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.

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Volman, Susan
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Rutgers University
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
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Yuan, Rui; Di, Xin; Taylor, Paul A et al. (2016) Functional topography of the thalamocortical system in human. Brain Struct Funct 221:1971-84
Di, Xin; Biswal, Bharat B (2015) Characterizations of resting-state modulatory interactions in the human brain. J Neurophysiol 114:2785-96
Fu, Zening; Chan, Shing-Chow; Di, Xin et al. (2014) Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI. IEEE Trans Biomed Circuits Syst 8:228-39