Brain functional & structural patterns predicting outcome in cannabis-induced psychosis

精神障碍是造成全球疾病负担的主要因素之一，并与精神病患者的不良功能结局有关。由于很大一部分患者对现有治疗的反应有限，最近的研究集中在早期识别和预防的方法上。目的是确定受试者将来发展成精神病的高风险，并避免他们的功能衰退以及完全精神障碍的发作。大麻已被确定为发展成精神病的主要风险因素之一。然而，人们对大麻和精神病风险之间关系的生物学基础知之甚少。研究表明，急性大麻中毒会导致类似于急性原发精神病(PP)的临床症状。最初已经宣布，这种由大麻引起的精神病(CIP)的症状在戒断后会自发缓解。然而，最近的研究表明，高达50%的CIP患者继续发展为一种永久性的精神障碍。尽管CIP患者代表了与发展一种长期形式的精神病的估计风险最高相关的人群，但他们在精神病的研究中被广泛忽视。本方案的目的是建立一项针对CIP患者的纵向、多模式神经影像研究，包括基线时的分子、结构和功能神经成像以及9个月后的随访检查，以记录患者的临床结果。它计划应用多变量模式分析(MVPA)来识别与临床和神经认知变量相关的神经成像数据中的大脑模式。这种多模式和多变量方法的设计是为了首次(1)识别与CIP相关的大脑功能和结构异常，从而揭示潜在的病理生理过程，以及(2)产生统计模型，允许预测CIP患者的临床结果。通过这种方式，将评估基于神经成像的模型作为识别精神病高危受试者的临床工具的潜力，潜在地为因CIP而具有精神病风险的受试者设置个性化和有效的治疗干预途径。

Psychotic disorders reside among the leading factors of global disease burden and are associated with poor functional outcome in psychiatric patients. Due to the limited response to established treatments in a large subset of patients, recent research focusses on an approach of early-recognition and prevention. The aim is to identify subjects with a high-risk to develop psychosis in the future and to avoid their functional decline as well as the onset of a full psychotic disorder. Cannabis has been identified as one of the major risk factors for developing psychosis. However the biological basis of the relationship between cannabis and psychosis risk is only poorly understood. Studies indicate that the acute intoxication with cannabis induces symptoms that resemble the clinical picture of acute primary psychosis (PP). Initially it has been proclaimed that symptoms of this cannabis-induced psychosis (CIP) remit spontaneously following abstinence. However recent studies show that up to 50 % of all patients with CIP continue to develop a permanent form of a psychotic disorder. Even though patients with CIP represent the population associated with the highest estimated risk to develop a long-lasting form of psychosis, they have been widely neglected in research of psychosis. The aim of the present proposal is to set up a longitudinal, multimodal neuroimaging study of patients with CIP, including molecular, structural and functional neuroimaging at baseline as well as a follow up examination to record the clinical outcome of patients after 9 months. It is planned to apply multivariate pattern analysis (MVPA) to identify brain patterns in neuroimaging data that relate to clinical and neurocognitive variables. This multimodal and multivariate approach is designed to allow for the first time (1) identification of cerebral functional and structural abnormalities associated with CIP, thus shedding light on the underlying pathophysiological processes and (2) generation of statistical models that allow the prediction of the clinical outcome in patients with CIP. In this way the potential of neuroimaging-based models as a clinical tool to identify subjects with high-risk for psychosis will be evaluated, potentially setting the path for personalized and efficient treatment interventions for subjects with psychosis risk due to CIP.