Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIG...

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Vydáno v:	Nature communications Ročník 15; číslo 1; s. 5996 - 15
Hlavní autoři:	Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars T., Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole A., Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda L., Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Salvador, Raymond, Fuentes-Claramonte, Paola, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali Saffet, Uslu, Ozgul, Burhanoglu, Birce Begum, Uyar Demir, Aslihan, Rootes-Murdy, Kelly, Calhoun, Vince D., Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young Chul, Kim, Woo-Sung, Sponheim, Scott R., Demro, Caroline, Ramsay, Ian S., Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min Tae M., Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn E., Rossell, Susan L., Hughes, Matthew, Carruthers, Sean P., Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana D., Preda, Adrian, Thomopoulos, Sophia I., Jahanshad, Neda, Cui, Long-Biao, Yao, Dezhong, Thompson, Paul M., Turner, Jessica A., van Erp, Theo G. M., Cheng, Wei, Feng, Jianfeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 17.07.2024 Nature Publishing Group Nature Portfolio
Témata:	692/53/2421 692/699/476/1799 Adult Algorithms Brain - diagnostic imaging Brain - pathology Cross-Sectional Studies Europe Female Gray Matter - diagnostic imaging Gray Matter - pathology Hippocampus - diagnostic imaging Hippocampus - pathology Humanities and Social Sciences Humans Inference Learning algorithms Machine Learning Magnetic Resonance Imaging Male Mental disorders Middle Aged multidisciplinary Neostriatum Neuroimaging North America Reproducibility Reproducibility of Results Schizophrenia Schizophrenia - diagnostic imaging Schizophrenia - pathology Science Science (multidisciplinary) Subgroups Substantia grisea Taxonomy North America Europe
ISSN:	2041-1723, 2041-1723
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Shrnutí:	Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors. Machine learning can be used to identify subtypes of psychiatric disease. Here the authors identified two neurostructural subgroups in schizophrenia, each showing reproducibility and generalizability across different collection locations and illness stages, using the SuStain algorithm.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-024-50267-3