Poster No:
1105
Submission Type:
Abstract Submission
Authors:
Lingchen Kong1, Norihide Maikusa1, Lin Cai1, Issei Ueda1, Shuhei Shibukawa1,2, Naohiro Okada3, Hidenori Yamasue4, Ryu-ichiro Hashimoto5,6, Tsutomu Takahashi7, Toshiya Murai8, Kazutaka Ohi9, Shinichiro Nakajima10, Kiyoto Kasai1,11,12,3, Shinsuke Koike1,11,3
Institutions:
1Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan, 2Faculty of Health Science, Department of Radiological Technology, Juntendo University, Tokyo, Japan, 3The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan, 4Department of Psychiatry, Hamamatsu University School of Medicine, Shizuoka, Japan, 5Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan, 6Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan, 7Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan, 8Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan, 9Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan, 10Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan, 11University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan, 12Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
First Author:
Lingchen Kong
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
Tokyo, Japan
Co-Author(s):
Norihide Maikusa
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
Tokyo, Japan
Lin Cai
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
Tokyo, Japan
Issei Ueda
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
Tokyo, Japan
Shuhei Shibukawa
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo|Faculty of Health Science, Department of Radiological Technology, Juntendo University
Tokyo, Japan|Tokyo, Japan
Naohiro Okada, M.D., Ph.D.
The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS)
Tokyo, Japan
Ryu-ichiro Hashimoto
Department of Language Sciences, Tokyo Metropolitan University|Medical Institute of Developmental Disabilities Research, Showa University
Tokyo, Japan|Tokyo, Japan
Tsutomu Takahashi, M.D., Ph.D.
Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences
Toyama, Japan
Toshiya Murai
Department of Psychiatry, Graduate School of Medicine, Kyoto University
Kyoto, Japan
Shinichiro Nakajima
Department of Neuropsychiatry, Keio University School of Medicine
Tokyo, Japan
Kiyoto Kasai, M.D., Ph.D.
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo|University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM)|Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo|The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS)
Tokyo, Japan|Tokyo, Japan|Tokyo, Japan|Tokyo, Japan
Shinsuke Koike
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo|University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM)|The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS)
Tokyo, Japan|Tokyo, Japan|Tokyo, Japan
Introduction:
Neuroanatomical alterations in schizophrenia include gray matter reduction, ventricular enlargement, and increased globus pallidus volume (Cheon et al., 2022). The heterogeneity observed in clinical features have spurred interest in identifying subtypes of schizophrenia. Machine learning applied to structural MRI data has identified two neuroanatomical subtypes: one characterized by widespread gray matter reductions and another by increased basal ganglia volumes (Chand et al., 2020). The Subtype and Stage Inference (SuStaIn) algorithm models spatiotemporal trajectories of brain structure changes using cross-sectional data, identifying two gray matter reduction patterns in schizophrenia: a cortex-dominant type starting in Broca's area and the insular cortex, and a subcortex-dominant type originating in the hippocampus (Jiang et al., 2024). These insights highlight schizophrenia's heterogeneity but require validation through harmonized dataset and longitudinal studies. In this study, we aimed to validate these subtypes in a harmonized multi-site dataset and to further explore progression trajectories in earlier clinical stages of psychosis.
Methods:
Structural brain MRI data from 11 sites, 14 procedures, including 469 individuals with schizophrenia (SCZ), 63 with first-episode psychosis (FEP), 98 at ultra-high risk for psychosis (UHR), and 1391 healthy controls (HC) were analyzed. A two-step harmonization process addressed inter-site biases. First, using 149 scans from 35 individuals imaged from 10 procedures, traveling subject (TS) harmonization were applied to diminish measurement bias. Second, ComBat-GAM harmonization reduced residual inter-site differences for 4 non-TS procedures. Cortical thickness and subcortical volumes were normalized for ICV, age and sex using HC data. Z-scores were calculated as deviations from HC predictions, scaled by residual standard deviations, and multiplied by -1 to reflect reductions. The Z-scores of the 16 regions of interest (ROIs) previously associated with schizophrenia-related structural changes were input into SuStaIn to estimate spatiotemporal trajectories and classify participants into subtypes with distinct progression patterns.
Results:
The SuStaIn algorithm identified two neurostructural subtypes and 48 progression stages. Subtype 1 exhibited characteristics similar to a subcortical-predominant pattern, with significant early reductions in subcortical volumes such as the amygdala (t = 13.05, p < 0.001). Subtype 2 resembled a cortical-predominant pattern, showing prominent reductions in cortical thickness, particularly in the isthmus of the cingulate gyrus (t = -8.43, p < 0.001). A one-way analysis of variance (ANOVA) revealed a significant effect of group on estimated stages (F (2, 627) = 7.41, p < 0.001). Post-hoc Tukey HSD tests revealed that SCZ group showed more progressive stages compared to UHR and FEP (UHR: mean stage = 2.38, FEP: mean stage =2.16, SCZ: mean stage = 4.10; p = 0.008, 0.014, respectively), while no significant difference was observed between FEP and UHR (p = 0.963). This finding supports the validity of SuStaIn in identifying differences in estimated progression stages among clinical groups, demonstrating its ability to reflect distinct disease progression patterns.
Conclusions:
This study identified two distinct subtypes of schizophrenia: subcortical-predominant and cortical-predominant subtypes, which is consistent with previous findings. The algorithm classified patients into estimated progression stages, revealing significant differences among clinical groups and supporting its validity in identifying heterogeneous disease progression. These findings suggest potential applications in refining diagnostics and interventions based on subtype-specific progression patterns. However, progression trajectory modeling still has need for longitudinal validation. Future research should utilize harmonized multi-site longitudinal imaging to confirm spatiotemporal progression.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Keywords:
Cortex
Data analysis
Machine Learning
Morphometrics
MRI
Psychiatric Disorders
Schizophrenia
Sub-Cortical
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
Structural MRI
Computational modeling
Provide references using APA citation style.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.
Chand, G. B. et al. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain : a journal of neurology, 143(3), 1027–1038.
Cheon, E. et al. (2022). Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings. Psychiatry and clinical neurosciences, 76(5), 140–161.
Jiang, Y. et al. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm. Nat Communications, 15, 5996 (2024).
No