Active Mental Health

Towards sleep and circadian diagnostic biomarkers for unipolar and bipolar depression

In plain English

AI plain-English summary

A young adult’s sleep patterns—how long they take to fall asleep, how their brainwaves behave during the night, and how their internal body clock ticks—could one day help doctors tell the difference between bipolar disorder and ordinary depression before a wrong diagnosis leads to harmful treatment. Current diagnosis relies on subjective reports and clinical interviews, yet sleep disruption is a core feature of both conditions. This project will collect detailed sleep and circadian data from 200 young adults with bipolar disorder or major depression, plus their unaffected relatives and healthy controls. By linking these measurements with genetic risk, cognitive performance, and mood, the researchers aim to identify objective electrical signatures in the brain that distinguish the two disorders. If successful, the work could produce diagnostic biomarkers for a critical clinical decision-point: whether a patient presenting with depression will later develop mania. Misdiagnosing bipolar depression as unipolar depression can trigger dangerous mood swings when antidepressants are prescribed alone. The resulting resource—linking sleep, circadian, genomic, cognitive, and mood data—would also help explain why patients with the same diagnosis have such different outcomes. This is fundamental science with a clear translational target, not an immediate bedside tool.

View original technical description
Altered sleep and circadian rhythms are common clinical features of both unipolar depression (major depressive disorder, MDD) and bipolar disorder (BD). Nonetheless, major knowledge gaps around predictive utility, specificity, heterogeneity and mechanism hamper the development of diagnostically useful, objective sleep and circadian biomarkers. Building on our work in schizophrenia (SCZ), we will collect a cohort (N = 200) of young adults with BD or MDD, their unaffected relatives (i.e. asymptomatic, high- risk individuals) and healthy controls, to identify electrophysiological signatures, shared across or unique to BD and MDD, integrating genetic risk and clinical moderators. Second, we will frame these sleep data within the broader context of ultradian and circadian rhythms, alterations of which are also linked to mood disorders, to generate more informative metrics. Finally, we will investigate the chronic and proximal functional consequences of altered sleep neurophysiology, via changes in cognition, emotion regulation and well-being. This proposed project aims to generate an invaluable resource linking sleep, circadian, genomic, cognitive and mood data. In particular, it will address two clinically relevant issues: 1) heterogeneous patient outcomes in BD and 2) the prediction of bipolarity in patients first presenting with depressive symptoms, an important clinical decision-point with potential for iatrogenic harm.

View the original record at the funder ↗

Researchers

Katherine Burdick (EPMC Awardee)Shaun Purcell (EPMC Awardee)Tamar Sofer (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Testing a causal model of sleep and circadian rhythm disturbance and youth- onset mood disorders
Ambient and passive collection of sleep and circadian rhythm data in bipolar disorder to understand symptom trajectories and clinical outcomes (AMBIENT- BD).
Integrating circadian, neuroimaging and genetic data to investigate major depression and bipolar disorder
Validation of a translatable chronobiological signature of early relapse in bipolar disorder
Sleep And Circadian Health Disturbances In Psychosis And Depression: The Cascading Impacts Of Impairments In Cognitive Control

Original classification

Directed Call

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.