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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21NS071221-02
Application #
8293093
Study Section
Special Emphasis Panel (ZRG1-SBIB-J (80))
Program Officer
Babcock, Debra J
Project Start
2011-08-01
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2014-07-31
Support Year
2
Fiscal Year
2012
Total Cost
$197,500
Indirect Cost
$72,500
Name
Stanford University
Department
Psychiatry
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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
Ryali, Srikanth; Shih, Yen-Yu Ian; Chen, Tianwen et al. (2016) Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions. Neuroimage 132:398-405
Ryali, Srikanth; Chen, Tianwen; Padmanabhan, Aarthi et al. (2015) Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI. J Neurosci Methods 240:128-40
Chen, Tianwen; Michels, Lars; Supekar, Kaustubh et al. (2015) Role of the anterior insular cortex in integrative causal signaling during multisensory auditory-visual attention. Eur J Neurosci 41:264-74
Cai, Weidong; Ryali, Srikanth; Chen, Tianwen et al. (2014) Dissociable roles of right inferior frontal cortex and anterior insula in inhibitory control: evidence from intrinsic and task-related functional parcellation, connectivity, and response profile analyses across multiple datasets. J Neurosci 34:14652-67
Chen, Tianwen; Ryali, Srikanth; Qin, Shaozheng et al. (2013) Estimation of resting-state functional connectivity using random subspace based partial correlation: a novel method for reducing global artifacts. Neuroimage 82:87-100
Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh et al. (2013) A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI. Neuroimage 65:83-96
Supekar, Kaustubh; Menon, Vinod (2012) Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model. PLoS Comput Biol 8:e1002374
Cho, Soohyun; Metcalfe, Arron W S; Young, Christina B et al. (2012) Hippocampal-prefrontal engagement and dynamic causal interactions in the maturation of children's fact retrieval. J Cogn Neurosci 24:1849-66
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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