Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
EBioMedicine. 2022 Apr;78:103977. doi: 10.1016/j.ebiom.2022.103977. Epub 2022 Apr 1.
Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.
Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.
GCN achieved an accuracy of 81·5% (95%CI: 80·5-82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.
These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.
This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).
在个体水平上建立客观和定量的神经影像学生物标志物可以辅助重度抑郁症（MDD）的早期和准确诊断。然而，大多数先前使用机器学习来识别 MDD 的研究都是基于小样本量，并且没有考虑到与 MDD 病理生理学相关的脑连接组。在这里，我们通过在大型多中心 MDD 数据集上应用图卷积网络（GCN）来解决这些局限性。
收集了来自 16 个 Rest-meta-MDD 联盟站点的 1586 名参与者（821 名 MDD 与 765 名对照）的静息态功能磁共振成像扫描数据。使用个体全脑功能网络训练 GCN 模型，以从对照组中识别 MDD 患者，表征对分类有贡献的最显著区域，并探索显著区域的拓扑特征与临床指标之间的关系。
GCN 实现了 81.5%的准确率（95%CI：80.5-82.5%，AUC：0.865），高于其他常见的机器学习分类器。对分类有贡献的最显著区域主要位于默认模式、额顶叶和扣带回-额盖网络内。左侧顶下小叶和左侧背外侧前额叶皮质的节点拓扑与抑郁严重程度和疾病持续时间分别相关。
这些基于大型多中心数据集的发现支持 GCN 用于表征 MDD 的可行性和有效性，并且还说明了 GCN 通过检测功能网络拓扑结构中的临床相关破坏来增强对 MDD 神经生物学的理解的潜在效用。
本研究得到了国家自然科学基金（Grant Nos. 81621003、82027808、81820108018）的支持。
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