Motor imagery training to improve language processing: What are the arguments?

Efficacy of different rehabilitation therapies on post-stroke aphasia patients: A network meta-analysis

BACKGROUND

Although several rehabilitation interventions are effective in post-stroke aphasia (PSA), the efficacy of different rehabilitation interventions compared to each other remains controversial. Here, we aimed to compare the effectiveness of varying rehabilitation interventions in PSA.

METHODS

Randomized controlled trials on 8 kinds of rehabilitation interventions to improve speech function in patients with PSA were searched by computer from 10 databases, including PubMed, Web of Science, Cochrane, OVID, CINAHL, Embase, CNKI, WanFang, CBM, and VIP. The search scope was from the establishment of the database to August 2023. The literature screening, extraction of basic information, and quality assessment of the literature were conducted independently by 2 researchers. Network meta-analysis (NMA) was performed using Stata 17.0 software.

RESULTS

Fifty-four studies involving 2688 patients with PSA were included. The results of NMA showed that: ① in terms of improving the severity of aphasia, the therapeutic effects of repetitive transcranial magnetic stimulation were the most significant; ② motor imagery therapy was the most effective in improving spontaneous speech, repetition, and naming ability; ③ in terms of improving listening comprehension ability, the therapeutic effects of mirror neuron therapy was the most significant.

CONCLUSION

The 8 rehabilitation interventions have different focuses in improving the speech function of PSA patients, and the clinical therapists can select the optimal rehabilitation interventions in a targeted manner according to the results of this NMA and the patients' conditions and other relevant factors.

Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?

Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.

Motor experience modulates neural processing of lexical action language: Evidence from rugby players

The perceptual symbol theory proposes a sensorimotor simulation in language processing, emphasizing the role of motor experience. However, the neural basis of motor experience on lexical-level language processing remains little known. In the current fMRI study, we compared brain activation and task-based functional connectivity in 28 rugby players and 28 novices during rugby- specialized and daily verb processing. Distinct differences were observed between the two groups in the bilateral superior temporal gyrus and left angular gyrus regions during specialized verb processing. Notably, intergroup functional connectivity was evident between the left superior temporal gyrus and the right precentral gyrus during specialized verb processing. This study contributes insights into the neural responses and connectivity patterns associated with motor experience at the lexical level, highlighting its potential impact on language processing.

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject’s unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain–computer interface systems.