Multi-modal brain imaging analysis method for brain modeling

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The primary goal of the proposed research program is to develop and improve the brain imaging analysis methods. The emergence of brain imaging techniques such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) significantly contributed to broadening our knowledge about how the brain functions. For example, with injection of [18F]fluorodeoxyglucose (FDG; the most widely used radiotracer for PET), we can locate where glucose is rapidly metabolized which is related with the level of neuronal activity in particular conditions that the subject is scanned under. Although illuminating, one of the limitations of the typical FDG-PET technique is that one cannot model the region-to-region connectivity. In other words, it is difficult to model the brain as a network where distant brain regions are communicating with each other.*******Utilizing the functional connectivity and graph theory on FDG-PET images, I have identified “hubs of information flow” in human brain. Stimulating (functionally interfering) the hub brain region may have the most influence on the brain network structure and alter how the brain processes information. Here, use of fMRI will enable personalized hub identification that is not possible with FDG-PET alone. Here, the first half of my proposed research program will focus on modelling mathematical brain networks based on large brain imaging database and validate the constructed models by “perturbing-and-measuring” experimental approach using non-invasive brain stimulation technique. This proposal will promote a culture of mathematical theory-experiment partnership similar to that prevailing in the physical sciences which has been lacking in the field of brain imaging.*******In basic neuroscience research, perhaps the biggest advantage of using PET is that one can monitor the activities of specific neurochemicals that the radiotracer is targeting. However, current approaches often require arterial blood sampling to estimate how much radiotracer is actually delivered to the brain region. This is associated with risks including arterial occlusion, bleeding and infection. By simultaneously acquiring blood flow information using hybrid PET-MRI, we will develop methods that accurately delineate arteries in the brain images thereby replacing the real blood sampling with “image-based blood sampling.” We will also develop a method that clarifies the sources of changes in the PET outcome measures which has been often obscured by concurrently changing blood flow.*******In sum, my team will develop methods that overcome the challenges associated with PET and MR imaging, including but not limited to the above-mentioned difficulties. One PhD student and three MSc students will be recruited. HQPs trained in the proposed research program and their research outcome will greatly contribute to the advances of brain imaging science.***