STUDY OBJECTIVES: The two-process model posits that sleep is regulated by 2 independent processes, a circadian Process C and a homeostatic Process S. EEG slow-wave activity (SWA) is a marker of NREM sleep intensity and is used as an indicator of sleep homeostasis. So far, parameters of the two-process model have been derived mainly from average data. Our aim was to quantify inter-individual differences. DESIGN: Polysomnographic recordings (analysis of existing data). SETTING: Sound attenuated sleep laboratory. PATIENTS OR PARTICIPANTS: Eight healthy young males. INTERVENTIONS: 40-h sustained wakefulness. MEASUREMENTS AND RESULTS: Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA (derivation C3A2) per NREM sleep episode at episode midpoint were used for parameter estimation. Parameters were estimated simultaneously by minimizing the mean square error between data and simulations of Process S. This approach was satisfactory for average data and most individual data. We further improved our methodological approach by limiting the time constants to a physiologically meaningful range. This allowed a satisfactory fit also for the one individual whose parameters were beyond a physiological range. The time constants of the buildup of Process S ranged from 14.1 h to 26.4 h and those of the decline from 1.2 h to 2.9 h with similar inter-individual variability of the buildup and decline of Process S. CONCLUSIONS: We established a robust method for parameter estimation of Process S on an individual basis.
STUDY OBJECTIVES: The two-process model posits that sleep is regulated by 2 independent processes, a circadian Process C and a homeostatic Process S. EEG slow-wave activity (SWA) is a marker of NREM sleep intensity and is used as an indicator of sleep homeostasis. So far, parameters of the two-process model have been derived mainly from average data. Our aim was to quantify inter-individual differences. DESIGN: Polysomnographic recordings (analysis of existing data). SETTING: Sound attenuated sleep laboratory. PATIENTS OR PARTICIPANTS: Eight healthy young males. INTERVENTIONS: 40-h sustained wakefulness. MEASUREMENTS AND RESULTS: Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA (derivation C3A2) per NREM sleep episode at episode midpoint were used for parameter estimation. Parameters were estimated simultaneously by minimizing the mean square error between data and simulations of Process S. This approach was satisfactory for average data and most individual data. We further improved our methodological approach by limiting the time constants to a physiologically meaningful range. This allowed a satisfactory fit also for the one individual whose parameters were beyond a physiological range. The time constants of the buildup of Process S ranged from 14.1 h to 26.4 h and those of the decline from 1.2 h to 2.9 h with similar inter-individual variability of the buildup and decline of Process S. CONCLUSIONS: We established a robust method for parameter estimation of Process S on an individual basis.
Authors: T Hori; Y Sugita; E Koga; S Shirakawa; K Inoue; S Uchida; H Kuwahara; M Kousaka; T Kobayashi; Y Tsuji; M Terashima; K Fukuda; N Fukuda Journal: Psychiatry Clin Neurosci Date: 2001-06 Impact factor: 5.188
Authors: Dennis A Dean; Ary L Goldberger; Remo Mueller; Matthew Kim; Michael Rueschman; Daniel Mobley; Satya S Sahoo; Catherine P Jayapandian; Licong Cui; Michael G Morrical; Susan Surovec; Guo-Qiang Zhang; Susan Redline Journal: Sleep Date: 2016-05-01 Impact factor: 5.849