The objective of this award is to develop a computational framework for identifying the critical network topology of brain connectivity in neuroimaging data, specifically functional magnetic resonance imaging (fMRI). In this framework, network optimization modeling and mathematical programming algorithms will be employed to characterize connectivity patterns in fMRI data from different brain regions. Machine learning techniques will be employed to construct a pattern recognition model used to detect biomarkers and predict the brain disease conditions (i.e., abnormals vs. controls). An information-theoretic approach will be used to select the most informative brain regions to improve the generalizability and to increase the accuracy of the diagnosis prediction model.
If successful, the results of this research will lead to improvements in efficiency and efficacy of brain functional connectivity modeling and new developments of optimization methods for handling large-scale spatio-temporal data. The developed computational framework will be extremely useful for neuroscientists and neurologists to identify abnormal functional connectivity in the brain and to gain a greater understanding of the brain function. The framework will be employed and tested as a novel biomarker for differential diagnoses of brain disorders. Alzheimer?s disease (AD), autism spectrum disorder (ASD), and Parkinson?s disease (PD) will be the case points in this project to test if our computational framework is a sensitive enough tool to detect alterations in brain connectivity associated with brain disorders. Accurate diagnosis can substantially extend a patient?s lifespan and some treatments have different outcomes at different disease stages. Additionally, the developed computational framework can be applied to other real-life large-scale spatio-temporal data that arise in other research areas such as manufacturing, medicine, bioinformatics, neuroscience, finance, and geosciences.
