Psychiatrists seeking to identify mental disorders must depend on less precise measures – observations of behaviors and various questionnaires that seek to elicit responses that can add up to a diagnosis. Research at Baylor College of Medicine, aimed at finding a less subjective measure of mental disorder, shows that a social interaction between a "normal" person and a person with a mental problem can help identify the disorder.
Surprisingly, the tell-tale reactions occur in the "normal" subject who is reacting to the partner with the mental disorder, said Dr. P. Read Montague, director of the Brown Human Neuroimaging Laboratory, professor of neuroscience at BCM and senior author of the report that appears online in PLoS Computational Biology. Researchers from BCM, the W.M. Keck Center for Interdisciplinary Bioscience Training and Rice University in Houston took part in the research.
"The relation between social interactions and disorders is very subtle. That is why it has not been fully detected before. In our research, sophisticated statistical algorithms running on powerful computer clusters allowed us to see disorder-related patterns behind the seemingly random social interactions. These algorithms are similar to powerful lenses that transform a blurry image into a clear picture," said first author Misha Koshelev of BCM and the Keck Center.
Koshelev and his colleagues studied the interaction of 287 pairs of research subjects who had previously participated in a simple "trust" game in which the testers give one person (the investor) $20. That person can then choose to send a fraction of that amount to the other person called the trustee. The amount sent is tripled on the way to the trustee, who then decides how much to send back. Again, the money is tripled on the way to the trustee. This continues for 10 rounds. During this time, the partners of the game learn what to expect from the other person. Typically, the two do not meet or speak before, after or during the game.
In this case, the investor had no mental disorder and the trustee was diagnosed with one of several disorders – autism spectrum disorder, borderline personality disorder, major depressive disorder and attention deficit hyperactivity disorder.
Koshelev and his colleagues classified the dynamic between the two members of the dyad using the numbers or amount of money exchanged – the investment and repayment ratios, the types or styles of play between the two and the dependence of the next investment on the ratio of the investment to repayment that had gone before.
"We wanted to quantify the way people interact," said Dr. Terry M. Lohrenz, instructor in the human neuroimaging laboratory. "We looked at 287 of these interactions and, using this data, clustered them. Then we looked to see if any of the various groups were overrepresented in the clusters, and they were."
The clustering was based on the reaction of the investor and not the person with the mental disorder.
"They were a sort of biosensor," said Dr. Marina Vannucci of the Keck Center and a professor of statistics at Rice University. "We were focusing on what the investor did and his/her reaction to the other person's response."
Later, the researchers created a computer version of the healthy investor and had it play the trust game against computerized versions of the various mental disorders represented in the dyad.
"We could tell a difference when the computer was playing against a computerized version of someone with borderline personality disorder," said Lohrenz. The same proved true with the other disorders.
"This opens up a whole new way of approaching diagnosis," he said.
"Game theory has been available to mathematicians and economists for years," said Dr. Kenneth Kishida, a postdoctoral fellow in the neuroimaging laboratory. "Only in the past decade has it been available to neuroscientists and now we are trying to bring it into the psychiatric domain."
Both he and Lohrenz emphasized that this could provide a tool in diagnosis. It does not replace the proven diagnostic precepts of psychiatry.
Funding for this work came from the Keck Center for Interdisciplinary Bioscience Training of the Gulf Cost Consortia, the National Human Genome Research Institute, the National Science Foundation and the National Institutes of Health.