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eNeuro. 2019 Jul 11;6(4). pii: ENEURO.0472-18.2019. doi: 10.1523/ENEURO.0472-18.2019. Print 2019 Jul/Aug.

Dynamic Brain Interactions during Picture Naming.

Author information

1
Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030.
2
Department of Neurosurgery, University of Texas Health Science Center, 6431 Fannin Street, Houston, TX 77030.
3
Department of Neurosurgery, University of Texas Health Science Center, 6431 Fannin Street, Houston, TX 77030 xaq@rice.edu nitin.tandon@uth.tmc.edu.
4
Mischer Neuroscience Institute, Memorial Hermann Hospital Texas Medical Center, Houston, TX 77030.
5
Department of Electrical and Computer Engineering, Rice University, 6100 Main St., Houston, TX 77005.
6
Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030 xaq@rice.edu nitin.tandon@uth.tmc.edu.
7
Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030.

Abstract

Brain computations involve multiple processes by which sensory information is encoded and transformed to drive behavior. These computations are thought to be mediated by dynamic interactions between populations of neurons. Here, we demonstrate that human brains exhibit a reliable sequence of neural interactions during speech production. We use an autoregressive Hidden Markov Model (ARHMM) to identify dynamical network states exhibited by electrocorticographic signals recorded from human neurosurgical patients. Our method resolves dynamic latent network states on a trial-by-trial basis. We characterize individual network states according to the patterns of directional information flow between cortical regions of interest. These network states occur consistently and in a specific, interpretable sequence across trials and subjects: the data support the hypothesis of a fixed-length visual processing state, followed by a variable-length language state, and then by a terminal articulation state. This empirical evidence validates classical psycholinguistic theories that have posited such intermediate states during speaking. It further reveals these state dynamics are not localized to one brain area or one sequence of areas, but are instead a network phenomenon.

KEYWORDS:

Hidden Markov Model; dynamics; electrocorticography; language; network

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