Learning Amyloid Pathology Progression from Longitudinal PIB-PET Images in Preclinical Alzheimer's Disease

Wei Hao, Nicholas M. Vogt, Zihang Meng, Seong Jae Hwang, Rebecca L. Koscik, Sterling C. Johnson, Barbara B. Bendlin, Vikas Singh

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

Abstract

Amyloid accumulation is acknowledged to be a primary pathological event in Alzheimer's disease (AD). The literature suggests that propagation of amyloid occurs along neural pathways as a function of the disease process (prion-like transmission), but the pattern of spread in the preclinical stages of AD is still poorly understood. Previous studies have used diffusion processes to capture amyloid pathology propagation using various strategies and shown how future time-points can be predicted at the group level using a population-level structural connectivity template. But connectivity could be different between distinct subjects, and the current literature is unable to provide estimates of individual-level pathology propagation. We use a trainable network diffusion model that infers the propagation dynamics of amyloid pathology, conditioned on an individual-level connectivity network. We analyze longitudinal amyloid pathology burden in 16 gray matter (GM) regions known to be affected by AD, measured using Pittsburgh Compound B (PiB) positron emission tomography at 3 different time points for each subject. Experiments show that our model outperforms inference based on group-level trends for predicting future time points data (using individual-level connectivity networks). For group-level analysis, we find parameter differences (via permutation testing) between the models for APOE positive and APOE negative subjects.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages572-576
Number of pages5
ISBN (Electronic)9781538693308
DOIs
Publication statusPublished - 2020 Apr
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 2020 Apr 32020 Apr 7

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City
Period20/4/320/4/7

Bibliographical note

Funding Information:
Acknowledgments. This project was supported in part by RF1 AG059312, R01 AG037639, R01 AG027161, R01 AG021155, P50 AG033514, UW CPCP (U54AI117924) and NSF CAREER award RI 1252725.

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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