Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback

Abstract

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults

“…Indeed, the amplitude of the signal measured during the resting state EEG or the first session seems to be a reliable predictor of an individual's ability to modulate this signal, in that the higher the level of baseline activity, the greater the modulation. This has been observed for the alpha (Su et al, 2021;Wan et al, 2014), sensorimotor rhythm (SMR, Blankertz et al, 2010;Li et al, 2021;Reichert et al, 2015;Weber et al, 2011) and low beta bands (12-15 Hz, Nan et al, 2015;Sho'ouri, 2021). Interestingly, although most of the cited studies aimed to increase the amplitude of the target signal during the resting state, one study (Nan et al, 2018) showed that a lower amplitude of the trained alpha frequency also predicted the ability to decrease it during NFB training.…”

Could neurofeedback improve therapist-patient communication? Considering the potential for neuroscience informed examinations of the psychotherapeutic relationship

“…Differences in brain anatomy and physiology, such as cortical thickness, white matter integrity, and neurotransmitter receptor distribution, could affect neurofeedback learning rates and outcomes (Haugg et al, 2020;Li et al, 2021;Misaki et al, 2019;Ninaus et al, 2015;Tsuchiyagaito et al, 2021b;Weber et al, 2020;Zhao et al, 2021). However, the literature has yet to fully explore and integrate these individual differences.…”

Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback

“…Further, some studies reported structural correlators of neurofeedback learning performance, such as gray matter volume of the right middle cingulate cortex and white matter volume of the cingulate tract for frontal-midline theta neurofeedback training [13], and gray matter volumes in the supplementary motor area and left middle frontal gyrus for gamma neurofeedback training [14]. A previous study by our group pointed out that the neurofeedback learning ability is related to both structural and functional brain imaging features [15]. However, no neuroimaging predictors for FAA neurofeedback protocol have been reported so far.…”