Subtyping Brain Diseases from Imaging Data

Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, Christos Davatzikos

    Research output: Chapter in Book/Report/Conference proceedingChapter

    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.

    Original languageEnglish (US)
    Title of host publicationNeuromethods
    PublisherHumana Press Inc.
    Pages491-510
    Number of pages20
    DOIs
    StatePublished - 2023

    Publication series

    NameNeuromethods
    Volume197
    ISSN (Print)0893-2336
    ISSN (Electronic)1940-6045

    Keywords

    • Heterogeneity
    • Machine learning
    • Neuroimaging
    • Semi-supervised clustering

    ASJC Scopus subject areas

    • General Neuroscience
    • General Biochemistry, Genetics and Molecular Biology
    • Pharmacology, Toxicology and Pharmaceutics(all)
    • Psychiatry and Mental health

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