Defining the Electrophysiological Dynamics of the Default Mode Network

描述(由申请人提供)：人类默认网络(DN)内的异常活动模式被认为与许多神经精神障碍相关的认知和行为障碍有关，如精神分裂症、抑郁症、自闭症谱系障碍或ADHD。默认网络是一系列功能相连的大脑区域，在内省思维时参与，在感觉运动处理过程中受到抑制。例如，功能磁共振成像(FMRI)研究表明，糖尿病肾病活动的异常模式可能在一定程度上协调精神分裂症的无序思维特征。如功能磁共振成像所示，各种神经精神疾病状态下的糖尿病肾病功能障碍可能不是共同的病理生理过程的结果。尽管有这一假设，但人们对糖尿病肾病的电生理动力学知之甚少，这些信息对于在系统水平上从机制上理解异常功能至关重要。这项提议中概述的实验的总体目标是使用两种技术来确定糖尿病肾病的电生理动力学：1，皮质电(ECoG)，在顽固性癫痫患者的术前评估中，利用标准的硬膜下ECoG电极植入；2，高频头皮脑电(EEG)。具体目标1将使用ECoG来确定在诱发的事件相关的内省、自我参照任务和由语言任务产生的DN抑制过程中，DN内神经反应的快速和动态的时序。在特定的目标2中，我们将使用皮层脑电图来表征DN与语言或任务正向网络之间的相互作用，以阐明DN干扰对任务绩效影响的神经元机制。然而，ECoG的使用仅限于因手术暴露而需要开颅手术的人群。因此，在具体目标3中，我们将利用EEG方法学中的新方法，并以ECoG为金标准，检查EEG将电活动定位到糖尿病肾病深层皮质区域的能力。以他在功能成像方面的背景为基础，设计了一个培训计划，并组建了一支由神经外科医生、神经学家和电生理学家组成的杰出团队，为候选人提供必要的知识，将电生理学纳入他的研究活动。多模式方法的未来应用，使用功能成像和电生理学在精神病人群中追求糖尿病肾病功能，提供了一种非常敏感的手段来描绘糖尿病肾病功能障碍的范围和对心理健康的生物学基础的贡献。 与公共健康相关：这些实验的预期结果将准确地描述人类默认网络的神经生理动力学。它们将有助于我们理解默认网络的生物学效用、相关性和功能，并提供更深入的见解，了解在精神分裂症、自闭症、抑郁症和ADHD等各种精神障碍中报告的网络异常是如何导致潜在病理的。

DESCRIPTION (provided by applicant): Abnormal patterns of activity within the human default network (DN), which is a constellation of functionally connected brain regions that is engaged during introspective thought and suppressed during sensorimotor processing, has been linked to the cognitive and behavioral impairments associated with host of neuropsychiatric disorders such as schizophrenia, depression, autism spectrum disorder or ADHD. For instance, functional magnetic resonance imaging (fMRI) studies have revealed that aberrant patterns of DN activity may in part orchestrate disordered-thinking characteristic to schizophrenia. DN dysfunction, as illuminated with fMRI, across various neuropsychiatric disease states is likely not a consequence of a shared pathophysiologic process. Despite this assumption, remarkably little is known about the electrophysiological dynamics of the DN, information that is crucial to a mechanistic understanding of abnormal function at a systems level. The overall goal of the experiments outlined in this proposal is to define the electrophysiological dynamics of the DN using two techniques: 1, electrocortiography (ECoG), capitalizing on the standard-of-care implantation of subdural ECoG electrodes in the presurgical evaluation of individuals with intractable epilepsy and 2, high frequency scalp-based electroencephalography (EEG). Specific Aim 1 will use ECoG to determine the rapid and dynamic timing of neural responses within the DN during an evoked event-related introspective, 'self-referential' task and during DN suppression engendered by a linguistic task. In Specific Aim 2, we will use ECoG to characterize the interactions between the DN and a language or task positive network to elucidate the neuronal mechanisms underlying DN interference on task performance. However, use of ECoG is limited to populations requiring craniotomies for surgical exposure. Therefore, in Specific Aim 3 we will capitalize on new approaches in EEG methodology and examine the ability of EEG to localize electrical activity to the deep cortical regions of the DN, using ECoG as the gold standard. Building upon his background in functional imaging, a training program has been designed and a distinguished team of neurosurgeons, neurologists and electrophysiologists has been assembled to provide the candidate with the knowledge necessary to incorporate electrophysiology into his research activities. The future application of a multimodal approach, employing both functional imaging and electrophysiology to the pursuits of DN function in psychiatric populations provides an exquisitely sensitive means of profiling the range and contributions of DN dysfunction to the biological basis of mental health. PUBLIC HEALTH RELEVANCE: The anticipated results from these experiments will precisely characterize the neurophysiological dynamics of the default network in humans. They will contribute to our understanding of the biological utility, relevance and function of the default network and provide greater insight into how reported network abnormalities occurring in various mental disorders such as Schizophrenia, Autism, depression and ADHD contribute to underlying pathology.