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Li Z, Sheth AB, Sheth BR. What drives slow wave activity during early non-REM sleep: Learning during prior wake or effort? PLoS One 2017; 12:e0185681. [PMID: 29028805 PMCID: PMC5640223 DOI: 10.1371/journal.pone.0185681] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 09/07/2017] [Indexed: 11/19/2022] Open
Abstract
What is the function of sleep in humans? One claim is that sleep consolidates learning. Slow wave activity (SWA), i.e. slow oscillations of frequency < 4 Hz, has been observed in electroencephalograms (EEG) during sleep; it increases with prior wakefulness and decreases with sleep. Studies have claimed that increase in SWA in specific regions of the sleeping brain is correlated with overnight improved performance, i.e. overnight consolidation, on a demanding motor learning task. We wondered if SWA change during sleep is attributable to overnight consolidation or to metabolic demand. Participants executed out-and-back movements to a target using a pen-like cursor with their dominant hand while the target and cursor position were displayed on a screen. They trained on three different conditions on separate nights, differing in the amount and degree of rotation between the actual hand movement direction and displayed cursor movement direction. In the no-rotation (NR) condition, there was no rotation. In the single rotation (SR) condition, the amount of rotation remained the same throughout, and performance improved both across pre-sleep training and after sleep, i.e. overnight consolidation occurred; in the random rotation (RR) condition, the amount of rotation varied randomly from trial to trial, and no overnight consolidation occurred; SR and RR were cognitively demanding. The average EEG power density of SWA for the first 30 min. of non-rapid eye movement sleep after training was computed. Both SR and RR elicited increase in SWA in the parietal region; furthermore, the topographic distribution of SWA in each was remarkably similar. No correlation was found between the overnight performance improvement on SR and the SWA change in the parietal region on measures of learning. Our results argue that regulation of SWA in early sleep is associated with high levels of cognitive effort during prior wakefulness, and not just overnight consolidation.
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Affiliation(s)
- Ziyang Li
- University of Houston, Houston, TX, United States of America
| | - Aarohi B. Sheth
- Carnegie Vanguard High School, Houston, TX, United States of America
| | - Bhavin R. Sheth
- University of Houston, Houston, TX, United States of America
- * E-mail:
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Chen L, Cai S, Li B, Jiang Q, Ke M, Zhao Y, Chen S, Zou M. A novel signal acquisition platform of human cardiovascular information with noninvasive method. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2017; 88:054301. [PMID: 28571401 DOI: 10.1063/1.4982952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cardiovascular diseases (CVDs) are considered the major cause of death worldwide, so more researchers pay more and more attention to the development of a non-invasive method to obtain as much cardiovascular information (CVI) as possible for early screening and diagnosing. It is known that considerable brain information could be probed by a variety of stimuli (such as video, light, and sound). Therefore, it is quite possible that much more CVI could be extracted via giving the human body some special interrelated stimulus. Based on this hypothesis, we designed a novel signal platform to acquire more CVI with a special stimulus, which is to give a gradual decrease and a different settable constant pressure to six air belts placed on two-side brachia, wrists, and ankles, respectively. During the stimulating process, the platform is able to collect 24-channel dynamic signals related with CVI synchronously. Moreover, to improve the measurement accuracy of signal acquisition, a high precision reference chip and a software correction are adopted in this platform. Additionally, we have also shown some collection instances and analysis results in this paper for its reliability. The results suggest that our platform can not only be applied on study in a deep-going way of relationship between collected signals and CVDs but can also serve as the basic tool for developing a new noninvasive cardiovascular function detection instrument and system that can be used both at home and in the hospital.
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Affiliation(s)
- Longcong Chen
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Shaoxi Cai
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Bo Li
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Qifeng Jiang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Ming Ke
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Yi Zhao
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Sijia Chen
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Misha Zou
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
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