Cognitive information processing depends on dynamical interactions between distributed brain areas. In the past decade, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function. Although fMRI research has primarily focused on identifying brain regions that are activated during performance of cognitive tasks, there is growing consensus that cognitive functions emerge as a result of dynamic, context-dependent, causal interactions between multiple brain areas. Devising and validating methods for investigating such interactions has therefore taken added significance. Despite the growing need, the accuracy of current methods for identifying causal interactions in fMRI data remain poorly understood. The overall goal of this proposal is to address a critical need in fMRI by developing and testing new algorithms and software for identifying context-dependent causal interactions between distributed brain regions. We will first develop and validate novel methods based on a Multivariate Dynamical Systems (MDS) framework that overcomes several limitations of existing methods. We will then compare the performance of our new methods with other methods on both simulated and real fMRI data. Important contributions of these proposed studies include (1) development of novel multivariate state space methods for estimating causal interactions between brain regions and (2) first and most detailed evaluation of not only MDS but also other effective connectivity methods using both simulated and experimental fMRI data. Together, these studies will lead to new and improved tools for analyzing functional brain connectivity using fMRI. More generally, our proposed methods will help to advance knowledge of the dynamical basis of human cognitive function and will provide new tools for investigating neurodevelopmental, psychiatric and neurological disorders such as autism, schizophrenia and Parkinson's disease. The proposed studies are highly relevant to the mission of the NIH Exploratory Innovations in Biomedical Computational Science and Technology Program Announcement (PA 09-219), which seeks to encourage development of innovative advanced computational tools for brain imaging.
In the past decade, functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for investigating human brain function and dysfunction. Although fMRI studies of brain function have primarily focused on identifying brain regions that are activated during performance of perceptual or cognitive tasks, there is growing consensus that cognitive functions emerge as a result of dynamic context-dependent interactions between multiple brain areas. Developing new methods for investigating causal interactions in fMRI data has therefore taken added significance;the overall goal of this proposal is to address this critical need by developing new methods for studying causal interactions and brain connectivity between distributed brain regions during cognition.
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|Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh et al. (2012) Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty. Neuroimage 59:3852-61|
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