Objective: To map evidence on the characteristics, effectiveness, and potential mechanisms of motor imagery interventions targeting cognitive function and depression in adults with neurological disorders and/or mobility impairments.
Data Sources: Six English databases (The Cochrane Library, PubMed, Embase, Scopus, Web of Sciences, and PsycINFO), two Chinese databases (CNKI and WanFang), and a gray literature database were searched from inception to December 2024.
Review Methods: This scoping review followed the Joanna Briggs Institute Scoping Review methodology. Interventional studies that evaluated motor imagery for cognitive function and/or depression in adults with neurological disorders and/or mobility impairments were included.
Results: A total of 24 studies, primarily involving adults with cerebrovascular diseases, multiple sclerosis, and Parkinson's disease, were identified. Motor imagery was typically conducted at home/clinic, occurring 2 to 3 sessions per week for approximately 2 months, with each session lasting 20 to 30 minutes. The 62.5% of studies (n = 10) reported significant improvements in cognitive function, exhibiting moderate-to-large effect sizes (Cohen's = 0.48-3.41), especially in memory, attention, and executive function, while 53.3% (n = 8) indicated alleviation in depression with moderate-to-large effect sizes (Cohen's = -0.72- -2.56). Motor imagery interventions could relieve pain perception and promote beneficial neurological changes in brains by facilitating neurotrophic factor expression and activating neural circuits related to motor, emotional, and cognitive functions.
Conclusion: Motor imagery could feasibly be conducted at home, with promising effects on cognitive function and depression. More high-quality randomized controlled trials and neuroimaging techniques are needed to investigate the effects of motor imagery on neuroplasticity and brain functional reorganization, thereby aiding in the development of mechanism-driven interventions.
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http://dx.doi.org/10.1177/02692155241313174 | DOI Listing |
The complementary strengths of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have driven extensive research into integrating these two noninvasive modalities to better understand the neural mechanisms underlying cognitive, sensory, and motor functions. However, the precise neural patterns associated with motor functions, especially imagined movements, remain unclear. Specifically, the correlations between electrophysiological responses and hemodynamic activations during executed and imagined movements have not been fully elucidated at a whole-brain level.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing, University, Chongqing, 400044, People's Republic of China.
Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300384, China.
Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!