Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography.

Autor:	Warsi NM; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada; Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada., Wong SM; Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada., Germann J; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada., Boutet A; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada., Arski ON; Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada., Anderson R; Maerospace Corporation, Waterloo, Ontario, Canada., Erdman L; Vector Institute for Artificial Intelligence, University Health Network, Toronto, Ontario, Canada., Yan H; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Suresh H; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada; Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada., Gouveia FV; Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada., Loh A; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada., Elias GJB; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada., Kerr E; Department of Psychology, The Hospital for Sick Children, University of Toronto, 555 University Ave., Toronto, Ontario, Canada, M5G 1X8., Smith ML; Department of Psychology, The Hospital for Sick Children, University of Toronto, 555 University Ave., Toronto, Ontario, Canada, M5G 1X8., Ochi A; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Otsubo H; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Sharma R; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Jain P; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Donner E; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Lozano AM; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada., Snead OC; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada., Ibrahim GM; Division of Neurosurgery, The Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada; Department of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada. Electronic address: george.ibrahim@sickkids.ca.
Jazyk:	angličtina
Zdroj:	Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2023 Oct; Vol. 167, pp. 827-837. Date of Electronic Publication: 2023 Jul 23.
DOI:	10.1016/j.neunet.2023.07.019
Abstrakt:	Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. We leverage stereotactic intracranial recordings to directly resolve local-field potentials from otherwise inaccessible structures to study moment-to-moment attentional activity in children with epilepsy performing a flexible attentional task. On an individual subject level, we employed deep learning to decode neural features predictive of task performance indexed by single-trial reaction time. These models were subsequently aggregated across participants to identify predictive brain regions based on AAL atlas and FIND functional network parcellations. Through this approach, we show that fluctuations in beta (12-30 Hz) and gamma (30-80 Hz) power reflective of increased top-down attentional control and local neuronal processing within relevant large-scale networks can accurately predict single-trial task performance. We next performed connectomic profiling of these highly predictive nodes to examine task-related engagement of distributed functional networks, revealing exclusive recruitment of the dorsal default mode network during shifts in attention. The identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze:	MEDLINE
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