Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts.

IF 11.3 1区化学 Q1 CHEMISTRY, PHYSICAL ACS Catalysis Pub Date : 2024-06-21 DOI:10.1016/j.biopsych.2024.06.011

Haley R Wang, Zhen-Qi Liu, Hajer Nakua, Catherine E Hegarty, Melanie Blair Thies, Pooja K Patel, Charles H Schleifer, Thomas P Boeck, Rachel A McKinney, Danielle Currin, Logan Leathem, Pamela DeRosse, Carrie E Bearden, Bratislav Misic, Katherine H Karlsgodt

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Abstract

Background: Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

Methods: A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

Results: In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

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解码早期精神病：揭示独立群体中与精神病理学相关的稳定微观结构特征。

背景：早期精神病患者（EP，精神病发病后 3 年内）表现出显著的差异性，使得结果预测具有挑战性。目前，几乎没有证据表明神经微结构特性与不同诊断的症状特征之间存在稳定的关系，这限制了早期干预措施的开发：方法：在两个独立的数据集中使用数据驱动方法--偏最小二乘法（PLS）相关性--来研究白质（WM）特性与症状之间的多变量关系，以确定 EP 中稳定且可推广的特征。主要队列包括 "人类连接组计划-早期精神病 "中的 EP 患者（n=124）。复制队列包括来自范斯坦医学研究所（Feinstein Institute for Medical Research）的 EP 患者（人数=78）。两个样本都包括精神分裂症、分裂情感障碍和精神情绪障碍患者：在这两组样本中，一个重要的潜伏成分（LC）与阴性症状（主要是表达能力减退）和特定躯体症状相结合的症状特征相对应。两个潜在成分都捕捉到了 WM 干扰的综合特征，主要是皮层下和额叶关联纤维的组合。令人吃惊的是，在原始队列中训练的 PLS 模型能准确预测复制队列中的微观结构特征和症状。研究结果不受诊断、药物或药物使用的影响：这种数据驱动的跨诊断方法揭示了 EP 中微观结构 WM 改变的稳定和可复制的神经生物学特征，跨越了诊断和数据集，显示出这些改变与阴性和躯体症状的独特特征之间存在很强的协方差。这一发现表明，应用数据驱动方法来揭示具有共同神经生物学基础的症状领域具有临床实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Catalysis CHEMISTRY, PHYSICAL-

CiteScore

20.80

自引率

6.20%

发文量

1253

审稿时长

1.5 months

期刊介绍： ACS Catalysis is an esteemed journal that publishes original research in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. It offers broad coverage across diverse areas such as life sciences, organometallics and synthesis, photochemistry and electrochemistry, drug discovery and synthesis, materials science, environmental protection, polymer discovery and synthesis, and energy and fuels. The scope of the journal is to showcase innovative work in various aspects of catalysis. This includes new reactions and novel synthetic approaches utilizing known catalysts, the discovery or modification of new catalysts, elucidation of catalytic mechanisms through cutting-edge investigations, practical enhancements of existing processes, as well as conceptual advances in the field. Contributions to ACS Catalysis can encompass both experimental and theoretical research focused on catalytic molecules, macromolecules, and materials that exhibit catalytic turnover.