Research

The human brain is a highly dynamic system, constantly processing information through the coordinated activity of neural circuits. Neural oscillations, or brain waves, are rhythmic fluctuations in electrical activity that help regulate cognitive functions, motor control, and even interactions between the brain and other physiological systems. These oscillations occur at different frequencies, from slow delta waves associated with deep sleep to high-frequency gamma waves linked to complex cognitive tasks. A key aspect of brain dynamics is the interplay between these oscillations, particularly through mechanisms like phase-amplitude coupling (PAC), where the phase of a slow wave modulates the amplitude of a faster wave. This cross-frequency coupling is increasingly recognized as a biomarker of brain function and dysfunction, playing a role in conditions such as epilepsy, Parkinson's disease, and psychiatric disorders. Recent research has extended the concept of PAC beyond the brain, revealing coupling between brain rhythms and peripheral physiological signals, such as the gastric slow wave, which suggests a deeper connection between neural activity and the gut-brain axis. To accurately measure PAC, traditional methods have relied on non-parametric techniques, but recent advancements introduce statistical models that better capture the underlying dependencies. One such approach utilizes mutual information with high-dimensional sparse models, allowing for more precise detection of low-level PAC, even in noisy conditions. By employing flexible parametric modeling with von-Mises function regressors and ℓ1 regularization, these methods refine our ability to study brain oscillations and their role in health and disease. As our understanding of neural dynamics advances, improved analytical tools will continue to shed light on how oscillatory activity supports cognition, behavior, and even inter-organ communication, opening new avenues for both neuroscience research and clinical applications.

• A mutual information measure of phase-amplitude coupling using gamma generalized linear models

  AS Perley, TP Coleman

  Frontiers in Computational Neuroscience

  2024