Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography

Warsi, N.; Wong, S. M.; Germann, J.; Boutet, A.; Arski, O.; Anderson, R.; Erdman, L.; Yan, H.; Suresh, H.; Venetucci Gouveia, F.; Loh, A.; Elias, G.; Kerr, E.; Smith, M. L.; Ochi, A.; Otsubo, H.; Sharma, R.; Jain, P.; Donner, E.; Lozano, A. M.; Snead, O. C.; Ibrahim, G.

doi:10.1101/2022.02.25.481973

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bioRxiv · 10.1101/2022.02.25.481973

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography

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Abstract

BackgroundCognitive 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. MethodsStereotactic intracranial recordings that permit direct resolution of local-field potentials from otherwise inaccessible structures were employed to study moment-to-moment attentional activity in children with epilepsy during the performance of an attentional set-shifting task. A combined deep learning and model-agnostic feature explanation approach was used to analyze these data and decode attentionally-relevant neural features. Connectomic profiling of highly predictive attentional nodes was further employed to examine task-related engagement of large-scale functional networks. ResultsThrough this approach, we show that beta/gamma power within executive control, salience, and default mode networks accurately predicts single-trial attentional performance. Connectomic profiling reveals that key attentional nodes exclusively recruit dorsal default mode subsystems during attentional shifts. ConclusionsThe 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. FundingNo funds supported this specific investigation. Awards and grants supporting authors include: Canadian Institutes of Health Research (CIHR) Vanier Scholarship (NMW, HY); CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award (SMW); CIHR Canada Graduate Scholarship Masters Award (ONA); and a CIHR project grant (GMI).

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Warsi, N., Wong, S. M., Germann, J., Boutet, A., Arski, O., Anderson, R., Erdman, L., Yan, H., Suresh, H., Venetucci Gouveia, F., Loh, A., Elias, G., Kerr, E., Smith, M. L., Ochi, A., Otsubo, H., Sharma, R., Jain, P., Donner, E., Lozano, A. M., Snead, O. C., Ibrahim, G.. 2022-02-26. Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography. https://doi.org/10.1101/2022.02.25.481973

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