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Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

Maryam H Mofrad1,2, Greydon Gilmore2,3, Dominik Koller4

  • 1Department of Mathematics, Western University, London, Canada.

Elife
|June 29, 2022
PubMed
Summary

Widespread sleep spindles, detected using deep learning, occur more frequently than previously thought. These large-scale brain rhythms may play a key role in consolidating memories across distributed cortical networks.

Keywords:
computational biologydeep learninghumanmemorymemory consolidationneurosciencerhesus macaquesleep spindlessystems biology

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sleep Science

Background:

  • Sleep is traditionally viewed as a synchronized state across the thalamus and neocortex.
  • Recent studies suggest localized sleep rhythms, challenging the large-scale synchrony hypothesis.
  • The spatial extent of these sleep rhythms, particularly sleep spindles, remains unclear.

Purpose of the Study:

  • To determine the spatial scale of sleep rhythms, specifically sleep spindles.
  • To investigate the frequency and spatiotemporal patterns of sleep spindles across different recording types.
  • To explore the role of widespread spindles in memory consolidation after a visual task.

Main Methods:

  • Adapted deep learning algorithms (originally for earthquake and gravitational wave detection) for neural data analysis.
  • Analyzed sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and human intracranial electroencephalogram (iEEG) recordings.
  • Examined spatiotemporal patterns of multi-area spindles and their changes post-visual memory task.

Main Results:

  • Widespread sleep spindles were found to occur significantly more frequently than previously reported across all recording types.
  • Analysis revealed distinct spatiotemporal patterns of these large-scale, multi-area spindles.
  • Changes in spindle patterns following a visual memory task suggest a role in memory consolidation.

Conclusions:

  • Sleep spindles are not isolated events but are widespread, multi-area phenomena.
  • These widespread spindles may be crucial for consolidating memories within broadly distributed cortical networks.
  • Deep learning offers a powerful tool for analyzing large-scale neural dynamics during sleep.