Global and non-Global slow oscillations differentiate in their depth profiles

Sang-Cheol Seok; Elizabeth McDevitt; Sara C Mednick; Paola Malerba

doi:10.3389/fnetp.2022.947618

Global and non-Global slow oscillations differentiate in their depth profiles

Front Netw Physiol. 2022 Oct 24:2:947618. doi: 10.3389/fnetp.2022.947618. eCollection 2022.

Authors

Sang-Cheol Seok¹, Elizabeth McDevitt², Sara C Mednick³, Paola Malerba^{1

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Affiliations

¹ Battelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, United States.
² Princeton University, Princeton, NJ, United States.
³ Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.
⁴ Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States.
⁵ School of Medicine, The Ohio State University, Columbus, OH, United States.

Abstract

Sleep slow oscillations (SOs, 0.5-1.5 Hz) are thought to organize activity across cortical and subcortical structures, leading to selective synaptic changes that mediate consolidation of recent memories. Currently, the specific mechanism that allows for this selectively coherent activation across brain regions is not understood. Our previous research has shown that SOs can be classified on the scalp as Global, Local or Frontal, where Global SOs are found in most electrodes within a short time delay and gate long-range information flow during NREM sleep. The functional significance of space-time profiles of SOs hinges on testing if these differential SOs scalp profiles are mirrored by differential depth structure of SOs in the brain. In this study, we built an analytical framework to allow for the characterization of SO depth profiles in space-time across cortical and sub-cortical regions. To test if the two SO types could be differentiated in their cortical-subcortical activity, we trained 30 machine learning classification algorithms to distinguish Global and non-Global SOs within each individual, and repeated this analysis for light (Stage 2, S2) and deep (slow wave sleep, SWS) NREM stages separately. Multiple algorithms reached high performance across all participants, in particular algorithms based on k-nearest neighbors classification principles. Univariate feature ranking and selection showed that the most differentiating features for Global vs. non-Global SOs appeared around the trough of the SO, and in regions including cortex, thalamus, caudate nucleus, and brainstem. Results also indicated that differentiation during S2 required an extended network of current from cortical-subcortical regions, including all regions found in SWS and other basal ganglia regions, and amygdala and hippocampus, suggesting a potential functional differentiation in the role of Global SOs in S2 vs. SWS. We interpret our results as supporting the potential functional difference of Global and non-Global SOs in sleep dynamics.

Keywords: machine learning; sleep; sleep EEG; slow oscillations; space-time.

Grants and funding

R01 AG046646/AG/NIA NIH HHS/United States