1/2: B-SNIP: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)

仅有临床现象学既不能(I)捕捉基于生物学的疾病实体，也不能(Ii)允许 基于神经生物学的个体化治疗处方。B-SNIP财团展示并复制 精神分裂症、分裂情感障碍和伴有精神病的双相情感障碍缺乏神经生物学特征。B- SNIP转变为基于生物标记物同源性对精神病病例进行分组。我们制作和复制 生物同源的精神病生物型(BT1，BT2，BT3)，可能有助于针对精神病的治疗。 这个为期12个月的项目将开发一种时间和资源高效的算法来推导B-SNIP生物型， 即使在资源不足的环境中也可以实施。就像在实验室医学中一样，这个过程 (ADEPT)将逐步(临床评估，然后是认知，然后是电生理学)为 哪些具体的治疗可以实施(既定干预措施)或评估(新的治疗 发展)。目标1：B-SNIP生物型目前需要专门的设备进行实验室检测，以及 多个测试，多个分数之间的统计整合。相反，我们将确定最好的个人 产生最有效和最高概率的生物型成员资格的措施。熟练适应能力强 在(临床、认知、电生理)范围内和跨领域(临床特征为选择提供信息 为电生理测试的选择提供信息的认知测试)。在每个阶段，Adet都会制作一个 生物型分类和置信度。这将允许在一定比例的情况下甚至进行生物型测定 当实验室检测资源有限时。目的2：医学评估中的首次接触涉及临床 人物刻画。仅临床特征就会产生足以用于治疗的生物型歧视 少量但意义重大的患者亚群(15%，主要是BT3)。目标3：认知测试是技术上最少的测试 要求严格的实验室评估，是生物类型的强大鉴别器。B-SNIP使用BACS，停止 信号(SST)，以及用于评估认知的抗眼跳。将认知添加到临床特征将产生？80% 识别BT3和BT2的准确率为40%(大部分为BT2，尽管BT1和BT2很难识别 在没有电生理的情况下进行区分)。患者将接受基于适应性的不同认知测试 算法(例如，在某些情况下，SST在生物型鉴定方面可能更好)。适应性方法 保持分类精度，同时减轻临床医生和患者的负担。目标4：最重要的 生物类型区分的电生理学特征是对显著刺激的低神经反应(BT1)和 旺盛的非特异性神经活动(BT2)。我们使用了多种复杂的电生理学方法，但我们将 确定产生最有效的生物型分化的测试和措施。将电生理学添加到 临床和认知信息将产生90%-95%的准确率来识别所有病例的生物型。再一次，对于一个 根据患者的情况，我们会自适应地选择特定的电生理措施，以实现最大限度的分类 该患者的准确性(例如，在某些情况下，P300在生物型测定中可能更好)。

Clinical phenomenology alone neither (i) captures biologically based disease entities, nor (ii) allows for individualized treatment prescriptions based on neurobiology. The B-SNIP consortium showed and replicated that schizophrenia, schizoaffective, and bipolar disorder with psychosis lack neurobiological distinctiveness. B- SNIP transitioned to subgrouping psychosis cases based on biomarker homology. We produced and replicated biologically homologous psychosis Biotypes (BT1, BT2, BT3) that may assist treatment targeting for psychosis. This twelve-month project will develop a time and resource efficient algorithm for deriving B-SNIP Biotypes that can be implemented in even under-resourced environments. Like in laboratory medicine, the procedure (ADEPT) will be stepwise (clinical evaluation, then cognition, then electrophysiology) to yield Biotypes for which specific treatments can be either implemented (established interventions) or evaluated (novel treatment development). Aim 1: B-SNIP Biotypes currently require specialized equipment for laboratory testing, and multiple tests with statistical integration across multiple scores. Instead, we will determine the best individual measures that yield the most efficient and highest probability Biotype memberships. ADEPT will be adaptive both within (clinical, cognitive, electrophysiological) and across the domains (clinical features inform selection of cognitive tests which inform selection of electrophysiological tests). At each stage, ADEPT will produce a Biotype classification and confidence. This will allow for Biotype determination in a proportion of cases even when laboratory testing resources are limited. Aim 2: The first contact in medical evaluation involves clinical characterization. Clinical features alone will yield Biotype discriminations sufficient for treatment targeting in a small but significant subset of patients (»15%, mostly BT3). Aim 3: Cognition tests are the least technically demanding laboratory assessments, and are powerful discriminators of Biotypes. B-SNIP uses BACS, Stop Signal (SST), and antisaccades to assess cognition. Addition of cognition to clinical features will yield »80% accuracy for identifying BT3s and »40% of all cases (mostly BT2, although BT1 and BT2 are difficulty to differentiate without electrophysiology). Patients will receive different cognitive tests based on the adaptive algorithm (e.g., SST may be superior for Biotype determination in some cases). The adaptive approach preserves classification precision while reducing clinician and patient burden. Aim 4: The most important Biotype differentiating electrophysiology features are low neural response to salient stimuli (BT1) and exuberant nonspecific neural activity (BT2). We used multiple complex electrophysiology measures, but we will identify tests and measures that yield the most efficient Biotype differentiation. Addition of electrophysiology to clinical and cognition information will yield 90-95% accuracy for identifying Biotypes for all cases. Again, for a given patient, we will adaptively select the specific electrophysiological measures to maximize classification accuracy for that patient (e.g., P300 may be superior for Biotype determination in some cases).