The Impact of Alpha‐Neurofeedback Training on Gastric Slow Wave Activity and Heart Rate Variability in Humans

Neurogastroenterology & Motility, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

ABSTRACT Introduction Neuromodulation of cortical brain regions associated with the gut‐brain axis may have potential to modulate gastric function. Previous studies shown phase‐amplitude coupling between electroencephalogram (EEG) alpha band frequency insula (Ins) and slow wave (GSW) activity. This study investigated first evidence EEG‐neurofeedback (EEG‐NF) training explore its effects on GSW activity heart rate variability (HRV). Methods A randomized crossover design was employed 20 healthy participants attending two separate sessions [ active‐training : uptraining left posterior Insula (LPIns) active‐control primary visual cortex (PVC Brodmann area 17)] following baseline recording period. 5‐min water loading test (5WLT) conducted EEG‐NF sessions. Finally, a post EEG‐NF/5WL period also recorded. Participants were blinded program, at least 48 h apart. Electrocardiogram (ECG), EEG, electrogastrogram (EGG) data recorded throughout theexperiment. In addition, duration successful NF extracted. Correlation analysis performed assess relationships outcome variables. Results Pearson correlation coefficient revealed significant relationship HRV metrics (RMSSD: r = 0.59; p 0.005, SI: −0.59; 0.006) in LPIns group EGG‐gastric rhythm index ( −0.40; 0.028) PVC group. Moreover, correlated EEG infraslow over anterior Ins 0.45; 0.043), right −0.5; 0.022), beta 0.44; 0.04) 0.04). Significant correlations observed connectivity gamma bands interest. Conclusion The demonstrated association HRV, (activity functional connectivity)measures did not show negative Gastric Alimetry Rhythm Index (GA‐RI) 5WLT as These findings underscore importance considering an important variable when evaluating efficacy future studies.

Language: Английский

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke

BioScience Trends, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments motor control, cognition, emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack individualization yield suboptimal outcomes. In recent years, brain-computer interface (BCI) technology has emerged as promising neurorehabilitation tool by decoding neural signals providing real-time feedback to enhance neuroplasticity. This review systematically explores the use BCI systems post-stroke rehabilitation, focusing on three core domains: function, cognitive capacity, outlines neurophysiological principles underpinning BCI-based including neurofeedback training, Hebbian plasticity, multimodal strategies. It then examines advances upper limb gait recovery using integrated with functional electrical stimulation (FES), robotics, virtual reality (VR). Moreover, it highlights BCI's potential language through EEG-based integration artificial intelligence (AI) immersive VR environments. addition, discusses role monitoring regulating disorders via closed-loop systems. While promising, technologies face challenges related signal accuracy, device portability, clinical validation. Future research should prioritize integration, AI-driven personalization, large-scale randomized trials establish efficacy. underscores transformative delivering intelligent, personalized, cross-domain solutions for stroke survivors.

Language: Английский