Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD.

Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes.

A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings.

Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms.

Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

人们越来越多地将重度抑郁症（MDD）理解为大脑连接体的破坏。研究大样本量的灰质结构网络可以为 MDD 的网络级神经病理学基础的结构基础提供有价值的见解。

本研究使用包括近 2000 名个体的多站点 MRI 数据集，旨在识别与 MDD 和相关临床表型相关的灰质结构网络的稳健拓扑和连接异常。

共有来自 23 个地点的 955 名 MDD 患者和 1009 名健康对照者被纳入研究。基于灰质体积图建立个体化结构协方差网络（SCN）。数据协调后，检查网络拓扑指标和焦点连接性，以进行组级比较、个体级分类性能以及与临床评级的关联。应用了各种验证策略来确认研究结果的可靠性。

与健康对照组相比，MDD个体表现出整体效率提高、区域中心性异常（即丘脑、中央前回、中扣带皮层和默认模式网络）和改变的回路连接（即腹侧注意网络和额顶叶网络）。首次用药和复发患者在网络拓扑和连接方面表现出不同的缺陷模式。此外，拓扑度量的个体级分类优于结构连通性的分类。丘脑-岛叶连接与抑郁症状的严重程度呈正相关。

基于这个高性能的数据集，我们确定了MDD和相关亚型中个体化SCN拓扑和连接受损的可靠模式，这增加了目前对MDD神经病理学的理解，并可能指导未来诊断和治疗标记物的开发。