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.
|Title of host publication||ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2020 Apr|
|Event||17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States|
Duration: 2020 Apr 3 → 2020 Apr 7
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||17th IEEE International Symposium on Biomedical Imaging, ISBI 2020|
|Period||20/4/3 → 20/4/7|
Bibliographical noteFunding 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.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging