Bipolar disorder – formerly known as manic depression – is a chronic, recurrent mental illness characterised by extreme swings in mood. The condition is thought to affect at least one in every 100 adults worldwide and has the highest rate of suicide among psychiatric disorders.
But despite its prevalence and severity, little is known about the processes underlying the disorder, while treatments remain limited. Researchers at Oxford University have set out to address this by using mathematical modelling to better understand the ‘mood dynamics’ of people with bipolar disorder.
In a new paper published in Journal of the Royal Society: Interface, academics investigate how the subjective experience of mood can be understood using oscillators that ‘map’ the fluctuations in mood reported by participants via the QIDS (Quick Inventory of Depressive Symptomatology) questionnaire system.
Michael Bonsall, Professor of Mathematical Biology in the Department of Zoology at Oxford, says: ‘Bipolar disorder affects a huge number of people across the globe, so it’s really important that we find new ways to understand it from both scientific and clinical points of view.
‘For the last four or five years, we’ve been working on what we call “mood maths” – using mathematical modelling to tell us more about the variation in mood experienced by people with bipolar disorder. Collecting mood scores over time allows us to ask how important previous mood state is on current mood – how is mood dependent on recent events?
‘And although episodes of depression or mania in people with bipolar disorder can be very infrequent, the inter-episode “average” mood can go up and down a lot.
‘We want to be able to build mathematical structures that will allow us to drill down from the subjective idea of mood to how individual neurones in the brain are interacting.’
Using the oscillators in tandem with data reported by 25 participants with bipolar disorder, the researchers were able to show, in mathematical terms, what drives these fluctuations in mood.
Professor Bonsall says: ‘In the future, we may be able to use maths in conjunction with self-reported data such as the QIDS score to help ascertain the efficacy of particular treatments for bipolar disorder. Eventually, that may even involve working out what is best for individual patients.
‘That makes a study of this type really powerful.’
Professor John Geddes, Head of the Department of Psychiatry and one of the paper’s authors, says: ‘The application of mathematical approaches to mood data is very exciting. They provide insight into the nature of the mood instability that occurs in bipolar disorder and other mental illnesses and will help us understand the mechanism of the illness and develop better treatments. These analytic approaches have been made possible by advances in our ability to capture high-resolution data from patients remotely, the engagement of patients and our colleagues in other academic disciplines, and the support of our funders.’
Funding: The research was supported by the Wellcome Trust and the National Institute for Health Research (NIHR).
Source: Stuart Gillespie – Oxford University
Image Source: The image is adapted from the Oxford University press release
Original Research: Full open access research for “Bipolar disorder dynamics: affective instabilities, relaxation oscillations and noise” by Michael B. Bonsall, John R. Geddes, Guy M. Goodwin, and Emily A. Holmes in Journal of the Royal Society: Interface. Published online November 18 2015 doi:10.1098/rsif.2015.0670
Bipolar disorder dynamics: affective instabilities, relaxation oscillations and noise
Bipolar disorder is a chronic, recurrent mental illness characterized by extreme episodes of depressed and manic mood, interspersed with less severe but highly variable mood fluctuations. Here, we develop a novel mathematical approach for exploring the dynamics of bipolar disorder. We investigate how the dynamics of subjective experience of mood in bipolar disorder can be understood using a relaxation oscillator (RO) framework and test the model against mood time-series fluctuations from a set of individuals with bipolar disorder. We show that variable mood fluctuations in individuals diagnosed with bipolar disorder can be driven by the coupled effects of deterministic dynamics (captured by ROs) and noise. Using a statistical likelihood-based approach, we show that, in general, mood dynamics are described by two independent ROs with differing levels of endogenous variability among individuals. We suggest that this sort of nonlinear approach to bipolar disorder has neurobiological, cognitive and clinical implications for understanding this mental illness through a mechacognitive framework.
“Bipolar disorder dynamics: affective instabilities, relaxation oscillations and noise” by Michael B. Bonsall, John R. Geddes, Guy M. Goodwin, and Emily A. Holmes in Journal of the Royal Society: Interface. Published online November 18 2015 doi:10.1098/rsif.2015.0670