Daichi Sone, Alexandra Young, Shunichiro Shinagawa, Sakiko Tsugawa, Yusuke Iwata, Ryosuke Tarumi, Kamiyu Ogyu, Shiori Honda, Ryo Ochi, Karin Matsushita, Fumihiko Ueno, Nobuaki Hondo, Akihiro Koreki, Edgardo Torres-Carmona, Wanna Mar, Nathan Chan, Teruki Koizumi, Hideo Kato, Keisuke Kusudo, Vincenzo de Luca, Philip Gerretsen, Gary Remington, Mitsumoto Onaya, Yoshihiro Noda, Hiroyuki Uchida, Masaru Mimura, Masahiro Shigeta, Ariel Graff-Guerrero, Shinichiro Nakajima
BACKGROUND AND HYPOTHESIS: Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Here, we adopted data-driven machine-learning techniques to identify the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance. STUDY DESIGN: In this cross-sectional multicenter study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging...
November 25, 2023: Schizophrenia Bulletin