Bio-inspired cognitive model of motor learning by imitation

Effects of Electrical Stimulation on Activation of Mirror Neuron Network in Healthy Adults during Motor Execution and Imitation: An fNIRS Study

BACKGROUND

Observation, execution, and imitation of target actions based on mirror neuron network (MNN) have become common physiotherapy strategies. Electrical stimulation (ES) is a common intervention to improve muscle strength and motor control in rehabilitation treatments. It is possible to enhance MNN's activation by combining motor execution (ME) and motor imitation (MI) with ES simultaneously. This study aims to reveal whether ES could impact cortical activation during ME and MI.

METHODS

We recruited healthy individuals and assigned them randomly to the control group (CG) or experiment group (EG). Participants in EG performed ME and MI tasks with ES, while participants in CG performed the same two tasks with sham ES. We utilized functional near-infrared spectroscopy (fNIRS) to detect brain activation of MNN during ME and MI with and without ES, a randomized block design experiment paradigm was designed. Descriptive analysis of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) were used to show the hemoglobin (Hb) concentration changes after different event onsets in both CG and EG, a linear mixed-effects model (LMM) of HbO data was employed to analyze the effect of ES on the activation of MNN.

RESULTS

A total of 102 healthy adults were recruited and 72 participants' data were analysed in the final report. The block averaged Hb data showed that HbO concentration increased and HbR concentration decreased in most MNN regions during ME and MI in both groups. The LMM results showed that ES can significantly improve the activation of inferior frontal gyrus, middle frontal gyrus, and precentral gyrus during MI, the supplementary motor area, inferior parietal lobule, and superior temporal gyri showed increased activation, but without statistical significance. Although the results did not reach statistical significance during ME, ES still showed positive effects on increased overall activations.

CONCLUSIONS

In this study, we present potential novel rehabilitation approaches that combines MNN strategies and low-frequency ES to enhance cortical activation. Our results revealed that ES has potential to increase activation of most MNN brain areas, providing evidence for related rehabilitative interventions and device development.

CLINICAL TRIAL REGISTRATION

This study was registered on the China Clinical Trial Registration Center (identifier: ChiCTR2200064082, registered 26, September 2022, https://www.chictr.org.cn/showproj.html?proj=178285).

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.