Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample. We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes. The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation. Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

中文翻译：

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解析大型单站点样本中抑郁症异质性的维度和分类解决方案

最近的研究报告了重度抑郁症异质性生物学基础建模方面的重大进展，但研究人员也发现了重要的技术挑战，包括扫描仪相关的伪影、多变量模型过度拟合的倾向以及需要更广泛的更大样本临床表型。当前研究的目标是评估解析抑郁症异质性的维度和分类解决方案，这些解决方案在大型单点样本中是稳定且可推广的。我们使用正则化典型相关分析来识别数据驱动的大脑行为维度，这些维度可以解释大型单中心数据集中抑郁症症状领域的个体差异，该数据集包括 328 名重度抑郁症患者的临床评估和静息态功能磁共振成像数据和 461 名健康对照参与者。我们检查了保留数据中临床负荷和模型性能的稳定性。最后，使用这些维度的层次聚类来识别抑郁症的分类亚型。最佳正则化典型相关分析模型产生了 3 个稳健且可概括的大脑行为维度，解释了抑郁情绪、焦虑、快感缺乏和失眠的个体差异。分层聚类确定了 4 种抑郁亚型，每种亚型都有不同的临床症状、异常的静息态功能连接模式以及对重复经颅磁刺激的抗抑郁反应。我们的结果定义了解析重度抑郁症神经生物学异质性的维度和分类解决方案，这些解决方案是稳定的、可推广的，并且能够预测治疗结果，每种解决方案在不同的情况下都有独特的优势。他们还提供了额外的证据，表明正则化典型相关分析和层次聚类是研究功能连接与临床症状之间关联的有效工具。