Network communications flexibly predict visual contents that enhance representations for faster visual categorization

Yan, Y., Zhang, J., Ince, R. A.A. and Schyns, P. G. (2023) Network communications flexibly predict visual contents that enhance representations for faster visual categorization. Journal of Neuroscience, 43(29), pp. 5391-5405. (doi: 10.1523/JNEUROSCI.0156-23.2023) (PMID:37369588) (PMCID:PMC10359031)

Abstract

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N=11, both sexes). Each was cued to the spatial location (left vs. right) and contents (Low vs. High Spatial Frequency, LSF vs. HSF) of a predicted Gabor stimulus that they then categorized. Using each participant’s concurrently measured MEG, we reconstructed networks that predict and categorize LSF vs. HSF contents for behavior. We found that predicted contents flexibly propagate top-down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF vs. HSF representations of the stimulus, all the way from occipital-ventral-parietal to pre-motor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e. 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Yan, Yuening and Schyns, Professor Philippe and Ince, Dr Robin
• Authors: Yan, Y., Zhang, J., Ince, R. A.A., and Schyns, P. G.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Journal of Neuroscience
• Publisher: The Society for Neuroscience
• ISSN: 0270-6474
• ISSN (Online): 1529-2401
• Published Online: 27 June 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Journal of Neuroscience 43(29): 5391-5405
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172413		Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time series	Philippe Schyns	Wellcome Trust (WELLCOTR)	107802/Z/15/Z	Centre for Cognitive Neuroimaging
304240		Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysis	Robin Ince	Wellcome Trust (WELLCOTR)	214120/Z/18/Z	Centre for Cognitive Neuroimaging