AI helps scientists predict depression outcomes : Study

Brain

Studies
show brain activity patterns influence effect of antidepressants

DALLAS – Sept. 26, 2019 – 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.”

Brain
activity

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.”

Improving
outcomes

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
.

AI and
depression

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.

Ongoing
research

Dr. Trivedi
has initiated other large research projects to further understand the
underpinnings of mood disorders, among them D2K, a study that will enroll 2,500 patients with depression and bipolar disorders and follow
them for 20 years. In addition, RAD is a 10-year study of 2,500
participants (ages 10-24)
that will uncover
factors for reducing the risk of developing mood or anxiety disorders.

Utilizing
some of these enrollees, Dr. Trivedi’s research team will study the results
from several other tests to augment brain imaging and more accurately assess
patients’ biological signatures to determine the most effective treatment. Dr.
Trivedi has had preliminary success developing a blood test but acknowledges it
may only benefit patients with a specific type of inflammation.

Combining
blood and brain tests, he said, will improve the chances of choosing the right
treatment the first time.

“We need to look at this issue in
several ways to identify the many different signatures of depression in the
body,” he said. “The findings from these new studies are significant and bring
us closer to using them clinically to improve outcomes for millions of people.”

About
EMBAR
C

Funded by
the National Institute of Mental Health, EMBARC (Establishing Moderators and
Biosignatures of Antidepressant Response for Clinical Care) was a multicenter
effort that also involved researchers at Harvard Medical School and
Massachusetts General Hospital, Columbia University, McLean Hospital, Stanford
University, University of Pittsburgh, and the University of Michigan.

Who is Dr.
Madhukar Trivedi

Dr. Trivedi,
who served as the Coordinating Principal Investigator, is a Professor of
Psychiatry in UT Southwestern’s Peter O’Donnell Jr. Brain Institute. He holds
the Betty Jo Hay Distinguished Chair in Mental Health and the Julie K. Hersh
Chair for Depression Research and Clinical Care. Disclosures for Dr. Trivedi
are listed in the AJP and Nature studies

About UT
Southwestern Medical Center

UT Southwestern, one of the premier academic medical centers in the USA, integrates pioneering biomedical research with exceptional clinical care and education.

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