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Schizotypal traits are associated with sleep spindles and rapid eye movement in adolescence.

Liisa KuulaIlona MerikantoTommi MakkonenRisto HalonenMarius Lahti-PulkkinenJari LahtiKati HeinonenKatri RäikkönenAnu-Katriina Pesonen
Published in: Journal of sleep research (2018)
Research suggests an association between schizophrenia and a decrease in sleep spindle activity, as well as a change in sleep architecture. It is unknown how the continuum of psychotic symptoms relates to different features in the sleep electroencephalogram. We set out to examine how sleep architecture and stage 2 spindle activity are associated with schizotypy in a healthy adolescent population. The participants in our study (n = 176, 61% girls) came from a community-based cohort. Schizotypal traits were evaluated using the Schizotypal Personality Scale (STA) in early adolescence (mean age 12.3 years, SD = 0.5) and the participants underwent ambulatory overnight polysomnography at mean age 16.9 years (SD = 0.1). Sleep was scored in 30-s epochs into stages 1, 2, 3 and rapid eye movement (REM) sleep. Stage 2 spindles were detected using an automated algorithm. Spindle analyses from central and frontal derivations included spindle duration and density for slow (10-13 Hz) and fast (13-16 Hz) ranges. Covariates included sex and age. Those with the highest STA scores had a higher percentage of REM (B = 2.07 [95% CI, 0.17, 4.0]; p = .03) than those with the lowest scores. Those with the highest scores had shorter spindle duration, as derived from the frontal regions, and a slower oscillation range (B = -0.04 [95% CI, -0.07, -0.01]; p = .023) than those with the lowest scores. We conclude that high levels of schizotypy characteristics measured in early adolescence may be associated with distinguished features of sleep architecture, namely with spindle morphology and a higher proportion of REM sleep.
Keyphrases
  • sleep quality
  • physical activity
  • depressive symptoms
  • bipolar disorder
  • blood pressure
  • mental health
  • young adults
  • deep learning