School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, 12 Qinghexiaoyingdong Road, Beijing, 100192, China.
Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), 51 Huayuanbei Road, Beijing, 100191, China.
Brain Imaging Behav. 2022 Dec;16(6):2744-2754. doi: 10.1007/s11682-022-00739-1. Epub 2022 Nov 4.
Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.
患有重度抑郁症（MDD）的患者表现出情感和认知障碍。尽管已经深入研究了与 MDD 相关的大脑功能和结构异常，但在大型样本数据集中尚未评估 MDD 与时空大规模功能网络之间的关系。我们采用了来自国际抑郁症大数据研究中心（IBCDR）的数据，纳入了 543 名可比的健康对照（HC）和 314 名首次发作的未用药 MDD 患者。我们使用多元模式分类方法来学习信息丰富的时空功能状态。使用独立成分分析对每个参与者的大脑状态进行提取，以进行功能动态估计。然后，开发了一种多核模式分类方法来识别与 FEDN MDD 相关的有区别的时空状态。最后，对内在和临床大脑特征进行了统计分析。与 HC 相比，FEDN MDD 患者表现出默认模式网络（DMN）、突显网络、中枢网络（以背外侧前额叶皮层为中心）和相对复杂的耦合网络（视觉、DMN、运动感觉和皮质下网络）的时空功能状态发生改变。用于区分患者和 HC 的多核分类模型获得的接收器工作特征曲线下面积高达 0.80。分类评分与汉密尔顿抑郁评定量表评分和 MDD 发病年龄相关。FEDN MDD 患者具有多种异常的时空功能状态。从这些状态得出的分类评分与症状严重程度相关。时空状态的评估可能代表一种强大的临床和研究工具，可用于区分神经精神科患者和对照者。
