Systems-Level Dysconnectivity in First Episode Psychosis

精神分裂症（Sz）是一种使人衰弱的严重精神疾病，具有终身残疾， 医疗系统和社会。尽管经过几十年的研究， Sz未知。确定Sz大脑异常的尝试已经超出了寻找一到几个 损伤位置，以关注系统级脑回路和相关的神经元之间的功能（dys）连接 这些神经事件是分布式电路中信息功能整合失败的基础。有效 连通性指的是一个区域的活动对另一个区域的活动的影响，从而可以进行推断 关于方向性。我们的总体假设是，长距离皮层有效连接是一个基本的 精神分裂症的生物系统异常，特别是前额叶皮层和感觉区之间的异常。这 建议包括对分布式系统中的结构、功能和连通性进行系统级检查 已知在Sz，颞叶听觉皮层和额下回的大脑皮层-执行皮层受损。 皮层参与产生失配负波（MMN），这是自动听觉变化检测的指标。 我们将检查这个分布式系统的节点之间的有效连接，并使用计算建模 将EEG和MEG的神经生理信息转换为突触电导，表明可能的 系统水平缺陷的分子机制。我们的方法的核心是在第一次测试个人 精神分裂症谱系精神病（首发精神分裂症谱系，FESz）的临床接触，其中 对脑结构和功能的进行性原发和继发疾病影响被最小化。病理生理 接近疾病发作很可能反映了疾病病因学的关键过程。在FESz中，我们测试了3个听觉 增加模式复杂性的测试，以区别地对听觉变化检测系统征税，测量大脑 活动与组合的高时间分辨率EEG & MEG神经生理学的措施，并构建 大脑结构的高空间分辨率测量，以将传感器活动投射到皮层源，比较和 FESz和健康个体之间的对比（AIM 1）。使用光谱有效连通性的高级测量 （相转移熵）的源分辨活动，我们将确定之间的不连通性的正面和 FESz中的颞叶皮质源对这些任务的影响（AIM 2）。接下来，使用层状皮质的计算机建模， 电路，以取代单个等效偶极子在每个来源，我们将确定突触电导赤字， AMPA、GABA和NMDA活性可能是FESz（AIM 3）连接障碍的基础。最后，评估 精神病发作后大脑结构、功能和连通性的变化，我们将测试参与者 纵向6个月后追踪进行性损伤（AIM 4）。我们相信研究这些相互作用 自下而上和自上而下的皮层过程之间的联系， 预测编码，学习，记忆，注意力和其他关键的大脑功能的机制， 被Sz破坏了

Schizophrenia (Sz) is a debilitating major mental illness with life-long disability that disproportionately burdens the healthcare system and society. Despite decades of research, the underlying mechanisms of pathophysiology in Sz are unknown. Attempts for determining brain abnormalities in Sz have gone beyond searching for one to a few lesion locations to focusing on functional (dys)connectivity between systems-level brain circuits and the associated neural events that underlie the failure in functional integration of information across distributed circuits. Effective connectivity refers to the influence of activity in one area on activity in another at a later time, allowing inferences about directionality. Our overarching hypothesis is that long-range cortical effective connectivity is a fundamental biological system abnormality in schizophrenia, particularly between prefrontal cortex and sensory areas. This proposal comprises a systems-level examination of structure, function, and connectivity in a distributed system known to be impaired in Sz, the temporal lobe auditory cortices and the inferior frontal gyrus auditory-executive cortex involved in the generation of mismatch negativity (MMN), an index of automatic auditory change detection. We will examine effective connectivity between nodes of this distributed system, and use computational modeling to translate neurophysiological information from EEG & MEG to synaptic conductances, indicating possible molecular mechanisms of the systems-level deficits. Central to our approach is testing of individuals at their first clinical contact for schizophrenia-spectrum psychosis (first episode schizophrenia-spectrum, FESz), where the progressive primary and secondary disease effects on brain structure and function are minimized. Pathophysiology proximal to disease onset very likely reflects processes critical to disease etiology. In FESz, we test 3 auditory tests of increasing pattern complexity to differentially tax the auditory change detection system, measure brain activity with combined high-temporal resolution EEG & MEG measures of neurophysiology, and construct high-spatial resolution measures of brain structure to project sensor activity to cortical sources, compared and contrasted between FESz and well individuals (AIM 1). Using advanced measures of spectral effective connectivity (phase transfer entropy) on the source-resolved activity, we will determine dysconnectivity between the frontal and temporal cortical sources in FESz on these tasks (AIM 2). Next, using computational modeling of a laminar cortical circuit to replace single equivalent dipoles at each source, we will determine synaptic conductance deficits in AMPA, GABA, and NMDA activity that may underlie the dysconnectivity in FESz (AIM 3). Finally, to assess changes in brain structure, function, and connectivity after the onset of psychosis, we will test participants longitudinally 6 months later to track progressive impairments (AIM 4). We believe studying the interactions between bottom-up and top-down cortical processes shows great promise for understanding the basic molecular mechanisms of predictive coding, learning, memory, attention, and other key brain functions that seem to be compromised by Sz.