title: Age and gender dependency of physiological networks in sleep.
abstract: Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics. We find moderate dependencies of the network on gender. Significantly higher link strengths up to 13% are found in women for some links in different frequency bands of central and occipital regions in REM and light sleep (N2). Higher link strengths are found in men consistently in cardio-cerebral links in N2, but not significant. Age dependency is more pronounced. In particular a significant overall weakening of the network with age is found for wakefulness and non-REM sleep stages. The largest overall decrease is observed in N2 with 0.017 per decade. For individual links decrease rates up to 0.08 per decade are found, in particular for intra-brain links in non-REM sleep. Many of them show a significant decrease with age. Non-linear regression employing an artificial neural network can predict the age with a mean absolute error (MAE) of about five years, suggesting that an age-resolution of about a decade would be appropriate in normative data for physiological networks.
authors.affiliation: Center of Biomedical Image and Information Processing, University of Applied Sciences Berlin, Berlin, Germany.
authors.forename: Dagmar
authors.initials: D
authors.lastname: Krefting
authors.forename: Christoph
authors.initials: C
authors.lastname: Jansen
authors.forename: Thomas
authors.initials: T
authors.lastname: Penzel
authors.forename: Fang
authors.initials: F
authors.lastname: Han
authors.forename: Jan W
authors.initials: JW
authors.lastname: Kantelhardt
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017286:Polysomnography; D012727:Sex Characteristics; D012890:Sleep / Q000502:physiology*; D055815:Young Adult