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Pattern Recognition Technology May Help Predict Future Mental Illness in Teens

Pattern Recognition Technology May Help Predict Future Mental Illness in Teens


Source: NIMH

A technique combining computer-based pattern recognition and brain imaging data accurately distinguished teens at risk for mental disorders from those with low risk and may someday be useful in predicting risk in individuals, according to an NIMH-funded study published February 15, 2012, in the journal PLoS One.

Background

Research on risk for mental disorders generally describes risk factors that apply to groups. To date, no biological measures can accurately predict an individual’s risk of future mental disorders.

Mary Phillips, M.D., of the University of Pittsburgh School of Medicine, and colleagues evaluated the use of computer-based techniques that automatically find patterns in data—these techniques are collectively called machine learning—with functional magnetic resonance imaging (fMRI) data. The researchers obtained fMRI data from 32 teens, half of whom had at least one biological parent diagnosed with bipolar disorder and were therefore at genetic risk for future psychiatric disorders. The other half of teens had no history of mental disorders either personally or in their immediate families.

The teens’ brain activity was assessed as they identified the gender of actors depicting various emotional facial expressions (happy, fearful, or neutral) in a series of photographs. Previous research has linked various mental disorders, especially depression and bipolar disorder, with abnormal patterns of brain activity during this task. Based on this fMRI data, the researchers used machine learning to calculate each participant’s odds for future mental illness social worker ceus

The participants were also assessed clinically and with fMRI at the start of the study, and clinically assessed again about two years later, on average. Long-term follow up is ongoing, with successive face-to-face assessments occurring every other year.

Results

Machine learning combined with fMRI accurately identified most of the healthy teens at genetic risk of future mental disorders vs. healthy teens with low genetic risk. Four of the 16 at-risk teens were misidentified as having low risk.

At the two-year follow up, none of the at-risk teens had developed bipolar disorder, but six were diagnosed with major depression or an anxiety disorder. Among all the at-risk teens identified through machine learning, these six had received the highest odds for belonging to the at-risk group.

Three of the four at-risk teens misidentified as belonging to the low risk group at the start of the study remained healthy at the second assessment. Clinical information for the fourth teen was not available at the time of follow-up.

Significance

Though still a very preliminary study, according to the researchers, machine learning combined with fMRI shows promise for predicting individual risk of developing future mental disorders, especially in at-risk populations.

The ongoing follow-up may also yield further insights into the relationship between depression, anxiety disorders, and bipolar disorder. Many studies have shown that bipolar disorder is often preceded by depression or anxiety disorders, and that these disorders may affect the course of subsequent bipolar disorder.

What’s Next

Larger studies using machine learning and fMRI will help to better define the extent to which pattern recognition techniques can accurately identify people at risk for future mental disorders. Research in this area may also inform early treatment or prevention efforts.

How stress influences disease: Carnegie Mellon study reveals inflammation as the culprit

PITTSBURGH—Stress wreaks havoc on the mind and body. For example, psychological stress is associated with greater risk for depression, heart disease and infectious diseases. But, until now, it has not been clear exactly how stress influences disease and health.

A research team led by Carnegie Mellon University's Sheldon Cohen has found that chronic psychological stress is associated with the body losing its ability to regulate the inflammatory response. Published in the Proceedings of the National Academy of Sciences, the research shows for the first time that the effects of psychological stress on the body's ability to regulate inflammation can promote the development and progression of disease continuing education for social workers

"Inflammation is partly regulated by the hormone cortisol and when cortisol is not allowed to serve this function, inflammation can get out of control," said Cohen, the Robert E. Doherty Professor of Psychology within CMU's Dietrich College of Humanities and Social Sciences.

Cohen argued that prolonged stress alters the effectiveness of cortisol to regulate the inflammatory response because it decreases tissue sensitivity to the hormone. Specifically, immune cells become insensitive to cortisol's regulatory effect. In turn, runaway inflammation is thought to promote the development and progression of many diseases.

Cohen, whose groundbreaking early work showed that people suffering from psychological stress are more susceptible to developing common colds, used the common cold as the model for testing his theory. With the common cold, symptoms are not caused by the virus — they are instead a "side effect" of the inflammatory response that is triggered as part of the body's effort to fight infection. The greater the body's inflammatory response to the virus, the greater is the likelihood of experiencing the symptoms of a cold.

In Cohen's first study, after completing an intensive stress interview, 276 healthy adults were exposed to a virus that causes the common cold and monitored in quarantine for five days for signs of infection and illness. Here, Cohen found that experiencing a prolonged stressful event was associated with the inability of immune cells to respond to hormonal signals that normally regulate inflammation. In turn, those with the inability to regulate the inflammatory response were more likely to develop colds when exposed to the virus.

In the second study, 79 healthy participants were assessed for their ability to regulate the inflammatory response and then exposed to a cold virus and monitored for the production of pro-inflammatory cytokines, the chemical messengers that trigger inflammation. He found that those who were less able to regulate the inflammatory response as assessed before being exposed to the virus produced more of these inflammation-inducing chemical messengers when they were infected.

"The immune system's ability to regulate inflammation predicts who will develop a cold, but more importantly it provides an explanation of how stress can promote disease," Cohen said. "When under stress, cells of the immune system are unable to respond to hormonal control, and consequently, produce levels of inflammation that promote disease. Because inflammation plays a role in many diseases such as cardiovascular, asthma and autoimmune disorders, this model suggests why stress impacts them as well."

He added, "Knowing this is important for identifying which diseases may be influenced by stress and for preventing disease in chronically stressed people."