Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data
Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data
复制标题
重度抑郁症脑网络标记的验证：新获取数据的重测可靠性和顺行泛化性能
DOI：
10.1016/j.jad.2023.01.087
复制
发表时间：
2023
影响因子：
6.6
通讯作者：
et al.
中科院分区：
文献类型：
--
作者：
Okada Go;Yoshioka Toshinori;Yamashita Ayumu;Itai Eri;Yokoyama Satoshi;et al.
背景最近，我们开发了一种可推广的脑网络标记物，用于使用静息态功能磁共振成像在多个成像部位诊断重性抑郁症（MDD）。在这里，我们应用这个大脑网络标记新获得的数据，以验证其重测信度和顺行泛化性能为新patients.MethodsWe测试的敏感性和特异性，我们的大脑网络标记的MDD使用数据从43个新的MDD患者以及新的数据从33名健康对照（HC）谁参加了我们以前的研究。为了检验我们的大脑网络标记物的重测信度，我们评估了基于大脑网络标记的分类器输出之间的组内相关系数（ICC），结果两组分类器输出之间的重测相关性来自HC的MDD（MDD概率）表现出中等可靠性，ICC为0.45（95%置信区间，0.13-0.68）。分类器区分MDD和HC患者的准确率为69.7%（敏感性72.1%;特异性，66.7%）.局限性本研究中MDD患者的数据是横断面的，标志物的临床意义，如它是否是MDD的状态或特质标志物及其与治疗反应性的关联，结论本研究的结果再次证实了我们的脑网络标记物诊断MDD的重测信度和泛化性能。
BackgroundRecently, 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.MethodsWe 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.ResultsTest-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 %).LimitationsThe 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.ConclusionsThe results of this study reaffirmed the test-retest reliability and generalization performance of our brain network marker for the diagnosis of MDD.