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Changes in brain functional network connectivity after stroke

Neural Regeneration Research 2014年第1期9卷 51-60页

作者： Wei Li Yapeng Li Wenzhen Zhu Xi Chen Key Laboratory of Image Processing and Intelligent Control Ministry of Education Department of Control Science and Engineering Huazhong University of Science and Technology Department of Radiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and Technology

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

关键词： nerve regeneration brain injury stroke motor areas functional magnetic resonanceimaging brain network independent component analysis functional network connectivity neuralplasticity NSFC grant neural regeneration

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General Neural Process in Cycling Exercise

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Journal of Science in Sport and Exercise 2023年第1期5卷 1-15页

作者： Victor Scholler Alain Groslambert Frederic Grappe Sidney Grosprêtre EA4660 C3S Culture Sport Health SocietyUniversity of Bourgogne Franche-Comté25000 BesançonFrance Equipe Cycliste Groupama-FDJ 25000 BesançonFrance Laboratory of Athlete-Material-Environment(LAME) 56 chemin des Montarmots25000 BesançonFrance C3S Laboratory UPFR Sport31Chemin de l’Epitaphe25000 BesançonFrance

Performance in cycling is frequently related to metabolic or biomechanical ***,the contribution of the neu-rophysiological system during cycling is often poorly considered in performance ***,cycling is a complex whole-body physical exercise that necessitates specific coordination and fine control of motor output to manage the dif-ferent *** ability to produce different levels of intensity of exercise would require optimizing many functions of the central nervous system from the brain’s treatment of sensory signals to complex motor command execution via the corticospinal *** review proposes an integrative approach to the factors that could influence cycling performance,based on neurophysiological and cognitive ***,we report data relying on brain activity signals,to account for the different brain areas and cognitive functions ***,because the motor command is highly dependent upon its regulation along the corticospinal pathway,we expose the modulation of corticospinal and spinal excitabilities during *** present these later by reviewing the literature of studies using transcranial magnetic or percutaneous nerve ***,we describe a model of neural and cognitive adjustments that occur with acute and chronic cycling practices,with several areas of improvement focusing on these factors,including mental and cognitive training.

关键词： Cognition motor areas Electroencephalography Transcranial magnetic stimulation Spinal excitability

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