DATA-DRIVEN DISEASE PROGRESSION PATTERNS OF BRAIN MORPHOLOGY IN SCHIZOPHRENIA: MORE PROGRESSED STAGES IN TREATMENT- RESISTANCE
Authors: Sone *Daichi , Young Alexandra L , Shinagawa Shunichiro , Tsugawa Sakiko , Iwata Yusuke , Tarumi Ryosuke , Ogyu Kamiyu , Honda Shiori , Ochi Ryo , Matsushita Karin , Ueno Fumihiko , Hondo Nobuaki , Koreki Akihiro , Torres-Carmona Edgardo , Mar Wanna , Chan Nathan , Koizumi Teruki , Kato Hideo , Kusudo Keisuke , De Luca Vincenzo , Gerretsen Philip , Remington Gary , Onaya Mitsumoto , Noda Yoshihiro , Uchida Hiroyuki , Mimura Masaru , Shigeta Masahiro , Graff-Guerrero Ariel , Nakajima Shinichiro 
Published: February 2025

Background

Given the heterogeneity, particularly in terms of treatment response, and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Treatment-resistant schizophrenia (TRS) is a distinct subpopulation showing poor response to conventional pharmacological treatment and, as a result, a form of the illness associated with serious social and economic burden. The neurobiological basis of TRS remains to be elucidated, despite numerous strategies including neuroimaging studies.

Aims & Objectives

Here, we applied a data-driven machine-learning technique to classify disease progression patterns and staging of brain morphology in schizophrenia, with the goal of identifying distinct biological subtypes in the context of illness progression and associations with clinical measures. We hypothesized that TRS may be associated with more progressed disease staging; in addition, we investigated relationship with other clinical characteristics.

Method

In this cross-sectional multi-center study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into Z-scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis.

Results

SuStaIn identified three different subtypes: 1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, 2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, 3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (p=0.001). The GP-CX type presented earlier stages than the pure CX type (p=0.009), but otherwise the subtypes did not show any relationship with clinical characteristics.

Discussion & Conclusions

The brain morphological progressions in schizophrenia can be classified into three subtypes, and treatment-resistance was associated with more progressed stages. These brain morphological subtypes and staging may, in turn, lead to the development of clinically useful individualized biomarkers. Schizophrenia is a syndrome with heterogeneity, and to date, no clinically available biomarkers have been established from the symptom-based approaches. Thus, individual-level classification and staging based solely on the biological features obtained in this study may lead to more biologically accurate classification, which would allow us to clarify pathophysiology and select personalized treatment. We clarified the relationship between stage progression and TRS, suggesting the possibility of individual-level disease monitoring for treatment resistance in clinical practice.