Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes

Junhao Wen, Erdem Varol, Aristeidis Sotiras, Zhijian Yang, Ganesh B. Chand, Güray Erus, Haochang Shou, Ahmed Abdulkadir, Gyujoon Hwang, Dominic B. Dwyer, Alessandro Pigoni, Paola Dazzan, René S. Kahn, Hugo G. Schnack, Marcus V. Zanetti, Eva Meisenzahl, Geraldo F. Busatto, Benedicto Crespo-Facorro, Romero-Garcia Rafael, Christos Pantelis et al. (10 additional authors not shown)

Abstract: Highlights•We propose a novel multi-scale semi-supervised clustering method, termed MAGIC, to disentangle the heterogeneity of brain diseases.•We perform extensive semi-simulated experiments on large control samples (UK Biobank, N = 4403) to precisely quantify performance under various conditions, including varying degrees of brain atrophy, different levels of heterogeneity, overlapping disease subtypes, class imbalance, and varying sample sizes.•We apply MAGIC to MCI and Alzheimer's disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to dissect their neuroanatomical heterogeneity, providing guidance regarding the use of the semi-simulated experiments to validate the subtypes found in actual clinical applications.