The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

• Published in: PloS one Vol. 6; no. 8; p. e23593
• Main Authors: Merica, Helli, Fortune, Ronald D.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.08.2011; Public Library of Science (PLoS)
• Subjects: Adult; Alternations; Arousal - physiology; Biology; Brain; Brain stem; Circadian rhythm; EEG; Electroencephalography; Eye movements; Frequencies; Homeostasis; Humans; Information services; Models, Neurological; Neurons; Neurons - physiology; Neurophysiology; NREM sleep; Rapid eye movement state; REM sleep; Sleep; Sleep (NREM); Sleep (REM); Sleep, REM - physiology; Stochastic Processes; Stochasticity; Substrates; Suprachiasmatic nucleus; Suprachiasmatic Nucleus - physiology; Time Factors; Trends; Young Adult; Los Angeles California; California; United States > US; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0023593

Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP)--in fitting the data well--successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN) activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for TIMING the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the PATTERN of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1-4, using data from 30 healthy subjects aged 20-30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN to the EEG.