Research on the functions of sleep has produced a wealth of evidence that sleep rhythms play an evolutionarily conserved role in the consolidation of multiple forms of memory. It has also revealed the breadth and importance of sleep-dependent memory consolidation to adaptive behavior across the lifespan.
A nascent literature documents abnormal sleep rhythms in people with autism spectrum disorder (ASD), which converges with genetic and neuroimaging evidence of thalamocortical circuit dysfunction1. In addition to disrupting sleep rhythms, thalamocortical circuit dysfunction may contribute to reduced attention and sensory sensitivities. But findings of abnormal sleep rhythms in ASD are inconsistent, likely reflecting small sample sizes, methodological limitations and the heterogeneity of ASD.
To realize the promise of understanding pathophysiology and developing mechanistically guided treatments, large-scale studies of genetically characterized of individuals with ASD are needed to characterize abnormal sleep rhythms and their genetic underpinnings. Because large-scale laboratory-based sleep studies are prohibitively expensive and burdensome, the first goal of this project is to validate the methodology and establish the feasibility of a large-scale at-home sleep study in ASD using a commercially available wearable electroencephalography (EEG) device.
Dara Manoach and her team will validate candidate devices against concurrent polysomnography in their sleep lab. They will select the optimal device, based on validity and feasibility of use at home in children with ASD, to characterize abnormal sleep physiology in ASD and explore its relationship to clinical features. These devices can also deliver auditory stimulation time-locked to specific sleep rhythms to enhance their coordination2, suggesting the possibility of a safe, scalable in-home treatment.
Findings from this project will lay the groundwork for large home-based studies that can illuminate the genetics and mechanisms of abnormal sleep physiology in ASD, establish its links with clinical phenotypes, identify novel treatment targets and test new treatments.