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

精神障碍是全球疾病负担的主要因素之一，与精神病患者的不良功能结局相关。由于在大部分患者中对既定治疗的反应有限，最近的研究集中在早期识别和预防的方法上。其目的是确定受试者在未来发展为精神病的高风险，避免他们的功能下降以及完全精神病性障碍的发作。大麻已被确定为发展精神病的主要危险因素之一。然而，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，大麻急性中毒引起的症状类似于急性原发性精神病的临床表现。最初，它已被宣布，这种大麻引起的精神病（CIP）的症状缓解后自发禁欲。然而，最近的研究表明，高达50%的CIP患者继续发展为永久性精神障碍。尽管CIP患者代表了与发展为长期精神病的最高估计风险相关的人群，但他们在精神病研究中被广泛忽视。本提案的目的是对CIP患者进行纵向、多模式神经影像学研究，包括基线时的分子、结构和功能神经影像学以及随访检查，以记录9个月后患者的临床结局。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旨在首次允许（1）识别与CIP相关的脑功能和结构异常，从而阐明潜在的病理生理过程，以及（2）生成统计模型，从而预测CIP患者的临床结局。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风险受试者的临床工具的潜力，可能为CIP所致精神病风险受试者的个性化和有效治疗干预提供途径。

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.