Search Results for author: Leo R. Zekelman

Found 5 papers, 0 papers with code

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations

no code implementations6 Apr 2024 Yijie Li, Wei zhang, Ye Wu, Li Yin, Ce Zhu, Yuqian Chen, Suheyla Cetin-Karayumak, Kang Ik K Cho, Leo R. Zekelman, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang

However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

no code implementations8 Jul 2023 Yuqian Chen, Leo R. Zekelman, Chaoyi Zhang, Tengfei Xue, Yang song, Nikos Makris, Yogesh Rathi, Alexandra J. Golby, Weidong Cai, Fan Zhang, Lauren J. O'Donnell

We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project.

regression

TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography

no code implementations5 Jan 2023 Yuqian Chen, Fan Zhang, Leo R. Zekelman, Tengfei Xue, Chaoyi Zhang, Yang song, Nikos Makris, Yogesh Rathi, Weidong Cai, Lauren J. O'Donnell

This work shows the potential of incorporating anatomical information, especially known anatomical similarities between input features, to guide convolutions in neural networks.

White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

no code implementations6 Jul 2022 Yuqian Chen, Fan Zhang, Chaoyi Zhang, Tengfei Xue, Leo R. Zekelman, Jianzhong He, Yang song, Nikos Makris, Yogesh Rathi, Alexandra J. Golby, Weidong Cai, Lauren J. O'Donnell

In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF).

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