Aim: The aim of this study was to examine the specificity of motor imagery (MI) difficulties in children with CP.
Method: Performance of 22 children with CP was compared to a gender and age matched control group. MI ability was measured with the Hand Laterality Judgment (HLJ) task, examining specifically the direction of rotation (DOR) effect, and the Praxis Imagery Questionnaire (PIQ).
Results: In the back view condition of the HLJ task both groups used MI, as evidenced by longer response times for lateral compared with medial rotational angles. In the palm view condition children with CP did not show an effect of DOR, unlike controls. Error scores did not differ between groups. Both groups performed well on the PIQ, with no significant difference between them in response pattern.
Conclusion And Implication: The present study suggests that children with CP show deficits on tasks that trigger implicit use of MI, whereas explicit MI ability was relatively preserved, as assessed using the PIQ. These results suggest that employing more explicit methods of MI training may well be more suitable for children with CP in rehabilitation of motor function.
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http://dx.doi.org/10.1016/j.ridd.2016.06.010 | DOI Listing |
Physiol Rep
January 2025
Gravitational Physiology and Medicine Research Unit, Division of Physiology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria.
Available evidence suggests that various medical/rehabilitation treatments evoke multiple effects on blood hemostasis. It was therefore the aim of our study to examine whether fascial manipulation, vibration exercise, motor imagery, or neuro-muscular electrical stimulation can activate the coagulation system, and, thereby, expose patients to thrombotic risk. Ten healthy young subject were enrolled in the study.
View Article and Find Full Text PDFFront Hum Neurosci
December 2024
Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Türkiye.
Introduction: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
College of Computer and Information Engineering/College of Artificial Intelligence, Nanjing Tech University, Nanjing, 210093, China.
Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.
View Article and Find Full Text PDFExp Brain Res
December 2024
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Understanding the complex activation patterns of brain regions during motor tasks is crucial. Integrated functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) offers advanced insights into how brain activity fluctuates with motor activities. This study explores neuronal activation patterns in the cerebral cortex during active, passive, and imagined wrist movements using these functional imaging techniques.
View Article and Find Full Text PDFSci Rep
December 2024
Creative Robotics Lab, UNSW, Sydney, 2021, Australia.
Unlike the conventional, embodied, and embrained whole-body movements in the sagittal forward and vertical axes, movements in the lateral/transversal axis cannot be unequivocally grounded, embodied, or embrained. When considering motor imagery for left and right directions, it is assumed that participants have underdeveloped representations due to a lack of familiarity with moving along the lateral axis. In the current study, a 32 electroencephalography (EEG) system was used to identify the oscillatory neural signature linked with lateral axis motor imagery.
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