Important scientific insights into psychosis and depression can be gained from studying the brain structure of people who have recently developed these conditions.
Researchers at the University of Birmingham have demonstrated in a recent study published in Biological Psychiatry that structural MRI scans of the brain may be used to identify individuals who are more likely to have poor outcomes.
When doctors can identify these individuals in the early phases of their condition, they will be able to provide them with more targeted and effective treatment options.
Paris Alexandros Lalousis, one of the study’s primary authors, argues that most mental health illnesses are now diagnosed by taking into account a patient’s medical history, symptoms, and clinical observations, rather than biological information. In other words, people with identical underlying biological pathways in their condition but distinct diagnoses might exist side by side. ‘By understanding those mechanisms more fully, we can give clinicians better tools to use in planning treatments,’ says the researcher.
The research process
The PRONIA trial, which included data from around 300 individuals with recent-onset psychosis and recent-onset depression, was used by the researchers. In the PRONIA study, which is being conducted in seven European research centers, including Birmingham, researchers are studying predictive tools for psychoses with funding from the European Union.
They utilized a machine-learning system to analyze data from patients’ brain scans and categorize them into groups or clusters, which they called clustering. Based on the scans, two clusters were discovered, each of which includes patients suffering from psychosis as well as individuals suffering from depression. Each cluster had various traits that were shown to be substantially associated with their chance of recovery.
What they found
Lower quantities of grey matter — the darker tissue inside the brain that is involved in muscular control as well as processes such as memory, emotions, and decision-making – were shown to be related with individuals who had poorer results in the first cluster. Patients in the second group, on the other hand, had larger amounts of grey matter, which indicated that they were more likely to recover fully from their illness.
After that, a second algorithm was employed to forecast the patient’s health nine months after the first diagnosis had been made. The researchers discovered that by employing biologically based clusters rather than standard diagnostic methods, they were able to anticipate outcomes with a better degree of accuracy.
Evidence also suggested that patients in the cluster with lower volumes of grey matter in their brain scans might have higher levels of inflammation, poorer concentration, and other cognitive impairments that have been previously associated with depression and schizophrenia, according to the researchers.
Finally, the researchers analyzed the clusters in additional large cohort studies conducted in Germany and the United States, and they were able to demonstrate that the identical clusters found could be used to predict patient outcomes in both countries.
‘While the PRONIA study contained people recently diagnosed with their illness, the other datasets we used contained people with chronic conditions,’ adds Lalousis.
They discovered that the longer a patient had been unwell, the more probable it was that he or she would fall into the first cluster with the lowest grey matter volume. This adds to the growing body of data that structural MRI scans may be able to provide important diagnostic information that may be used to assist guide targeted treatment decisions.
For the time being, the team will focus on validating the clusters in the clinic while simultaneously collecting patient data in real time, before moving on to bigger scale clinical trials.