CRCNS: Neurophysiological Basis of Brain Connectivity

CRCNS：大脑连接的神经生理学基础

基本信息

批准号：
8902101
负责人：
Bharat Bhusan Biswal
金额：
$ 12.74万
依托单位：
NEW JERSEY INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
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/8902101
关键词：
Aging Alzheimer&apos 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

项目摘要

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巨大的研究努力，但对神经系统的原理和动态的彻底了解仍在其中。计算神经科学是更好地理解大脑功能的关键方法之一。但是，探索和模型的大脑功能以及神经元和大脑区域的相互作用，以及检查模型的特定边界条件的有效性，这些边界条件需要从体内实验数据及其分析。获得体内功能代谢信息的一种强大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，揭示了多个时间链接的体内参数。因此，PET/MRI和计算神经科学以完美的方式相互补充。在此提案中，多模式成像领域的两个世界领先机构（Tuebingen大学）和大脑连接映射（NIJT新泽西理工学院）联合起来探索迄今未知的代谢大脑连接性地形。到目前为止，关于大脑中大规模网络甚至在休息条件下都很活跃的许多问题尚未得到解答。到目前为止，他们的确切起源，解释以及对能耗的需求尚不清楚。在过去的三年中，静止状态，功能连通性（RSFC）功能磁共振成像（FMRI）通常也称为功能连通性fMRI（FC-FMRI）的功能连通性（FC-FMRI）已经看到，从基本脑部成像到临床外科手术计划或神经性疾病中的临床应用或神经性疾病的应用的兴趣大大增加。 FCMRI首先是由美国和同事的PI开发相关的皮质网络，例如运动皮层，但在功能无关的网络之间不进行。尽管广泛使用了这项技术，但尚不清楚其生理和代谢基础知识。最近，来自德国和同事的PI还发现，有关大脑连通性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。小动物中的PET/MR成像合并，具有研究大脑连通性的这些代谢和神经生理基础知识的能力。该技术的一种优势是可以同时获取PET和功能磁共振成像数据，从而最大程度地减少了混杂因素，例如温度，呼吸率或动物位置的变化。但是，PET/MRI生成的大量数据及其复杂的起源要求对先进的计算分析方法的要求，但这些数据又为数学模型提供了一种新颖的输入。通过将新型数据与计算模型结合使用，我们建议在确定大脑连通性的代谢基础上迈出重要的一步。为此，我们希望使用各种宠物追踪器在静止和刺激过程中使用多种宠物追踪器获取如此独特的宠物/MR数据。该数据将与FC-FMRI结合使用，因此可以直接比较FC-PET和FC-FMRI。我们将基于独立组件分析（ICA）以及基于图的网络测量方法进一步开发特定的计算神经科学方法，以分析FC-PET数据。到目前为止，此类方法尚未提出。然后，我们将使用合并的数据采集和数据分析方法来研究FC-PET和FC-FMRI信息是多余的还是互补的。我们还设想，PET数据的定量性质提供了对大型大脑网络使用的充满活力预算的新见解。这项美国 - 德国研究提案具有对科学的巨大影响，但总体上也具有巨大影响。基于大脑中特定的转运蛋白系统，新颖而独特的大脑连接数据可能会产生对大脑网络的详细见解 - 这对基础研究，神经生理学以及旨在在其建模和理论框架中实现新颖网络的计算神经科学家具有根本性。所提出的分析方法将对医学成像科学家有用，因为它从PET成像数据中得出了未利用的信息。此外，临床医生还可以将开发的FC-PET技术应用于从脑肿瘤到阿尔茨海默氏病或帕金森病的多种神经系统疾病。特别是这些疾病的代谢基础，可以使用 FC-PET和FC-FMRI方法在此提案中开发了。尤其是在衰老社会中，不仅对这种疾病的经济负担产生巨大影响，而且还可能是由于更好的治疗监测给数百万患者带来了新的希望。因此，我们的项目可以视为可以应用于大量基础研究以及涉及FC网络的临床挑战的框架的基础。