Objective: Virtual reality (VR) has been proposed as a potentially useful tool for motor assessment and rehabilitation. The objective of this study was to investigate the usefulness of VR in the assessment of short-term motor learning in multiple sclerosis (MS).
Methods: Twelve right-handed MS patients and 12 control individuals performed a motor-tracking task with their right upper limb, following the trajectory of an object projected on a screen along with online visual feedback on hand position from a sensor on the index finger. A pretraining test (3 trials), a training phase (12 trials), and a posttraining test (3 trials) were administered. Distances between performed and required trajectory were computed.
Results: Both groups performed worse in depth planes compared to the frontal (x,z) plane (P < .006). MS patients performed worse than control individuals in the frontal plane at both evaluations (P < .015), whereas they had lower percent posttraining improvement in the depth planes only (P = .03).
Conclusions: The authors' VR system detected impaired motor learning in MS patients, especially for task features requiring a complex integration of sensory information (movement in the depth planes). These findings stress the need for careful customization of rehabilitation strategies, which must take into account the patients' motor, sensory, and cognitive limitations.
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http://dx.doi.org/10.1177/1545968306294913 | DOI Listing |
J Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFNeurology
January 2025
Department of Neurology, Massachusetts General Hospital, Boston.
Background And Objectives: Rolandic epilepsy (RE), the most common childhood focal epilepsy syndrome, is characterized by a transient period of sleep-activated epileptiform activity in the centrotemporal regions and variable cognitive deficits. Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in RE and related sleep-activated epileptic encephalopathies.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials-pieces of paper. Furthermore, the products of origami may reflect children's ages and their visual-motor integration (VMI) development.
View Article and Find Full Text PDFOccup Ther Int
January 2025
Department of Motor Behavior, Faculty of Physical Education and Sport Sciences, Razi University, Kermanshah, Iran.
This study is aimed at investigating the impact of internal and external attention focus on learning a throwing skill in children with autism, as well as the relationship between working memory and learning rate. Twenty-four children aged 6-8 years with autism were assigned to internal and external attention groups. Participants performed a throwing task while their working memory was assessed using Cornoldi's working memory test.
View Article and Find Full Text PDFKorean J Neurotrauma
December 2024
Department of Neurosurgery, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea.
Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management.
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