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In training, the tool was 85% accurate at classifying the results, while in the final test using new data it was 73% accurate at predicting which participants were at high risk of psychosis onset. Credit: Neuroscience News

Predicting Psychosis Before Symptom Onset

Summary: Researchers developed a machine-learning tool that accurately identifies individuals at high risk of psychosis through MRI brain scans. This innovative approach, which achieved an 85% accuracy rate in training and 73% using new data, offers a promising avenue for early intervention in psychosis, potentially improving treatment outcomes.

The study involved over 2,000 participants from 21 global locations, highlighting the tool’s potential in diverse clinical settings. By detecting structural brain differences before the onset of psychosis, this tool marks a significant advancement in psychiatric care, aiming for better prediction and prevention strategies.

Key Facts:

  1. The machine-learning classifier can distinguish between individuals at high risk of psychosis and those not at risk with high accuracy, using MRI brain scans.
  2. Early identification of psychosis risk through MRI scans could lead to more effective interventions and reduce the impact on individuals’ lives.
  3. The research emphasizes the need for further development to ensure the classifier’s applicability across different data sets and clinical environments.

Source: University of Tokyo

The onset of psychosis can be predicted before it occurs, using a machine-learning tool which can classify MRI brain scans into those who are healthy and those at risk of a psychotic episode.

An international consortium including researchers from the University of Tokyo, used the classifier to compare scans from over 2,000 people from 21 global locations. About half of the participants had been identified as being clinically at high risk of developing psychosis.

Using training data, the classifier was 85% accurate at 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 helpful in future clinical settings, as while most people who experience psychosis make a full recovery, earlier intervention typically leads to better outcomes with less negative impact on people’s lives.

Anyone might experience a psychotic episode, which commonly involves delusions, hallucinations or disorganized thinking. There is no single cause, but it can be triggered by illness or injury, trauma, drug or alcohol use, medication, or a genetic predisposition.

Although it can be scary or unsettling, psychosis is treatable and most people recover. As the most common age for a first episode is during adolescence or early adulthood, when the brain and body are undergoing a lot of change, it can be difficult to identify young people in need of help. 

“At most only 30% of clinical high-risk individuals later have overt psychotic symptoms, while the remaining 70% do not,” explained Associate Professor Shinsuke Koike from the Graduate School of Arts and Sciences at the University of Tokyo.

“Therefore, clinicians need help to identify those who will go on to 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 have worked together to create a machine-learning tool which uses brain MRI scans 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 reportedly the first time that differences in the brains of those who are at very high risk but have not yet experienced psychosis have been identified.

The team from 21 different institutions in 15 different countries gathered a large and diverse group of adolescent and young adult participants.

According to Koike, MRI research into psychotic disorders can be challenging because variations in brain development and in MRI machines make it difficult to get very accurate, comparable results. Also, with young people, it can be difficult to differentiate between changes that are taking place because of typical development and those due to mental illness. 

“Different MRI models have different parameters which also influence the results,” explained Koike.

“Just like with cameras, varied 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 which is well tuned to predicting psychosis onset.”

The participants were divided into three groups of people at clinical high risk: those who later developed psychosis; those who didn’t develop psychosis; and people with uncertain follow-up status (1,165 people in total for all three groups), and a fourth group of healthy controls for comparison (1,029 people). Using the scans, the researchers trained a machine-learning algorithm to identify patterns in the brain anatomy of the participants.

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 at classifying the results, while in the final test using new data it was 73% accurate at predicting which participants were at high risk of psychosis onset.

Based on the results, the team considers that providing brain MRI scans for people identified as being at clinically high risk may be helpful for predicting future psychosis onset. 

“We still have to test whether the classifier will work well for new sets of data. Since some of the software we used is best for a fixed data set, we need to build a classifier that can robustly classify MRIs from new sites and machines, a challenge which a national brain science project in Japan, called Brain/MINDS Beyond, is now taking on,” said Koike.

“If we can do this successfully, we can create more robust classifiers for new data sets, which can then be applied to real-life and routine clinical settings.”

Funding: This research was supported in part by AMED (Grant Number JP18dm0307001, JP18dm0307004, and JP19dm0207069), JST Moonshot R&D (JPMJMS2021), JSPS KAKENHI (JP23H03877 and JP21H02851), Takeda Science Foundation and SENSHIN Medical Research Foundation. This study was also supported by the International Research Center for Neurointelligence (WPI-IRCN), the University of Tokyo.

About this psychosis research news

Author: Joseph Krisher
Source: University of Tokyo
Contact: Joseph Krisher – University of Tokyo
Image: The image is credited to Neuroscience News

Original Research: Open access.
Using Brain Structural Neuroimaging Measures to Predict Psychosis Onset for Individuals at Clinical High-Risk” by Shinsuke Koike et al. Molecular Psychiatry


Using Brain Structural Neuroimaging Measures to Predict Psychosis Onset for Individuals at Clinical High-Risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown.

We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other.

We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites.

We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier.

The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively.

Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%).

We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample.

The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis.

Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

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