This is a revised application for an NIH K25 Mentored Quantitative Research Career Development Award entitled Methods for dynamic causal interactions in the developing human brain. 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 methods for investigating such interactions has therefore taken great significance. The human brain undergoes a protracted period of development, and understanding the maturation of causal brain dynamics underlying information processing is critical for gaining insights into brain and cognitive development. The first major goal of this proposal is to address a critical need in fMRI research by developing novel algorithms for identifying context- dependent causal interactions between distributed brain regions. To this end, we will first develop and validate novel methods based on a Multivariate Dynamical Systems (MDS) framework that overcomes major limitations of several existing methods for investigating causal interactions in the human brain. The second major goal of this proposal is to use the MDS framework to investigate dynamic causal interactions underlying sensory-motor and attention processing in adults, and their differential maturation from childhood to adulthood. The MDS-based methods I propose to develop, validate, test, and apply here will allow me to investigate these processes for the first time, leading to a more complete understanding of fundamental mechanisms underlying human brain function and development. Taken together, the proposed studies will provide important new methods for investigating distributed cortical networks underlying cognition and lead to a more complete understanding of fundamental mechanisms underlying human brain function and development. In conjunction with this research plan, a rigorous education and training program will address critical gaps in my knowledge of brain anatomy, cognitive neuroscience, cognitive psychology and developmental psychology. I will be mentored and trained by leading experts in these fields. I will also gain critical experience in designing fMRI experiments, fMRI data acquisition and analysis, and interpretation of fMRI findings in the context of brain function and development. My training program and the research project will enable me to acquire skills necessary for becoming a successful independent investigator in the fields of computational, cognitive and developmental neuroscience.

Public Health Relevance

Understanding fundamental aspects of brain function is a critical first step towards the development of more targeted treatments, therapies, and interventions for multiple psychiatric and neurological disorders which are becoming urgent public health concerns. Dynamical causal interactions across multiple brain areas play a prominent role in cognitive function. The proposed research seeks to develop and validate novel methods for estimating dynamic causal interactions between brain regions using fMRI data and apply these methods for investigating causal networks in adults underlying sensory-motor mapping and attention, and their different maturational trajectories in the developing brain.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Mentored Quantitative Research Career Development Award (K25)
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Biobehavioral and Behavioral Sciences Subcommittee (CHHD)
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Freund, Lisa S
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Stanford University
Schools of Medicine
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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; 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
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
Abrams, Daniel A; Chen, Tianwen; Odriozola, Paola et al. (2016) Neural circuits underlying mother's voice perception predict social communication abilities in children. Proc Natl Acad Sci U S A 113:6295-300
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
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
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
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
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