The ability to identify biomarkers of psychosis risk is essential in defining effective preventive measures to potentially circumvent the transition to psychosis. Using samples of people at clinical high risk for psychosis (CHR) and Healthy controls (HC) who were administered a task fMRI paradigm, we used a framework for labelling time windows of fMRI scans as ‘integrated’ FC networks to provide a granular representation of functional connectivity (FC). Periods of integration were defined using the ‘cartographic profile’ of time windows and k‐means clustering, and sub‐network discovery was carried out using Network Based Statistics (NBS). There were no network differences between CHR and HC groups. Within the CHR group, using integrated FC networks, we identified a sub‐network negatively associated with longitudinal changes in the severity of psychotic symptoms. This sub‐network comprised brain areas implicated in bottom‐up sensory processing and in integration with motor control, suggesting it may be related to the demands of the fMRI task. These data suggest that extracting integrated FC networks may be useful in the investigation of biomarkers of psychosis risk.

中文翻译：

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综合转移功能连接网络预测精神病临床高危患者症状严重程度的变化

识别精神病风险生物标志物的能力对于确定有效的预防措施以潜在地规避向精神病的转变至关重要。使用接受任务 fMRI 范式的精神病临床高危人群 (CHR) 和健康对照 (HC) 样本，我们使用一个框架将 fMRI 扫描的时间窗口标记为“集成”FC 网络，以提供粒度表示功能连接（FC）。使用时间窗和 k-means 聚类的“制图剖面”定义集成周期，并使用基于网络的统计 (NBS) 进行子网络发现。CHR 和 HC 组之间没有网络差异。在 CHR 集团内，使用集成的 FC 网络，我们确定了一个与精神病症状严重程度的纵向变化负相关的子网络。该子网络包含与自下而上的感觉处理和与运动控制相结合的大脑区域，表明它可能与 fMRI 任务的需求有关。这些数据表明，提取整合的 FC 网络可能有助于调查精神病风险的生物标志物。