Resistance exercise has been shown to be a potent stimulus for neuromuscular adaptations. These adaptations are not confined to the exercising muscle and have been consistently shown to produce increases in strength and neural activity in the contralateral, homologous resting muscle; a phenomenon known as cross-education. This observation has important clinical applications for those with unilateral dysfunction given that cross-education increases strength and attenuates atrophy in immobilized limbs. Previous evidence has shown that these improvements in the transfer of strength are likely to reside in areas of the brain, some of which are common to the mirror neuron system (MNS). Here we examine the evidence for the, as yet, untested hypothesis that cross-education might benefit from observing our own motor action in a mirror during unimanual resistance training, thereby activating the MNS. The hypothesis is based on neuroanatomical evidence suggesting brain areas relating to the MNS are activated when a unilateral motor task is performed with a mirror. This theory is timely because of the growing body of evidence relating to the efficacy of cross-education. Hence, we consider the clinical applications of mirror training as an adjuvant intervention to cross-education in order to engage the MNS, which could further improve strength and reduce atrophy in dysfunctional limbs during rehabilitation.
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http://dx.doi.org/10.3389/fnhum.2013.00396 | DOI Listing |
iScience
January 2025
Division of Optometry, Health Sciences, City University of London, London EC1V 0HB, UK.
A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes.
View Article and Find Full Text PDFNeural Netw
January 2025
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address:
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance.
View Article and Find Full Text PDFBackground And Aims: Body composition parameters associated with aerobic fitness, mirrored by maximal oxygen consumption (V̇Omax), have recently gained interest as indicators of physical efficiency in facioscapulohumeral dystrophy (FSHD). Bioimpedance analysis (BIA) allows a noninvasive and repeatable estimate of body composition but is based on the use of predictive equations which, if used in cohorts with different characteristics from those for which the equation was originally formulated, could give biased results. Instead, the phase angle (PhA), a BIA raw bioelectrical parameter reflecting body fluids distribution, could provide reliable data for such analysis.
View Article and Find Full Text PDFNat Commun
January 2025
School of Public Health, Shanghai Institute of Infectious Disease and Biosecurity, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai, China.
Heatwaves are commonly simplified as binary variables in epidemiological studies, limiting the understanding of heatwave-mortality associations. Here we conduct a multi-country study across 28 East Asian cities that employed the Cumulative Excess Heatwave Index (CEHWI), which represents excess heat accumulation during heatwaves, to explore the potentially nonlinear associations of daytime-only, nighttime-only, and day-night compound heatwaves with mortality from 1981 to 2010. Populations exhibited high adaptability to daytime-only and nighttime-only heatwaves, with non-accidental mortality risks increasing only at higher CEHWI levels (75th-90th percentiles).
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Department of Anatomy, Medical College, Jinan University, Guangdong, China.
Objective: To examine the medical students' awareness of laparoscopic surgery as well as assess the perceived importance of laparoscopic simulation training, and its impact on students' confidence, career aspirations, proficiency, spatial skills, and physical tolerance.
Design: Descriptive and comparative study using pre- and post-training assessments.
Setting: Simulation training sessions centred on laparoscopic surgery techniques.
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