TY - JOUR
T1 - Resting-state dynamic functional connectivity in major depressive disorder
T2 - A systematic review
AU - Sun, Shuting
AU - Yan, Chang
AU - Qu, Shanshan
AU - Luo, Gang
AU - Liu, Xuesong
AU - Tian, Fuze
AU - Dong, Qunxi
AU - Li, Xiaowei
AU - Hu, Bin
N1 - Publisher Copyright:
© 2023
PY - 2024/12/20
Y1 - 2024/12/20
N2 - As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
AB - As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
KW - Classification
KW - Depression
KW - Dynamic functional connectivity
KW - Graph theory
KW - Major depressive disorder
KW - Resting-state
UR - http://www.scopus.com/inward/record.url?scp=85199942215&partnerID=8YFLogxK
U2 - 10.1016/j.pnpbp.2024.111076
DO - 10.1016/j.pnpbp.2024.111076
M3 - Review article
C2 - 38972502
AN - SCOPUS:85199942215
SN - 0278-5846
VL - 135
JO - Progress in Neuro-Psychopharmacology and Biological Psychiatry
JF - Progress in Neuro-Psychopharmacology and Biological Psychiatry
M1 - 111076
ER -