The Imaging Core can assist with image acquisition and state of the art analyses using multi-modal MRI sequences and methods. Imaging data is uploaded to the ADRC Data and Informatics core, where several imaging databases are available (e.g., ADNI, Aging Brain program project) to investigators.)
Meng Law MD., Leader
Dr. Law investigates advanced MRI methods for acquisition and image analysis. His research focuses on the application of magnetic resonance technology to measure physiological parameters for the diagnosis and treatment of human disease. These include MR spectroscopy, perfusion, vascular permeability, diffusion tensor imaging and resting state fMRI. Dr. Law is Chief of the Division of Neuroradiology and Director of the neuroradiology fellowship program at USC. Dr. Law serves as the chair of the research and executive committees of the American Society of Neuroradiology and as President of the American Society of Functional Neuroradiology.
As the Neuroimaging Core leader he works with the Laboratory for Neuro Imaging, USC Viterbi School of Engineering, USC Dana and David Dornsife Cognitive Neuroscience Imaging Center on facilitating research on image acquisition and processing.
Arthur Toga, PhD., Co-Leader
Dr. Toga has a career-long record of funding and publication in the analysis, modeling and atlasing of images obtained from many species including humans. He has developed unique mathematical techniques for global and local warping of images, atlasing and multidimensional modeling as well as visualization of composite brain image sets. He directs several national and international collaborative studies that involve multisite acquisition of data, coordination of analysis, and dissemination of data and results. The USC Institute for Neuroimaging and Informatics (INI) and his laboratory coordinates neuroimage analysis and related informatics activities with national and international collaborators and, as such, is a worldwide resource by virtue of these distributive activities. Dr. Toga has experience and expertise in local and wide area computer networks, data transmission, storage and archiving as well as data compression and scientific visualization. He has developed a comprehensive supercomputing facility for brain mapping incorporating the latest technology in the analysis of structure and function of the brain in health and disease.
Paul Thompson, PhD
Dr. Thompson’s research focuses on the neuroscience, mathematics, software engineering and clinical aspects of neuroimaging and brain mapping. His group of 30 trainees (4 faculty, 11 PhD students in biomedical engineering, biomedical physics and neuroscience, 7 postdocs, and 8 research assistants) develops new methods to analyze brain images. They rack how diseases spread in the brain over time, often before symptoms begin, and how medications resist them. They specialize in multi-site neuroimaging efforts on an unprecedented scale, such as the ENIGMA Consortium (Thompson + 287 authors, 2013). All data and analysis results are available to all interested investigators.
Yonggang Shi, PhD.
Dr. Shi’s research focuses on the development of general mathematical frameworks and algorithms in mapping neuroanatomical structures and function. Using intrinsic geometry, he has developed novel surface mapping algorithms for both cortical and sub-cortical structures. For example, using Laplace-Beltrami eigenfunctions, he has developed robust algorithms for intrinsic surface reconstruction, modeling, mapping, and automated identification of major sulci. Related software tools have been released and publicly available as workflows in the LONI Pipeline.
Kristi Clark, PhD.
Dr. Clark has extensive training in neuroimaging methods, particularly those related to diffusion-weighted imaging. Her unique expertise relates complex signals to underlying neuroanatomical phenonema. Her research is based on developing and applying complex structural and functional models of brain networks using cutting-edge neuroimaging methods to study the ways in which the human brain responds to experiences. Her goal is to measure how behavioral interventions alter brain networks, through a combination of normalization and compensation mechanisms, in order to identify biomarkers to monitor treatment response.