The goal of this R21 project is to enable biophysically realistic, whole-brain, "macro-scale" network anal- ysis from neuroimaging data, at data scales that are not currently possible. Mapping the connectivity of the human brain is one of the great open problems of neuroscience. Understanding the "connectome" is likely to be a key step in unraveling the function of the brain and its connection to both normal behavior and mental illness. Current computational methods for identifying the brain activity networks from neuroimag- ing data span a spectrum from descriptively simple, but computationally efficient, to descriptively detailed and biophysically motivated, but computationally expensive. While popular, the latter models are currently so computationally expensive that it is infeasible to use them for whole-brain, data-driven network inference - the kind of modeling that is necessary to reconstructing the human connectome. In this work, we will develop and validate a new generation of network inference algorithms that will enable whole-brain, data- driven, biophysically-detailed network modeling of large neuroimaging data sets. Our approach focuses on the computational expense of evaluating, or "scoring," individual network models - a key step in the heart of the combinatorial optimization algorithms used for network inference. We replace the exact scoring function with a fast approximation learned from data, yielding orders of magnitude speedup in the optimization as a whole. In this work, we will develop, validate, characterize, and distribute versions of this technique for two classes of widely-used network models for neuroimaging data: dynamic Bayesian networks (DBNs) and dynamic causal models (DCMs). We accomplish this through three specific aims:
Specific Aim 1 : Extend our prior work on fast network identification in general data analysis problems to nonlinear measurable activity networks (sensor space DBNs) for neuroimaging analysis.
Specific Aim 2 : Develop appropriate proxy functions for biophysically-grounded, latent variable network models, such as dynamic causal models (DCMs).
Specific Aim 3 : Test the hypothesis that proxy-based search identifies DBNs and DCMs faster, and at larger scale, with no significant loss of model quality, compared to exact scoring search. This work paves the way for future studies of the network underpinnings of brain function and dysfunction, including probing the effective connectivity substrates of mental illnesses such as schizophrenia, psychopa- thy, or chronic drug addiction.

Public Health Relevance

This work will enable the identification of biophysically-detailed network models of brain activity from neuroimaging data at scales that are currently impractical. That will allow us to better understand the brain activity networks underlying behavior and mental illnesses such as schizophrenia, psychopathy, Alzheimer's disease, or chronic drug addiction. In turn, improved models of the brain's activity in these mental illnesses may pave the way to better prediction of onset, diagnosis, monitoring, and treatment.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (NOIT)
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Cavelier, German
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University of New Mexico
Biostatistics & Other Math Sci
Schools of Engineering
United States
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