Researchers from the AIRI Institute of Artificial Intelligence, the Skolkovo Institute of Science and Technology, Moscow State University, and the IEPH RAS explored a new kind of brain signal traveling across the cerebral cortex. This marks the first direct look at cortical traveling waves and their potential role in neural processing. The work points to a fresh path for improving brain-computer interfaces and deep brain stimulation strategies used to treat neurological disorders. The AIRI press service informed socialbites.ca about these findings, and the results have been published in Scientific Reports.
Today, one of the main approaches for studying brain function during motor tasks is the analysis of hand movements recorded via implanted electrodes in motor areas of the brain, often in reaching experiments. In scientific practice, two interpretive models are typically used: the representational model, which assigns specific tuning to individual neurons for movement parameters such as speed, direction, acceleration, and joint angles; and the dynamic model, which treats motor activity as a collective dynamic process rather than focusing on single cells.
The new study shifts the focus to brain oscillations themselves. It demonstrates a link between two brain phenomena: cortical traveling waves and the rotational dynamics observed in motor cortex activity. Importantly, it introduces two practical methods for measuring these waves.
One method estimates wave curvature without data compression, enabling analysis of rotation and speed in a two-dimensional projection while preserving essential information. The second method gauges the degree of rotation, allowing researchers to gauge the true dynamics of neural activity in the data and compare different measurements against each other.
Findings indicate that rhythmic fluctuations in neuronal activity are tied to specific waves propagating along the surface of the motor cortex. Building on this insight, the researchers developed two mathematical algorithms that leverage the properties of these waves to track how neuronal activity changes during brain function. The anticipated impact is an increase in the effectiveness of neuromodulation therapies and the overall precision of brain-computer interfaces as they are applied to clinical settings.
These results contribute to a growing understanding of how macroscopic wave patterns relate to motor control, complementing existing views from both the representational and dynamic frameworks. By focusing on the properties of cortical waves, the study provides a complementary lens through which to assess neural responses during movement and how these responses might be harnessed during therapeutic interventions. The work also emphasizes the potential for wave-based metrics to serve as biomarkers for evaluating brain states during treatment and rehabilitation, raising possibilities for more targeted and adaptive neuromodulation strategies in the future.
The researchers acknowledge that while their approach offers new tools for analyzing cortical activity, further work is needed to translate these wave-based measurements into routine clinical practice. Additional studies will examine how these waves interact with other neural signals across different brain regions and how patient-specific factors might influence wave dynamics. Nonetheless, the present findings lay a solid groundwork for integrating wave-centric analyses into the design and optimization of brain-computer interfaces and neuromodulation therapies, with the promise of improved outcomes for individuals living with neurological conditions.
Recent work from AI-focused groups has already shown sex-specific differences in certain brain activity patterns, underscoring the importance of comprehensive studies that account for biological diversity while evaluating neural signals and their clinical applications. Ongoing research aims to clarify how such factors may influence wave dynamics and treatment responses across diverse populations.