Brain images of thousands of people around the world have been used to create a machine learning-based classifier that could help in early diagnosis
The onset of psychosis can be predicted before it occurs, using a machine learning tool that can classify MRI brain scans between those who are healthy and those at risk of suffering a psychotic episode.
An international consortium including researchers from the University of Tokyo used the classifier to compare scans of more than 2,000 people from 21 global locations. Approximately half of the participants had been identified as clinically at high risk of developing psychosis.
Using training data, the classifier was 85% accurate in differentiating between people who were not at risk and those who later experienced overt psychotic symptoms. Using new data, it was 73% accurate. This tool could be useful in future clinical settings as, while most people who experience psychosis make a full recovery, earlier intervention generally leads to better outcomes with less negative impact on people’s lives.
Anyone can experience a psychotic episode, commonly involving delusions, hallucinations, or disorganized thoughts.
There is no single cause, but it can be triggered by an illness or injury, trauma, drug or alcohol use, medications, or a genetic predisposition. Although it can be frightening or disturbing, psychosis is treatable and most people recover. Because the most common age for a first episode is during adolescence or early adulthood, when the brain and body are undergoing many changes, it can be difficult to identify young people who need help.
"At most, only 30% of individuals at high clinical risk subsequently develop overt psychotic symptoms, while the remaining 70% do not," explained associate professor Shinsuke Koike of the University’s Graduate School of Arts and Sciences. from Tokyo. "Therefore, clinicians need help identifying those who will have psychotic symptoms using not only subclinical signs, such as changes in thinking, behavior and emotions, but also some biological markers."
The consortium of researchers has worked to create a machine learning tool that uses brain MRIs to identify people at risk of psychosis before it starts. Previous studies using brain MRI have suggested that structural differences occur in the brain after the onset of psychosis. However, this is the first time that differences have been identified in the brains of those who are at very high risk but have not yet experienced psychosis.
The team from 21 different institutions in 15 different countries brought together a large and diverse group of adolescent and young adult participants. According to Koike, investigating psychotic disorders using MRI can be challenging because variations in brain development and MRI machines make it difficult to obtain very precise and comparable results. Additionally, for young people, it can be difficult to differentiate between changes that occur due to typical development and those that occur due to mental illness.
"Different MRI models have different parameters that also influence the results," Koike explained. "As with cameras, various instruments and shooting specifications create different images of the same scene, in this case the participant’s brain. However, we were able to correct for these differences and create a classifier that is well suited to predicting the onset of psychosis".
Participants were divided into three groups of people at high clinical risk : those who later developed psychosis; those who did not develop psychosis; and people with uncertain follow-up status (1165 people total for the three groups), and a fourth group of healthy controls for comparison (1029 people).
Using the scans, the researchers trained a machine learning algorithm to identify patterns in the participants’ brain anatomy. From these four groups, the researchers used the algorithm to classify participants into two main groups of interest: healthy controls and those at high risk who later developed overt psychotic symptoms.
In training, the tool was 85% accurate in classifying results, while in the final test using new data it was 73% accurate in predicting which participants were at high risk for the onset of psychosis. Based on the results, the team believes that providing brain MRIs to people identified as being at high clinical risk may be useful in predicting the future onset of psychosis.
"We still need to test whether the classifier will work well for new data sets. Since some of the software we use is better for a fixed data set, we need to create a classifier that can robustly classify MRIs from new sites and machines . a challenge that is now being taken on by a national brain science project in Japan, called Brain/MINDS Beyond," Koike said. "If we can do this successfully, we will be able to create more robust classifiers for new data sets, which can then be applied to routine and real-life clinical settings."
The results suggest that, when considering adolescent brain development, baseline MRIs for individuals at high clinical risk may be useful in identifying their prognosis. Future prospective studies are required on whether the classifier could be truly useful in clinical settings.
