, during the dynamic acute state change phases of FC, the relationship between FC and SC may be distinctive and embody the abnormality inherent in depression. This study investigated the depression-related inter-network FC-SC coupling within particular dynamic acute state change phases of FC. METHODS: Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data were collected from 26 depressive patients (13 women) and 26 age-matched controls (13 women). We constructed functional brain networks based on MEG data and structural networks from DTI data. The dynamic connectivity regression algorithm was used to identify the state change points of a time series of inter-network FC. The time period of FC that contained change points were partitioned into types of dynamic phases (acute rising phase, acute falling phase,acute rising and falling phase and abrupt FC variation phase) to explore the inter-network FC-SC coupling. The selected FC-SC couplings were then fed into the support vector machine (SVM) for depression recognition. RESULTS: The best discrimination accuracy was 82.7% (P=0.0069) with FC-SC couplings, particularly in the acute rising phase of FC. Within the FC phases of interest, the significant discriminative network pair was related to the salience network vs ventral attention network (SN-VAN) (P=0.0126) during the early rising phase (70-170ms). LIMITATIONS: This study suffers from a small sample size, and the individual acute length of the state change phases was not considered. CONCLUSIONS: The increased values of significant discriminative vectors of FC-SC coupling in depression suggested that the capacity to process negative emotion might be more directly related to the SC abnormally and be indicative of more stringent and less dynamic brain function in SN-VAN, especially in the acute rising phase of FC. We demonstrated that depressive brain dysfunctions could be better characterized by reduced FC-SC coupling flexibility in this particular phase.
BACKGROUND: Dynamic functional-structural connectivity (FC-SC) coupling might reflect the flexibility by which SC relates to functional connectivity (FC). However, during the dynamic acute state change phases of FC, the relationship between FC and SC may be distinctive and embody the abnormality inherent in depression. This study investigated the depression-related inter-network FC-SC coupling within particular dynamic acute state change phases of FC. METHODS: Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data were collected from 26 depressivepatients (13 women) and 26 age-matched controls (13 women). We constructed functional brain networks based on MEG data and structural networks from DTI data. The dynamic connectivity regression algorithm was used to identify the state change points of a time series of inter-network FC. The time period of FC that contained change points were partitioned into types of dynamic phases (acute rising phase, acute falling phase,acute rising and falling phase and abrupt FC variation phase) to explore the inter-network FC-SC coupling. The selected FC-SC couplings were then fed into the support vector machine (SVM) for depression recognition. RESULTS: The best discrimination accuracy was 82.7% (P=0.0069) with FC-SC couplings, particularly in the acute rising phase of FC. Within the FC phases of interest, the significant discriminative network pair was related to the salience network vs ventral attention network (SN-VAN) (P=0.0126) during the early rising phase (70-170ms). LIMITATIONS: This study suffers from a small sample size, and the individual acute length of the state change phases was not considered. CONCLUSIONS: The increased values of significant discriminative vectors of FC-SC coupling in depression suggested that the capacity to process negative emotion might be more directly related to the SC abnormally and be indicative of more stringent and less dynamic brain function in SN-VAN, especially in the acute rising phase of FC. We demonstrated that depressive brain dysfunctions could be better characterized by reduced FC-SC coupling flexibility in this particular phase.
Authors: Allison C Nugent; Cristan Farmer; Jennifer W Evans; Sam L Snider; Dipavo Banerjee; Carlos A Zarate Journal: Hum Brain Mapp Date: 2019-06-09 Impact factor: 5.038