Learning the Latent Heat Diffusion Process through Structural Brain Network from Longitudinal $\beta$-Amyloid Data

Md Asadullah Turja, Guorong Wu, Defu Yang, Martin Andreas Styner
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:761-773, 2021.

Abstract

The excessive deposition of misfolded proteins such as amyloid-$\beta$ (A$\beta$) protein is an aging event underlying several neurodegenerative diseases. Mounting evidence shows that the spreading of neuropathological burden has a strong association to the white matter tracts in the brain which can be measured using diffusion-weighted imaging and tractography technologies. Most of the previous studies analyze the dynamic progression of amyloid using cross-sectional data which is not robust to the heterogeneous A$\beta$ dynamics across the population. In this regard, we propose a graph neural network-based learning framework to capture the disease-related dynamics by tracking the spreading of amyloid across brain networks from the subject-specific longitudinal PET images. To learn from limited (2 – 3 timestamps) and noisy longitudinal data, we restrict the space of amyloid propagation patterns to a latent heat diffusion model which is constrained by the anatomical connectivity of the brain. Our experiments show that restricting the dynamics to be a heat diffusion mechanism helps to train a robust deep neural network for predicting future time points and classifying Alzheimer’s disease brain.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-turja21a, title = {Learning the Latent Heat Diffusion Process through Structural Brain Network from Longitudinal $\beta$-Amyloid Data}, author = {Turja, Md Asadullah and Wu, Guorong and Yang, Defu and Styner, Martin Andreas}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {761--773}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/turja21a/turja21a.pdf}, url = {https://proceedings.mlr.press/v143/turja21a.html}, abstract = {The excessive deposition of misfolded proteins such as amyloid-$\beta$ (A$\beta$) protein is an aging event underlying several neurodegenerative diseases. Mounting evidence shows that the spreading of neuropathological burden has a strong association to the white matter tracts in the brain which can be measured using diffusion-weighted imaging and tractography technologies. Most of the previous studies analyze the dynamic progression of amyloid using cross-sectional data which is not robust to the heterogeneous A$\beta$ dynamics across the population. In this regard, we propose a graph neural network-based learning framework to capture the disease-related dynamics by tracking the spreading of amyloid across brain networks from the subject-specific longitudinal PET images. To learn from limited (2 – 3 timestamps) and noisy longitudinal data, we restrict the space of amyloid propagation patterns to a latent heat diffusion model which is constrained by the anatomical connectivity of the brain. Our experiments show that restricting the dynamics to be a heat diffusion mechanism helps to train a robust deep neural network for predicting future time points and classifying Alzheimer’s disease brain.} }
Endnote
%0 Conference Paper %T Learning the Latent Heat Diffusion Process through Structural Brain Network from Longitudinal $\beta$-Amyloid Data %A Md Asadullah Turja %A Guorong Wu %A Defu Yang %A Martin Andreas Styner %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-turja21a %I PMLR %P 761--773 %U https://proceedings.mlr.press/v143/turja21a.html %V 143 %X The excessive deposition of misfolded proteins such as amyloid-$\beta$ (A$\beta$) protein is an aging event underlying several neurodegenerative diseases. Mounting evidence shows that the spreading of neuropathological burden has a strong association to the white matter tracts in the brain which can be measured using diffusion-weighted imaging and tractography technologies. Most of the previous studies analyze the dynamic progression of amyloid using cross-sectional data which is not robust to the heterogeneous A$\beta$ dynamics across the population. In this regard, we propose a graph neural network-based learning framework to capture the disease-related dynamics by tracking the spreading of amyloid across brain networks from the subject-specific longitudinal PET images. To learn from limited (2 – 3 timestamps) and noisy longitudinal data, we restrict the space of amyloid propagation patterns to a latent heat diffusion model which is constrained by the anatomical connectivity of the brain. Our experiments show that restricting the dynamics to be a heat diffusion mechanism helps to train a robust deep neural network for predicting future time points and classifying Alzheimer’s disease brain.
APA
Turja, M.A., Wu, G., Yang, D. & Styner, M.A.. (2021). Learning the Latent Heat Diffusion Process through Structural Brain Network from Longitudinal $\beta$-Amyloid Data. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:761-773 Available from https://proceedings.mlr.press/v143/turja21a.html.

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