Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.
Psychol Med. 2023 Aug;53(11):5312-5321. doi: 10.1017/S0033291722002380. Epub 2022 Aug 12.
Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.
T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls ( = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.
As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.
In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.
阐明个体异常是实现异质障碍（如抑郁症）精准医学的关键第一步。抑郁症的神经病理学与异常的区域间结构协方差有关，表明大脑成熟受到干扰。然而，大多数研究都集中在群体水平的结构协方差异常，而忽略了个体间的异质性。因此，我们旨在借助个体化差异结构协变网络（IDSCN）分析来识别个体化结构协变异常。
对 195 名首发未治疗的抑郁症患者和 78 名匹配的健康对照者进行 T1 加权解剖图像采集。我们为每位患者获得 IDSCN，并根据共享的差异边缘确定抑郁症的亚型。
结果显示，抑郁症患者的差异结构协变边缘分布存在巨大的异质性。尽管存在这种异质性，但皮质下-小脑网络内的改变边缘经常被大多数患者共享。确定了两种稳健的神经解剖亚型。具体而言，亚型 1 中的患者通常共享与运动网络相关的减少的边缘。亚型 2 中的患者通常共享与皮质下-小脑网络相关的减少的边缘。功能注释进一步表明，亚型 2 的差异边缘主要与奖励/动机相关的功能术语有关。
总之，我们研究了个体化差异结构协变，并发现抑郁症患者经常共享皮质下-小脑网络内的减少边缘。所确定的两种亚型为分类学提供了新的见解，并为抑郁症的精准诊断和治疗提供了潜在线索。
