Speaker
Description
Closed-loop auditory stimulation (CLAS) is a promising non-invasive technique for modulating brain oscillations by delivering auditory stimuli phase-locked to ongoing neural activity. Recent work applying CLAS to theta (4.5 – 7.5 Hz) and alpha (7.5 – 12.5 Hz) oscillations during REM sleep has demonstrated phase-dependent changes in EEG power and frequency \cite{jaramillo_closed-loop_2024}. It has been hypothesized that these effects arise from phase-resetting..
We developed a mathematical model to test the extent to which a phase-reset mechanism can predict the observed changes. Using a Kuramoto oscillator model \cite{mirkhani_response_2025} with all-to-all coupling, we tracked the collective network activity of oscillators with natural frequencies in the theta and alpha bands separately, allowing band-specific phase-locked stimulation and analyses.
Baseline parameters were fitted to unperturbed REM activity, with stimulation parameters determined from single-pulse evoked responses using phase-response curves. The system was simulated with phase-locked stimulation, including realistic phase-locking error, to predict phase-dependent frequency and power changes. We compared predictions against the data, analysed parameter sensitivity, and assessed how phase-locking accuracy affects outcomes.
Our work provides a quantitative framework for understanding how stimulation timing influences CLAS effects and for guiding future protocol design.
Bibliography
@article{jaramillo_closed-loop_2024,
title = {Closed-loop auditory stimulation targeting alpha and theta oscillations during rapid eye movement sleep induces phase-dependent power and frequency changes},
volume = {47},
copyright = {https://academic.oup.com/pages/standard-publication-reuse-rights},
issn = {0161-8105, 1550-9109},
url = {https://academic.oup.com/sleep/article/doi/10.1093/sleep/zsae193/7745355},
doi = {10.1093/sleep/zsae193},
abstract = {Abstract
Study Objectives
Alpha and theta oscillations characterize the waking human electroencephalogram (EEG) and can be modulated by closed-loop auditory stimulation (CLAS). These oscillations also occur during rapid eye movement (REM) sleep, but their function here remains elusive. CLAS represents a promising tool to pinpoint how these brain oscillations contribute to brain function in humans. Here we investigate whether CLAS can modulate alpha and theta oscillations during REM sleep in a phase-dependent manner.
Methods
We recorded high-density EEG during an extended overnight sleep period in 18 healthy young adults. Auditory stimulation was delivered during both phasic and tonic REM sleep in alternating 6-second ON and 6-second OFF windows. During the ON windows, stimuli were phase-locked to four orthogonal phases of ongoing alpha or theta oscillations detected in a frontal electrode.
Results
The phases of ongoing alpha and theta oscillations were targeted with high accuracy during REM sleep. Alpha and theta CLAS induced phase-dependent changes in power and frequency at the target location. Frequency-specific effects were observed for alpha trough (speeding up) and rising (slowing down) and theta trough (speeding up) conditions. CLAS-induced phase-dependent changes were observed during both REM sleep substages, even though auditory evoked potentials were very much reduced in phasic compared to tonic REM sleep.
Conclusions
This study provides evidence that faster REM sleep rhythms can be modulated by CLAS in a phase-dependent manner. This offers a new approach to investigating how modulation of REM sleep oscillations affects the contribution of this vigilance state to brain function.},
language = {en},
number = {12},
urldate = {2026-03-12},
journal = {SLEEP},
author = {Jaramillo, Valeria and Hebron, Henry and Wong, Sara and Atzori, Giuseppe and Bartsch, Ullrich and Dijk, Derk-Jan and Violante, Ines R},
month = dec,
year = {2024},
pages = {zsae193},
}
@article{mirkhani_response_2025,
title = {Response of {Neuronal} {Populations} to {Phase}-{Locked} {Stimulation}: {Model}-{Based} {Predictions} and {Validation}},
volume = {45},
copyright = {https://creativecommons.org/licenses/by-nc-sa/4.0/},
issn = {0270-6474, 1529-2401},
shorttitle = {Response of {Neuronal} {Populations} to {Phase}-{Locked} {Stimulation}},
url = {https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.2269-24.2025},
doi = {10.1523/JNEUROSCI.2269-24.2025},
abstract = {Modulation of neuronal oscillations holds promise for the treatment of neurological disorders. Nonetheless, conventional stimulation in a continuous open-loop manner can lead to side effects and suboptimal efficiency. Closed-loop strategies such as phase-locked stimulation aim to address these shortcomings by offering a more targeted modulation. While theories have been developed to understand the neural response to stimulation, their predictions have not been thoroughly tested using experimental data. Using a mechanistic coupled oscillator model, we elaborate on two key predictions describing the response to stimulation as a function of the phase and amplitude of ongoing neural activity. To investigate these predictions, we analyze electrocorticogram recordings from a previously conducted study in parkinsonian rats and extract the corresponding phase and amplitude response curves. We demonstrate that the amplitude response to stimulation is strongly correlated to the derivative of the phase response (
ρ
{\textgreater} 0.8) in all animals except one, thereby validating a key model prediction. The second prediction postulates that the stimulation becomes ineffective when the network synchrony is high, a trend that appeared missing in the data. Our analysis explains this discrepancy by showing that the neural populations in parkinsonian rats did not reach the level of synchrony for which the theory would predict ineffective stimulation. Our results highlight the potential of fine-tuning stimulation paradigms informed by mathematical models that consider both the ongoing phase and amplitude of the targeted neural oscillation.},
language = {en},
number = {15},
urldate = {2026-03-12},
journal = {The Journal of Neuroscience},
author = {Mirkhani, Nima and McNamara, Colin G. and Oliviers, Gaspard and Sharott, Andrew and Duchet, Benoit and Bogacz, Rafal},
month = apr,
year = {2025},
pages = {e2269242025},
}
