Introduction: Motor imagery (MI) involves recreating a movement mentally without physically performing the movement itself. MI has a positive impact on motor performance, motor learning and neural plasticity. We analysed the connection between motor imagination and altered movement execution in individuals with dystonia, a complex sensorimotor disorder. The aim of our study was to examine MI ability in patients with functional dystonia (FD) in comparison to organic dystonia (OD).
Methods: Our case-control study involved 46 patients, 22 with FD and 24 with OD. The assessment consisted of specific questionnaire and standardized motor, cognitive and psychiatric scales. The KVIQ-20 was used to test MI in each patient.
Results: Patients with FD scored lower on both global visual and kinaesthetic scales of the KVIQ-20 exam compared to patients with OD (63.1 ± 18.5 vs. 73.7 ± 13.2, and 54.9 ± 21.9 vs. 68.8 ± 18.2, respectively). Patients with FD also exhibited visual and/or kinaesthetic MI impairment in different body segments. The internal perspective when imagining movements was preferred in both patients with FD and OD.
Conclusion: FD patients showed global dysfunction of visual and kinaesthetic MI abilities. Techniques for MI improvements might have a potential role in dystonia rehabilitation.
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http://dx.doi.org/10.1016/j.jpsychores.2024.111911 | 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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