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Chapitre D'ouvrage Année : 2023

Subtyping Brain Diseases from Imaging Data

Erdem Varol
  • Fonction : Auteur
Zhijian Yang
  • Fonction : Auteur
Gyujoon Hwang
  • Fonction : Auteur
Dominique Dwyer
  • Fonction : Auteur
Anahita Fathi Kazerooni
  • Fonction : Auteur
Paris Alexandros Lalousis
  • Fonction : Auteur
Christos Davatzikos
  • Fonction : Auteur

Résumé

Abstract The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases, as well as cancer, are often heterogeneous in terms of their clinical manifestations, neuroanatomical patterns, or genetic underpinnings. Therefore, in such cases, seeking a single disease signature might be ineffectual in delivering individualized precision diagnostics. The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. Work from Alzheimer’s disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications.
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Dates et versions

hal-04239807 , version 1 (12-10-2023)

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Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, et al.. Subtyping Brain Diseases from Imaging Data. Olivier Colliot. Machine Learning for Brain Disorders, 197, Springer, pp.491-510, 2023, ⟨10.1007/978-1-0716-3195-9_16⟩. ⟨hal-04239807⟩
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