Dynamic Inverse Solutions for Multimodal Imaging

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DESCRIPTION (provided by applicant): In recent years, there has been rapid progress in the development of imaging technologies to study brain function. An important focus of current research in many imaging centers is the development of functional neural imaging tools to carry out multimodal image fusion using various combinations of functional magnetic resonance imaging (fMRI), diffuse optical tomography (DOT), electroencephalography (EEG) and magnetoencephalography (MEG) measurements. This requires conducting experiments in which imaging is performed from two or more modalities simultaneously or in sequence so that the information from the different sources can be optimally combined. Using two or more imaging modalities simultaneously offers the exciting prospect of tracking the dynamics of brain activity on different spatial and time-scales. In response to > PAR-04-023, we propose to form a Bioengineering Research Partnership at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital to develop computational resources for fusing imaging measurements from two or more modalities. Using EEG, MEG, fMRI and DOT, this Partnership will develop an integrated state-space, computational framework based on the biophysics, physiology and anatomy of these imaging modalities. The model components for this computational framework will be identified and validated through a series of cross-modality experiments. High-speed supercomputing resources will be used to design and test the state-space data analysis algorithms on simulated and actual multimodal experimental imaging data. The data analysis algorithms developed as part of this Partnership and the data collected in the cross-modality experiments will be freely disseminated to the brain imaging community. The long-term goals of this project are to provide the brain imaging community with a unified computational framework for combining measurements from two or more imaging modalities that can be used in both real-time research studies and clinical management of patients. Real-time analysis of brain function will have important implications for understanding the dynamics of normal brain function, how these dynamics change in pathological conditions such as epilepsy, Alzheimer's disease, stroke and Parkinson's disease and for monitoring brain function during sleep, under anesthesia and in patients treated in the intensive care unit. The partnership is lead by Drs. Boas, Bonmassar, Brown, and Hamalainen. Collectively, they are experts in the multi-modal combination of fMRI (All), EEG and MEG (Bonmassar, Brown, and Hamalainen), and DOT (Boas), spannin experiments, analysis, and clinical application.