Combining Neurophysiological and Neuroimaging Tecniques to Understand Human Brain Function

结合神经生理学和神经影像技术来了解人脑功能

• 批准号: RGPIN-2014-06646
• 负责人: Daskalakis, Zafiris
• 金额: $ 2.48万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612349
• 关键词: Combining; Neurophysiological; Neuroimaging; Tecniques; Understand

Recent advances in technology and computing power have driven a revolution in neuroscience research. These advancements have improved understanding of physiology in the healthy and diseased brain. We propose to combine two complimentary research streams (i.e., neurophysiology and neuroimaging) to better understand key physiological mechanisms (e.g., connectivity) and understand how perturbation of these physiological mechanisms underlie severe neurologic and psychiatric disorders. Combined neurophysiologic, cognitive and imaging data will be collected from a large cohort of healthy individuals (n = 100). During a neuroimaging session, magnetic resonance imaging (MRI) will be utilized to collect functional and structural brain measures. These measures will allow us to examine cortical structure (through cortical thickness analysis), white matter connectivity (through diffusion tensor imaging), and functional connectivity between regions (resting state functional MRI). This will be followed by a neurophysiological testing session. Transcranial magnetic stimulation (TMS), a non-invasive method of brain stimulation, will be combined with electroencephalography (EEG) to probe neurophysiological function. TMS-EEG will be utilized to examine cortical excitability, inhibition, plasticity, and connectivity. Finally, we will utilize an innovative method which combines neuroimaging and neurophysiology, concurrent TMS-fMRI. TMS-fMRI allows us to administer neural stimulation while measuring neural activity. TMS-fMRI allows us to directly probe connectivity between brain regions with a temporal resolution not currently possible with MRI alone. Finally, cognitive testing will be administered. The purpose of the proposed study is two-fold: to examine relationships between neurophysiology and neuroimaging (and how these may relate to cognition), and to examine inter-individual variability within a large data set. To date, very few studies have combined neuroimaging and neurophysiology. A reductionist approach to data analysis will allow us to take these complex data sets and extract a few key measures from each modality. Then, by utilizing partial least squares analysis, we can examine complex relationships between various measures collected over the course of the data. The large sample size will ensure we have statistical power to detect such relationships within the data. Individual variability will be examined using a data clustering approach. We will identify individuals who cluster together according to relationships between data extracted from our imaging, neurophysiological, and cognitive measures. In this way, we hope to identify how particular characteristics amongst individuals may drive variability within data sets. As an example, individuals showing deficits in neural plasticity may demonstrate differences in neuroimaging findings (e.g., decreased cortical thickness and connectivity) that may help us to reconcile cognitive performance. Examining these sources of individual variability may be key to understanding optimal human brain function. The proposed study will be the first large-scale database of its kind that combines neurophysiological, neuroimaging and cognitive measures. To allow maximum impact from the proposed study, data will be made accessible to researchers from around the world via online databases. Combining neuroimaging and neurophysiology with data sharing will not only provide important findings for researchers separately studying neurophysiology or neuroimaging, but will also significantly advance our understanding of human brain functioning. These advances can then be translated into clinical settings, with the potential for significant impact on the theory and treatment of neurological and psychiatric disea

技术和计算能力的最新进展推动了神经科学研究的革命。这些进步提高了对健康和患病大脑生理学的理解。我们建议联合收割机结合两个互补的研究流（即，神经生理学和神经成像）以更好地理解关键的生理机制（例如，连接），并了解这些生理机制的干扰如何成为严重神经和精神疾病的基础。将从一大群健康个体（n = 100）中收集神经生理学、认知和成像数据。在神经影像学检查期间，将利用磁共振成像（MRI）收集功能和结构脑测量结果。这些措施将使我们能够检查皮质结构（通过皮质厚度分析），白色物质连接（通过扩散张量成像）和区域之间的功能连接（静息状态功能MRI）。随后将进行神经生理学测试。经颅磁刺激（TMS）是一种非侵入性的脑刺激方法，将与脑电图（EEG）相结合，以探测神经生理功能。TMS-EEG将用于检查皮质兴奋性、抑制性、可塑性和连通性。最后，我们将利用一种创新的方法，结合神经影像学和神经生理学，同时TMS功能磁共振成像。TMS-fMRI允许我们在测量神经活动的同时进行神经刺激。TMS-fMRI使我们能够直接探测大脑区域之间的连接，其时间分辨率目前仅用MRI是不可能的。最后，将进行认知测试。 这项研究的目的是双重的：检查神经生理学和神经影像学之间的关系（以及这些可能与认知有关），并检查大型数据集内的个体间变异性。迄今为止，很少有研究将神经影像学和神经生理学结合起来。数据分析的简化方法将使我们能够采用这些复杂的数据集，并从每种模态中提取一些关键指标。然后，通过利用偏最小二乘分析，我们可以检查在数据过程中收集的各种度量之间的复杂关系。大样本量将确保我们有统计能力检测数据中的此类关系。 将使用数据聚类方法检查个体变异性。我们将根据从我们的成像，神经生理学和认知测量中提取的数据之间的关系来识别聚集在一起的个体。通过这种方式，我们希望确定个体之间的特定特征如何驱动数据集内的可变性。例如，显示神经可塑性缺陷的个体可能表现出神经成像结果的差异（例如，皮质厚度和连通性降低），这可能有助于我们协调认知表现。研究这些个体差异的来源可能是理解人类大脑最佳功能的关键。 这项研究将是第一个结合神经生理学、神经成像和认知测量的大型数据库。为了使拟议的研究产生最大的影响，来自世界各地的研究人员将通过在线数据库访问数据。将神经影像学和神经生理学与数据共享相结合，不仅可以为分别研究神经生理学或神经影像学的研究人员提供重要的发现，而且还将大大促进我们对人类大脑功能的理解。这些进展可以转化为临床环境，对神经和精神疾病的理论和治疗产生重大影响。