Understanding how the human brain produces cognition ultimately depends on precise quantitative characterization of context-dependent dynamic functional networks (DFN) that transiently link distributed brain regions. Progress in achieving this goal has been limited due to a lack of theoretical frameworks for characterizing DFNs and appropriate computational methods to test them. Devising and validating computational methods for investigating DFNs in the human brain is thus of great significance. The first major goal of this proposal is to address a critical need in human brain research by developing novel algorithms for identifying DFNs and characterizing dynamic network interactions between distributed brain regions. To achieve this goal, we will develop and validate novel computational methods within the framework of Bayesian switching linear dynamical systems (BSDS) with vector autoregressive models (VAR) and factor analysis (FA) that overcome major limitations of existing methods for investigating dynamic interactions in the human brain. The second major goal of this proposal is to use BSDS to investigate DFNs underlying cognitive function in healthy adults, and in patients with Parkinson's disease (PD). Severe cognitive impairment is one of the most devastating behavioral outcomes in patients with PD, yet little is known about the temporal properties of dysfunctional neurocognitive systems in this debilitating disorder. The computational algorithms we propose to develop, validate, and apply will allow us to rigorously investigate brain dynamics that support critical cognitive functions and significantly advance our understanding of dynamic processes underlying human brain function and dysfunction. Our proposed studies will also, for the first time, investigate DFNs in simulated, rodent in vivo optogenetic fMRI, as well as human data using state-of-the-art (sub- second) high-temporal resolution fMRI data generated by the NIH-funded Stanford Alzheimer's Disease Research Center (ADRC), highlighting critical translational applications of our proposed methods. Our proposed studies will provide novel tools for investigating dynamic functional networks in the human brain, with innovative applications to the Human Connectome Project (HCP) and the study of neurological disorders and clinical neuroscience more broadly. The proposed studies are highly relevant to the mission of the BRAIN Initiative (RFA-EB-15-006), which calls for the development and dissemination of innovative computational tools for probing human brain function and dysfunction. Our computational tools will be widely disseminated to facilitate research into the dynamical aspects of human brain function.

Public Health Relevance

/Public Health Statement We propose to develop and validate advanced computational tools for investigating dynamic functional networks in the human brain. The novel tools we develop will be used to investigate aberrant functional circuits associated with cognitive impairments in Parkinson's disease (PD). The proposed studies have significant public health relevance and are highly relevant to the mission of the BRAIN initiative, which calls for the development and dissemination of innovative computational tools for understanding human brain function and dysfunction.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB022907-01
Application #
9170593
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
2016-09-30
Project End
2019-06-30
Budget Start
2016-09-30
Budget End
2017-06-30
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Stanford University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Cai, Weidong; Chen, Tianwen; Szegletes, Luca et al. (2018) Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits. Biol Psychiatry Cogn Neurosci Neuroimaging 3:263-273
Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen et al. (2017) Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI. Neuroimage 155:271-290
Ryali, Srikanth; Supekar, Kaustubh; Chen, Tianwen et al. (2016) Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling. PLoS Comput Biol 12:e1005138