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DOI 10.1016/j.biopsych.2024.01.026

Медицинская библиотека · Biological psychiatry · 2024

Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder.

Qin Kun, Li Huiru, Zhang Huawei, Yin Li, Wu Baolin, Pan Nanfang, Chen Taolin, Roberts Neil, Sweeney John A, Huang Xiaoqi, Gong Qiyong, Jia Zhiyun

СтатьяМногоцентровое исследованиеПеревод в очереди

Аннотация

BACKGROUND

Although brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood.

METHODS

Using a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression.

RESULTS

Group comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling.

CONCLUSIONS

These findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

Для цитирования: Qin Kun, Li Huiru, Zhang Huawei, Yin Li, Wu Baolin, Pan Nanfang, Chen Taolin, Roberts Neil, Sweeney John A, Huang Xiaoqi, Gong Qiyong, Jia Zhiyun. Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder.. Biological psychiatry. 2024. DOI: 10.1016/j.biopsych.2024.01.026.

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