The characterization of static and dynamic brain functional networks in suicide attempters with major depressive disorder and its relation to psychological pain

Exploring the neurocognitive correlates of suicidal ideation in major depressive disorder: The role of frontoparietal and default mode networks

Suicidal ideation (SI) is a common symptom of major depressive disorder (MDD), often accompanied by cognitive alterations and emotional dysregulation. However, it is unclear whether cognitive dysfunction in patients with MDD is related to the presence or absence of SI and impaired connectivity within or between large-scale neurocognitive networks. Previous studies have shown that the frontoparietal network (FPN) and default mode network (DMN) are critical for cognitive control and emotional regulation. Participants were 51 MDD patients with suicidal ideation (MDDSI), 52 MDD patients without suicidal ideation (MDDNSI), and 55 healthy controls (HC). Using areas located within FPN and DMN networks as regions of interest (ROIs), we compared the cognitive performance of the three groups and the strength of the resting state functional connections (RSFC) within and between the FPN and DMN networks. Additionally, we examined the correlation between the strength of FC within the FPN and cognitive function in the SI group. Furthermore, network-based statistics (NBS) were used to correct for the strength of FPN and DMN functional connections. The study identified significant cognitive deficits in MDD patients. Reduced strength of FC was observed within the FPN and DMN networks in the SI group compared to the NSI group. In the SI group, the strength of FC within the FPN network was positively correlated with attention/vigilance. These insights underscore the critical roles of the FPN and DMN in the suicidal ideation, shedding light on the cognitively relevant neurobiological characteristics of MDDSI, providing new insights into the neural mechanisms of MDDSI. URL: https://www.chictr.org.cn/bin/project/edit?pid=131537. Registration number: ChiCTR2100049646.

Identifying suicide attempter in major depressive disorder through machine learning: the importance of pain avoidance, event-related potential features of affective processing

How to achieve a high-precision suicide attempt classifier based on the three-dimensional psychological pain model is a valuable issue in suicide research. The aim of the present study is to explore the importance of pain avoidance and its related neural features in suicide attempt classification models among patients with major depressive disorder. By recursive feature elimination with cross-validation and support-vector-machine algorithms, scores from the measurements and the task-based EEG signals were chosen to achieve a suicide attempt classification model. In the multimodal suicide attempt classifier with an accuracy of 83.91% and an area under the curve of 0.90, pain avoidance ranked as the top one in the optimal feature set. Theta (reward positive feedback minus neutral positive feedback) was the shared neural representation ranking as the top one of event-related potential features in pain avoidance and suicide attempt classifiers. In conclusion, the suicide attempt classifier based on pain avoidance and its related affective processing neural features has excellent accuracy among patients with major depressive disorder. Pain avoidance is a stable and strong indicator for identifying suicide risks in both traditional analyses and machine-learning approaches. A novel methodology is needed to clarify the relationship between cognitive and affective processing evoked by punishment stimuli and pain avoidance.