Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People's Republic of China.
Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000, People's Republic of China.
Transl Psychiatry. 2022 Sep 12;12(1):383. doi: 10.1038/s41398-022-02147-x.
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.
重度抑郁症（MDD）是一种严重的脑部疾病，与自杀风险显著相关。识别自杀倾向有时可以挽救 MDD 患者的生命。我们旨在探索动态功能网络连接（dFNC）在 MDD 患者自杀倾向检测中的应用。共有 173 名 MDD 患者参与了本研究，其中 48 名无自杀风险（NS），74 名有自杀意念（SI），51 名有自杀企图（SA）。还招募了 38 名健康对照者进行比较。采用滑动窗口方法获取 dFNC，采用 K-均值聚类方法对窗口化的 dFNC 进行聚类。采用线性支持向量机进行分类，采用留一交叉验证进行验证。还使用了其他机器学习方法进行比较。MDD 患者在强连接状态（状态 2 和 5）和弱连接状态（状态 4）中均表现出广泛的连接减少，而弱连接状态（状态 4）内的功能连接障碍主要由自杀企图引起。此外，dFNC 矩阵，特别是弱连接状态，可用于区分 MDD 与健康对照组（曲线下面积 [AUC] = 82），甚至可用于识别 MDD 患者的自杀倾向（NS 与 SI 相比 AUC = 78，NS 与 SA 相比 AUC = 88，SA 与 SI 相比 AUC = 74），与视觉相关和默认相关的网络间连接作为重要特征。因此，本研究中观察到的 dFNC 异常可能进一步加深我们对 MDD 患者自杀倾向神经基础的理解。
