Delineating the Role of Alpha Waves in Exercise-induced Neural Changes through Resting-state EEG

Summary

This protocol aims to decode prefrontal alpha-band neural oscillatory reprogramming induced by aerobic exercise in high-trait-anxiety individuals, using EEG-deep learning integration. The developed predictive model (81.82% accuracy) identifies alpha oscillation as the core mechanism for exercise-mediated anxiety alleviation, advancing precision neuromodulation targets for emotional disorders.

Abstract

Exercise intervention demonstrates unique potential in treating emotional dysregulation, yet the ambiguity of its neuromodulation targets hinders the development of precise exercise prescriptions. This study investigates trait anxiety as a representative emotional disorder in 40 high-trait-anxiety university students, who were randomly assigned to either an exercise intervention group (40 min moderate-intensity aerobic exercise, n = 20) or a non-exercise control group (40 min quiet reading, n = 20), followed by resting EEG data collection. By integrating resting-state electroencephalography (EEG) after exercise with deep learning algorithms, we developed an alpha-band time-frequency predictive model to systematically decode the neural oscillatory reprogramming mechanisms in the prefrontal cortex induced by exercise. The deep learning model exhibited superior classification efficacy (accuracy 83.33%, F1 score 0.83, Kappa coefficient 0.67) in identifying exercise-induced alpha-band power spectral entropy alterations. This study pioneers the identification of prefrontal Alpha excitatory rebalancing through neural oscillation remodeling as the core mechanism underlying exercise-mediated anxiety mitigation.

Introduction

In contemporary society, the accelerating pace of life and the increasing burden of life pressures have led to a significant upsurge in the prevalence of emotional dysregulation. Among various manifestations of emotional dysregulation, anxiety, a prevalent subtype, poses a great challenge to individuals. Pharmacological therapies have long been regarded as a fundamental approach in the treatment of emotional dysregulation, particularly anxiety. However, research has shown that approximately 30% of individuals with emotional dysregulation do not respond to first-line medications. Moreover, long-term use of these drugs may give rise to various risks, such as metabolic d....

Protocol

This study was approved by the Institutional Research Ethics Committee of Wuhan Sports University (2023016).

1. Study participants

1. Recruit 550 non-sports major students from a university on a voluntary basis. Perform pre-screening for sample selection. Use the Chinese Revised State-Trait Anxiety Inventory⁴¹ for this purpose, as shown in Table 1.
2. Classify individuals scoring 55 or higher on the Trait Anxiety subscale as having high trait anxiety and select them for the EEG experiment. Use the Trait Anxiety subscale of this inventory as the key assessment tool, following establish....

Results

EEG data processing and statistical analysis

The raw EEG data were segmented into 2 s epochs centered on the event onset, consistent with standard practices in time-frequency analysis to capture transient neural dynamics while minimizing edge artifacts. Each epoch underwent continuous wavelet transformation (CWT) using a complex Morlet wavelet with 3 cycles, which optimally balances temporal and frequency resolu.......

Discussion

In the realm of mental health research, understanding the underlying neural mechanisms of interventions for individuals with high trait anxiety is of paramount importance. This study was designed with the explicit aim of exploring the neural biomarkers associated with exercise intervention in such individuals through the utilization of artificial intelligence models. The adoption of advanced deep neural networks, including models like EEGNet, has revolutionized electroencephalogram (EEG) signal processing by enabling the.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BrainAmp SN	Brain Products	AMP12081737 Standard	Acquisition of electroencephalogram (EEG) signals
Eprime Professional	PSYCHOLOGY SOFTWARE TOOLS	2.0.10.92	Psychology Experiment Software
motion cycle 600	emotion fitness GmbH & Co. KG	F-EF-MC-650	Bicycle Ergometer
DCU(Deep Computing Unit)	HYGON	HYGON Z100L	Model analysis
Python	Python Software Foundation	Python 3.8	Model analysis