Identification of Distinct Biotypes in Clinical High Risk for Psychosis State Using Objective Brain-Based Biomarkers

识别预测因素和阐明精神病发病的潜在机制对于 制定有针对性的干预措施。当前以症状为基础的临床高危(CHR) 症状已被证实为未来精神病风险的指标，但不能提供 关于个人临床轨迹的准确预测或分层模型 结果或治疗反应。CHR阶段在风险因素和结果方面是不同的， 有相当大比例的CHR转换者(&lt；30%)发展为非情感性精神病 (~73%)和一小部分人发展为情感性精神病(~11%)。此外，CHR Non- 转化者有不同的结果，从完全缓解到持续的多方面后遗症。 此外，慢性再生障碍性贫血个体之间的神经生物学异质性很好。 有记录在案。尽管如此，很少有研究试图利用收集到的丰富的多模式数据 在大规模的CHR研究中，以形成和验证神经生物驱动的CHR亚群。 成功解析CHR的潜在异质性可能会产生更多定义的子群 有不同的临床轨迹和结果。我们小组最近的研究检查了 用双相情感-精神分裂症网络研究精神病谱系的神经生物学异质性 中间表型数据集，采用数据驱动的聚类分析方法定义 使用不可知的认知和电生理生物标记物的生物群(或生物类型) 传统的基于症状的诊断类别。我们的目标是从神经生物学的角度识别 具有假定不同的潜在病理生理学的同质生物型。然而，样本 由慢性精神病受试者组成，因此无法告知早期 干预和预防应用，缺乏临床相关的纵向结果数据。 一种侧重于CHR的方法来定义生物型并使用纵向临床评估，以及 功能结果数据尚未尝试。前瞻性特征的可用性 和来自北美前驱纵向研究2的深表型ChR样本 3是一个独特的机会，可以用客观的方法来解决这个问题。在当前 建议，我们将使用聚类分析来确定不同的CHR亚群或生物型，并将 比较、评估和验证所产生的生物类型。这项提议将产生重要影响 关于我们对生物异质性如何影响慢性阻塞性肺疾病和慢性阻塞性肺疾病临床结局的理解 阐明了一种表征该种群中生物异质性的方法，提供了生物 更有效的诊断和早期治疗干预的目标。

Identifying predictors and elucidating mechanisms underlying psychosis onset are critical for the development of targeted interventions. The current symptom-based clinical high risk (CHR) syndrome has been validated as an indicator of future psychosis risk but does not provide accurate predictions regarding individual clinical trajectories or a stratification model that informs outcome or treatment response. The CHR phase is diverse in terms of risk factors and outcomes, with a significant proportion of CHR converters (<30%) developing non-affective psychosis (~73%) and a smaller group developing affective psychoses (~11%). In addition, CHR non- converters have variable outcomes, ranging from full remission to ongoing multifaceted sequelae. Furthermore, substantial neurobiological heterogeneity among CHR individuals is well- documented. Despite this, little research attempted to harness the rich multimodal data collected in large-scale CHR studies to form and validate neurobiologically driven CHR subgroups. Successful parsing of the underlying heterogeneity of CHR may yield more defined subgroups with distinct clinical trajectories and outcomes. Our group’s recent studies examining neurobiological heterogeneity in psychosis spectrum using the Bipolar-Schizophrenia Network for Intermediate Phenotypes dataset, adopted a data-driven cluster analytic method to define biological clusters (or biotypes) using cognitive, and electrophysiological biomarkers agnostic to traditional symptom-based diagnostic categories. The goal was to identify neurobiologically homogeneous biotypes with presumed distinct underlying pathophysiology. However, the sample consisted of chronic psychosis subjects and thus was unable to inform the development of early intervention and prevention applications, and lacked clinically relevant longitudinal outcome data. An approach focusing on CHR to define biotypes and assess them using longitudinal clinical, and functional outcome data has yet to be attempted. The availability of prospectively characterized and deeply phenotyped CHR samples from the North American Prodrome Longitudinal Study 2 and 3 is a unique opportunity to address this question using an objective approach. In the current proposal, we will identify distinct subgroups or ‘biotypes’ for CHR using cluster analysis and will compare, evaluate and validate the resulting biotypes. This proposal will have an important impact on our understanding of how biological heterogeneity contributes to clinical outcomes in CHR and elucidates a way to characterize biological heterogeneity in this population, providing biological targets for more effective diagnosis, and early therapeutic intervention.