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Schizophrenia, a psychiatric disorder affecting about 1% of the population, is one of the leading causes of functional disability in the United States. The diagnosis is usually based on the display of visible "positive symptoms", such as hallucinations and delusions, but one of the key elements of early identification and treatment is recognition of negative symptoms, and neuroscientists at the University of Georgia are developing new technological tools to catch these symptoms. and improve risk prediction.
Negative symptoms usually appear years before the onset of positive symptoms and are often what primarily puts young people who later develop schizophrenia into contact with the mental health care system. These symptoms are characterized by a reduction of emotions, motivation and expressive communication. They include behaviors such as suppression, lack of motivation to engage in targeted activities, asociality or decreased participation in social activities.
Historically, early detection and prevention efforts for schizophrenia have focused on positive symptoms. These symptoms are often disruptive and require urgent clinical attention when they appear. However, it is the negative symptoms of the disorder on which it may be even more important to focus for early identification and prevention. "
Gregory Strauss, Assistant Professor of Psychology and Neuroscience at UGA Franklin College of Arts and Sciences
Founded with $ 3 million from the National Institute of Mental Health, Strauss is the principal investigator of a project aimed at collecting data at UGA, Northwestern University and Emory University in order to improve the quality of life. evaluate new methods of identifying risks such as digital phenotyping. The project capitalizes on an omnipresent 21sttechnology of the century.
"We ask participants to record videos on their cell phone throughout the day, where they tell us what they have done and how they feel," Strauss said. "We then use automated algorithms to deal with the emotions in the participant's face and voice to quantify the negative symptoms."
The videos can also be used for lexical badysis, to determine if certain keywords related to positive emotions are present or if the speech may be tangential or inconsistent. These help identify other key symptoms such as speech disorganization or even suicidal thoughts.
"We believe that these numerical phenotyping variables can predict risk much more sophisticated than what we can achieve with traditional clinical badessment scales," Strauss said.
Video has long been a method used in the laboratory, often requiring careful manual badysis. Strauss and his colleagues want to automate the whole process and move it to the real world where emotions occur naturally.
"We are developing a risk monitoring system in which a young person could use some of these measures to monitor their risks," Strauss said. "If a phone could help them monitor their symptoms and prevent them from having to come back for frequent clinical rebadessments, that would represent a new level of use of technology for mental health care."
This evaluation work is part of a broader research program conducted in Strauss's laboratory to determine the mechanisms underlying negative symptoms.
"My hope," he said, "is that our findings on brain mechanisms will uncover new treatment targets, as there is currently no drug that can effectively treat negative symptoms. 39 is one of the biggest challenges in the field of psychiatry. "
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