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Related Experiment Video

Updated: May 15, 2026

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

Individual differences in white matter diffusion affect sleep oscillations.

Giovanni Piantoni¹, Simon-Shlomo Poil, Klaus Linkenkaer-Hansen

¹Department of Sleep and Cognition, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands. gio@gpiantoni.com

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|January 4, 2013

Summary

This summary is machine-generated.

Individual differences in brain oscillations during sleep, like sleep spindles and slow waves, are linked to white matter microstructure. This suggests the brain's structural connections influence sleep patterns and cognitive functions.

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Optogenetic Manipulation of Neural Circuits During Monitoring Sleep/wakefulness States in Mice

Optogenetic Manipulation of Neural Circuits During Monitoring Sleep/wakefulness States in Mice

Published on: June 19, 2019

Area of Science:

Neuroscience
Sleep Science
Neuroimaging

Background:

Sleep oscillations, including sleep spindles and slow waves, exhibit stable individual characteristics and variability linked to cognitive functions like memory and intelligence.
The underlying mechanisms for these individual differences in sleep oscillations remain largely unknown.
Sleep oscillations are influenced by synaptic plasticity and neuronal network connectivity.

Purpose of the Study:

To investigate the relationship between individual differences in sleep spindle and slow wave parameters and white matter microstructure.
To explore whether white matter integrity across distributed brain networks underlies variations in sleep oscillation characteristics.

Main Methods:

Simultaneous recording of whole-night, high-density electroencephalography (EEG) and diffusion-weighted magnetic resonance imaging (dMRI).
Quantification of white matter fractional anisotropy (FA) and axial diffusivity (AD) using tract-based spatial statistics (TBSS).
Correlation analysis between sleep oscillation parameters (spindle power, slow wave slope) and dMRI metrics.

Main Results:

Higher sleep spindle power was associated with greater axial diffusivity in specific white matter tracts, including the forceps minor, anterior corpus callosum, temporal lobe fascicles, and thalamic pathways.
A steeper slow wave rising slope correlated with higher axial diffusivity in temporal fascicles and frontal white matter tracts (forceps minor, anterior corpus callosum).

Conclusions:

Individual variations in sleep oscillations (spindles and slow waves) are associated with the microstructural properties of underlying white matter tracts.
Sleep oscillation profiles reflect both synaptic network dynamics and the integrity of the brain's structural white matter connections.