Repetitive Transcranial Magnetic Stimulation (rTMS) studies are used to test motor imagery hypothesis. Motor Imagery (MI) represents conscious access to contents of movement intention, generally executed unconsciously during motor preparation. The main objective of this study was to investigate electrophysiological changes, which occurred before and after low-frequency rTMS application when we compared three different tasks: execution, action observation and motor imagery of finger movement. We hypothesize that absolute theta power over frontal regions would change between sensorimotor integration tasks and after 1 Hz of rTMS application. Eleven healthy, right-handed volunteers of both sexes (5 males, 6 females; mean age 28 ± 5 years), with no history of psychiatric or neurological disorders, participated in the experiment. After performing the tasks randomly, subjects were submitted to 15 min of low-frequency rTMS applied on Superior Parietal Cortex (SPC) and performed the tasks again. All tasks were executed simultaneously with Eletroencephalography (EEG) signals recording. Our results clarified the specificity of each sub-region during MI activity. Frontopolar cortex presented involvement with motor process and showed main effect for task and moment. Inferior frontal gyrus presented involvement with long-term memory retrieval and showed interaction between task and moment in the left hemisphere while the right hemisphere showed a main effect for task and moment. The lack of the main effect for conditions on the anterior frontal cortex collaborates with the hypothesis that in this region an integrated circuit of performance monitoring exists.
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http://dx.doi.org/10.1016/j.neulet.2018.09.036 | DOI Listing |
Brain Behav
January 2025
School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background: Different modes of motor acquisition, including motor execution (ME), motor imagery (MI), action observation (AO), and mirror visual feedback (MVF), are often used when learning new motor behavior and in clinical rehabilitation.
Purpose: The aim of this study was to investigate differences in brain activation during different motor acquisition modes among healthy young adults.
Methods: This cross-sectional study recruited 29 healthy young adults.
Front Neurol
December 2024
Department of Physical Therapy, School of Health Sciences, Ariel University, Ariel, Israel.
Children with attention deficit hyperactivity disorder (ADHD) exhibit various degrees of motor and cognitive impairments in fine and gross motor skills. These impairments impact social functioning, while also hindering academic achievement, self-esteem, and participation. Specifically, motor impairments are not fully addressed by current therapies.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
View Article and Find Full Text PDFLife (Basel)
December 2024
CESPU, Instituto Politécnico de Saúde do Norte, Escola Superior de Saúde do Vale do Ave, 4760-409 Vila Nova de Famalicão, Portugal.
Arthrogenic muscle inhibition (AMI) following ACL injury or reconstruction is a common issue that affects muscle activation and functional recovery. Thus, the objective of this study was to systematize the literature on the effects of physiotherapy interventions in the rehabilitation of AMI after ACL injury or reconstruction. A systematic review was conducted following the PRISMA guidelines.
View Article and Find Full Text PDFBrain Sci
December 2024
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/a, 1083 Budapest, Hungary.
: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems.
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