Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning
Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning
复制标题
睡眠障碍相关的神经影像特征作为诊断重度抑郁症的潜在生物标志物：基于机器学习的多中心研究
DOI：
10.1016/j.jad.2021.08.027
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发表时间：
2021-08-27
影响因子：
6.6
通讯作者：
Zhang, Zhijun
中科院分区：
文献类型：
--
作者：
Shi, Yachen;Zhang, Linhai;Zhang, Zhijun
背景资料：客观的生物标志物是克服重性抑郁障碍（MDD）临床困境的关键，个体化诊断是MDD精确治疗的基础。在内部数据集中确定了睡眠障碍相关的磁共振成像（MRI）特征（92例MDD患者），并在独立多中心数据集的460例MDD患者中进一步验证。随后，使用这些MRI特征，在当前多中心数据集（460例MDD患者和470例正常对照）中构建了eXtreme Gradient Boosting分类模型。同时，还研究了分类输出与抑郁症状严重程度之间的关系。在抑郁症患者中，脑灰质密度与低频波动分数幅值的结合可以准确预测由第4项评分、第5项评分和17项汉密尔顿抑郁量表（HAMD-17）第6项评分（内部数据集R-2 = 0.158;多中心数据集R-2 = 0.110）。此外，基于这些MRI特征的分类模型以86.3%的准确度（曲线下面积= 0.937）将MDD患者与正常对照区分开。重要的是，分类输出与MDD患者的HAMD-17评分显著相关。限制条件：结论：神经影像学特征能准确反映个体睡眠障碍的表现，可作为MDD的潜在诊断指标。
Background: Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD.Methods: Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated.Results: In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R-2 = 0.158 in the internal dataset; R-2 = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients. Limitation: Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index.Conclusion: Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.