A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance that may lead to biomarkers that could be used to treat and diagnose patients more accurately, says a new report on Science Daily. The Penn State research team, led by Theodore D. Satterthwaite, MD, mapped abnormalities in brain networks to mood, psychosis, disruptive externalizing behavior, and fear, linking each to distinct patterns of brain connectivity.

Previous studies using the clinical diagnostic categories considered to be standard have been able to show evidence of brain-based abnormalities, but the findings were limited due to a few factors, which included the high probability of comorbid mental health conditions. The research was published in Nature Communications.

The findings reveal that Satterthwaite and colleagues were able to identify brain network patterns “strongly related to psychiatric symptoms,” the article reports. The current gold-standard in diagnosing mental health disorders relies on patients reliably self-reporting, and physician observations.

That’s a stark difference when compared to other areas of medicine using “biomarkers to aid in diagnosis, determination of prognosis, and selection of treatment for patients.” Satterthwaitte explained just how behind the rest of medicine psychiatry truly is. “For example, when a patient comes in to see a doctor with most problems, in addition to talking to the patient, the physician will recommend lab tests and imaging studies to help diagnose their condition.

Right now, that is not how things work in psychiatry. In most cases, all psychiatric diagnoses rely on just talking to the patient. Read more from inquisitr.com…

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