Biomedical scientists tie improved learning processes to reduced
symptoms of depression

Date:
July 28, 2021
Source:
Virginia Tech
Summary:
Brain imaging and mathematical modeling reveal previously unreported
mechanistic features of symptoms associated with major depressive
disorder.



FULL STORY ========================================================================== Virginia Tech scientists with the Fralin Biomedical Research Institute
at VTC have identified neural learning processes to be associated with
symptoms of depression and linked improvements in these processes to
improved symptoms in research participants being treated for depression.


==========================================================================
The findings, described in a study published July 28, 2021 in the Journal
of the American Medical Association (JAMA) Psychiatry, suggest distinct
paths to depression symptoms and new mathematically guided approaches
for treating clinical depression.

Major depression is one of the most common mental disorders in the
United States and can cause severe impairment, according to the National Institute of Mental Health. An estimated 7.1% of all U.S. adults have
had at least one major depressive episode.

"Current medications and behavioral therapies are helpful, but for many
people struggling with depression, existing treatments don't work well,"
said Pearl Chiu, an associate professor at the Fralin Biomedical Research Institute Computational Psychiatry Unit and the study's corresponding
author. "We need to consider other possible paths to depression. These
paths, or mechanisms, could point to new treatment targets to explore."
The scientists used computational models of brain functioning as a
new way to consider mechanisms of depression. In a key discovery, the researchers found that the symptom improvements that followed cognitive behavioral therapy were related to improvements in reinforcement learning components that were disrupted prior to therapy.

"Depression is a very serious illness and a leading cause of disability
in the world. We hope that our work can be a bridge between behavioral clinicians and computational scientists to more precisely identify
what causes depression and new ways to treat the illness," said first
author Vanessa Brown, a former doctoral student with Chiu in Virginia
Tech's Department of Psychology and who is now an assistant professor
of psychiatry at the University of Pittsburgh.



==========================================================================
The research team began studying a baseline group of 101 adults with and without clinical depression. A subset of the participants with depression
were treated with up to 12 weeks of cognitive behavioral therapy --
a treatment that involves learning how to identify and correct negative
thought patterns.

Participants with depression played a learning game during functional
MRI brain scanning before and after cognitive behavioral therapy,
and participants without depression played the same game at time points
matched to participants who took part in cognitive behavioral therapy. The scientists used computational modeling to identify different processes
that contribute to learning. They found that distinct components of
learning about rewards and losses -- known as reinforcement learning --
were connected to certain symptoms of depression.

"Two of the most exciting parts of the findings are that people with
depression learn in different ways and that these learning processes
changed when depression symptoms improved after cognitive behavioral
therapy. The link between the learning components and symptoms is
critical," said Brooks King- Casas, co-author of the study and an
associate professor with the Fralin Biomedical Research Institute and
in the Department of Psychology in Virginia Tech's College of Science.

The researchers say using computational models has potential to help
other investigators and mental health professionals precisely identify
new contributors to depression, which in turn could be new targets
for therapies.

"An example is that for someone with depression, losing a few cents
in the game could feel like losing several hundred dollars or the loss
could be very hard to forget. These processes are different, but both
affect how we learn and the choices we make," King-Casas said.



==========================================================================
"We quantified some of these learning processes with computational
modeling and show that they relate to depression in very different
ways," said Chiu, who is also an associate professor of psychology in
Virginia Tech's College of Science. "The idea is similar to how stress
or too much sodium can both contribute to high blood pressure, but what contributes to a particular person's hypertension could suggest whether
they focus on decreasing stress or reducing salt consumption as part
of treatment. Similarly, for depression, the parts of learning that
contribute to a person's depression could call for different approaches
to treatment." Chiu says forming a computational understanding of how cognitive processes align with symptoms of depression is a promising
approach.

"Now that we've linked specific components of learning to depression and
show that they change with specific depression symptoms, perhaps we can
develop new therapies that focus on adjusting these learning components
as a way to reduce depression," she said.

Additional former students and postdoctoral associates who contributed
to the study include Lusha Zhu, Alec Solway, John Wang, and Katherine
McCurry.

The study was funded in part by the National Institute of Mental Health,
part of the National Institutes of Health.

========================================================================== Story Source: Materials provided by Virginia_Tech. Original written by
John Pastor. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Vanessa M. Brown, Lusha Zhu, Alec Solway, John M. Wang, Katherine L.

McCurry, Brooks King-Casas, Pearl H. Chiu. Reinforcement Learning
Disruptions in Individuals With Depression and Sensitivity to
Symptom Change Following Cognitive Behavioral Therapy. JAMA
Psychiatry, 2021; DOI: 10.1001/jamapsychiatry.2021.1844 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210728111331.htm

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