Recently, a team led by Professor Jia Zhiyun from the Department of Nuclear Medicine at West China Hospital of Sichuan University and Professor Gong Qiyong from the MRI Research Center published a paper in "Biological Psychiatry," revealing for the first time the correlation between suicidal tendencies in depression patients and abnormal expression patterns of brain structural covariance networks.
The high disability and mortality rates associated with depression are closely linked to suicidal thoughts and behaviors. Globally, nearly 800,000 people die by suicide each year, with over half of them attributed to depression. Understanding the neurobiological mechanisms underlying suicide in depression patients can aid in accurately identifying suicide risks and implementing early interventions. Previous studies often relied on small single-center samples, resulting in low reliability. Few multicenter studies focused solely on local morphological changes, lacking in-depth exploration of network-level abnormalities and the unclear gene expression patterns behind these network anomalies.
Based on multicenter data, the study examined 218 depression patients with suicidal tendencies, 230 depression patients without suicidal tendencies, and 263 controls without depression, using a brain structural covariance network (SCN) approach. Machine learning models were applied to explore the diagnostic and predictive value of brain morphological and network topological features, and an in-depth analysis was conducted on the similarities and differences in brain structural covariance network changes between depression patients with and without suicidal ideation or behavior. For the first time, it was found that brain structural network topological indicators have greater predictive value in identifying suicidal ideation and behavior than traditional morphological parameters.
Furthermore, utilizing the Allen Brain Atlas, the research revealed differences in brain networks between patients with and without suicidal ideation or behavior, primarily related to synaptic signal transmission and cellular biomolecular metabolism processes. This elucidated the underlying gene expression patterns and corresponding cellular molecular biology processes, emphasizing the importance of structural covariance networks in identifying and detecting suicide risks in depression patients. It provides new insights into the neurobiological basis of suicidal ideation and behavior in depression patients.
For more information on the paper, please visit: Link
