CRCNS: Neurophysiological Basis of Brain Connectivity

CRCNS：大脑连接的神经生理学基础

• 批准号: 8838312
• 负责人: Bharat Bhusan Biswal
• 金额: $ 12.76万
• 依托单位: NEW JERSEY INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8838312
• 关键词: Aging; Alzheimer' s Disease; Anesthesia procedures; Animal Model; Animals; Base of the Brain; Basic Science; Behavioral; Blood flow; Body Temperature; Brain; Brain Neoplasms; Brain imaging; Brain region; Budgets; Clinical; Communities; Complement; Complex; Computer Analysis; Computer Simulation; Computing Methodologies; Consumption; Data; Data Analyses; Data Set; Databases; Development; Disease; Equipment; Ethics; Functional Magnetic Resonance Imaging; German population; Germany; Graph; Human; Image; Image Analysis; Individual; Institutes; Institution; Investigation; Knowledge; Laboratories; Link; Literature; Magnetic Resonance Imaging; Maps; Measures; Medical Imaging; Metabolic; Methodology; Methods; Modeling; Monitor; Motor Cortex; Nature; Nervous system structure; Network-based; Neurodegenerative Disorders; Neurons; Neurosciences; New Jersey; Organ; Parkinson Disease; Patients; Physiological; Positioning Attribute; Positron-Emission Tomography; Radiation; Rattus; Reporting; Research; Research Personnel; Research Proposals; Respiration; Rest; Science; Scientist; Signal Transduction; Societies; Specificity; System; Techniques; Technology; Temperature; Theoretical model; Tracer; Translations; Universities; Work; base; brain surgery; clinical application; computational neuroscience; data acquisition; design; glucose metabolism; imaging modality; improved; in vivo; independent component analysis; infancy; insight; interest; mathematical model; multimodality; nervous system disorder; neurophysiology; non-invasive imaging; novel; programs

DESCRIPTION (provided by applicant): The brain is the major organ of humans and animals for their interaction with nature. Despite huge research efforts in the field of neurosciences a thorough understanding of the principles and dynamics of the nervous system is still in its infancies. Computational neuroscience is one of the key methodologies for a better understanding of brain function. However, explore and model aspects of brain function and the interplay of neurons and brain regions, as well as to check the validity of models specific boundary conditions evolving from in vivo experimental data and their analysis are needed. A powerful method to gain in vivo functional-metabolic information is non-invasive imaging, specifically represented by the recent advances in combined positron emission tomography and magnetic resonance imaging (PET/MRI), which reveals multiple temporal linked in vivo parameters. Thus, PET/MRI and computational neuroscience complement each other in a perfect way. In this proposal two world leading institutions in the fields of multimodality imaging (University of Tuebingen) and brain connectivity mapping (New Jersey Institute of Technology, NIJT) join forces to explore so far uncharted terrains of metabolic brain connectivity. Many questions regarding large scale networks in the brain, which are even active during resting conditions, are so far unanswered. Their exact origin, interpretation and also their demand on energy consumption are so far not understood. In the last three years, resting state, functional connectivity (RSFC) functional magnetic resonance imaging (fMRI) often also termed functional connectivity fMRI (fc-fMRI) has seen a tremendous increase in interest in applications ranging from basic brain imaging to clinical applications in brain surgery planning or neurodegenerative disease. fcMRI was first developed by the PI from the USA and colleagues who observed that fluctuations in fMRI signals during behavioral "rest" are temporally correlated within functionally related cortical networks such as motor cortex but not between functionally unrelated networks. Despite its widespread use of this technology its physiological and metabolic basics are not understood. Recently the PI from Germany and colleagues also found that useful information about brain connectivity appears to be hidden in dynamic as well as static positron emission tomography (PET) data. Combined PET/MR imaging in small animals, offers the ability to investigate these metabolic and neurophysiological basics of brain connectivity. One strength of this technology is that PET and fMRI data can be acquired simultaneously, therefore minimizing confounding factors such as changes in temperature, respiration rate or animal position. However, the wealth of data generated by PET/MRI and their complex origin requests for advanced computational analysis methods, but in turn these data provide a novel input for mathematical models. By using novel data in conjunction with computational models, we propose to take an important step in determining the metabolic basis of brain connectivity. For this we want to acquire so far unique, combined PET/MR data using a variety of PET-tracers investigating glucose metabolism, blood flow, the serotonergic and the dopaminergic system of the rat brain during rest and stimulation. This data will be acquired in combination with fc-fMRI, and will hence allow a direct comparison of fc-PET and fc-fMRI. We will further develop specific computational neuroscience methods for the analysis of fc- PET data, based on independent component analysis (ICA) as well as graph based network measures. Such methods have so far not been presented. Our combined data acquisition and data analysis approach will then be utilized to investigate if fc-PET and fc-fMRI information are redundant or complimentary. We also envision that the quantitative nature of PET data gives novel insights into the energetic budget used by large-scale brain networks. This US-German research proposal has the potential of a tremendous impact on science but also society in general. The novel and unique brain connectivity data might yield detailed insights into brain networks, based on specific transporter systems in the brain - this is of fundamental interest for basic research, neurophysiology but also for computational neuroscientists who aim to implement novel networks in their modeling and theoretical framework. The proposed analysis methods will be useful for medical imaging scientists, since it derives so far unutilized information from PET imaging data. Moreover, also clinicians can apply the developed fc-PET techniques in a variety of neurological diseases ranging from brain tumors to Alzheimer`s or Parkinson disease. It is especially the metabolic basis of these diseases, which can potentially earlier be identified using fc-PET and fc-fMRI methods developed in this proposal. This would especially in aging societies have a tremendous effect not only on the economical burden of such diseases, but might also due to a better treatment monitoring give new hope to millions of patients. Therefore our project can be seen as the basis of a framework that can be applied to a huge variety of basic research as well as clinical challenges involving fc networks.

描述(申请人提供)：大脑是人类和动物与自然互动的主要器官。尽管在神经科学领域做出了巨大的研究努力，但对神经系统的原理和动力学的彻底了解仍处于初级阶段。计算神经科学是更好地了解大脑功能的关键方法之一。然而，需要探索和模拟大脑功能以及神经元和大脑区域的相互作用，以及检查模型的有效性，以及检查从体内实验数据演变而来的特定边界条件及其分析的有效性。无创成像是获取体内功能代谢信息的一种有效方法，具体表现为正电子发射断层扫描和核磁共振成像(PET/MRI)的最新进展，它揭示了体内多个时间相关的参数。因此，PET/MRI和计算神经科学以一种完美的方式相辅相成。在这份提案中，两家在多模式成像领域处于世界领先地位的机构 (Tuebingen大学)和大脑连接图谱(新泽西理工学院，NIJT)联手探索迄今未知的新陈代谢大脑连接区域。关于大脑中的大规模网络的许多问题，甚至在休息条件下都是活跃的，到目前为止还没有答案。它们的确切来源、解释以及它们对能源消耗的需求到目前为止还不清楚。在过去的三年里，静息状态，功能连接(RSFC)功能磁共振成像(FMRI)也被称为功能连接功能磁共振成像(FC-fMRI)，在从基础脑成像到临床应用于脑外科计划或神经退行性疾病的应用中，人们的兴趣大大增加。FcMRI首先是由来自美国的PI和他的同事们开发的，他们观察到，在行为“休息”期间，fMRI信号的波动在时间上与 相关的皮质网络，如运动皮质，但不在功能无关的网络之间。尽管这项技术被广泛使用，但人们并不了解它的生理和代谢基础。最近，来自德国的PI和他的同事还发现，关于大脑连接的有用信息似乎隐藏在动态和静态正电子发射断层扫描(PET)数据中。结合小动物的PET/MR成像，提供了研究这些大脑连接的代谢和神经生理学基础的能力。这项技术的一个优点是可以同时获取PET和fMRI数据，从而将温度、呼吸频率或动物位置变化等混杂因素降至最低。然而，PET/MRI产生的丰富数据及其复杂的来源要求先进的计算分析方法，但反过来这些数据又为数学模型提供了新的输入。通过使用新的数据结合计算模型，我们建议在确定大脑连接的代谢基础方面迈出重要的一步。为此，我们希望获得到目前为止唯一的、结合的PET/MR数据，使用各种PET示踪剂研究休息和刺激期间大鼠大脑的葡萄糖代谢、血流、5-羟色胺能系统和多巴胺能系统。这些数据将与FC-fMRI一起获得，因此可以对FC-PET和FC-fMRI进行直接比较。我们将进一步开发基于独立分量分析(ICA)和基于图形的网络测量的特定计算神经科学方法来分析FC-PET数据。到目前为止，还没有人提出这样的方法。我们的数据采集和数据分析相结合的方法将被用来调查FC-PET和FC-fMRI信息是冗余的还是补充的。我们还设想，PET数据的量化性质为大规模大脑网络使用的能量预算提供了新的见解。这项美德合作的研究计划有可能对科学乃至整个社会产生巨大影响。这些新颖而独特的大脑连接数据可能会基于大脑中特定的转运蛋白系统对大脑网络产生详细的见解-这对基础研究、神经生理学以及旨在在他们的建模和理论框架中实现新网络的计算神经科学家来说都是基本感兴趣的。建议的分析方法将对医学成像科学家有用，因为它从PET成像数据中获得了迄今未被利用的信息。此外，临床医生还可以将开发的FC-PET技术应用于各种神经疾病，从脑瘤到阿尔茨海默氏症或帕金森病。它尤其是这些疾病的代谢基础，有可能更早地被发现 本提案中开发的FC-PET和FC-fMRI方法。这将特别是在老龄化社会中产生巨大影响，不仅对此类疾病的经济负担产生巨大影响，还可能因为更好的治疗监测给数百万患者带来新的希望。因此，我们的项目可以被视为一个框架的基础，该框架可以应用于各种基础研究以及涉及FC网络的临床挑战。