Brain activation during motor imagery has been the subject of a large number of studies in healthy subjects, leading to divergent interpretations with respect to the role of descending pathways and kinesthetic feedback on the mental rehearsal of movements. We investigated patients with complete spinal cord injury (SCI) to find out how the complete disruption of motor efferents and sensory afferents influences brain activation during motor imagery of the disconnected feet. Eight SCI patients underwent behavioral assessment and functional magnetic resonance imaging. When compared to a healthy population, stronger activity was detected in primary and all non-primary motor cortical areas and subcortical regions. In paraplegic patients the primary motor cortex was consistently activated, even to the same degree as during movement execution in the controls. Motor imagery in SCI patients activated in parallel both the motor execution and motor imagery networks of healthy subjects. In paraplegics the extent of activation in the primary motor cortex and in mesial non-primary motor areas was significantly correlated with the vividness of movement imagery, as assessed by an interview. The present findings provide new insights on the neuroanatomy of motor imagery and the possible role of kinesthetic feedback in the suppression of cortical motor output required during covert movements.
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http://dx.doi.org/10.1093/cercor/bhh116 | DOI Listing |
Sensors (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 PDFBrain Sci
January 2025
IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy.
: Parkinson's disease (PD) is a neurodegenerative disorder, characterised by cardinal motor features and a multitude of non-motor manifestations. Among them, cognitive impairment in PD has been recognised as a defined clinical entity, and it might lead to an increased risk of developing dementia. Consequently, the present review aimed to ascertain the available interventions for the training of cognitive abilities in persons with PD (PwPD).
View Article and Find Full Text PDFBrain Sci
January 2025
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.
Objectives: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.
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