An Atlas-Guided Diffusion MRI Tractometry Approach for Fine-Scale Along-Tract Profiling Analysis
Poster No:
1784
Submission Type:
Abstract Submission
Authors:
Ruixi Zheng1, Wei Zhang1, Yijie Li1, Lauren O’Donnell2, Fan Zhang1
Institutions:
1University of Electronic Science and Technology of China, Chengdu, Sichuan, 2Brigham and Women's Hospital, Harvard Medical School, Boston, MA
First Author:
Co-Author(s):
Introduction:
Diffusion MRI (dMRI) tractography is the only technique for in vivo reconstruction of the brain's white matter (WM) connections (Zhang et al., 2022). Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has increasingly contributed to our understanding of brain connectivity and its effects on health and disease (Neher et al., 2024).
This study presents a novel atlas-guided dMRI tractometry technique that enables simultaneous analysis across multiple brain regions for population-wise comparison. It leverages an anatomical WM ORG-atlas (Zhang et al., 2018), allowing for finer-scale fiber tract parcellation. A clique-percolation method (CPM) with nonparametric permutation testing is employed to identify significant WM differences between populations. Compared to Automated Fiber Quantification (AFQ) (Yeatman et al., 2012) and Bundle Analytics (BUAN) (Chandio et al., 2020), our method demonstrates superior sensitivity and specificity in detecting population-wise WM differences.
This study presents a novel atlas-guided dMRI tractometry technique that enables simultaneous analysis across multiple brain regions for population-wise comparison. It leverages an anatomical WM ORG-atlas (Zhang et al., 2018), allowing for finer-scale fiber tract parcellation. A clique-percolation method (CPM) with nonparametric permutation testing is employed to identify significant WM differences between populations. Compared to Automated Fiber Quantification (AFQ) (Yeatman et al., 2012) and Bundle Analytics (BUAN) (Chandio et al., 2020), our method demonstrates superior sensitivity and specificity in detecting population-wise WM differences.
Methods:
Datasets: The Human Connectome Project Young Adult (HCP-YA) dataset (Van Essen et al., 2013) (29.1±3.7 y; 54F, 46M) for studying sex differences, and the Autism Brain Imaging Data Exchange II database (ABIDE) (Di Martino et al., 2014) (10.2±5.7; 30ASD, 22HC) for studying autism disease.
Overview: Fig 1 gives the method overview. First, whole-brain tractography is performed using the two-tensor Unscented Filter (UKF) algorithm (Reddy & Rathi, 2016) to reconstruct WM streamlines per subject. Second, WM parcellation is performed to subdivide the tractography through streamline clustering, centerline computation, and distance-based point assignment to generate along-tract WM parcels. Third, parcel community construction is performed to integrate neighboring WM parcels into a larger community for the following statistical analysis. Fourth, community-based statistical inference is performed to identify significant group differences at the level of anatomical communities by: 1) performing parcel-level statistical tests on diffusion metrics, 2) grouping significant parcels into anatomical communities with CPM (Palla et al., 2005), and 3) using nonparametric permutation tests to assess community significance with multiple comparison correction.
Synthetic data analysis with ground truth: We apply AFQ, BUAN and our method to a synthetic dataset of the arcuate fasciculus (AF), corticospinal tract (CST), and corpus callosum (CC) with synthetically generated known group differences, following the process used in (Zhang et al., 2018). Accuracy (ACC) and precision (PR) with respect to the ground truth are computed for each method.
Real data analysis: We apply AFQ, BUAN and our method to the HCP-YA dataset to investigate sex-related WM differences, and to the ABIDE Dataset to examine autism-related WM alterations.
Overview: Fig 1 gives the method overview. First, whole-brain tractography is performed using the two-tensor Unscented Filter (UKF) algorithm (Reddy & Rathi, 2016) to reconstruct WM streamlines per subject. Second, WM parcellation is performed to subdivide the tractography through streamline clustering, centerline computation, and distance-based point assignment to generate along-tract WM parcels. Third, parcel community construction is performed to integrate neighboring WM parcels into a larger community for the following statistical analysis. Fourth, community-based statistical inference is performed to identify significant group differences at the level of anatomical communities by: 1) performing parcel-level statistical tests on diffusion metrics, 2) grouping significant parcels into anatomical communities with CPM (Palla et al., 2005), and 3) using nonparametric permutation tests to assess community significance with multiple comparison correction.
Synthetic data analysis with ground truth: We apply AFQ, BUAN and our method to a synthetic dataset of the arcuate fasciculus (AF), corticospinal tract (CST), and corpus callosum (CC) with synthetically generated known group differences, following the process used in (Zhang et al., 2018). Accuracy (ACC) and precision (PR) with respect to the ground truth are computed for each method.
Real data analysis: We apply AFQ, BUAN and our method to the HCP-YA dataset to investigate sex-related WM differences, and to the ABIDE Dataset to examine autism-related WM alterations.
Results:
Fig 2a provides the results on the synthetic dataset, consistently outperforming AFQ and BUAN in ACC and PR. Fig 2b gives the sex difference analysis results on the HCP-YA data. Only BUAN and our method can identify group differences. Our method is potentially more anatomically specific in identifying expected sex differences near Wernicke's area. Fig 2c displays the autism analysis results on the ABIDE data, where only our method can detect group differences.
Conclusions:
This study presents a novel atlas-guided dMRI tractometry approach for fine-scale along-tract analysis. It outperforms the widely used approaches in sensitivity and specificity, enabling more accurate investigation of complex brain WM structures and supporting comparative neuroimaging research across diverse datasets.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Methods Development
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Neuroinformatics and Data Sharing:
Workflows
Keywords:
Autism
Computational Neuroscience
Data analysis
Experimental Design
Machine Learning
Modeling
Morphometrics
White Matter
Workflows
Other - dMRI, Tractography, Tractometry
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Acknowledgment:
National Key R&D Program of China (No. 2023YFE0118600), National Natural Science Foundation of China (No. 62371107), National Institutes of Health (R01MH108574, P41EB015902, R01MH074794, R01MH125860, R01MH119222, R01MH132610, R01NS125781).