Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, China; Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
J Affect Disord. 2024 Nov 15;365:9-20. doi: 10.1016/j.jad.2024.08.082. Epub 2024 Aug 14.
One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.
一种最常见的精神疾病是重度抑郁症（MDD），它增加了自杀意念或过早死亡的可能性。在言语流畅性任务（VFT）中，功能性近红外光谱（fNIRS）检测到的异常额部血流动力学变化有可能作为评估临床症状的客观指标。然而，对于表现出抑郁症状的个体，全面的定量和客观评估工具仍未开发。本研究从一个包含 289 名 MDD 患者和 178 名健康对照的大规模数据集的 467 个样本中，在整个 VFT 期间获得了 fNIRS 测量值。为了确定独特的 MDD 生物标志物，本研究引入了一种从 fNIRS 信号中提取时空特征的数据表示方法，随后将其用作潜在的预测因子。实现了机器学习分类器（例如梯度提升决策树（GBDT）和多层感知机），以评估预测选定特征的能力。交叉验证的平均值和标准差表明，当与 180 个特征模式结合使用时，GBDT 模型可以最有效地将 MDD 患者与健康对照组区分开来。测试集的正确分类准确性为 0.829±0.053，AUC 为 0.895（95%CI：0.864-0.925），敏感性为 0.914±0.051。使用 Shapley 加法解释方法确定了对 MDD 识别贡献最大的通道，这些通道位于额极区和背外侧前额叶皮层以及Broca 三角区。评估 MDD 患者在 VFT 期间的前额叶活动异常可作为一种客观可测量的生物标志物，用于评估认知缺陷并促进 MDD 的早期筛查。本研究中提出的模型可以应用于大规模病例对照 fNIRS 数据集，以检测 MDD 的独特特征，并为临床医生提供一种基于客观生物标志物的分析工具，以帮助评估可疑病例。
