Background

Suicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC).

Methods

A cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation.

Results

The models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately.

Limitations

This study was a single center cohort study without external validation.

Conclusion

These findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction.

中文翻译：

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基于机器学习方法和全脑功能连接的主要抑郁症患者的自杀风险分层

背景

重度抑郁症 (MDD) 的自杀风险分层和个体水平预测很重要，但尚未得到认可。在这里，我们使用机器学习 (ML) 和全脑功能连接 (FC) 构建模型来检测 MDD 中的自杀倾向。

方法

对 200 名受试者进行了横断面评估，包括 126 名具有高自杀风险的 MDD（HSR；73 名有自杀意念 [SI] 的患者，53 名有自杀企图的患者 [SA]），36 名具有低自杀风险（LSR）的患者和38 个健康对照 (HC)。计算全脑FC特征，采用最小绝对收缩和选择算子（LASSO）方法进行特征选择。执行支持向量机 (SVM) 来构建模型以区分 MDD 和 HC，以及 MDD 之间的自杀风险分层。进行留一法交叉验证 (LOOCV) 进行验证。

结果

在全脑 FC 上使用 SVM 构建的模型在从 HC 筛选 MDD 方面具有强大的分类效率（准确度 = 88.50 %），并且在 MDD 患者的自杀风险分层中（准确度 = 84.56 % 和 74.60 % 对 HSR 或 LSR 患者进行分类和 SA 或 SI）。随后的分析表明，感觉运动网络中的网络内连接异常和默认和背侧注意网络之间的网络间连接异常可以分别表征 MDD 中的 HSR 和 SA。

限制

本研究是一项未经外部验证的单中心队列研究。

结论

这些发现表明 ML 方法可用于基于全脑 FC 的 MDD 自杀风险分层，这可能有助于识别 MDD 中具有不同自杀风险的个体并提供个体水平的预测。