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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.

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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 diagnosis of either MDD or bipolar type I (bipolar I), which is a form of bipolar disorder that features full manic episodes, as well as an additional 33 research participants with no history of mental illness. 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 illness, 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 mood disorders 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 medication. 

“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 patients, 11 out of 12 responded to the medication predicted by the algorithm. 

“Machine learning is an approach that learns in a data-centric way, providing information that can be used to predict future data sets. In this case, that’s the prediction of MDD from bipolar I,” says Dr. Vince Calhoun, President of The Mind Research Network; Distinguished Professor, Departments of Electrical and Computer Engineering, Neurosciences, Computer Science, and Psychiatry at The University of New Mexico; and co-lead investigator on the study. “There are multiple layers of algorithms in this project. The first layer includes an approach that automatically extracts brain networks from the data provided and the second layer includes automatically identifying which combinations of networks are most sensitive or predictive of MDD and bipolar I.”

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Above: Dr. Vince Calhoun

“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.”

The study, “Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients,” is published online in Acta Psychiatrica Scandinavica. Local support included donor funding through London Health Sciences Foundation. 

Learn more about this research:

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Above: Drs. Elizabeth Osuch and Jean Théberge, Lawson scientists

“I just wasn’t myself,” says Major Ronald Miller when reflecting on his experience with post-traumatic stress disorder (PTSD). “I could see a significant change in my personality that just wasn’t me.”

Major Miller first joined the Canadian Armed Forces in 1971 as a young infantry private in the 1st Battalion, The Royal Canadian Regiment and later became an Officer in the Royal Canadian Artillery, which included a tour in Germany during the Cold War.

After the Cold War, he reoriented his career towards peacekeeping which saw him deployed to seven different conflict zones.  “It was during the Civil War in El Salvador in 1991 that I experienced a number of incidents that first triggered my PTSD,” explains Major Miller. “From that time forward the PTSD was always there but I suppressed it.”

It wasn’t until 2016 that Major Miller’s PTSD resurfaced.  After retiring from the Canadian Armed Forces in 2008, he started working a number of contracts in support of the military.

“Over the years, I was exposed to the kind of death and destruction that can be rather difficult from a psychological standpoint. The older I got, the less I was able to suppress those experiences. In fall 2016, I was supporting a NATO military exercise in the UK when I began experiencing horrible nightmares every night. I knew I needed to seek help.”

He reached out to Veteran Affairs Canada and was referred to the Operational Stress Injury (OSI) Clinic at Parkwood Institute, a part of St. Joseph’s Health Care London, where he was diagnosed with PTSD. It was there that he learned about PTSD research being conducted by Dr. Ruth Lanius, Scientist at Lawson Health Research Institute and Psychiatrist at London Health Sciences Centre. 

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“I saw the need to help by participating in research, not only for myself but for my friends who have succumbed to the illness.” 

Major Miller participated in Dr. Lanius’ neuroimaging research which uses advanced imaging technologies like PET/MRI to study differences in brain activity and neural connections between healthy individuals and those with different subtypes of PTSD. Dr. Lanius hopes that patterns of brain activity can one day be used as an objective biomarker to accurately diagnosis different subtypes of PTSD and uncover new targets for therapy.

“The research experience was interesting. I was interviewed while in the MRI and had to talk about incidents that trigger my PTSD to study my brain waves,” explains Major Miller. 

In one study, Dr. Lanius is studying patterns of brain activity for those with ‘moral injury,’ an intense feeling of shame or guilt that can sometimes affect veterans with PTSD. 

“Sometimes you’re involved in situations that you don’t have control over but that you feel responsible for,” says Major Miller. “You wonder, ‘Could I have done something differently?’”

 

Major Miller was happy to participate in any study that might help. Today, he is managing his PTSD through a combination of therapies that work for him. 

“My biggest fear was giving up the things I love. I’ve been a soldier since day one and I’m sure I will be until the day I die. It’s important we come up with solutions to ensure our veterans receive proper care. Through research, we can tailor treatment to the individual rather than looking for a broad brush solution that might not suit everyone.”

This story is also featured on Research Canada’s Patient Stories website.