• Corpus ID: 247025913

Subtyping brain diseases from imaging data

@article{Wen2022SubtypingBD,
  title={Subtyping brain diseases from imaging data},
  author={J. Wen and E. Varol and Zhijian Yang and Gyujoon Hwang and Dominic B. Dwyer and Anahita Fathi Kazerooni and Paris Alexandros Lalousis and Christos Davatzikos},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.10945}
}

• J. Wen, E. Varol, +5 authors C. Davatzikos
• Published 16 February 2022
• Psychology, Medicine
• ArXiv

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. To appear in O. Colliot (Ed.), Machine Learning for Brain Disorders, Springer ar X iv :2 20 2. 10 94 5v 1 [ cs .L G ] 1 6 Fe b 20 22