Infra-Low-Frequency Neurofeedback Alters EEG Network Efficiency: Exploratory Evidence from Healthy Volunteers

Area of Science:

• Neuroscience
• Neurofeedback Research
• Brain-Computer Interfaces

Background:

• Infra-Low-Frequency Neurofeedback (ILF-NFB) integrates classic frequency-band (FB) and infra-low-frequency (ILF) electroencephalography (EEG) for implicit training.
• The precise neurophysiological mechanisms driving ILF-NFB efficacy require further investigation.
• ILF-NFB is gaining traction in clinical settings, necessitating a deeper understanding of its effects.

Purpose of the Study:

• To investigate the immediate effects of a single ILF-NFB session on EEG correlates in healthy individuals.
• To explore potential alterations in functional brain connectivity during ILF-NFB compared to sham feedback.
• To provide preliminary insights into the network-level mechanisms of ILF-NFB.

Main Methods:

• A randomized, sham-controlled, double-blind crossover study design was employed.
• Continuous 31-channel EEG data were recorded during verum and sham ILF-NFB conditions.
• Functional connectivity was assessed using the debiased weighted phase-lag index (dwPLI) and analyzed with graph theory.

Main Results:

• A significant increase in global efficiency within the Beta1 band (12-15 Hz) was observed during verum ILF-NFB compared to sham.
• This effect was prominent in the initial phase of the neurofeedback session, with a consistent trend in the latter half.
• No significant differences were found in other frequency bands or for betweenness centrality measures.

Conclusions:

• Preliminary findings suggest that ILF-NFB may induce measurable changes in brain network efficiency, specifically in the Beta1 frequency band.
• These results, while exploratory, support the hypothesis of network-level effects associated with ILF-NFB.
• Further research with extended protocols and clinical populations is warranted to validate these observations and elucidate the underlying mechanisms.