Cognitive states like motor imagery (MI; simulating actions without overtly executing them) share a close correspondence with action execution, and hence, activate the motor system in a similar way. However, as people age, reduction in specific cognitive abilities like motor action simulation and action planning/prediction are commonly experienced. The present study examined the effect of visual-spatial processing for both typical and challenging upper-limb movements using the Hand Laterality Judgment Task (HLJT), in which participants were asked to judge whether the depicted hand is a left or right hand. Several main findings emerged: (1) Compared to younger adults, older adults exhibited slower responses and greater error rates in both Experiment 1 and 2. This suggests that visual-spatial transformations undergo alterations with age; (2) Older adults displayed higher error rates with realistic hands at both back and palm viewpoints of the hands compared to younger adults. However, this pattern did not hold for response times; (3) Participants responded faster to medial hand orientations (i.e., closer to the midline of the body) compared to lateral hand orientations (i.e., farther from the midline of the body) for palm-views in both Experiment 1 and Experiment 2. Given that we observed better performance on medial orientations compared to lateral orientations, this suggests that participants follow the same motor rules and biomechanical constraints of the represented movement. Novel information is provided about differences in individuals' use of strategies (visual vs. motor imagery) to solve the HLJT for both mannequin and real hands.
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http://dx.doi.org/10.3389/fpsyg.2024.1445152 | DOI Listing |
HRB Open Res
September 2024
UCD School of Public Health, Physiotherapy and Sports Science, Health Sciences Centre, University College Dublin, Dublin, Leinster, Ireland.
Background: Following Spinal Cord Injury (SCI), 53% of people develop neuropathic pain (NP). NP can be more debilitating than other consequences of SCI, and a persistent health issue. Pharmacotherapies are commonly recommended for NP management in SCI, although severe pain often remains refractory to these treatments in many sufferers.
View Article and Find Full Text PDFFront Neural Circuits
January 2025
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan.
Introduction: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.
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December 2024
Department of Critical Care Medicine, Hospital for Sick Children, Toronto, Ontario, Canada; and.
Background: Physicians practicing in pediatric critical care medicine (PCCM) should maintain procedural skills competency. Faculty practicing in academic centers face challenges that may affect their procedural skills maintenance. The overall clinical opportunities are decreasing in PCCM.
View Article and Find Full Text PDFArch Rehabil Res Clin Transl
December 2024
Research Centre for Nutrition, Lifestyle and Exercise, School of Physiotherapy, Zuyd University of Applied Sciences, Faculty of Health, Heerlen, The Netherlands.
Objective: To provide a broad overview of the current state of research regarding the effects of 7 commonly used motor learning strategies to improve functional tasks within older neurologic and geriatric populations.
Data Sources: PubMed, CINAHL, and Embase were searched.
Study Selection: A systematic mapping review of randomized controlled trials was conducted regarding the effectiveness of 7 motor learning strategies-errorless learning, analogy learning, observational learning, trial-and-error learning, dual-task learning, discovery learning, and movement imagery-within the geriatric and neurologic population.
PLoS One
January 2025
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, Tamil Nadu, India.
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.
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