Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition

Jalil Taghia (), Weidong Cai (), Srikanth Ryali, John Kochalka, Jonathan Nicholas, Tianwen Chen and Vinod Menon ()
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Jalil Taghia: Stanford University School of Medicine
Weidong Cai: Stanford University School of Medicine
Srikanth Ryali: Stanford University School of Medicine
John Kochalka: Stanford University School of Medicine
Jonathan Nicholas: Stanford University School of Medicine
Tianwen Chen: Stanford University School of Medicine
Vinod Menon: Stanford University School of Medicine

Nature Communications, 2018, vol. 9, issue 1, 1-19

Abstract: Abstract Human cognition is influenced not only by external task demands but also latent mental processes and brain states that change over time. Here, we use novel Bayesian switching dynamical systems algorithm to identify hidden brain states and determine that these states are only weakly aligned with external task conditions. We compute state transition probabilities and demonstrate how dynamic transitions between hidden states allow flexible reconfiguration of functional brain circuits. Crucially, we identify latent transient brain states and dynamic functional circuits that are optimal for cognition and show that failure to engage these states in a timely manner is associated with poorer task performance and weaker decision-making dynamics. We replicate findings in a large sample (N = 122) and reveal a robust link between cognition and flexible latent brain state dynamics. Our study demonstrates the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.

Date: 2018
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DOI: 10.1038/s41467-018-04723-6

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