Background: Balance is sensed through peripheral and central receptors and mediated by central control through the brain and spinal cord. Although some evidence exists as to the areas of the brain involved and how processing of data occurs in young individuals, nothing has been published on people with diabetes. The purpose of this study was to examine the electroencephalogram (EEG) during common sensorimotor and balance training tasks and to relate these to task difficulty.
Subjects And Methods: Postural sway and EEG change of alpha, beta, and sigma wave bands were measured in 17 young subjects, 10 older subjects, and 10 subjects with diabetes during eight progressively more difficult balance tasks with eyes open and closed, feet in tandem or apart, and on foam or a firm surface.
Results: EEG power of beta and sigma wave bands showed significant increases on the cortical and parietal areas of the brain relative to the control tasks when eyes were open (P<0.05). The cortical involvement decreased as the task became more difficult with vision and somatosensory information reduced, whereas that of the parietal area increased with task difficulty. The greatest increase was in subjects with diabetes, and the least was in younger people. Individuals with diabetes had increased sigma and beta EEG power in all regions of the brain examined with increased complexity of the balance task.
Conclusions: This study demonstrated cortical and parietal involvement in static balance tasks commonly used in sensorimotor training. The results support the proposal that there was increased subcortical control with increase in task difficulty in the young subjects, but in subjects with diabetes, there was a major increase in activity across the brain.
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Biomimetics (Basel)
January 2025
School of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, China.
To reduce hip joint muscles' activation during walking with an active hip exoskeleton. Few studies examine the optimal active assistance timing of the hip exoskeleton based on muscle activation characteristics. Sixteen gender-balanced healthy adults (mean age 28.
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Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA.
Objective: Functional magnetic resonance imaging data pose significant challenges due to their inherently noisy and complex nature, making traditional statistical models less effective in capturing predictive features. While deep learning models offer superior performance through their non-linear capabilities, they often lack transparency, reducing trust in their predictions. This study introduces the Time Reversal (TR) pretraining method to address these challenges.
View Article and Find Full Text PDFComput Biol Med
January 2025
Hamburg University of Technology, Hamburg, Germany.
The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image.
View Article and Find Full Text PDFGait Posture
January 2025
School of Psychology, David Keir Building, Queen's University Belfast, Belfast, UK. Electronic address:
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View Article and Find Full Text PDFGait Posture
January 2025
Sports Engineering / Movement Science Departement, Faculty for Human Sciences, Otto-von-Guericke University, Magdeburg, Germany.
Background: Postural stability is a key factor in maintaining an upright standing position. Children with average height (CAH) have elaborate general postural stability up to the age of seven years. Children with achondroplasia (ACH) face body disproportions like shorter arms and legs, bowing of the legs as well as hyperlordosis and hypokyphosis in the spine.
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