Resting-state Neural Networks in Awake Rodents

项目总结/摘要 大脑是一个高度互联的网络系统，分布在大脑中的区域协调一致， 正常的大脑功能和调解复杂的行为。越来越清楚的是，改变的大脑网络 这些特性是精神疾病的基础，但引起这些大规模网络变化的神经基质 仍然未知。一个关键的假设是大脑中枢区域的功能障碍。中枢是大脑的区域， 与大脑网络其他部分的高度连接。由于他们的核心作用，枢纽功能障碍 节点可以改变整体整合过程，并被假设为改变大脑的直接原因 网络功能和脑疾病的病理生理学。然而，直接在人类身上测试这一假设是 具有挑战性，因为选择性地操纵枢纽中的活动并剖析其对大脑网络的因果影响， 难我们将使用尖端工具来弥合这一关键差距，以操纵枢纽的活动，并监测 利用静息态功能磁共振成像研究这些操作对大脑网络的影响 （rsfMRI）和清醒大鼠模型中的行为。在当前的资助周期中，我们已经建立了rsfMRI方法 在清醒大鼠中，这使我们能够可靠地测量静息状态功能连接（RSFC）并表征 大鼠的大脑网络特性。我们在清醒大鼠rsfMRI方法的基础上， 受体专门激活的设计师药物（DREADDs），光遗传学，电生理学和动物 行为。有了这些能力，我们可以随意操纵中枢脑区的活动，并测量大脑中的活动。 相应的全脑网络重构。利用光遗传学，我们可以操纵神经活动， 使用DREADDs，我们可以在几小时的时间尺度上操纵神经活动， 几天。最后，同步电生理学-功能磁共振成像将使我们能够直接将大脑网络和行为联系起来， 神经基础的变化。我们的目标是阐明操纵中枢区域活动对 使用rsfMRI、DREADDs、光遗传学和 电生理学在目标1中，我们将记录网络属性的变化，包括网络拓扑 组织和全脑RSFC动力学诱导的半急性抑制脑枢纽。带着手机- 类型特异性的DREADDs，我们将研究的作用，兴奋和抑制的平衡，在大脑中的枢纽 网络动态阐明网络节点活动与特定脑网络的关系 功能，在目标2中，我们将剖析默认模式网络中每个节点的功能角色以及相关的 老鼠的行为在目标3中，我们将进一步确定枢纽的慢性抑制对长期 网络重组和行为。成功完成拟议的研究将阐明因果关系 中枢的短期和长期功能障碍与大脑网络重构之间的关系， 将有助于理解导致大规模大脑网络变化的神经基质。

Project Summary/Abstract The brain is a highly inter-connected network system, with distributed brain regions orchestrating to maintain normal brain function and mediate complex behavior. It is becoming increasingly clear that altered brain network properties underlie mental illness, but the neural substrates causing these large-scale network changes remain unknown. A key hypothesis is the dysfunction of brain hub regions. Hubs are brain regions that have high degrees of connections with the rest of the brain network. Because of their central roles, dysfunction of hub nodes can change global integrative process, and has been hypothesized to be a direct cause of altered brain network function and pathophysiology of brain disorders. However, directly testing this hypothesis in humans is challenging, as selectively manipulating activity in a hub and dissecting its causal impact on brain networks is difficult. We will bridge this critical gap using cutting-edge tools to manipulate the activity of a hub, and monitor the impact of these manipulations on brain networks using resting state functional magnetic resonance imaging (rsfMRI) and behavior in an awake rat model. In the current grant cycle we have established the rsfMRI approach in awake rats, which allows us to reliably measure resting-state functional connectivity (RSFC) and characterize brain network properties in rats. We have built on our awake rat rsfMRI approach by incorporating Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), optogenetics, electrophysiology and animal behaviors. With these capacities, we can causally manipulate the activity in a hub brain region, and measure the corresponding brain-wide network reconfigurations. Using optogenetics, we can manipulate neural activity on the millisecond timescale, and using DREADDs, we can manipulate neural activity on the times scales of hours to days. Finally, concurrent electrophysiology-fMRI will allow us to directly relate brain network and behavioral changes to their neural basis. Our goal is to elucidate the causal impact of manipulating hub region activity on brain network organization, function and behavior in awake rats using rsfMRI, DREADDs, optogenetics and electrophysiology. In Aim 1, we will document changes in network properties including the network topological organization and brain-wide RSFC dynamics induced by semi-acute suppression of a brain hub. With the cell- type specificity of DREADDs, we will examine the role of the balance of excitation and inhibition in a hub in brain network dynamics. To elucidate the relationship between activity of a network node and specific brain network function, in Aim 2 we will dissect the functional role of each node in the default mode network and related behaviors in rats. In Aim 3, we will further determine the impact of chronic suppression of a hub on long-term network reorganization and behavior. Successful completion of the proposed research will elucidate the causal relationship between short-term and long-term dysfunction of a hub and brain network reconfigurations, which will help understand the neural substrates causing large-scale brain network changes.