15 citations · 49 across the 18 of their papers we have counts for
18 papers
Cross-Population White Matter Atlas Creation for Concurrent Mapping of Brain Connections in Neonates and Adults with Diffusion MRI Tractography
Wei Zhang, Yijie Li, Ruixi Zheng +9
Comparing white matter (WM) connections between adults and neonates using diffusion MRI (dMRI) can advance our understanding of typical brain development and potential biomarkers f…
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
Yui Lo, Yuqian Chen, Dongnan Liu +8
Shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and c…
TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View
Yuqian Chen, Leo Zekelman, Yui Lo +7
Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical s…
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
Haolin He, Ce Zhu, Le Zhang +9
Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation…
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
Yui Lo, Yuqian Chen, Dongnan Liu +9
Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship…
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Yui Lo, Yuqian Chen, Dongnan Liu +9
The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and acro…
Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
Ari Tchetchenian, Leo Zekelman, Yuqian Chen +8
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been succes…
White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
Yui Lo, Yuqian Chen, Fan Zhang +6
Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype clas…
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography
Yuqian Chen, Fan Zhang, Meng Wang +10
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of brain white ma…
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
Yijie Li, Wei Zhang, Ye Wu +11
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental he…
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Yui Lo, Yuqian Chen, Dongnan Liu +8
Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help inter…
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
Yuxiang Wei, Yuqian Chen, Tengfei Xue +6
Large datasets often contain multiple distinct feature sets, or views, that offer complementary information that can be exploited by multi-view learning methods to improve results.…
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
Haolin He, Ce Zhu, Le Zhang +9
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomi…
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
Yuqian Chen, Leo R. Zekelman, Chaoyi Zhang +8
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise…
TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractography
Yuqian Chen, Fan Zhang, Leo R. Zekelman +7
The structure and variability of the brain's connections can be investigated via prediction of non-imaging phenotypes using neural networks. However, known neuroanatomical relation…
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
Xiao Xu, Yuqian Chen, Leo Zekelman +4
As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connecti…
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography
Tengfei Xue, Fan Zhang, Leo R. Zekelman +9
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using…
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning
Yuqian Chen, Fan Zhang, Chaoyi Zhang +9
White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data h…