CNS neuroscience & therapeutics · 2026
Altered Salience-Default Mode Network Dynamics in Subclinical Depression: A Preclustering-Based Co-Activation Pattern Analysis.
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Zhang Bo, Yu Zhinan, Yan Feifan, Sun Yiwei, Ye Jiao, Liu Xiaoya, Qi Shouliang, Wei Xinhua, Liu Shuang, Ming Dong
Аннотация
BACKGROUND: Neuroimaging studies frequently report aberrant spontaneous brain activity and functional connectivity within core functional networks, including the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in subclinical depression (SD). However, the dynamic coordination among these networks remains poorly understood, impeding comprehensive elucidation of the underlying neuropathology of SD.
METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (n = 26) and healthy controls (HCs, n = 33). A preclustering-based co-activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.
RESULTS: Subjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN-DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.
CONCLUSION: These findings underscore the potential of altered SN-DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN-DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self-related depressive symptoms like rumination.
METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (n = 26) and healthy controls (HCs, n = 33). A preclustering-based co-activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.
RESULTS: Subjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN-DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.
CONCLUSION: These findings underscore the potential of altered SN-DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN-DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self-related depressive symptoms like rumination.