Gray matter volume could inform processing dec

The brain structure of patients with new-onset psychosis and depression can offer important biological information about these illnesses and how they might develop.

In a new study published today [date] in biological psychiatry, researchers from the University of Birmingham show that by examining the structure MRI Brain scans can identify patients most likely to have poor outcomes.

By identifying these patients in the early stages of their disease, clinicians will be able to offer more targeted and effective treatments.

“Currently, the way we diagnose most mental health disorders is based on a patient’s history, symptoms, and clinical observations, rather than biological information,” says lead author Paris Alexandros Lalousis. “This means that patients may have similar underlying biological mechanisms in their disease, but different diagnoses. By better understanding these mechanisms, we can give clinicians better tools to use in treatment planning. »

In the study, researchers used data from approximately 300 patients with new-onset psychosis and depression participating in the PRONIA study. PRONIA is a European Union-funded cohort study investigating prognostic tools for psychoses and taking place at seven European research centres, including Birmingham.

The researchers used a machine learning algorithm to evaluate data from patients’ brain scans and sort them into groups or clusters. Two clusters were identified based on the scans, each containing both patients with psychosis and patients with depression. Each group revealed distinctive characteristics strongly related to their likelihood of recovery.

In the first group, lower volumes of gray matter – the darker tissue inside the brain involved in muscle control and functions such as memory, emotions and decision-making – were associated with patients who then had less good results. In the second group, on the other hand, higher levels of gray matter signaled that patients were more likely to recover well from their disease.

A second algorithm was then used to predict the condition of patients nine months after the initial diagnosis. The researchers found a higher level of accuracy in predicting outcomes when using the biologically based clusters compared to traditional diagnostic systems.

Evidence also showed that patients in the group with lower gray matter volumes in their brain scans may have higher levels of inflammation, lower concentration, and other cognitive impairments previously associated with depression and moodiness. schizophrenia.

Finally, the team tested the clusters in other large cohort studies in Germany and the United States and were able to show that the same identified clusters could be used to predict patient outcomes.

“While the PRONIA study contained people recently diagnosed with their disease, the other datasets we used contained people with chronic conditions,” says Lalousis. “We found that the longer the disease duration, the more likely a patient was to fit into the first group with lower gray matter volume. This really adds to the evidence that structural MRIs can be in able to offer useful diagnostic information to help guide targeted treatment decisions.

The next step for the team is to start validating the clusters in the clinic, collecting real-time patient data, before planning larger-scale clinical trials.

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