Since 1994, NIH has supported the standardized collection of polysomnography (PSG) studies as part of landmark multi-center studies which have used well-defined methods for data collection and quality assurance. Across studies, information on disease risk factors and outcomes, cardiovascular and neurocognitive function, and biochemical marker data, are available for children and adults representing diverse backgrounds. These data could serve as an invaluable national resource, providing opportunities to engage the scientific community, including trainees, in efforts to discover predictive bio-physiological signals for disease incidence and progression and to address critical questions regarding disease susceptibility and subgroup differences not possible using data from single cohorts. Improved access to those data would add value to already funded projects, ensuring maximal and enduring impact. Optimal use of such data requires the systematic organization and structuring of these data and access to procedures and computational tools for easy but secure access and curation of well-defined data subsets. The NHLBI National Sleep Research Resource (NSRR) aims to meet these compelling needs, thus leveraging the NIH's investments in the collection of sleep data in well characterized cohort studies and clinical trial to create a unique national resource of reliably-scored, well-annotated research PSGs from many major NHLBI cohorts or clinical trials (~ 50,000 sleep studies). The NSRR also will include: a) an electronic database of polysomnography data (raw signals, scored annotations, and summary sleep metrics);b) quantitative metrics of heart rate and EEG signals;b) linked data on clinical, physiological and biochemical parameters;c) a tool set to allow the user to search and access de-identified research data using a secure, cloud platform;d) an open-source suite of tools for offline analyses for signal processing, file structure editing, data harmonization and statistic generation;and e) user support for resource usage. NSRR will be a scalable and expandable resource constructed by capitalizing on major advances in informatics, developed with leading-edge agile software engineering methodology, user-centered interface design and an ontology-driven architecture.
The creation of a central library of well-defined sleep studies linked to clinical and physiological data will transform data sharing approaches across the scientific community, enhancing the ability of researchers to address critical questions regarding the role of sleep disorders in the etiology of chronic health conditions, such as cardiovascular disease and identify subgroups of the population at greatest risk for sleep disorders and their related co-morbidities.
|Mariani, Sara; Tarokh, Leila; Djonlagic, Ina et al. (2018) Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource. Sleep Med 47:126-136|
|Ogilvie, Rachel P; Lakshminarayan, Kamakshi; Iber, Conrad et al. (2018) Joint effects of OSA and self-reported sleepiness on incident CHD and stroke. Sleep Med 44:32-37|
|Li, Xiaojin; Cui, Licong; Tao, Shiqiang et al. (2018) HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring. IEEE J Biomed Health Inform 22:375-385|
|Quante, Mirja; Mariani, Sara; Weng, Jia et al. (2018) Zeitgebers and their association with rest-activity patterns. Chronobiol Int :1-11|
|Kang, Seung-Gul; Mariani, Sara; Marvin, Stephanie A et al. (2018) Sleep EEG spectral power is correlated with subjective-objective discrepancy of sleep onset latency in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 85:122-127|
|Purcell, S M; Manoach, D S; Demanuele, C et al. (2017) Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat Commun 8:15930|
|Pamidi, Sushmita; Redline, Susan; Rapoport, David et al. (2017) An Official American Thoracic Society Workshop Report: Noninvasive Identification of Inspiratory Flow Limitation in Sleep Studies. Ann Am Thorac Soc 14:1076-1085|
|Mitchell, Jonathan A; Quante, Mirja; Godbole, Suneeta et al. (2017) Variation in actigraphy-estimated rest-activity patterns by demographic factors. Chronobiol Int 34:1042-1056|
|Valdez, Joshua; Kim, Matthew; Rueschman, Michael et al. (2017) ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies. AMIA Annu Symp Proc 2017:1705-1714|
|Costa, Madalena D; Davis, Roger B; Goldberger, Ary L (2017) Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics. Front Physiol 8:255|
Showing the most recent 10 out of 22 publications