Statistical disease mapping for heterogeneous neuroimaging studies

Rongjie Liu, Chao Huang, Tengfei Li, Liuqing Yang, Hongtu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Most cancers and neuro-related diseases (e.g., autism and stroke) display significant phenotypic and genetic heterogeneity. Characterizing such heterogeneity could transform our understanding of the etiology of these conditions and inspire new approaches to urgently needed preventions, diagnoses, and treatments. However, existing statistical methods face major challenges in delineating such heterogeneity at both group and individual levels. The aim of this paper is to propose a novel statistical disease mapping (SDM) framework to address some of these challenges. We develop an efficient estimation method to estimate unknown parameters in SDM and individual and group disease maps. Both simulation studies and real data analysis on the ADNI PET dataset indicate that our SDM can not only effectively detect diseased regions in each patient, but also provide a group disease map analysis of Alzheimer (AD) subgroups.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1415-1418
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period4/4/184/7/18

Keywords

  • Hidden Markov model
  • Multivariate varying coefficient model
  • Statistical disease mapping

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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