Dr Bianchi's Sleep Informatics Laboratory focuses on understanding human sleep, in health and disease, through a combination of diverse analytical methods and novel device technology. The patient-oriented research goals involve three major categories.
Matt Bianchi, MD, PhD, MMSc
Current and Past Team Members
Yvonne Alameddine, Research Assistant
Mihaela Bazalakova, Neurology Resident
Kathy Chuang, Neurology Resident
Preethi Srinivasan (now PhD student at Northeastern)
New computational methods for extracting maximal information from standard clinical testing ("sleep studies")
When patients undergo sleep studies, multiple aspects of physiology are monitored, such as brain waves (EEG), muscle tone, breathing, heart beat (EKG), etc. These signals enable us to determine whether a patient is awake or asleep, what stage of sleep is occurring, and whether the sleep is normal or abnormal. We have used our large database of patient recordings, as well as analysis of independent databases, to study what is known as "sleep architecture": the pattern of transitions among the various sleep-wake stages throughout the night. Despite the common view that sleep is a continuous or smooth process, in reality even normal sleepers make many stage transitions during a typical night of sleep, and the brain even shows repeated episodes of wake-like activity even though a person might not realize (or remember) these brief transitions. In addition, despite the common view that "dream" sleep (REM) and "deep" sleep are the most important parts of sleep, the timing and duration of these stages in a given person are highly variable from night to night, and the idea that these stages are critical for the refreshing aspects of sleep has not been verified outside of highly controlled (and thus unrealistic) experimental settings. This common challenge, known as "external validity", is addressed in section 3.
We have employed a combination of methods to understand sleep architecture and its relationship to sleep disorders, including spectral analysis, transition analysis, Markov models, and machine learning. These endeavors are helping to address some critical limitations in the field of sleep medicine. For example, we cannot predict based on sleep architecture, or even based on the presence of an obvious disease such as sleep apnea, whether a patient will experience sleepiness or fatigue. Perhaps even more interesting is the fact that we cannot predict, based on sleep architecture, whether a patient will even be able to accurately report how long they slept. These seemingly basic questions are of critical importance in research as well as clinical arenas. If a patient with sleep apnea is just as likely to report being sleepy as to report feeling fine, then, a) we will miss many diagnoses if we only test those patients with symptoms, and, b) we either can't quantify sleepiness effectively or we can't quantify sleep effectively. If a patient reporting insomnia may be sleeping more than they think, then, a) some patients may overly concerned or over-medicated, and, b) we must understand the basis for this mismatch ("misperception") because diagnostic and therapeutic decision-making depend entirely on patient self-report of sleep-wake durations.
New approaches for understanding sleep through autonomic nervous system signals (such as cardiac and respiratory activity).
The traditional method to measure sleep is based largely on brainwave activity (EEG). Although there are some advantages to EEG analysis, there are also several important limitations. For example, it is very difficult to obtain quality EEG recordings in the home, especially over multiple nights. In addition, it is difficult to automate EEG analysis, in part because the brainwaves are influenced by many factors (age, gender, caffeine and alcohol intake, and medications) that differ between patients. Autonomic nervous system signals are an attractive complement to the EEG, and much existing research supports autonomic signals as important markers of sleep physiology. We are using EKG-based home monitors developed initially by my mentor and collaborator, Dr Thomas from BIDMC, to improve the diagnosis and management of patients with sleep disorders. We are also developing tools to extract information about normal and abnormal sleep from the breathing patterns. The autonomic nervous system signals have two main advantages over EEG analysis of sleep: they are easy to collect in the home over multiple nights, and 2) the analysis of these signals can be automated.
Advancement of novel wearable sleep devices for individualizing diagnostic and therapeutic decision-making
Building upon our experience with quantifying sleep using traditional and novel methods, we are rapidly translating our efforts into the "real world" of sleep - that is, what happens night after night, in the home, as our sleep is impacted by factors such as caffeine, alcohol, smoking exercise, diet, naps, shift work, recent sleep patterns, etc. This is a departure from traditional experimental research that seeks to eliminate or tightly control these factors. However, the extent to which one controls or limits these factors is the extent to which the experimental conclusions lose relevance to the real world. Thus, our studies engage the opposite philosophy: we want to understand sleep in the most natural context, understanding that this context is unique to each individual. To accomplish this, we utilize home sleep-monitoring devices. We developed a novel sleep monitor that uses fabric sensors in a light-weight and comfortable tee-shirt to track breathing patterns in the comfort of home. The shirts are washable, inexpensive, do not require special fitting, and contain no wires or adhesives. Our validation research has shown that breathing patterns can be used not only to quantify the presence (and severity) of sleep apnea, but also to provide information about sleep-wake stage architecture. Ongoing work aims to use this combination of autonomic nervous system analysis with the novel sleep shirt technology to improve diagnosis, optimize treatment monitoring, and even provide individualized feedback to patients with sleep disorders.
Medical Decision Making
In addition to the major thrust of research in the field of sleep medicine, we have active efforts in the area of Medical Decision Making. I teach a course at Harvard Medical School on the topic with my colleague Dr Westover. This exciting field uses quantitative modeling (cost-benefit, cost-effectiveness, Markov models, etc) to explore challenging questions facing patients and clinicians alike. Finally, I maintain an interest in neuropharmacology dating back to my PhD thesis work, and in particular, the mechanisms by which certain drugs affect the brain. My most recent work in this area advances the concept of rational promiscuity as a theoretical platform for novel drug discovery in the fields of neurology and psychiatry.
Read about and apply for residency, fellowship and observership programs at http://www.massgeneral.org/neurology/education.
Apply for temporary positions (summer interns) through the Bulfinch Temporary Service Web site at http://www.massgeneral.org/careers/temporary.aspx. Search for all opportunities using ID# 2200484.
The impact of body posture and sleep stages on sleep apnea severity in adults.
Eiseman NA, Westover MB, Ellenbogen JM, Bianchi MT. J Clin Sleep Med. 2012 Dec 15;8(6):655-66.
Publication embargo until July 2013
Sleep misperception in healthy adults: implications for insomnia diagnosis.
Bianchi MT, Wang W, Klerman EB. J Clin Sleep Med. 2012 Oct 15;8(5):547-54.
Publication embargo until June 2013
Sleep telemedicine: a survey study of patient preferences.
Kelly JM, Schwamm LH, Bianchi MT. ISRN
Decreased nocturnal awakenings in young adults performing bikram yoga.
Kudesia RS, Bianchi MT.
ISRN Neurol. 2012;2012:153745.
Instantaneous monitoring of sleep fragmentation by point process heart rate variability and respiratory dynamics.
Citi L, Bianchi MT, Klerman EB, Barbieri R.
Conf Proc IEEE Eng Med Biol Soc. 2011;2011:7735-8.
Probabilistic sleep architecture models in patients with and without sleep apnea.
Bianchi MT, Eiseman NA, Cash SS, Mietus J, Peng CK, Thomas RJ. J
Sleep Res. 2012 Jun;21(3):330-41.
Classification algorithms for predicting sleepiness and sleep apnea severity.
Eiseman NA, Westover MB, Mietus JE, Thomas RJ, Bianchi MT. J
Sleep Res. 2012 Feb;21(1):101-12.
Power law versus exponential state transition dynamics: application to sleep-wake architecture.
Chu-Shore J, Westover MB, Bianchi MT.
PLoS One. 2010 Dec 2;5(12):e14204.
Obstructive sleep apnea alters sleep stage transition dynamics.
Bianchi MT, Cash SS, Mietus J, Peng CK, Thomas R.
PLoS One. 2010 Jun 28;5(6):e11356.
Complete List of Publications
Reviews and Commentaries
Clinical pharmacology in sleep medicine.
Proctor A, Bianchi MT.
ISRN Pharmacol. 2012;2012:914168.
Technical advances in the characterization of the complexity of sleep and sleep disorders.
Bianchi MT, Thomas RJ.
Prog Neuropsychopharmacol Biol Psychiatry. 2012 Nov 19.
Recent developments in home sleep-monitoring devices.
Kelly JM, Strecker RE, Bianchi MT.
ISRN Neurol. 2012;2012:768794.
Mammalian sleep genetics.
Kelly JM, Bianchi MT.
Neurogenetics. 2012 Nov;13(4):287-326.
Hypnotics and mortality risk.
Bianchi MT, Thomas RJ, Ellenbogen JM.
J Clin Sleep Med. 2012 Aug 15;8(4):351-2.
Targeting ligand-gated ion channels in neurology and psychiatry: is pharmacological promiscuity an obstacle or an opportunity?
Bianchi MT, Botzolakis EJ.
BMC Pharmacol. 2010 Mar 2;10:3.
Decision Theory and Bayesian Statistics
Propagation of uncertainty in Bayesian diagnostic test interpretation.
Srinivasan P, Westover MB, Bianchi MT.
South Med J. 2012 Sep;105(9):452-9.
Should risky treatments be reserved for secondary prevention? Theoretical considerations regarding risk-benefit tradeoffs.
Westover MB, Eiseman NA, Bianchi MT.
J Clin Epidemiol. 2012 Aug;65(8):877-86.
Significance testing as perverse probabilistic reasoning.
Westover MB, Westover KD, Bianchi MT.
BMC Med. 2011 Feb 28;9:20.
Statin use following intracerebral hemorrhage: a decision analysis.
Westover MB, Bianchi MT, Eckman MH, Greenberg SM.
Arch Neurol. 2011 May;68(5):573-9.
Incorporating uncertainty into medical decision making: an approach to unexpected test results.
Bianchi MT, Alexander BM, Cash SS.
Med Decis Making. 2009 Jan-Feb;29(1):116-24.
Screening for Obstructive Sleep Apnea: Bayes Weighs In.
The Open Sleep Journal. 2009. 2:56-59.
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