Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily ph...Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily physical activity on cognitive function, elucidating the neural mechanisms using near-infrared spectroscopy(NIRS).Methods: A total of 23 healthy young adults(age 22.0 ± 2.2 years) participated in this study. Exercise amount was assessed using a uniaxial accelerometer. We evaluated total sleep time(TST) and sleep efficiency by actigraphy. Cognitive function was tested using the N-back task, the Wisconsin Card Sorting Test(WCST), and the Continuous Performance Test—Identical Pairs(CPT-IP), and the cortical oxygenated hemoglobin levels during a word fluency task were measured with NIRS.Results: Exercise amount was significantly correlated with reaction time on 0- and 1-back tasks(r = —0.602, p = 0.002; r = —0.446, p = 0.033,respectively), whereas TST was significantly correlated with % corrects on the 2-back task(r = 0.486, p = 0.019). Multiple regression analysis,including exercise amount, TST, and sleep efficiency, revealed that exercise amount was the most significant factor for reaction time on 0- and 1-back tasks(b = —0.634, p = 0.002; b = —0.454, p = 0.031, respectively), and TST was the most significant factor for % corrects on the 2-back task(b = 0.542, p = 0.014). The parameter measured by WCST and CPT-IP was not significantly correlated with TST or exercise amount. Exercise amount, but not TST, was significantly correlated with the mean area under the NIRS curve in the prefrontal area(r = 0.492, p = 0.017).Conclusion: Exercise amount and TST had differential effects on working memory and cortical activation in the prefrontal area. Daily physical activity and appropriate sleep duration may play an important role in working memory.展开更多
Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, th...Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.展开更多
基金supported by the Japan Society for the Promotion of Science, KAKENHI (25282210,15H05935)by Grants-in-Aid from the Comprehensive Research on Disability Health and Welfare+3 种基金the Ministry of Health,Labor and Welfare of Japanthe Academic Frontier Project for Private UniversitiesComparative Cognitive Science InstitutesMeijo University
文摘Purpose: Although exercise and sleep duration habits are associated with cognitive function, their beneficial effects on cognitive function remain unclear. We aimed to examine the effect of sleep duration and daily physical activity on cognitive function, elucidating the neural mechanisms using near-infrared spectroscopy(NIRS).Methods: A total of 23 healthy young adults(age 22.0 ± 2.2 years) participated in this study. Exercise amount was assessed using a uniaxial accelerometer. We evaluated total sleep time(TST) and sleep efficiency by actigraphy. Cognitive function was tested using the N-back task, the Wisconsin Card Sorting Test(WCST), and the Continuous Performance Test—Identical Pairs(CPT-IP), and the cortical oxygenated hemoglobin levels during a word fluency task were measured with NIRS.Results: Exercise amount was significantly correlated with reaction time on 0- and 1-back tasks(r = —0.602, p = 0.002; r = —0.446, p = 0.033,respectively), whereas TST was significantly correlated with % corrects on the 2-back task(r = 0.486, p = 0.019). Multiple regression analysis,including exercise amount, TST, and sleep efficiency, revealed that exercise amount was the most significant factor for reaction time on 0- and 1-back tasks(b = —0.634, p = 0.002; b = —0.454, p = 0.031, respectively), and TST was the most significant factor for % corrects on the 2-back task(b = 0.542, p = 0.014). The parameter measured by WCST and CPT-IP was not significantly correlated with TST or exercise amount. Exercise amount, but not TST, was significantly correlated with the mean area under the NIRS curve in the prefrontal area(r = 0.492, p = 0.017).Conclusion: Exercise amount and TST had differential effects on working memory and cortical activation in the prefrontal area. Daily physical activity and appropriate sleep duration may play an important role in working memory.
文摘Event-related potentials (ERP) is an important type of brain dynamics in human cognition research. However, ERP is often submerged by the spontaneous brain activity EEG, for its relatively tiny scale. Further more, the brain activities collected from scalp electrodes are often inevitably contaminated by several kinds of artifacts, such as blinks, eye movements, muscle noise and power line interference. A new approach to correct these disturbances is presented using independent component analysis (ICA). This technique can effectively detect and extract ERP components from the measured electrodes recordings even if they are heavily contaminated. The results compare favorably to those obtained by parametric modeling. Besides, auto-adaptive projection of decomposed results to ERP components was also given. Through experiments, ICA proves to be highly capable of ERP extraction and S/N ratio improving.
