Predicting psychosis before onset

The onset of psychosis can be predicted before it occurs using machine learning tools that can classify brain MRI scans into healthy people and those at risk of psychotic episodes. An international consortium, including researchers from the University of Tokyo, used the classifier to compare more than 2,000 scans of her from 21 locations around the world. Approximately half of the participants were clinically identified as being at high risk of developing psychosis. Using the training data, the classifier distinguished between people who were not at risk and those who later experienced overt psychotic symptoms with her 85% accuracy. Using new data, accuracy was 73%. This tool may be useful in future clinical settings. Most people who experience psychosis fully recover, because early intervention usually has less negative impact on people's lives and produces better outcomes.

Anyone can experience a psychotic episode that involves delusions, hallucinations, or confused thinking. There is no single cause, but it can be caused by illness, injury, trauma, drug or alcohol use, medication, or genetic predisposition. Although it can be scary and worrying, mental illness is treatable and most people recover. It can be difficult to identify young people who need help because the age when first symptoms are most common is during adolescence or early adulthood, when major changes are occurring in the brain and body.

“At most, only 30% of clinically high-risk people will later develop clear psychotic symptoms, but the remaining 70% will not,” says Shinsuke Koike, associate professor at the University of Tokyo's Graduate School of Arts and Sciences. explains. “Clinicians therefore need help identifying people with persistent psychotic symptoms, using not only underlying signs such as changes in thinking, behavior and emotion, but also some biological markers. is.”

A consortium of researchers has teamed up to create a machine learning tool that uses brain MRI scans to identify people at risk of psychosis before they develop. Previous studies using brain MRI have suggested that structural differences occur in the brain after the onset of psychosis. But this is reportedly the first time that brain differences have been identified in people who are at very high risk but have not yet experienced psychosis.

Teams from 21 different institutions in 15 countries brought together a large and diverse group of youth and young adult participants. Koike said MRI studies of mental disorders can be difficult because differences in brain development and MRI equipment make it difficult to obtain highly accurate and comparable results. Additionally, in young people, it may be difficult to distinguish between changes due to neurotypical development and changes due to mental illness.

“Different MRI models have different parameters, which also affect the results,” Koike explained. “Just like with cameras, different equipment and filming specifications create different images of the same scene, in this case a participant's brain. But we are correcting for these differences and trying to detect the onset of psychosis. We were able to create a well-tuned classifier to make predictions.”

Participants were divided into three groups of clinically high-risk people. People who did not develop mental illness. those with uncertain follow-up status (3 groups total he 1,165 people), and his fourth group of healthy controls for comparison (1,029 people). The researchers used the scans to train a machine learning algorithm that identified patterns in the participants' brain anatomy. The researchers used an algorithm from these four groups to divide participants into two main target groups: healthy controls and a group at high risk of later developing overt psychotic symptoms. Classified.

In training, the tool showed 85% accuracy in classifying outcomes, but in final testing with new data, it was 73% accurate in predicting which participants were at high risk of developing psychosis. Based on this result, the team believes that offering brain MRI scans to people clinically identified as at high risk could help predict future development of psychosis. .

“We need to test whether the classifier performs well on new datasets. Some of the software we used is best suited for fixed datasets, so we can test MRIs from new facilities and machines. We need to build a classifier that can classify reliably. This is a challenge that Japan's national neuroscience project called Brain/MINDS Beyond is working on right now,” Koike said. “If this is successful, we will be able to create more robust classifiers on new datasets and apply them in real-world, routine clinical settings.”

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Paper title:

Immaculate Baeza, Xiaogang Chen, Suna Choi, Cheryl M. Corcoran, Bea H. Ebdorp, Adriana Fortea, Ranzini RG. Galani, Birte Eading Glentoy, Louise Birkedal Glentoy, Sharila S. Haas, Holly K. Hamilton, Rebecca A. Hayes, Yin Ha, Karsten Hekelen, Kyoto Kasai, Naoyuki Katagiri, Mina Kim. , Tina D. Christensen, Jun Su Kwon, Stephen M. Rowley, Irina Lebedeva, Jimmy Lee, Rachel L. Lowy, Daniel H. Matalon, Philip McGuire, Romina Mizrahi, Masafumi Mizuno, Paul Moeller, Takahiro Nemoto, Dorte Nordholm, Maria A. Omelchenko, Jayachandra M. Raghava, Jan I. Rothberg, Wolf Ressler, Dean F. Salisbury, Daiki Sasaba, Lukasz Smielski, Gisela Sugranies, Tsutomu Takahashi, Christian K. Tamnes. , Jinson Tan, Anastasiadar S. Tomishev, Peter J. AMJ. van Amelsvoort, James A. Waltz, Lars T. Westlye, Juan H. Zhou, Paul M. Thompson, Dennis Hernaus, Maria Jalbrzikowski, Koike Shinuke, and the ENIGMA Clinical High Risk for Psychosis Working Group. The use of structural brain neuroimaging measures to predict the onset of psychosis in clinically high-risk individuals. molecular psychiatry. DOI: 10.1038/s41380-024-02426-7

Funding:

This research was supported in part by AMED (grant numbers JP18dm0307001, JP18dm0307004, JP19dm0207069), JST Moonshot Research and Development (JPMJMS2021), JSPS KAKENHI (JP23H03877 and JP21H02851), the Takeda Science Foundation, and the Senshin Medical Research Foundation. I received a grant. This research was also supported by the University of Tokyo International Research Center for Neurointelligence (WPI-IRCN).

Conflict of interest:

Author OAO serves as a consultant for cortechs.ai and has received speaking fees from Lundbeck, Janssen, and Sunovion.

Author BYG is the leader of the Center of Excellence for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS) at the Lundbeck Foundation (January 2009 – December 2021). This center was partially funded by an independent grant from the Lundbeck Foundation under international review. by the Mental Health Service of the Danish Capital Region, the University of Copenhagen, and other foundations. All grants are the property of and will be administered by the Danish Capital Region Mental Health Service.

The other authors have no conflicts of interest to declare in relation to the content of this article.

Useful links
Graduate School of Arts and Sciences: https://www.cu-tokyo.ac.jp/eng_site/

Center for Evolutionary Cognitive Science: https://ecs.cu-tokyo.ac.jp/ja/home/home.html

Koike Laboratory: https://klab.cu-tokyo.ac.jp/

Research contact:

Shinsuke Koike, Associate Professor, Doctor of Medicine
Center for Evolutionary Cognitive Science

Graduate School of Arts and Sciences, University of Tokyo
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
Email: [email protected]

Phone number: +81-3-5454-4327; FAX: 03-5454-4327

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