| Project/Area Number |
19K12199
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
Diaz Javier 筑波大学, 国際統合睡眠医科学研究機構, 研究員 (10835911)
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| Co-Investigator(Kenkyū-buntansha) |
佐藤 誠 筑波大学, 国際統合睡眠医科学研究機構, 教授 (50242409)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | Sleep physiology / Sleep mutants / Envelope analysis / EEG / Human Polysomnography / Random Walk / Time‐domain‐analysis / Computer simulations / Human Polysomnograpjy / random walk / time-domain-analysis / LFP / Sleep mutant |
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| Outline of Research at the Start |
In sleep the brain shows characteristic electrical signals. We want to verify and expand novel computational methods to analyze these signals to help and diagnose normal and disturbed sleep. We will use mouse data and data from humans with normal sleep and sleep problems.
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| Outline of Final Research Achievements |
Starting from computational models of signals arising from a superposition of arrhythmic pulses, we discovered a fundamental mathematical relationship linking the function describing the pulse and the auto-covariance function of the emerging wave superposition. For a superposition involving a mixture of different pulses, the response of the method corresponds to the linear superposition of individual pulses aligned at tau=0 (i.e. auto-covariance analysis origin) and scaled in accordance with their energy contribution. Applied to EEG, the technique reveals unique patterns characterizing NREM sleep, REM sleep, active wake, and grooming behavior. Remarkably, the method allows, for the first time, the detection of an EEG feature specific to wakefulness. From a theoretic perspective, these results strongly support the view that EEG arises mainly from arrhythmic (phasic) neuronal activity.
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| Academic Significance and Societal Importance of the Research Achievements |
EEG is a powerful tool for scientific research, medical diagnosis, and the development of brain-computer interfaces. This research aims to resolve fundamental open questions about the origin of EEG. Learning to extract EEG features directly linked to its ontology will greatly enhance its usefulness.
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