Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior
Takuya Ito (taku.ito1@gmail.com),
Guangyu Robert Yang,
Patryk Laurent,
Douglas H. Schultz and
Michael W. Cole
Additional contact information
Takuya Ito: Rutgers University
Guangyu Robert Yang: Massachusetts Institute of Technology
Patryk Laurent: Independent Researcher
Douglas H. Schultz: University of Nebraska-Lincoln
Michael W. Cole: Rutgers University
Nature Communications, 2022, vol. 13, issue 1, 1-16
Abstract:
Abstract The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in “conjunction hubs”—brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28323-7
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DOI: 10.1038/s41467-022-28323-7
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