In the past two decades, 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 interest in examining how cognitive functions emerge as a result of context-dependent, dynamic causal interactions between distributed brain regions. Devising and validating methods for investigating such interactions has therefore taken on great significance. The first major goal of this proposal is to address a critical need in fMRI research by developing novel algorithms for identifying context-dependent dynamic causal interactions between distributed brain regions. To this end, we will develop and validate novel computational methods using Multivariate Dynamical Systems based Markov chain Monte Carlo (MDS-MCMC) algorithms that overcome major limitations of existing methods for investigating dynamic causal interactions and connectivity in the human brain. A comprehensive validation framework will be use evaluate MDS-MCMC and compare it with existing dynamic causal estimation methods. The second major goal of this proposal is to use the MDS-MCMC framework to investigate dynamic causal interactions underlying cognition in normal healthy adults, and in patients with Parkinson's disease (PD). Cognitive impairment is one of the most devastating symptoms in PD. Once thought of as an insignificant feature of the disease, it is now clear that cognitive impairment is present in the majority of PD patients and that this impairment is significantly linked to increased disability and the risk of mortality, yetlittle is known about the brain basis of cognitive impairment in PD. The computational algorithms we develop, validate, and apply here will allow us to rigorously investigate brain dynamics support critical cognitive processes in the human brain, leading to a more complete understanding of fundamental mechanisms underlying human brain function and dysfunction. Our proposed studies will also, for the first time, examine casual interactions in simulated, open-source, opto-genetic, experimental and clinical brain imaging data using state-of-the-art sub-second high-temporal resolution fMRI, based on the Human Connectome Project (HCP). Critically, we will maintain a tight link between our computational and systems neuroscience goals algorithms to solve important problems in cognitive, systems and clinical neuroscience. Together, our proposed studies will lead to new and improved computational tools for examining dynamical causal interactions between distributed brain regions, with broad applications to the HCP and clinical neuroscience. The proposed studies are highly relevant to the mission of the NIH Innovations in Biomedical Computational Science and Technology and the Big Data to Knowledge Programs, which seek to encourage development and dissemination of innovative advanced computational tools for brain imaging and neuroscience. We will disseminate our algorithms and software to the research community via NITRC .

Public Health Relevance

In the past two decades, 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 computational 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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
1R01NS086085-01A1
Application #
8774725
Study Section
(NOIT)
Program Officer
Babcock, Debra J
Project Start
2014-07-01
Project End
2019-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Stanford University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
City
Stanford
State
CA
Country
United States
Zip Code
94304
Kim, Jeehyun; Zhang, Kai; Cai, Weidong et al. (2018) Dopamine-related dissociation of cortical and subcortical brain activations in cognitively unimpaired Parkinson's disease patients OFF and ON medications. Neuropsychologia 119:24-33
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
Ng, Bernard; Varoquaux, Gael; Poline, Jean Baptiste et al. (2017) Distinct alterations in Parkinson's medication-state and disease-state connectivity. Neuroimage Clin 16:575-585
Cai, Weidong; Chen, Tianwen; Ide, Jaime S et al. (2017) Dissociable Fronto-Operculum-Insula Control Signals for Anticipation and Detection of Inhibitory Sensory Cue. Cereb Cortex 27:4073-4082
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Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh et al. (2016) Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data. J Neurosci Methods 268:142-53
Poston, Kathleen L; YorkWilliams, Sophie; Zhang, Kai et al. (2016) Compensatory neural mechanisms in cognitively unimpaired Parkinson disease. Ann Neurol 79:448-63
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
Cai, Weidong; Chen, Tianwen; Ryali, Srikanth et al. (2016) Causal Interactions Within a Frontal-Cingulate-Parietal Network During Cognitive Control: Convergent Evidence from a Multisite-Multitask Investigation. Cereb Cortex 26:2140-53

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