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（由申请人提供）：大脑是人类和动物与自然相互作用的主要器官。尽管在神经科学领域进行了大量的研究工作，但对神经系统的原理和动力学的透彻理解仍处于起步阶段。计算神经科学是更好地理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，从体内实验数据及其分析中演变出特定的边界条件。获得体内功能代谢信息的一种强有力的方法是非侵入性成像，特别是正电子发射断层扫描和磁共振成像（PET/MRI）的最新进展，揭示了多个时间相关的体内参数。因此，PET/MRI和计算神经科学以完美的方式相互补充。在这项提案中，两个多模态成像领域的世界领先机构 （图宾根大学）和大脑连接映射（新泽西理工学院，NIJT）联手探索迄今为止未知的代谢大脑连接领域。许多关于大脑中大规模网络的问题，甚至在休息条件下也是活跃的，到目前为止还没有答案。它们的确切起源、解释以及它们对能源消耗的要求至今还不清楚。在过去的三年里，静息状态功能连接（RSFC）功能磁共振成像（fMRI）通常也称为功能连接功能磁共振成像（fc-fMRI）已经看到了巨大的增长，从基本的脑成像到脑外科手术规划或神经退行性疾病的临床应用。fcMRI首先由来自美国的PI及其同事开发，他们观察到在行为“休息”期间fMRI信号的波动在功能上是时间相关的。 相关的皮层网络，如运动皮层，但不是功能无关的网络之间。尽管这项技术被广泛使用，但其生理和代谢基础尚不清楚。最近，来自德国的PI及其同事还发现，有关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。在小动物中组合PET/MR成像，提供了研究大脑连接的这些代谢和神经生理学基础的能力。该技术的优势之一是可以同时采集PET和fMRI数据，从而最大限度地减少混淆因素，如温度，呼吸率或动物位置的变化。然而，PET/MRI产生的大量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。通过使用新的数据与计算模型相结合，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，我们希望获得迄今为止独特的，结合PET/MR数据，使用各种PET示踪剂调查葡萄糖代谢，血流，多巴胺能和多巴胺能系统的大鼠大脑在休息和刺激。这些数据将与fc-fMRI结合获得，因此将允许fc-PET和fc-fMRI的直接比较。我们将进一步开发特定的计算神经科学方法，用于基于独立成分分析（伊卡）以及基于图形的网络测量的fc-PET数据分析。这种方法迄今尚未提出。我们结合数据采集和数据分析的方法，然后将被用来调查，如果fc-PET和fc-fMRI信息是多余的或互补的。我们还设想，PET数据的定量性质为大规模大脑网络所使用的能量预算提供了新的见解。这项美国和德国的研究计划有可能对科学和整个社会产生巨大的影响。新颖独特的大脑连接数据可能会基于大脑中特定的转运系统对大脑网络产生详细的见解-这不仅对基础研究，神经生理学，而且对旨在在其建模和理论框架中实现新型网络的计算神经科学家具有根本意义。所提出的分析方法将是有用的医学成像科学家，因为它得到迄今未利用的信息从PET成像数据。此外，临床医生还可以将开发的fc-PET技术应用于各种神经系统疾病，从脑肿瘤到阿尔茨海默病或帕金森病。尤其是这些疾病的代谢基础，可以通过使用 fc-PET和fc-fMRI方法在这个建议中开发。这将特别是在老龄化社会中，不仅对此类疾病的经济负担产生巨大影响，而且还可能由于更好的治疗监测而给数百万患者带来新的希望。因此，我们的项目可以被看作是一个框架的基础，可以应用于各种各样的基础研究以及涉及fc网络的临床挑战。