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Antidepressant Medications and Therapy can provide much-needed relief to those suffering from mental health illnesses.
But there are many people whose symptoms do not respond to treatment and whose road to recovery often begins with trial and error with different medications and/or treatment methods. And their symptoms may get worse in this time. By one estimate, people with such treatment-resistant depression 30% of seekers of mental health care.
This has come to light in a recent study conducted by an international team of researchers. published in Naturopathy,offers a solution – and it requires reimagining psychiatric diagnosis.
“The way we think about depression in the clinic is a label,” said neuroscientist and first author of the study, Leonardo Tozzi, who was at Stanford before joining a neurodiagnostics firm in the US last year.
The brain is the center of our minds and people suffering from depression manifest it in different ways in their brains. These manifestations appear as faulty brain patterns, which psychiatrists often do not take into account, the study researchers said.
Dr. Tozzi and his colleagues showed that these patterns could be classified into six distinct subtypes of depression. The team also found that at least three of these subtypes could predict antidepressant medications and/or treatment approaches that might be effective in treating these people.
Brain biomarkers
Dr. Tozzi joined Stanford Medicine’s Center for Precision Mental Health and Wellness as a postdoctoral researcher in 2018. He compared the study’s purpose to cardiologists who evaluate a patient’s heart condition using electrocardiogram data. “We’re trying to turn psychiatry into that.”
Like the heart, the brain also has electrical activity. magnetic resonance imaging (fMRI) machine can capture this activity and its changes over time through electrical signals.
In people with mental illness, the underlying brain circuits that connect different regions do not activate normally. One region may have more intense electrical activity than a healthy person.
Different people have different patterns, both common and uncommon. When some of them were shared among people with a specific mental illness, researchers called it a subtype.
In this way, many studies have classified subtypes of depression based on brain activity. But the new study used a “theory-driven” approach to create subtypes that are also clinically relevant.
Data Crunching
In 2021, Dr. Tozzi and company began working on the study, scouring 801 patient records from clinical trials conducted over the past two decades. All of these individuals had been diagnosed with depression and/or an anxiety disorder but were untreated at the time. “Many had comorbid anxiety,” Dr. Tozzi said.
The records contained patients’ responses to symptom questionnaires and behavioral performance results, as well as fMRI data — lots of it. Scanning each brain volume involved tracking changes in 100,000 data inputs over 20 minutes, which is the typical scan duration in trials.
“We had to do some sort of compression, and reduce the data into a meaningful, smaller set of variables,” Dr. Tozzi said.
They limited their analysis to six brain circuits that previous studies had linked to depression and anxiety disorders. But these circuits did not shine in the fMRI data all the time, or together.
Instead, when the person was sitting idle, immersed in his thoughts, his attention, salience and ‘default mode’ circuits were activated. Several tasks were needed to clarify the other three circuits of cognitive, positive and negative affect.
During clinical trials, subjects were scanned with an fMRI machine while they were not performing any task, and also while they were reacting to smiling or sad faces, or following commands on a computer screen.
Dr. Tozzi recalled a “eureka” moment when the team figured out the best way to spot faulty brain activity in people using a machine-learning algorithm. “Everything else fell into place after that,” he said, as the algorithm revealed six subtypes.
Overcoming depression
The team was then able to prove, based on the symptoms people reported and their performance on behavioral tests, that these subtypes were real, and not artifacts of the data.
The team also analyzed treatment response data from 250 participants from clinical trials. These individuals were randomly given some common antidepressants — sertraline, venlafaxine XR, and escitalopram — and therapy. Team members found that people with three of the six depressive subtypes could benefit from treatment and expect improvements in their symptoms.
The study found that one subtype has overactive cognitive circuits. The researchers determined that people with this subtype tend to have a lot of anxiety, a lack of interest in engaging with the outside world, and feelings of threat in general. Their analysis showed that they may respond better to Venlafaxine XR.
According to the analysis, individuals with the two other subtypes may respond better to treatment, although one of them, who had overactive attention circuits, had a “worse response to behavioral treatment,” the paper said.
The researchers were not able to link people with the other three subtypes to treatment options that could help them. However, for one of these subtypes the researchers did not find much associated defective brain activity and for the other two there was not enough data to reliably measure treatment responses.
Dr. Tozzi said the next step will be to find more treatments that can address depression symptoms across all subtypes. In fact, he said Stanford has been running a clinical trial for a year — after the team’s study concluded — to test the ability of subtypes to help predict treatment response.
“People are scanned, their (subtype) is determined, and then they’re given a drug that’s designed to target that specific (subtype),” Dr. Tozzi said of the new test’s approach.
Karthik Vinod is a freelance science journalist and co-founder of Ad Publica. He has a master’s degree in astrophysics and science, technology and society.
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