Abstract：
BACKGROUND: Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions.
METHODS: A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.
RESULTS: In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS 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 WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.
METHODS: A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.
RESULTS: In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS 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 WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.
摘要：
背景：早期精神病患者（EP，精神病发作后3年内)表现出显著的变异性，使结果预测具有挑战性。目前,几乎没有证据表明在EP诊断中神经微结构特性和症状谱之间存在稳定的关系，限制早期干预的发展。
方法：数据驱动方法，偏最小二乘(PLS)相关，在两个独立的数据集上使用，以检查白质(WM)属性和症状学之间的多变量关系，在EP中识别稳定和可推广的特征。主要队列包括来自人类连接组项目-早期精神病的EP患者(n=124)。复制队列包括来自Feinstein医学研究所的EP患者(n=78)。两个样本都包括精神分裂症患者，分裂情感障碍,和精神病性情绪障碍。
结果：在这两个队列中，显著的潜在成分(LC)对应于结合阴性症状的症状概况，主要是表达减少，有特定的躯体症状.两个LC都捕获了WM中断的全面特征，主要是皮层下和额叶联合纤维的组合。引人注目的是，在主要队列上训练的PLS模型准确预测了复制队列中的微结构特征和症状.发现不是由诊断驱动的，药物,或物质使用。
结论：这种数据驱动的诊断方法揭示了EP中微结构WM改变的稳定且可复制的神经生物学特征，跨诊断和数据集，显示这些改变的强烈协方差，具有独特的阴性和躯体症状。这一发现表明应用数据驱动的方法来揭示共享神经生物学基础的症状域的临床实用性。
方法：数据驱动方法，偏最小二乘(PLS)相关，在两个独立的数据集上使用，以检查白质(WM)属性和症状学之间的多变量关系，在EP中识别稳定和可推广的特征。主要队列包括来自人类连接组项目-早期精神病的EP患者(n=124)。复制队列包括来自Feinstein医学研究所的EP患者(n=78)。两个样本都包括精神分裂症患者，分裂情感障碍,和精神病性情绪障碍。
结果：在这两个队列中，显著的潜在成分(LC)对应于结合阴性症状的症状概况，主要是表达减少，有特定的躯体症状.两个LC都捕获了WM中断的全面特征，主要是皮层下和额叶联合纤维的组合。引人注目的是，在主要队列上训练的PLS模型准确预测了复制队列中的微结构特征和症状.发现不是由诊断驱动的，药物,或物质使用。
结论：这种数据驱动的诊断方法揭示了EP中微结构WM改变的稳定且可复制的神经生物学特征，跨诊断和数据集，显示这些改变的强烈协方差，具有独特的阴性和躯体症状。这一发现表明应用数据驱动的方法来揭示共享神经生物学基础的症状域的临床实用性。
