Brain Connectivity Modeling using Neuroimaging DataHuman brain is the most complex system known to human being. How the brain system efficiently and robustly support the neural computation and information flow is still an open question. Higher function (language, conscious recognition, etc.) is neither the result of activity strictly localized in specific neural structures, nor of the brain as a whole, but emerges from joint dynamics in distributed cortical regions, each relatively specialized for one or more aspects of the function. It is at the systems level that anatomy, physiology, and adaptive function come into correspondence. The composition of such systems is fundamentally constrained by patterns of anatomical connectivity that connect different cortical centers, but the systems’ architecture shifts dynamically so that individual cortical regions are involved in multiple distributed systems. On the other hand, modern neuroimaging techniques have enabled high-throughput measurements and characterization of the function and structure of the brain. Commonly used neuroimaging modalities include Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Diffusion Tensor Imaging (DTI), and functional MRI (fMRI). Neuroimaging has been found to be a powerful method with enormous implications on both scientific discovery and clinical applications, such as understanding how the brain structure support the cognitive functions, how to identify brain regions disrupted by neurodegenerative diseases, how the disease processes such as Alzheimer’s Disease disrupt the functions, how to monitor the disease progression, and how to evaluate the treatment effect as a more sensitive and reliable index than conventional subjective cognitive measurements, etc. We are interested on the modeling and analysis of the brain systems using multi-modality neuroimaging data, focusing on system-level understanding and innovations in machine learning and statistical analysis, to create  an analytic framework that can convert the high-dimensional and noisy neuroimaging data into scientific knowledge and better clinical practices.

 


Collaborators
Integrated Brain Imaging Center at UW: http://www.ibic.washington.edu
Banner Alzheimer's Institute: http://www.banneralz.org/
Byrd Alzheimer's Institute: http://health.usf.edu/byrd/