TY - GEN
T1 - Statistical disease mapping for heterogeneous neuroimaging studies
AU - Liu, Rongjie
AU - Huang, Chao
AU - Li, Tengfei
AU - Yang, Liuqing
AU - Zhu, Hongtu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - 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.
AB - 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.
KW - Hidden Markov model
KW - Multivariate varying coefficient model
KW - Statistical disease mapping
UR - http://www.scopus.com/inward/record.url?scp=85048074765&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048074765&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2018.8363837
DO - 10.1109/ISBI.2018.8363837
M3 - Conference contribution
AN - SCOPUS:85048074765
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1415
EP - 1418
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -