Studies provide evidence for the impact of biology by using AI to identify...
Studies provide evidence for the impact of biology by using AI to identify patterns of brain activity that make people less responsive to certain antidepressants.
Source: UT Southwestern Medical Center

AI helps scientists predict depression outcomes

Studies provide evidence for the impact of biology by using AI to identify patterns of brain activity that make people less responsive to certain antidepressants.

The psychiatry field has long sought answers to explain why antidepressants help only some people. Is a patient’s recovery due merely to a placebo effect—the self-fulfilling belief that a treatment will work—or can the biology of the person influence the outcome? Two studies led by UT Southwestern provide evidence for the impact of biology by using artificial intelligence to identify patterns of brain activity that make people less responsive to certain antidepressants. Put simply, scientists showed they can use imaging of a patient’s brain to decide whether a medication is likely to be effective.

The studies include the latest findings from a large national trial (EMBARC) intended to establish biology-based, objective strategies to remedy mood disorders and minimize the trial and error of prescribing treatments. If successful, scientists envision using a battery of tests such as brain imaging and blood analyses to increase the odds of finding the right treatment. “We need to end the guessing game and find objective measures for prescribing interventions that will work,” said Dr. Madhukar Trivedi, who oversees EMBARC and is founding Director of UT Southwestern’s Center for Depression Research and Clinical Care. “People with depression already suffer from hopelessness, and the problem can become worse if they take a medication that is ineffective.”

The studies—which each included more than 300 participants—used imaging to examine brain activity in both a resting state and during the processing of emotions. Both studies divided the participants into a healthy control group and people with depression who either received antidepressants or placebo.

Of the participants who received medication, researchers found correlations between how the brain is wired and whether a participant was likely to improve within two months of taking an antidepressant. Dr. Trivedi said imaging the brain’s activity in various states was important to get a more accurate picture of how depression manifests in a particular patient. For some people, he said, the more relevant data will come from their brains’ resting state, while in others the emotional processing will be a critical component and a better predictor for whether an antidepressant will work. “Depression is a complex disease that affects people in different ways,” he said. “Much like technology can identify us through fingerprints and facial scans, these studies show we can use imaging to identify specific signatures of depression in people.”

Scientists examined the brain activity of study participants while they were...
Scientists examined the brain activity of study participants while they were presented with “emotional conflicts” – in this case photographs that offered sometimes conflicting messages.
Source: UT Southwestern Medical Center

AI and depression

Data from both studies derive from the 16-week EMBARC trial, which Dr. Trivedi initiated in 2012 at four U.S. sites. The project evaluated patients with major depressive disorder through brain imaging and various DNA, blood, and other tests. His goal was to address a troubling finding from another study he led (STAR*D) that found up to two-thirds of patients do not adequately respond to their first antidepressant.

EMBARC’s first study, published in 2018, focused on how electrical activity in the brain can indicate whether a patient is likely to benefit from an SSRI (selective serotonin reuptake inhibitor), the most common class of antidepressant. The finding has been followed by related research that identifies other predictive tests for SSRIs, most recently the resting-state brain imaging study published in the American Journal of Psychiatry and the second imaging study published in Nature Human Behaviour.

The Nature research used artificial intelligence to determine correlations between the effectiveness of an antidepressant and how a patient’s brain processes emotional conflict. Participants undergoing brain imaging were shown photographs in quick succession that offered sometimes conflicting messages such as an angry face with the word “happy,” or vice versa. Each participant was asked to read the word on the photograph before clicking to the next image.

However, rather than observe only neural regions believed to be relevant to predicting antidepressant benefits, scientists used machine learning to analyze activity in the entire brain. “Our hypotheses for where to look have not panned out, so we wanted to try something different,” Dr. Trivedi explained.

AI identified specific brain regions—for example in the lateral prefrontal cortices—that were most important in predicting whether participants would benefit from an SSRI. The results showed that participants who had abnormal neural responses during emotional conflict were less likely to improve within eight weeks of starting the medication.

Subscribe to our newsletter

Related articles

Can chatbots help fill the empathy gap?

Can chatbots help fill the empathy gap?

Stressed out? Need to talk? Turning to a chatbot for emotional support might help, research from Michigan State University shows.

AI may alter how doctors treat depression

AI may alter how doctors treat depression

Artificial intelligence may soon play a critical role in choosing which depression therapy is best for patients.

AI can detect depression in a child’s speech

AI can detect depression in a child’s speech

Researchers have used artificial intelligence to detect hidden depression in young children, a condition that can lead to increased risk of substance abuse and suicide later in life if left untreated.

Research programme to bioprint ear and noses launched

Research programme to bioprint ear and noses launched

The Scar Free Foundation has launched a research programme that aims to revolutionise surgeons’ ability to reconstruct nose and ear cartilage in patients affected by facial difference.

AI measures fat around heart to predict diabetes

AI measures fat around heart to predict diabetes

Researchers have developed a new artificial intelligence tool that is able to automatically measure the amount of fat around the heart from MRI scan images.

COVID-19: AIs shortcuts lead to misdiagnosis

COVID-19: AIs shortcuts lead to misdiagnosis

Researchers discovered that AI models have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.

Researchers use virtual reality to reach youth

Researchers use virtual reality to reach youth

Researchers found that patients participating in VR sessions experienced reduced levels of anxiety and depression.

Key task in computer vision gets a boost

Key task in computer vision gets a boost

A researcher has demonstrated a technique that reduces the computing time for non-rigid point set registration relative to other approaches.

Microfluidics and AI microscopy measure hemoglobin

Microfluidics and AI microscopy measure hemoglobin

Researchers at the Indian Institute of Science and SigTuple Technologies have developed a method to measure hemoglobin levels in small-volume blood samples.

Popular articles

Subscribe to Newsletter