Brain Scans Can Predict The Success Of Treatment For Social Anxiety Disorder

But new MIT research suggests that it might be possible to do better than a coin toss whenever choosing psychiatric remedies for patients. Susan Whitfield-Gabrieli, a research scientist in the McGovern Institute for Brain Research at MIT. The researchers used a form of brain imaging that scans patients in circumstances of rest. Greg Siegle, an associate professor of psychiatry at the University of Pittsburgh School of Medicine who was simply not involved with this study.

The findings are reported in today’s problem of the journal Molecular Psychiatry. The ongoing work was carried out in the lab of primary investigator John Gabrieli, the Grover Hermann Professor of Health Sciences and Cognitive Neuroscience at MIT and a known member of the McGovern Institute. Social anxiety disorder impacts approximately 6.8 percent of Americans, about 15 million individuals, and is the country’s third-most-common mental health disorder, based on the National Institutes of Mental Health. It is symptoms include extreme stress in social settings that can hinder quality and work of life.

Patients living with this disorder are also at higher risk of other psychiatric disorders, such as depression and drug abuse. The study analyzed SAD patients from the guts for Anxiety and Related Disorders at Boston University and the guts for Anxiety and Traumatic Stress at Massachusetts General Hospital. The patients were scanned to participation in 12 weeks of group-based CBT prior.

They also were evaluated utilizing a behavioral assessment tool called the Liebowitz Social Anxiety Scale (LSAS) before and after CBT to determine who acquired improved. In 2013, co-author Satrajit Ghosh, a primary research scientist at the McGovern Institute, examined task-based scans of this same band of patients. He and Whitfield-Gabrieli found that scans of patients’ brains as they taken care of immediately angry or neutral faces and emotional or neutral scenes predicted CBT final results.

Whitfield-Gabrieli says: Behavioral distinctions among patients can affect performance. Also, task-based scans can only just be utilized on patients who are able to follow a task, which excludes newborns and some elderly or very sick patients. So Whitfield-Gabrieli followed this earlier research with a report of the predictive power of resting-state imaging, which she and co-workers experienced performed prior to CBT also. During a resting-state scan, the patient just lies there. Resting-state imaging provides a go through the way a patient’s brain is wired, both and functionally structurally.

For example, resting-state useful magnetic resonance imaging (fMRI) shows which parts of the mind synchronize with one another during rest, suggesting that they are connected functionally. In addition, analysis of diffusion-weighted magnetic resonance imaging (dMRI) shows the underlying anatomy of the white matter tracts that interconnect distant brain regions. Predicated on findings using their earlier research, Whitfield-Gabrieli and colleagues used resting-state fMRI to check out contacts to the amygdala first, the seat of dread in the brain. They found that patients with higher connection to the amygdala from certain other areas were more likely to have lower stress after CBT.

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They then performed a second analysis of the resting-state fMRI data, this right time looking across the entire brain for patterns of connectivity. This analysis revealed additional markers which were predictive of treatment. The research workers also analyzed dMRI data and found that more robust connectivity in the tract that attaches visible cues with psychological responses is also predictive of improvement with CBT.

Higher LSAS scores, which indicate more severe SAD, correlate with bigger improvements after CBT modestly. In this scholarly study, each brain scan analysis had predictive value beyond the LSAS, and the three analyses together produced a fivefold improvement in predictive power within the LSAS alone. The next phase for Whitfield-Gabrieli and co-workers is to validate their predictive model on hundreds or perhaps a large number of patients.

Such a large-scale study may be possible because resting-state scans are similar even though performed in different labs or by different research workers. Such comparisons weren’t feasible using task-based scans, which have a tendency to vary from lab to lab. Other next steps for Whitfield-Gabrieli’s group are studies to forecast the success greater than one form of therapy, and to look at other psychiatric conditions, such as attention and depression disorders.

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