Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan; XNef, Inc., Kyoto, Japan.
J Affect Disord. 2023 Apr 1;326:262-266. doi: 10.1016/j.jad.2023.01.087. Epub 2023 Jan 27.
Recently, we developed a generalizable brain network marker for the diagnosis of major depressive disorder (MDD) across multiple imaging sites using resting-state functional magnetic resonance imaging. Here, we applied this brain network marker to newly acquired data to verify its test-retest reliability and anterograde generalization performance for new patients.
We tested the sensitivity and specificity of our brain network marker of MDD using data acquired from 43 new patients with MDD as well as new data from 33 healthy controls (HCs) who participated in our previous study. To examine the test-retest reliability of our brain network marker, we evaluated the intraclass correlation coefficients (ICCs) between the brain network marker-based classifier's output (probability of MDD) in two sets of HC data obtained at an interval of approximately 1 year.
Test-retest correlation between the two sets of the classifier's output (probability of MDD) from HCs exhibited moderate reliability with an ICC of 0.45 (95 % confidence interval,0.13-0.68). The classifier distinguished patients with MDD and HCs with an accuracy of 69.7 % (sensitivity, 72.1 %; specificity, 66.7 %).
The data of patients with MDD in this study were cross-sectional, and the clinical significance of the marker, such as whether it is a state or trait marker of MDD and its association with treatment responsiveness, remains unclear.
The results of this study reaffirmed the test-retest reliability and generalization performance of our brain network marker for the diagnosis of MDD.
最近，我们使用静息态功能磁共振成像开发了一种可推广的大脑网络标志物，用于在多个成像部位诊断重度抑郁症（MDD）。在这里，我们将该大脑网络标志物应用于新获取的数据，以验证其对新患者的测试-重测可靠性和前瞻性泛化性能。
我们使用来自 43 名新 MDD 患者以及我们之前研究中参与的 33 名健康对照者（HC）的新数据，测试了我们的 MDD 大脑网络标志物的敏感性和特异性。为了检验我们的大脑网络标志物的测试-重测可靠性，我们评估了在大约 1 年的间隔时间内两次获得的 HC 数据中，基于大脑网络标志物的分类器输出（MDD 的概率）的组内相关系数（ICC）。
HC 中两个分类器输出（MDD 的概率）之间的测试-重测相关性表现出中度可靠性，ICC 为 0.45（95%置信区间，0.13-0.68）。该分类器区分 MDD 患者和 HC 的准确率为 69.7%（敏感性，72.1%；特异性，66.7%）。
本研究中 MDD 患者的数据为横断面，标志物的临床意义，如它是否是 MDD 的状态或特征标志物以及它与治疗反应的关系，尚不清楚。
本研究结果再次证实了我们的大脑网络标志物用于诊断 MDD 的测试-重测可靠性和泛化性能。
