0112 Classification of reconstructed depth profiles shows Global and non-Global slow oscillations differentiate in the hippocampus and thalamus

Abstract

Abstract Introduction Sleep slow oscillations (SOs, 0.5-1.5 Hz) 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. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects.Sleep slow oscillations (SOs, 0.5-1.5 Hz) 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. In this study, we estimate the current density within the brain that generates a Global SO, to evaluate which sub-cortical structures are involved in Global SO dynamics. We then train multiple machine learning algorithms to distinguish between Global SOs and other SO types, and probe variance of Global/non-Global SO profiles within and across subjects. Methods 32 volunteers (18 females) slept in the lab with polysomnography including 24 head EEG channels; their sleep was scored according to AASM criteria. SOs were algorithmically detected at each channel and classified as Global or non-Global using our method (Malerba et al., 2019). The depth profile of each SO was reconstructed with current source estimation (in Brainstorm followed by sLORETA), with a standardized head model including 17 regions. Each depth profile was embedded in a matrix averaging current by region and in three 200ms-long time bins: before, during and after the SO trough. Thirty classifiers were applied to this dataset, leveraging Matlab’s supervised learning application. We compared accuracy within and across subjects and identified best-performing algorithms across dataset size. We then used univariate feature selection to quantify the relevance of each region-time pair to successful classification. Results Global/non-Global SOs current depth profiles have higher variance across subjects, and accuracy improves when data is sampled between rather than within individuals. Ensemble subspace methods reached highest accuracy (98.5%). Feature selectivity identified cortical, hippocampal, and thalamic currents at the trough of the SO as the most relevant for Global/non-Global SO classifications. Conclusion We introduce an analytical framework enabling the study of SO depth profiles, including their time evolution, as matrices. The predominant differentiation of Global/non-Global SOs in cortical, hippocampal, and thalamic currents supports the potential functional difference of these SO types. Support (If Any) NIH grant (R01 AG046646) to S.C.M.