The Stanford Center for Sleep Sciences and Medicine

Overview of Current Areas of Research

Researchers at Stanford are always seeking to learn more about how sleep works and the cause of various sleep disorders. At Stanford, we believe that the evaluation of new therapies in clinical settings should feed vital information back to the basic research scientists at the beginning of the process. Connecting the cycle of research and treatment depends on new systems that bridge the two, so that the flow of data from basic research is combined with continuous monitoring from clinical settings. At Stanford, the PhD at the laboratory bench and the MD at the bedside are often one and the same person.

Patient Oriented Research

At this stage in the development of sleep medicine, findings are coming fast and treatments are continually being piloted and refined. For this reason, Stanford places great emphasis on clinical research: this is where basic science, which uncovers the fundamental mechanisms of sleep and waking, intersects with applied science, where such knowledge leads to new treatments.

Stanford Technology Analytics and Genomics in Sleep (STAGES) Study

The Stanford Center for Sleep Sciences and Medicine is launching a large scale multi-site, study designed to develop and disseminate essential tools and data to the scientific community to advance the field of sleep medicine. The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is funded by the Klarman Family Foundation and will explore sleep through genetics and technology in a large-scale patient-oriented study.

Sleep is a biological "black box" and sleep disorders have enormous medical, psychological and societal impact. Variation in sleep (notably EEG characteristics) and propensity to sleep disorders is highly heritable. Among the various methods that could be used to make progress in this area, one of the most powerful is genetics, as findings can be made without apriori knowledge of underlying mechanisms. STAGES will study all major sleep disorders affecting our population. This study will collect clinical, objective sleep data, and biological samples from 30,000 patients at 20 different sleep clinic sites, including:

a. On-line sleep/medical history questionnaire (the Alliance Sleep Questionnaire, a branching logic questionnaire developed by 5 institutions)
b. In lab nocturnal Polysomnography data (one night PSG)
c. Computerized Neurocognitive Battery (U Penn CNB)
d. Actigraphy over 2-4 weeks
e. Genetics - Genome wide association data
f. Stored biological samples (DNA, plasma, serum) for future biomarker research
g. 3-D facial image

In parallel, the team will develop and share analytical tools such as machine learning and new statistical methods that will assist in the analysis and interpretation of these data. Access to these data and tools will spark new research opportunities and genetic analysis, which will result in new diagnostic biomarkers for sleep disorders and a better molecular understanding of sleep regulation.

This project has several multi-disciplinary committees to oversee the study, including a multi-institutional Statistics Committee.

More information will be available when the study begins recruitment.

Restless Legs Syndrome (RLS) Research

Restless Legs Syndrome is characterized by an urge to move the legs that is worse at rest and at bedtime, and is temporary relieved by leg movement. If your  adolescent has symptoms of Restless Legs Syndrome, they may be eligible to participate in a clinical trial at the Stanford Sleep Medicine Center regarding an investigational medication that may be able to treat his/her condition. Participants will receive extensive sleep evaluation and reimbursement for participation. 

We are actively recruiting for this study. For more information, please call Kary at (650) 721-7574.  (For general information about participant rights, contact 866-680-2906.)

Narcolepsy Research

Narcolepsy is a chronic sleep disorder affecting more than 1 in 2,000 Americans. The disease is principally characterized by a permanent and overwhelming feeling of sleepiness and fatigue. Other symptoms involve abnormalities of dreaming sleep, such as dream-like hallucinations and finding oneself physically weak or paralyzed for a few seconds. In addition, narcoleptics usually experience disturbed night-time sleep. Tragically, most individuals with the disorder are not diagnosed and thus are not treated.

The Stanford Center for Narcolepsy Research (SCNR) is currently conducting a study to identify the genes that cause narcolepsy and related sleep disorders such as idiopathic hypersomnia (excessive daytime sleepiness). By collecting and analyzing blood samples, throat swabs and spinal fluid we hope to reveal the responsible genes or proteins. If we can identify one or more unique gene or protein, physicians would have a simple, minimally invasive test for diagnosing narcolepsy and related sleep disorders.

To learn more, click here.

We are actively recruiting subjects and controls. To learn more about becoming a research participant in a narcolepsy study, contact Kerry [kbreuer].

Kleine-Levin Syndrome

Kleine-Levin Syndrome (KLS) is a rare and complex disorder characterized by recurring periods of excessive sleep, as well as altered cognition and behavior. Symptoms occur in cycles that can last anywhere from a few days to several weeks. While experiencing symptoms, patients sleep most of the day and night. During the brief periods when they are awake, they are often confused, disoriented and may lack emotions. In some cases, excessive food cravings (hyperphagia) and/or uninhibited hypersexuality are present during an episode.

During an episode, patients report feeling separated from the reality, like they are in a fog. However, between cycles, patients are healthy and completely aysymptomatic. The disorder usually begins in adolescence, and is more common in males than females. Typically, the episodes become shorter and less frequent until disappearing all together in middle age.

At Stanford we are working to identify blood markers or gene(s) associated with or responsible for the development of KLS and then to recognize how those genes affect people at the molecular level. If we can identify one or more unique gene or blood marker, physicians would have a simple, minimally invasive test to help diagnose KLS. Additionally, if the Stanford Center for Narcolepsy Research (SCNR) succeeds in identifying a marker, any lingering debate about the legitimacy of KLS will all but cease.

To learn more, click here.

We are actively recruiting subjects and controls, to learn more about becoming a research participant in a KLS study, contact Kerry [kbreuer].

Academic Alliance for Sleep Research (AASR) Consortium of Sleep Data

The Stanford Center for Sleep Sciences & Medicine is collaborating with four major academic sleep centers throughout the country to improve patient care and support on-going research efforts in the field of sleep medicine. As part of this effort, we have designed a comprehensive, web-based questionnaire that will help clinicians as well as researchers

The goal is standardize the information commonly collected before a patient’s initial visit to a sleep clinic and make this data available to researchers as well as the clinician. This study is only recruitng patients of the Stanford Sleep Disorders Clinic.

In addition, for some participants we will look at genetic and blood markers associated with sleep disorders as part of a Genome Wide Association Study. We hope to identify the genes, proteins, or other biomarkers associated with the development of disorders such as sleep apnea, restless legs syndrome, insomnia, narcolepsy and idiopathic hypersomnia (excessive daytime sleepiness).

If one or more unique genes or biomarkers is identified, physicians would have a simple, minimally invasive tool to diagnose sleep disorders.

To learn more about this project, contact Eileen Leary [eleary].

Software for Evaluating Medical Diagnostic Tests

Determining diagnostic criteria for specific disorders is often a tedious task that involves determining optimal diagnostic thresholds for symptoms and biomarkers using receiver-operating characteristic (ROC) statistics.  To help this endeavor, we developed softROC, a user-friendly graphic-based tool that let users visually explore possible ROC tradeoffs. The software requires MATLAB installation and an Excel file containing threshold symptoms/biological measures, with corresponding gold standard diagnoses for a set of patients.  The software scans the input file for diagnostic and symptom/biomarkers columns, and populates the graphical-user-interface (GUI).  Users select symptoms/biomarkers of interest using Boolean algebra as potential inputs to create diagnostic criteria outputs.  The software evaluates subtests across the user-established range of cut points and compares them to gold standard generating ROC and quality ROC scatter plots.  These plots can be examined interactively to find optimal cut points of interest for a given application (e.g. sensitivity versus specificity needs).  Split-set validation can also be used to set up criteria and validate these in independent samples.  Bootstrapping is used to produce confidence intervals.  Additional statistics and measures are provided, such as the area under the ROC curve (AUC). As a testing set, softROC is used to investigate nocturnal polysomnogram measures as diagnostic features for narcolepsy.  All measures can be outputted to a text file for offline analysis.

The softROC package, with clinical training data and tutorial instruction manual, can be obtained from this website.


Completed Clinical Research Projects

COMET Project

In 2010, Dr. Clete Kushida received a grant from the Agency for Healthcare Research and Quality (AHRQ) for the Comparative Outcomes Management with Electronic Data Technology (COMET) project.  COMET, is a large scale, multi-center informatics and comparative effectiveness project of positive airway pressure (PAP) versus dental appliance. 

The COMET team plans to develop an electronic network that will enable the transfer of information from various hospitals and medical centers, patients and research subjects, different types and severity of medical problems, various equipment and test types, and across several patient visits.  This network will allow physicians and scientists to access comprehensive information about their patients and research subjects, and the sharing of anonymized data across several academic institutions (initially at Stanford, Harvard, University of Pennsylvania, and University of Wisconsin-Madison) may ultimately lead to improvement in medical outcomes.  We also plan to conduct a study evaluating two common treatments for obstructive sleep apnea (OSA), a highly prevalent sleep-related breathing disorder, and we anticipate that this study will provide comparative data for determining the effectiveness of these treatments in reducing cardiovascular risk in a population at high risk for cardiovascular disease, and will enhance clinical decision making in determining the optimal treatment strategies for patients with OSA.

Enrollment for this project is currently closed.


Hypoxia occurs when you are oxygen-deprived which is a regular occurrence for many people with Obstructive Sleep Apnea. Our researchers are exploring the effects of Hypoxia (decreased Oxygen) and Hypercapnea (excess Carbon Dioxide) on individuals by performing a breathing test and also collecting questionnaire data and blood samples. We hope to identify the environmental and genetic factors involved in breathing regulation in response to slight changes in the oxygen and /or carbon dioxide levels in the blood. Identifying such factors will be a milestone in our understanding of breathing control disorders like sleep apnea and will open a window for the development of new treatment options for this common disorder.

Please note this is not a Sleep Apnea study, but rather a study to understand biological factors which also are in play in Sleep Apnea. The test is conducted on participants while awake.

Recruitment for this study is currently on hold.


Basic Research

There have been significant advances over the last decade which have revealed the association of particular genetic and biological markers with sleep disorders. For example, research has found genes that correlate with sleep apnea and Restless Legs Syndrome (RLS), as well as the link between narcolepsy and hypocretin levels. Compiling data from a large number of patients will guide lab science toward ever more detailed genetic patterns associated with specific disorders and even with the effectiveness of particular treatments.

Genome Wide Association Studies (GWAS)

The goal of a GWAS project is to identify common genetic factors affecting proteins, sugars or other molecules that’s presence or concentration in the body is unique to individuals with a particular condition. The researchers at Stanford hope to identify blood markers or gene(s) associated with or responsible for the development of sleep disorders in people, and identify how those genes affect people on a molecular level.

By compiling genetic information and questionnaire data from the many patients who visit the Stanford Sleep Disorders Center, we are able to identify more detailed genetic patterns associated with specific disorders and even the effectiveness of particular treatments. If one or more unique gene(s) or blood marker(s) can be identified, physicians would have a simple, minimally invasive test to help with the diagnosis of sleep disorders.

Electroencephalography (EEG)

During sleep, the brain generates rhythmic brain activity that can be measured by electroencephalography (EEG).  Many features of the EEG signal are stable and trait-like and appear to be regulated by the genes we inherit from our parents.  Changes in sleep EEG have been demonstrated in a variety of psychiatric and neurodegenerative disorders.  The Stanford Center for Sleep Sciences and Medicine is working to identify variations in genes that influence EEG patterns of brain activity during sleep.  Features of the normal sleep EEG will be identified and quantified using automated analysis and genetic association analysis will include both genome-wide and candidate polymorphisms.  We hypothesize that uncovering the genetic basis of basic sleep EEG will serve as a platform for understanding, diagnosing, and treating sleep disorders and mental illness such as depression, anxiety, autism, schizophrenia, and Alzheimer’s and Parkinson’s diseases. 



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