Health
The amount of gray matter can help make treatment decisions for developing mental health disorders-ScienceDaily
The brain structure of patients with recently developed psychosis and depression can provide important biological insights into these illnesses and how they develop.
In a new study published today Biological psychiatry, Researchers at the University of Birmingham have shown that structural MRI scans of the brain can identify patients who are most susceptible to poor outcomes.
Identifying these patients in the early stages of the disease enables clinicians to provide more targeted and effective treatment.
“Currently, most methods of diagnosing mental health disorders are based on the patient’s medical history, symptoms, and clinical observations, not biological information,” said lead author Paris Alexandros Larosis. “That is, patients may have similar underlying biological mechanisms in their illness, but the diagnosis is different. A more complete understanding of these mechanisms can be used in treatment planning. We can provide clinicians with better tools for. “
In this study, researchers used data from approximately 300 patients with recently-onset psychosis and recently-onset depression participating in the PRONIA study. PRONIA is a European Union-funded cohort study investigating psychosis prognosis tools conducted at seven European research centers, including Birmingham.
Researchers used machine learning algorithms to evaluate data from patient brain scans and classify them into groups or clusters. Two clusters were identified based on the scan, each containing both psychotic and depressed patients. Each cluster revealed unique characteristics that are strongly associated with recovery potential.
In the first cluster, low levels of gray matter-darker tissues in the brain involved in functions such as muscle control, memory, emotions, and decision-making were associated with poorer results. In contrast, in the second group, higher levels of gray matter signaled patients who were more likely to recover from the disease.
The second algorithm was then used to predict the patient’s condition 9 months after the initial diagnosis. Researchers have found that they are more accurate in predicting outcomes when using biology-based clusters compared to traditional diagnostic systems.
Evidence also suggests that patients in clusters with low levels of gray matter on brain scans may have higher, lower levels, and other cognitive deficits in inflammation previously associated with depression and schizophrenia. Showed that there is.
Finally, the team was able to test the clusters in other large cohort studies in Germany and the United States and show that the same identified clusters can be used to predict patient outcomes.
“The PRONIA study included people who were recently diagnosed with the disease, but the other datasets we used included people with chronic illness,” explains Lalousis. .. “We found that the longer the duration of the illness, the more likely it was that the patient would fit into the first cluster with a low amount of gray matter, which could allow structural MRI scans to provide useful diagnostic information. It actually adds evidence of sex. It helps guide targeted treatment decisions. “=
The team’s next step is to begin validation of the cluster in the clinic and collect patient data in real time before planning a large clinical trial.
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material Provided by University of Birmingham.. Note: Content can be edited in style and length.
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