Response of Neuronal Populations to Phase-Locked Stimulation: Model-Based Predictions and Validation

Journal article

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 > 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.

Authors: Mirkhani, N; McNamara, CG; Oliviers, G; Sharott, A; Duchet, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Society for Neuroscience
• Journal: The Journal of Neuroscience
• Volume: 45
• Issue: 15
• Pages: e2269242025-e2269242025
• Publication date: 2025-03-11
• DOI: 10.1523/jneurosci.2269-24.2025
• EISSN: 1529-2401
• ISSN: 0270-6474
• Language: English
• Keywords: Phase (matter); Stimulation; Psychology; Neuroscience; Artificial intelligence; Brain stimulation; Computer science; Artificial neural network; Physics; Amplitude