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This study aimed to explore the effect of prolonged speed-accuracy motor task on the indicators of psychological, cognitive, psychomotor and motor function. Ten young men aged 21.1 ± 1.0 years performed a fast- and accurate-reaching movement task and a control task. Both tasks were performed for 2 h. Despite decreased motivation, and increased perception of effort as well as subjective feeling of fatigue, speed-accuracy motor task performance improved during the whole period of task execution. After the motor task, the increased working memory function and prefrontal cortex oxygenation at rest and during conflict detection, and the decreased efficiency of incorrect response inhibition and visuomotor tracking were observed. The speed-accuracy motor task increased the amplitude of motor-evoked potentials, while grip strength was not affected. These findings demonstrate that to sustain the performance of 2-h speed-accuracy task under conditions of self-reported fatigue, task-relevant functions are maintained or even improved, whereas less critical functions are impaired.
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http://dx.doi.org/10.1007/s00221-018-5251-1 | DOI Listing |
J Neurosci
September 2025
Jefferson Moss Rehabilitation Research Institute, Thomas Jefferson University, Elkins Park, PA 19027.
Tool use is a complex motor planning problem. Prior research suggests that planning to use tools involves resolving competition between different tool-related action representations. We therefore reasoned that competition may also be exacerbated with tools for which the motions of the tool and the hand are incongruent (e.
View Article and Find Full Text PDFJ Neurophysiol
September 2025
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Limiting cognitive resources negatively impacts motor learning, but its cognitive mechanism is still unclear. Previous studies failed to differentiate its effect on explicit (or cognitive) and implicit (or procedural) aspects of motor learning. Here, we designed a dual-task paradigm requiring participants to simultaneously perform a visual working memory task and a visuomotor rotation adaptation task to investigate how cognitive load differentially impacted explicit and implicit motor learning.
View Article and Find Full Text PDFPsychol Res
September 2025
Neurorehabilitation Research Center, Kio University, Nara, Japan.
The ability to detect small errors between sensory prediction in the brain and actual sensory feedback is important in rehabilitation after brain injury, where motor function needs to be restored. To date in the recent study, a delayed visual error detection task during upper limb movement was used to measure this ability for healthy participants or patients. However, this ability during walking, which is the most sought-after in brain-injured patients, was unclear.
View Article and Find Full Text PDFJ Mot Behav
September 2025
Department Department of Physical Therapy, Faculty of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.
Visual-motor illusion (VMI) is a kinesthetic illusion produced by viewing an image showing joint motion. VMI with enhanced joint movement intensity (power-VMI; P-VMI) is expected to activate a wide range of motor association brain regions, and when combined with electrical stimulation that activates the motor sensory cortex, further activation of brain activity can be expected. This study aimed to verify the effectiveness of VMI using functional near-infrared spectroscopy to confirm brain activity during combined P-VMI and electrical stimulation.
View Article and Find Full Text PDFMov Disord Clin Pract
September 2025
Neurology Unit, Neuromotor and Rehabilitation Department, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
Background: GBA1 variants are the major genetic risk factor for Parkinson's Disease (PD) and account for 5-30% of PD cases depending on the population and age at onset of the disease.
Objectives: The aim of this study was to assess whether Artificial Intelligence (AI) could predict GBA1-mutated genotype in PD (GBA1-PD). Particularly, the main objective was to identify a Machine Learning (ML) model capable of accurately providing a pre-test estimate of GBA1-mutated status, relying on the clinical and demographic variables with the highest predictive value.