Purpose: Time-trial require cyclists to have an acute control on their sensory cues to regulate their pacing strategies. Pacing an effort accurately requires an individual to process sensory signals with efficacy, a factor that can be characterized by a high neural efficiency. This study aimed to investigate the effect of a cycling time-trial on neural efficiency in comparison to a low intensity endurance exercise, the latter supposedly not requiring high sensory control.
Methods: On two separate days, 13 competitive cyclists performed a session comprising of two 10 min treadmill tests, performed at different intensity zones from 1 to 5 on the rating subjective exercise intensity scale. The tests were performed before and after both a time-trial and endurance cycling exercise. Electroencephalography activity was measured during each intensity zones of the treadmill exercises. Neural efficiency was then calculated for each intensity block using the α/β electroencephalography activity ratio.
Results: The neural efficiency averaged on the 5 IZ decreased following the time-trial in the motor cortex (- 13 ± 8%) and prefrontal cortex (- 10 ± 12%), but not after the endurance exercise.
Conclusion: To conclude, the time-trial impaired the neural efficiency and increasing the RPE of the cyclists in the severe intensity zone.
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http://dx.doi.org/10.1007/s00421-023-05216-1 | DOI Listing |
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Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, P. R. China.
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View Article and Find Full Text PDFNat Comput Sci
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Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges.
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