Dr. Andrea T. Shafer Profile

Dr. Andrea T. Shafer

at National Institutes of Health

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Publications (7)

SPIE Journal Paper | 5 January 2024 Open Access

Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging

Tianyuan Yao, Francois Rheault, Leon Y. Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Andrea Shafer, Susan Resnick, Kurt Schilling, Bennett A. Landman, Yuankai Huo

JMI, Vol. 11, Issue 01, 014005, (January 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.1.014005

KEYWORDS: Data modeling, Diffusion, Voxels, Education and training, Reproducibility, Modeling, 3D modeling, Brain, Silver, Performance modeling

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PurposeDiffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies.ApproachIn this paper, we propose a data-driven deep CSD method to explicitly constrain the scan–rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test–retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation.ResultsFrom the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers.ConclusionWe propose a deep CSD method to explicitly reduce the scan–rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

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Proceedings Article | 3 April 2023 Presentation + Paper

Automatic preprocessing pipeline for white matter functional analyses of large-scale databases

Yurui Gao, Dylan Lawless, Muwei Li, Yu Zhao, Kurt Schilling, Lyuan Xu, Andrea Shafer, Lori Beason-Held, Susan Resnick, Baxter Rogers, Zhaohua Ding, Adam Anderson, Bennett Landman, John Gore

Proceedings Volume 12464, 124640U (2023) https://doi.org/10.1117/12.2653132

KEYWORDS: Functional magnetic resonance imaging, Matrices, Databases, White matter, Signal processing, Signal detection, Reliability, Quality control, Neuroimaging, Correlation coefficients

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Proceedings Article | 3 April 2023 Poster + Paper

Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging

Nancy Newlin, Leon Cai, Tianyuan Yao, Derek Archer, Kurt Schilling, Timothy Hohman, Kimberly Pechman, Angela Jefferson, Andrea Shafer, Susan Resnick, Bennett Landman

Proceedings Volume 12464, 124642B (2023) https://doi.org/10.1117/12.2653947

KEYWORDS: Diffusion weighted imaging, Scanners, Brain, Statistical analysis, Neuroimaging, Voxels, Cognitive modeling, Alzheimer's disease

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Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN, and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Proceedings Article | 4 April 2022 Paper

Longitudinal changes of connectomes and graph theory measures in aging

Yuzhe Wang, Francois Rheault, Kurt Schilling, Lori Beason-Held, Andrea Shafer, Susan Resnick, Bennett Landman

Proceedings Volume 12032, 120321U (2022) https://doi.org/10.1117/12.2611845

KEYWORDS: Brain, Matrices, Diffusion, Neuroimaging, Image processing, Diffusion weighted imaging, Statistical analysis, Data modeling, Brain mapping

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Proceedings Article | 4 April 2022 Presentation + Paper

Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer’s disease

Leon Cai, Francois Rheault, Cailey Kerley, Katherine Aboud, Lori Beason-Held, Andrea Shafer, Susan Resnick, Lori Jordan, Adam Anderson, Kurt Schilling, Bennett Landman

Proceedings Volume 12032, 120321H (2022) https://doi.org/10.1117/12.2611562

KEYWORDS: Independent component analysis, Visualization, Magnetic resonance imaging, Alzheimer's disease, Brain, Diffusion tensor imaging, Principal component analysis, Neuroimaging, Data modeling, Shape analysis

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Joint cortical surface and structural connectivity analysis of Alzheimer’s disease

Leon Cai, Cailey Kerley, Chang Yu, Katherine Aboud, Lori Beason-Held, Andrea Shafer, Susan Resnick, Lori Jordan, Adam Anderson, Kurt Schilling, Ilwoo Lyu, Bennett Landman

Proceedings Volume 11596, 1159630 (2021) https://doi.org/10.1117/12.2580956

KEYWORDS: Alzheimer's disease, Principal component analysis, Magnetic resonance imaging, Electroencephalography, Neuroimaging, Independent component analysis, Hemodynamics, Functional magnetic resonance imaging, Diffusion, Control systems

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Validation of group-wise registration for surface-based functional MRI analysis

Chang Yu, Yue Liu, Leon Cai, Cailey Kerley, Kaiwen Xu, Warren Taylor, Hakmook Kang, Andrea Shafer, Lori Beason-Held, Susan Resnick, Bennett Landman, Ilwoo Lyu

Proceedings Volume 11596, 115961X (2021) https://doi.org/10.1117/12.2580771

KEYWORDS: Magnetic resonance imaging, Image registration, Distortion, Independent component analysis, Data analysis, Spherical lenses, Optical inspection, Brain mapping, Brain, Associative arrays

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Showing 5 of 7 publications

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