Characterizing Schizophrenia Progression via Multi-modal Neuroimaging and Computation

 描述（由申请人提供）：精神分裂症（SCZ）是一种致残性神经发育障碍，导致严重的认知障碍。SCZ被假设是由影响大规模神经连接的突触紊乱引起的。这一观点得到了神经影像学研究的支持，这些研究反复显示前额叶皮层（PFC）功能和连接的改变以及丘脑-皮层和联合皮层回路的中断。然而，早期SCZ的复杂神经生物学仍然没有特征化，限制了干预至关重要的早期疾病阶段的治疗。这是改善靶向治疗、预测预后和促进早期检测的主要目标。我们的首要目标是纵向表征并发的功能和结构连接障碍，在早期课程SCZ的认知缺陷，通过国家的最先进的神经影像学。反过来，我们的目标是通过基于生物药理学的计算建模扩展到神经网络的水平，告知突触假说的临床神经成像效果。为了解决这些知识差距，我们将研究早期SCZ患者在首次进入耶鲁大学精神病早期专门治疗（STEP）诊所后神经连接障碍的纵向进展。反过来，我们将纵向跟踪患者 与50名匹配的健康对照相比，在6个月，12个月和24个月后。为了量化连接障碍，该项目将使用由人类连接组项目（HCP）优化的领先功能和结构方法，与NIMH人类疾病相关连接组倡议一致。首先，我们的目标是测试，如果最近确定的PFC和丘脑皮质标记表现出并发（或分离）的结构和功能的改变。这种平衡的纵向设计可以区分“状态”与“特质”的神经影像学标记在早期疾病过程中的临床相关变量。具体而言，检查药物治疗的效果，治疗依从性，未治疗精神病的持续时间和症状严重程度，告知这些有前途的神经影像学标记物的临床效用。其次，该项目将测试这些神经影像学标记物是否与认知缺陷的严重程度相关-SCZ的一个临床特征。我们的目标是同时检查工作记忆（WM），通过我们验证的神经影像学范式，以测试是否特定方面的结构和功能障碍预测WM赤字。这提供了SCZ中连接障碍和认知障碍之间急需的联系。最后，为了告知神经连接障碍背后的突触假设，例如由N-甲基-D-天冬氨酸谷氨酸受体（NMDAR）功能减退引起的皮质兴奋-抑制（E/I）不平衡，我们的目标是使用基于生物药理学的计算模型，该模型包含相关的细胞细节。我们的目标是迭代地探索突触参数的E/I平衡拟合在硅片上的影响，在体内的临床神经影像学研究结果。这种计算精神病学方法可以通过计算拟合来帮助解释SCZ中的动态神经连接障碍，并为专注于早期SCZ阶段的治疗研究产生新的突触靶点，此时干预是最重要的。

 DESCRIPTION (provided by applicant): Schizophrenia (SCZ) is a disabling neurodevelopmental disorder causing profound cognitive impairment. SCZ is hypothesized to arise from synaptic disturbances affecting large-scale neural connectivity. This view is supported by neuroimaging studies that repeatedly show alterations in prefrontal cortex (PFC) function and connectivity and disruptions across thalamo-cortical and associative cortex circuits. However, the complex neurobiology of early-course SCZ remains uncharacterized, limiting treatments for early illness phases when intervention is crucial. This is a major objective for improving targeted therapies, predicting prognosis, and promoting early detection. Our overarching goal is to longitudinally characterize concurrent functional and structural dysconnectivity in early-course SCZ in relation to cognitive deficits via state-of-the-art neuroimaging. In turn, we aim to inform synaptic hypotheses underlying clinical neuroimaging effects via biophysically-based computational modeling scaled to the level of neural networks. To address these knowledge gaps, we will examine longitudinal progression of neural dysconnectivity in early-course SCZ patients after their initial admission into the Specialized Treatment Early in Psychosis (STEP) Clinic at Yale. In turn, we will follow patients longitudinally at 6, 12, and 24 months later in comparison with 50 matched healthy controls. To quantify dysconnectivity the project will use leading functional and structural methods optimized by the Human Connectome Project (HCP), in line with the NIMH Connectomes Related to Human Disease initiative. First, we aim to test if the recently identified PFC and thalamo-cortical markers exhibit concurrent (or dissociable) structural and functional alterations. This balanced longitudinal design can distinguish `state' versus `trait' neuroimaging markers during early illness course in relation to clinically-relevant variables. Specifically, examining effects of pharmacotherapy, treatment compliance, duration of untreated psychosis, and symptom severity, informs the clinical utility of these promising neuroimaging markers. Second, the project will test if these neuroimaging markers relate to severity of cognitive deficits - a hallmak clinical feature of SCZ. We aim to concurrently examine working memory (WM) via our validated neuroimaging paradigms to test if specific aspects of structural and functional dysconnectivity predict WM deficits. This provides a much-needed link between dysconnectivity and cognitive impairment in SCZ. Finally, to inform synaptic hypotheses behind neural dysconnectivity, such as cortical excitation-inhibition (E/I) imbalance resulting from hypo-function of the N-methyl-D-aspartate glutamate receptor (NMDAR), we aim to use biophysically-based computational models that incorporate relevant cellular detail. We aim to iteratively explore synaptic parameters governing E/I balance by fitting in silico effects with in vivo clinical neuroimaging findings. This computational psychiatry approach can help interpret dynamic neural dysconnectivity in SCZ via computational fits and yield new synaptic targets for treatment studies focused on early SCZ stages, when intervention is most vital.