This study investigated the effects of training using motor imagery on balance, gait parameters, and ataxia severity in children after they underwent medulloblastoma tumour resection. Fifty participated children, aged seven-nine years and diagnosed with cerebellar ataxia after medulloblastoma resection were selected from the Tumor Hospital of Cairo University. Two groups of patients were randomly divided: the study group and the control group. The control group received a physical therapy program, whereas the study group received training in motor imagery along with a traditional physical therapy program. Each group was assessed using the Scale for the Assessment and Rating of Ataxia (SARA), Pediatric Berg Balance Scale (PBBS), and kinematic gait analysis using the Kinovea software. Significant improvements were noted in balance, ataxia, and spatial and temporal gait parameters in both groups, which favoured the study group (P > 0.05). Training in motor imagery is an effective rehabilitation treatment for medulloblastoma resection and may be applied in combination with an appropriate physical therapy.Trial registration: ClinicalTrials.gov identifier, NCT05992207, 08-07-2023.
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http://dx.doi.org/10.1038/s41598-024-78900-7 | DOI Listing |
Acta Psychol (Amst)
December 2024
School of Psychology, University College Dublin, Ireland. Electronic address:
Background: Motor imagery (MI) can be an effective strategy for learning and enhancing movement or as an alternative training modality when physical practice is compromised. Individual differences in MI ability are widely documented but the role of experience in different activities in influencing MI is not well understood. The present study examined how experience in activities associated with the use of MI influences implicit and explicit MI.
View Article and Find Full Text PDFBiomed Phys Eng Express
December 2024
Biomechatronics Laboratory Mechatronics Department, University of Sao Paulo, Av Prof Mello Moraes 2331, Cidade Universitaria, 05508-030 Sao Paulo-SP, Sao Paulo, 05508-900, BRAZIL.
Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes.
View Article and Find Full Text PDFFront Physiol
December 2024
College of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China.
Objective: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.
Methods: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones.
JMIR Form Res
December 2024
Neuroplasticity, Imagery, and Motor Behaviour Lab, Department of Psychology, University of British Columbia, Kelowna, BC, Canada.
Background: Markerless motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation are not feasible. Artificial intelligence (AI)-based systems can offer a markerless, lightweight approach to motion capture. However, the accuracy of such systems, such as MediaPipe, for tracking fine upper limb movements involving the hand has not been explored.
View Article and Find Full Text PDFTransl Psychiatry
December 2024
Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Targeted Memory Reactivation (TMR) during sleep benefits memory integration and consolidation. In this pre-registered study, we investigated the effects of TMR applied during non-rapid eye movement (NREM) sleep following modulation and updating of aversive autobiographical memories using imagery rescripting (ImR). During 2-5 nights postImR, 80 healthy participants were repeatedly presented with either idiosyncratic words from an ImR updated memory during sleep (experimental group) or with no or neutral words (control groups) using a wearable EEG device (Mobile Health Systems Lab-Sleepband, MHSL-SB) [1] implementing a close-loop cueing procedure.
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