Learning Optimal White Matter Tract Representations from Tractography using a Deep Generative Model for Population Analyses

Whole brain tractography is commonly used to study the brain's white matter fiber pathways, but the large number of streamlines generated - up to one million per brain - can be challenging for large-scale population studies. We propose a robust dimensionality reduction framework for tractograph...

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Published in:bioRxiv
Main Authors: Feng, Yixue, Bramsh Qamar Chandio, Chattopadhyay, Tamoghna, Thomopoulos, Sophia I, Owens-Walton, Conor, Jahanshad, Neda, Garyfallidis, Eleftherios, Thompson, Paul M
Format: Paper
Language:English
Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 02.08.2022
Cold Spring Harbor Laboratory
Edition:1.1
Subjects:
Alzheimer's disease
Embedding
Neurodegenerative diseases
Neuroscience
Population studies
Statistical analysis
Substantia alba
diffusion MRI
tractography
anomaly detection
Alzheimer’s disease
variational autoencoder
ISSN:2692-8205, 2692-8205
Online Access:Get full text
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Summary:Whole brain tractography is commonly used to study the brain's white matter fiber pathways, but the large number of streamlines generated - up to one million per brain - can be challenging for large-scale population studies. We propose a robust dimensionality reduction framework for tractography, using a Convolutional Variational Autoencoder (ConvVAE) to learn low-dimensional embeddings from white matter bundles. The resulting embeddings can be used to facilitate downstream tasks such as outlier and abnormality detection, and mapping of disease effects on white matter tracts in individuals or groups. We design experiments to evaluate how well embeddings of different dimensions preserve distances from the original high-dimensional dataset, using distance correlation methods. We find that streamline distances and inter-bundle distances are well preserved in the latent space, with a 6-dimensional optimal embedding space. The generative ConvVAE model allows fast inference on new data, and the smooth latent space enables meaningful decodings that can be used for downstream tasks. We demonstrate the use of a ConvVAE model trained on control subjects' data to detect structural anomalies in white matter tracts in patients with Alzheimer's disease (AD). Using ConvVAEs to facilitate population analyses, we identified 6 tracts with statistically significant differences between AD and controls after controlling for age and sex effect, visualizing specific locations along the tracts with high anomalies despite large inter-subject variations in fiber bundle geometry. Competing Interest Statement The authors have declared no competing interest.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
2692-8205
DOI:10.1101/2022.07.31.502227