Brain scans may help predict success of therapy for psychiatric disorders

Brain scans that look at biomarkers of brain connectivity may help predict which patients with psychiatric disorders are likely to benefit from therapy.

So conclude researchers from the Massachusetts Institute of Technology (MIT) who analyzed brain scans of patients receiving cognitive behavioral therapy (CBT) for the treatment of social anxiety disorder.

CBT is a talking therapy that aims to help people change the way they think and behave. While it can be a useful treatment for a range of mental and physical health problems, it is most commonly used to treat anxiety and depression.

Around 15 million Americans are affected by social anxiety disorder or social phobia – a condition where the fear of being judged and of being embarrassed is so strong it can get in the way of going to work or school and doing everyday things.

According to the National Institutes of Mental Health, social anxiety disorder is the third most common mental health disorder in the US.

Patients with social anxiety disorder are also at higher risk of other psychiatric disorders, such as depression and substance abuse.

Currently, even after weeks of therapy, only around half of patients with social anxiety disorder find their symptoms improve, leaving the other half having to start again with something else. The lack of effective treatment selection tools means trial and error is the only avenue open to patients, many of whom give up because it is so time consuming and expensive.

Susan Whitfield-Gabrieli, a research scientist at MIT’s McGovern Institute for Brain Research and first author of the new study, says:

“Choice of therapy is like a wheel of chance. We’re hoping to use brain imaging to help provide more reliable predictors of treatment response.”

She and her colleagues report their findings in the journal Molecular Psychiatry.

Study uses resting-state as opposed to task-based scans

The team analyzed brain scans of 38 patients with social anxiety disorder and found they could be used to predict – with 80% accuracy – which patients would most benefit from CBT. Using scans improved prediction accuracy five-fold compared with clinician assessment on its own.

After undergoing brain-scanning, the patients took part in 12 weeks of group-based CBT.

An important aspect of the study is the type of brain scan that the researchers used. They used “resting-state” scans as opposed to “task-based” scans. Resting state scans are taken when the patient is at rest, not thinking or paying attention to anything in particular. Task-based scans are taken when the patient is focusing on a given task.

 

In previous work, some of the authors had found that task-based scans, where patients responded to angry or neutral faces as they underwent their brain scans, could also be used to predict CBT outcomes.

But task-based scans are not ideal, because behavioral differences among patients can affect performance. In addition, they can only be used on patients who can follow the instructions, which rules out the very young and some of the very old or very ill.

The appeal of resting-state imaging is that it can be done reliably and quickly – in about 15 minutes – without the patient having to follow instructions. The patient just lies there and lets their mind drift. This makes resting-state scans ideal for doctors to use in clinical settings to help select the best treatments for their patients.

Resting-state scans give an idea of the brain’s connectivity – or what the researchers call its “connectomics” – a functional and structural map of its connections.

The functional view can be seen in resting-state functional magnetic resonance imaging (fMRI). This shows which parts of the brain work together during rest.

Clinicians can see the structural view with the help of diffusion-weighted magnetic resonance imaging (dMRI), which reveals the physical white matter connections between distant brain regions.

Three types of brain-scan analysis predicted CBT outcomes

Building on earlier research, the team first used resting-state fMRI to look at connections to the amygdala – the part of the brain that deals with fear.

They found patients with higher connectivity to the amygdala from certain other parts of the brain were more likely to have lower symptoms of anxiety after CBT.

A second analysis of the same scans – this time looking at connectivity across the whole brain – revealed more markers that were predictive of treatment outcomes.

When they examined dMRI scans, the researchers found the more robust connectivity between tracts that connect visual cues with emotional responses was also predictive of better CBT outcomes.

The patients were assessed before and after their CBT treatment with a behavioral assessment tool called the Liebowitz Social Anxiety Scale (LSAS). Higher LSAS scores indicate more severe social anxiety, and usually correlate modestly with better improvements following CBT.

However, the study shows that each brain scan analysis had predictive value beyond the LSAS, and the three together led to a five-fold improvement in predictive power over the LSAS alone.

The team now plans to validate the predictive tool on hundreds, and possibly thousands, of patients. Such a large-scale study is possible because, unlike task-based scans, you can compare resting-state scans even when they are performed in different labs or by different researchers.

Greg Siegle, an associate professor of psychiatry at the University of Pittsburgh School of Medicine who was not involved with the study, comments on its potential implications:

“Knowing who to give which therapy to upfront would save time, money, and health care resources. This ability would be staggering to have at our disposal for the health care system.”

Earlier this year, Medical News Today reported a world-first MRI study that found babies experience pain like adults. In the journal eLife, researchers from Oxford University in the UK show how many of the brain regions in the adult brain that are active in response to pain are also active in the brains of babies.

Written by Catharine Paddock PhD

http://www.medicalnewstoday.com/articles/298072.php

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