Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

• Main Authors: Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp G., Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole A., Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl M., Ebdrup, Bjørn H., Fortea, Adriana, Garani, Ranjini Rg, Glenthøj, Birte Yding, Glenthøj, Louise Birkedal, Haas, Shalaila S., Hamilton, Holly K., Hayes, Rebecca A., He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina D., Kwon, Jun Soo, Lawrie, Stephen M., Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel L., Mathalon, Daniel H., McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A., Raghava, Jayachandra M., Røssberg, Jan I., Rössler, Wulf, Salisbury, Dean F., Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian K., Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander S., Uhlhaas, Peter J., Værnes, Tor G., van Amelsvoort, Therese A. M. J., Waltz, James A., Westlye, Lars T., Zhou, Juan H., Thompson, Paul M., Hernaus, Dennis, Jalbrzikowski, Maria, Koike, Shinsuke
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Neuroimaging; Psychosis
• Online Access: https://hdl.handle.net/10356/180088
• Institution: Nanyang Technological University

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)...