Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification. The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

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通过丘脑 sMRI 特征的多变量模式分析重度抑郁症的个体化诊断

磁共振成像（MRI）研究发现重度抑郁症（MDD）存在丘脑异常。尽管MDD患者和健康对照（HC）之间的丘脑结构和功能在群体水平上存在显着差异，但尚不清楚丘脑的结构和功能特征是否适合用作MDD的诊断预测辅助工具。个人水平。在这里，我们将使用多变量测试丘脑中灰质密度（GMD）、灰质体积（GMV）、低频波动幅度（ALFF）和低频波动分数幅度（fALFF）的预测价值模式分析（MVPA）。招募了 74 名 MDD 患者和 44 名 HC 受试者。高斯过程分类器 (GPC) 被训练来区分 MDD 患者和 HC，高斯过程回归 (GPR) 被训练来预测抑郁评分，多核学习 (MKL) 被用来探索丘脑每个子区域的贡献。主要研究结果如下： [1] 丘脑 GMD 训练的 GPC 的平衡准确率为 96.59%（P < 0.001）。使用丘脑 GMV 训练的 GPC 的准确率为 93.18%（P < 0.001）。在使用 GMD 训练的 GPR 中，汉密尔顿抑郁量表 (HAMD) 评分目标与预测之间的相关性为 0.90（P < 0.001，r2 = 0.82），在使用 GMV 训练的 GPR 中，HAMD 评分目标与预测之间的相关性为 0.89（ P < 0.001，r2 = 0.79）。[2] 在丘脑中使用 ALFF 和 fALFF 训练的模型未能区分 MDD 患者和 HC 参与者。[3] MKL模型显示，左侧前额叶丘脑、右侧颞侧丘脑和右侧感觉丘脑对诊断分类的贡献更大。结果表明，GMD 和 GMV（而非丘脑功能指标）对于 MDD 的个体化诊断具有良好的潜力。此外，丘脑在预测 MDD 方面表现出丘脑亚区域结构特征的异质性。据我们所知，这是第一项针对丘脑使用机器学习方法在个体层面预测 MDD 的研究。