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

Linling Li,Zhiguo Zhang,Lijie Ren,Yixuan Zeng,Nan Yan,Shaohui Hou,Yinxue Wang,Li Zhang,Gan Huang

doi:10.3389/fnins.2021.699999

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.

Highlights

Electroencephalographic (EEG) neurofeedback (NFB) training is a popular neuromodulation method to train brain functions
General linear model (GLM) analysis revealed that the NFB learning index LI was positively associated with GMV localized in the inferior temporal gyrus and superior parietal gyrus, and negatively associated with GMV localized in the frontal gyrus, middle temporal gyrus, and supramarginal gyrus (Figure 3A)
The results suggested that, in view of the underspecified and complex character of NFB training task, the individual difference in NFB learning performance is not attributed to single modality, the individual difference in Sensory motor rhythm (SMR) NFB performance is not attributed to one single factor, but modulated by the brain’s baseline neural activity, structure, function, and functional connections

Summary

Introduction

Electroencephalographic (EEG) neurofeedback (NFB) training is a popular neuromodulation method to train brain functions. Since its first attempts in the 1960s, EEG NFB has rapidly received much attention because of its numerous potential applications for healthy participants and patients (Gruzelier, 2014a,b,c; Sitaram et al, 2017; Omejc et al, 2019). SMR NFB protocol has been applied to improve cognitive performance, such as sustained attention and visuomotor skills of healthy participants (Gruzelier, 2014a; Kober et al, 2020). SMR NFB training could improve the impaired cognitive functions in post-stroke patients (Kober et al, 2015). It is of great importance to investigate the neural mechanisms of the huge individual differences in EEG NFB learning performance, because the successful learning during EEG NFB training can directly contribute to the improvement of disease symptoms in clinical patients (Gruzelier, 2014c). It is highly desirable to discover predictors of EEG NFB learning success and to establish a machine learning model to predict the learning performance based on identified predictors

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