Humans and animals often exhibit brief awakenings from sleep (arousals), which are traditionally viewed as random disruptions of sleep caused by external stimuli or pathologic perturbations. However, our recent findings show that arousals exhibit complex temporal organization and scale-invariant behavior, characterized by a power-law probability distribution for their durations, while sleep stage durations exhibit exponential behavior. Such complex scale-invariant organization of the arousals makes it unlikely that they are merely a linear response to random external stimuli. The co-existence of both scale-invariant and exponential processes generated by a single regulatory mechanism has not been observed in physiological systems until now. Such co-existence resembles the dynamical features of non-equilibrium systems exhibiting self-organized criticality (SOC). Thus, we hypothesize that arousals are an integral part of sleep regulation and may be necessary to maintain and regulate healthy sleep by releasing accumulated excitations in the regulatory neuronal networks, following a SOC-type temporal organization. To address this hypothesis we propose to combine data from sleep physiology and bio-molecular/genetic experiments with modern concepts from statistical physics and the theory of complex networks. Utilizing the framework of SOC, our specific aim is: (i) to elucidate the mechanisms leading to scale-invariant organization of arousals during sleep;(ii) to uncover how pathologic conditions affect the SOC organization of arousals and sleep-stage transitions;(iii) to derive novel and more sensitive diagnostic markers of sleep disorders. We will analyze a large database from (i) healthy human subjects, and (ii) subjects with insomnia, narcolepsy, sleep apnea and other disorders;and (iii) from healthy wild type mice and rats. We will also utilize data from experimental animal models of various sleep disorders, where specific sleep-related neuronal groups and brain areas are targeted, to discern which key elements of the neurobiological interactions may be responsible for the emergence of SOC complexity in sleep dynamics at the system level. Establishing SOC-type complexity in sleep dynamics will challenge the current dominant homeostasis-based paradigm of sleep regulation, as it indicates the need of continuous fluctuations (arousals) over a broad range of time scales. How neuronal signaling interactions lead to SOC-type complexity at the system level is not known, and we will develop approaches based on the modern theory of scale-invariant networks to probe the role of the neuronal network topology in generating SOC in sleep dynamics.
Brief awakenings from sleep (arousals) are traditionally viewed as disruptions of sleep, and their temporal dynamics as well as the underlying mechanisms are not well understood. We have recently discovered that the temporal organization of arousal episodes and sleep-stage durations in healthy sleep exhibit a self-organized criticality (SOC) behavior, which has not been previously reported in integrated physiological systems, and is not accounted for by the current homeostasis-based framework of sleep dynamics. This proposal focuses on identifying the basic control mechanisms leading to SOC behavior by utilizing available data from bio-molecular and genetic animal experiments as well as modern concepts from statistical physics applied to data from animal models and human polysomnographic recordings, that will allow us to link biochemical signaling pathways at the cellular level, through functional neuronal networks of sleep- and wake-promoting neurons, with sleep dynamics at the system level, and to derive novel clinical diagnostic and prognostic markers of sleep disorders.
|Bashan, Amir; Bartsch, Ronny P; Kantelhardt, Jan W et al. (2012) Network physiology reveals relations between network topology and physiological function. Nat Commun 3:702|
|Carretero-Campos, Concepcion; Bernaola-Galvan, Pedro; Ivanov, Plamen Ch et al. (2012) Phase transitions in the first-passage time of scale-invariant correlated processes. Phys Rev E Stat Nonlin Soft Matter Phys 85:011139|