Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Hansen, Colin B.; Yang, Qi; Lyu, Ilwoo; Rheault, François; Kerley, Cailey I.; Chandio, Bramsh Qamar; Fadnavis, Shreyas; Williams, Owen A.; Shafer, Andrea T.; Resnick, Susan M.; Zald, David H.; Cutting, Laurie E.; Taylor, Warren D.; Boyd, Brian D.; Garyfallidis, Eleftherios; Anderson, Adam W.; Descoteaux, Maxime; Landman, Bennett A.; Schilling, Kurt G.

doi:10.1007/s12021-020-09497-1

Cited by 24 publications

(29 citation statements)

References 73 publications

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“…The probabilities of association pathways are visualized to illustrate their anatomical relation and relative location. The results are consistent with a recent population-based tractography atlas 17 .…”

Section: Population-based Tractography Of Young Adultssupporting

confidence: 91%

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Population-Based Tract-To-Region Connectome of the Human Brain and Its Hierarchical Topology

Yeh

2021

Preprint

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Connectome maps region-to-region connectivities but does not inform which white matter pathways form the connections. Here we constructed the first population-based tract-to-region connectome to fill this information gap. The constructed connectome quantifies the population probability of a white matter tract innervating a cortical region. The results show that ~85% of the tract-to-region connectome entries are consistent across individuals, whereas the remaining (~15%) have substantial individual differences requiring individualized mapping. Further hierarchical clustering on cortical regions revealed their parcellations into dorsal, ventral, and limbic networks based on the tract-to-region connective patterns. The clustering results on white matter bundles revealed the connectome-based categorization of fiber bundle systems in the association pathways. This new tract-to-region connectome provides insights into the connective topology between cortical regions and white matter bundles. The derived hierarchical relation further offers a connectome-based categorization of gray matter and white matter structures.

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Section: Population-based Tractography Of Young Adultssupporting

confidence: 91%

“…The tractography results shown in Fig. 2 are consistent with known neuroanatomy 15 and existing tractography results 10,16,17 . The lateralization of left AF can be readily observed by its substantially larger volume.…”

Section: Population-based Tractography Of Young Adultssupporting

confidence: 87%

Population-Based Tract-To-Region Connectome of the Human Brain and Its Hierarchical Topology

Yeh

2021

Preprint

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“…We compare the quantitative performance of transferring labels with the traditional atlas‐based approach as the baseline method. Here, we use the Pandora atlas, 16 which is a 4D collection population‐based atlases. The Pandora atlas used the same cohorts and diffusion tractography algorithms to generate each corresponding WM bundle same as we learn in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Fortunately, image registration is an established way of transferring different WM labels from population‐based atlases to T1w MRI and can isolate different WM regions in T1w MRI. In general, WM atlases from the diffusion‐weighted MRI (dMRI) community can be divided into two categories: streamline‐based atlases 10–13 and volumetric atlases 14–16 . Streamline‐based WM atlases contain streamlines assigned to various WM pathways, while volumetric WM atlases contain labels indicating the pathway assignment(s) of a given voxel.…”

Section: Introductionmentioning

confidence: 99%

Learning white matter subject‐specific segmentation from structural MRI

et al. 2022

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Purpose Mapping brain white matter (WM) is essential for building an understanding of brain anatomy and function. Tractography‐based methods derived from diffusion‐weighted MRI (dMRI) are the principal tools for investigating WM. These procedures rely on time‐consuming dMRI acquisitions that may not always be available, especially for legacy or time‐constrained studies. To address this problem, we aim to generate WM tracts from structural magnetic resonance imaging (MRI) image by deep learning. Methods Following recently proposed innovations in structural anatomical segmentation, we evaluate the feasibility of training multiply spatial localized convolution neural networks to learn context from fixed spatial patches from structural MRI on standard template. We focus on six widely used dMRI tractography algorithms (TractSeg, RecoBundles, XTRACT, Tracula, automated fiber quantification (AFQ), and AFQclipped) and train 125 U‐Net models to learn these techniques from 3870 T1‐weighted images from the Baltimore Longitudinal Study of Aging, the Human Connectome Project S1200 release, and scans acquired at Vanderbilt University. Results The proposed framework identifies fiber bundles with high agreement against tractography‐based pathways with a median Dice coefficient from 0.62 to 0.87 on a test cohort, achieving improved subject‐specific accuracy when compared to population atlas‐based methods. We demonstrate the generalizability of the proposed framework on three externally available datasets. Conclusions We show that patch‐wise convolutional neural network can achieve robust bundle segmentation from T1w. We envision the use of this framework for visualizing the expected course of WM pathways when dMRI is not available.

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“…The majority of the ROI-based methods leverage a brain ROI atlas and use an image registration to automatically align the ROIs in the atlas space to the subject space [482,203,543,544,510,520,552,13,473,248,388,9]. Currently, popularly used brain ROI atlases include those provided in Freesurfer [117,143], MNI-ICBM152 [286,297,320,319], and JHU-DTI [477,476] (see [184] for a review of more atlases). There are also ROI-based methods that directly predict ROIs in subject space using machine learning [13,264].…”

Section: Anatomical Tract Identificationmentioning

confidence: 99%

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

Zhang¹,

Daducci²,

He³

et al. 2021

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Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Cited by 24 publications

References 73 publications

Population-Based Tract-To-Region Connectome of the Human Brain and Its Hierarchical Topology

Population-Based Tract-To-Region Connectome of the Human Brain and Its Hierarchical Topology

Learning white matter subject‐specific segmentation from structural MRI

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

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