Machine learning could predict medication response in patients with complex mood disorders

August 8, 2018, Lawson Health Research Institute

Mood disorders like major depressive disorder (MDD) and bipolar disorder are often complex and hard to diagnose, especially among youth when the illness is just evolving. This can make decisions about medication difficult. In a collaborative study by Lawson Health Research Institute, The Mind Research Network and Brainnetome Center, researchers have developed an artificial intelligence (AI) algorithm that analyzes brain scans to better classify illness in patients with a complex mood disorder and help predict their response to medication.

The full study included 78 emerging adult patients from mental health programs at London Health Sciences Centre (LHSC), primarily from the First Episode Mood and Anxiety Program (FEMAP). The first part of the study involved 66 patients who had already completed treatment for a clear of either MDD or bipolar type I (bipolar I), which is a form of that features full manic episodes, as well as an additional 33 research participants with no history of . Each individual participated in scanning to examine different brain networks using Lawson's functional magnetic resonance imaging (fMRI) capabilities at St. Joseph's Health Care London.

The research team analyzed and compared the scans of those with MDD, bipolar I and no history of mental , and found the three groups differed in particular brain networks. These included regions in the default mode network, a set of regions thought to be important for self-reflection, as well as in the thalamus, a 'gateway' that connects multiple cortical regions and helps control arousal and alertness.

The data was used by researchers at The Mind Research Network to develop an AI algorithm that uses machine learning to examine fMRI scans to classify whether a patient has MDD or bipolar I. When tested against the research participants with a known diagnosis, the algorithm correctly classified their illness with 92.4 per cent accuracy.

The research team then performed imaging with 12 additional participants with complex for whom a diagnosis was not clear. They used the algorithm to study a participant's brain function to predict his or her diagnosis and, more importantly, examined the participant's response to .

"Antidepressants are the gold standard pharmaceutical therapy for MDD while mood stabilizers are the gold standard for bipolar I," says Dr. Elizabeth Osuch, a clinician-scientist at Lawson, medical director at FEMAP and co-lead investigator on the study. "But it becomes difficult to predict which medication will work in patients with complex mood disorders when a diagnosis is not clear. Will they respond better to an antidepressant or to a mood stabilizer?"

The research team hypothesized that participants classified by the algorithm as having MDD would respond to antidepressants while those classified as having bipolar I would respond to mood stabilizers. When tested with the complex , 11 out of 12 responded to the medication predicted by the algorithm.

"This study takes a major step towards finding a biomarker of medication response in emerging adults with complex mood disorders," says Dr. Osuch. "It also suggests that we may one day have an objective measure of psychiatric illness through brain imaging that would make diagnosis faster, more effective and more consistent across health care providers."

Psychiatrists currently make a diagnosis based on the history and behavior of a patient. Medication decisions are based on that diagnosis. "This can be difficult with complex mood disorders and in the early course of an illness when symptoms may be less well-defined," says Dr. Osuch. "Patients may also have more than one diagnosis, such as a combination of a mood disorder and a substance abuse disorder, further complicating diagnosis. Having a biological test or procedure to identify what class of medication a patient will respond to would significantly advance the field of psychiatry."

Explore further: Sleep and mood in bipolar disorder

More information: E. Osuch et al, Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients, Acta Psychiatrica Scandinavica (2018). DOI: 10.1111/acps.12945

Related Stories

Sleep and mood in bipolar disorder

October 12, 2017
Sleep loss can trigger relapse, particularly in the form of mania, in people with a diagnosis of bipolar disorder, finds a study by Cardiff University.

Study finds apparent benefits of addition of adjunctive antidepressants to mood stabilizers for bipolar disorder

March 30, 2018
Bipolar disorder patients, who comprise 1-4 percent of the population, suffer from chronic mood swings that alternate between "manic" episodes, characterized by inflated energy, self-esteem and risky behavior, and depression, ...

Brain structural effects of psychopharmacological treatment in bipolar disorder

February 16, 2016
Advances in magnetic resonance imaging (MRI) acquisition and analyses over the last two decades have enabled the identification of neuroanatomical abnormalities in a range of mental disorders, however one question which has ...

Study finds average six-year delay between onset and diagnosis of bipolar disorder

July 25, 2016
Crucial opportunities to manage bipolar disorder early are being lost because individuals are waiting an average of almost six years after the onset of the condition before diagnosis and treatment.

Menopausal mood swings can signal more serious mental illness

April 9, 2018
Most women expect to experience the effects of hormonal changes when they come to menopause and many anticipate increased irritability and mood swings. But mood swings that can be just an annoyance for some women can develop ...

Imaging in mental health and improving the diagnostic process

August 15, 2013
What are some of the most troubling numbers in mental health? Six to 10—the number of years it can take to properly diagnose a mental health condition. Dr. Elizabeth Osuch, a Researcher at Lawson Health Research Institute ...

Recommended for you

Schadenfreude sheds light on darker side of humanity

October 23, 2018
Schadenfreude, the sense of pleasure people derive from the misfortune of others, is a familiar feeling to many—perhaps especially during these times of pervasive social media.

Is big-city living eroding our nice instinct?

October 23, 2018
A new study by University of Miami psychology researchers of anonymous interactions suggests that humans switch off their automatic inclination to share in dealings with strangers.

Does putting the brakes on outrage bottle up social change?

October 23, 2018
While outrage is often generally considered a hurdle in the path to civil discourse, a team of psychologists suggest outrage—specifically, moral outrage—may have beneficial outcomes, such as inspiring people to take part ...

Brain training app helps reduce OCD symptoms, study finds

October 23, 2018
A 'brain training' app developed at the University of Cambridge could help people who suffer from obsessive compulsive disorder (OCD) manage their symptoms, which may typically include excessive handwashing and contamination ...

Closing the gender gap in competitiveness with a psychological trick

October 23, 2018
Women are still disadvantaged in society, particularly professionally. They are frequently paid less than men and find it more difficult to have a successful career. One reason for this may be the fact that women are observed ...

First impressions count, new speech research confirms

October 22, 2018
Human beings make similar judgements of the trustworthiness and dominance of an unfamiliar speaker after hearing just a single word, new research shows, suggesting the old saying that 'first impressions count' might well ...

0 comments

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.