Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease
Feng, Y.; Chandio, B. Q.; Villalon-Reina, J. E.; Benavidez, S.; Chattopadhyay, T.; Chehrzadeh, S.; Laltoo, E.; Thomopoulos, S. I.; Joshi, H.; Venkatasubramanian, G.; John, J. P.; Jahanshad, N.; Thompson, P. M.
Show abstract
This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNTs generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNTs capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimers disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain 96%
- WMH-DualTasker: A weakly-supervised deep learning model for automated white matter hyperintensities segmentation and visual rating prediction 96%
- Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- ComBat Harmonization: Empirical Bayes versus Fully Bayes Approaches 94%
- QSMRim-Net: Imbalance-Aware Learning for Identification of Chronic Active Multiple Sclerosis Lesions on Quantitative Susceptibility Maps 94%
- Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.