Different computations over the same inputs produce selective behavior in algorithmic brain networks

Jaworska, K. , Yan, Y., Van Rijsbergen, N. J. , Ince, R. A.A. and Schyns, P. G. (2022) Different computations over the same inputs produce selective behavior in algorithmic brain networks. eLife, 11, e73651. (doi: 10.7554/eLife.73651) (PMID:35174783) (PMCID:PMC8853655)

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Abstract

A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.

Item Type:Articles
Additional Information:PGS received support from the Wellcome Trust (Senior Investigator Award, UK; 107802) and the Multidisciplinary University Research Initiative/Engineering and Physical Sciences Research Council (USA, UK; 172046–01). RAAI was supported by the Wellcome Trust [214120/Z/18/Z].
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jaworska, Miss Katarzyna and Schyns, Professor Philippe and Van Rijsbergen, Dr Nicola and Ince, Dr Robin
Creator Roles:
Jaworska, K.Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing
Van Rijsbergen, N.Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Writing – review and editing
Ince, R.Conceptualization, Formal analysis, Investigation, Methodology, Software, Supervision, Visualization, Writing – review and editing
Schyns, P.Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review and editing
Authors: Jaworska, K., Yan, Y., Van Rijsbergen, N. J., Ince, R. A.A., and Schyns, P. G.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:eLife
Publisher:eLife Sciences Publications
ISSN:2050-084X
ISSN (Online):2050-084X
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in eLife 11:e73651
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)
172413Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time seriesPhilippe SchynsWellcome Trust (WELLCOTR)107802/Z/15/ZCentre for Cognitive Neuroimaging