Neural Modulation By Repetitive Transcranial Magnetic Stimulation (rTMS) for BCI Enhancement in Stroke Patients

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery

BACKGROUND

Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

OBJECTIVE

We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach.

METHODS

Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance.

RESULTS

Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections.

CONCLUSIONS

Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

Alpha Frequency Intervention by Electrical Stimulation to Improve Performance in Mu-Based BCI

The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the most widely used systems, of which the related methods to improve users' accuracy are still poorly studied, especially for the BCI illiteracy. Here, we examined a way to enhance mu-based BCI performance by electrically stimulating the ulnar nerve of the contralateral wrist at the alpha frequency (10 Hz) during left- and right-hand motor imagination in two BCI groups (literate and illiterate). We demonstrate that this alpha frequency intervention enhances the classification accuracy between left- and right-hand motor imagery from 66.41% to 81.57% immediately after intervention and to 75.28% two days after intervention in the BCI illiteracy group, while classification accuracy improves from 82.12% to 91.84% immediately after intervention and to 89.03% two days after intervention in the BCI literacy group. However, the classification accuracy did not change before and after the sham intervention (no electrical stimulation). Furthermore, the ERD on the primary sensorimotor cortex during left- or right-hand motor imagery tasks was more visible at the mu-rhythm (8-13 Hz) after alpha frequency intervention. Alpha frequency intervention increases the mu-rhythm power difference between left- and right-hand motor imagery tasks. These results provide evidence that alpha frequency intervention is an effective way to improve BCI performance by regulating the mu-rhythm which might provide a way to reduce BCI illiteracy.

Enhancing Mu-based BCI Performance with Rhythmic Electrical Stimulation at Alpha Frequency

The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the widely used systems, of which the related methods to improve users' accuracy is still poorly studied. Here, we examined the way to enhance the mu-based BCI performance by rhythmic electrical stimulation on the ulnar nerve at the contralateral wrist at the alpha frequency (10 Hz) during the left-and right-hand motor imagery. Time-frequency analysis, spectral analysis, and discriminant analysis were performed on the electroencephalograph (EEG) data before and after the intervention of electrical stimulation in 9 healthy subjects. We found that the ERD/S on the somatosensory and motor cortex during left-or right-hand imagination was more obvious at the mu rhythm after intervention. Furthermore, average classification accuracy between left-and right-hand imagery significantly increased from 78.43% to 88.17% after intervention, suggesting that the electrical stimulation at alpha frequency effectively regulates the brain's mu rhythm and enhances the discriminability of the left-hand and right-hand imagination tasks. These results provide evidence that the electrical stimulation at the alpha frequency is an effective way to improve the mu-based BCI performance.

High-Frequency Repetitive Transcranial Magnetic Stimulation Mediates Autophagy Flux in Human Bone Mesenchymal Stromal Cells via NMDA Receptor-Ca2+-Extracellular Signal-Regulated Kinase-Mammalian Target of Rapamycin Signaling

Aim

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive and safe technique for treatment of central and peripheral nerve injury. In recent years, this technique has been widely used in clinic, and an increasing number of studies have reported its mechanisms. In this study, we investigated the mechanisms of rTMS-mediated autophagy flux in human bone mesenchymal stromal cells (BMSCs).

Methods

A frequency of 50 Hz was employed. Cells were divided into five groups: (1) normal, (2) sham, (3) 0.5 T, (4) 1.0 T, and (5) 1.5 T. Cells were stimulated for 20 min/day. The levels of p62, LC3-II/I, phosphorylated extracellular signal-regulated kinase (p-ERK), ERK, phosphorylated-AKT (p-AKT), AKT, phosphorylated mammalian target of rapamycin (p-mTOR), mTOR, phosphorylated protein kinase A (p-PKA), PKA, phosphorylated epidermal growth factor receptor (p-EGFR), EGFR, Nanog, Oct4, Sox2, and NMDA receptor (NMDAR1) were investigated by western blotting. Intracellular calcium (Ca²⁺) levels were quantified by flow cytometry. p62 and LC3 expression was also assessed by immunofluorescence analysis.

Results

In the 0.5 T group, rTMS increased the expression of LC3-II/I, p-ERK/ERK, and NMDAR1 and decreased the levels of p62 and p-mTOR/mTOR than in the normal group. The ratio of p-AKT/AKT, p-PKA/PKA, and p-EGFR/EGFR and the expression of Nanog, Oct4, and Sox2 remained unchanged. Immunofluorescence analysis revealed colocalization of p62 with LC3 puncta, and flow cytometry analysis displayed that Ca²⁺ levels were elevated. However, in the 1.0 and 1.5 T groups, no changes in the expression of these autophagy markers were observed.

Conclusion

In the 0.5 T group, high-frequency rTMS can induce autophagy through NMDAR–Ca²⁺–ERK–mTOR signaling in BMSCs. In the 1.0 and 1.5 T groups, autophagy is not activated.