Dr. Angela L. Jefferson Profile

Dr. Angela L. Jefferson

at Vanderbilt University Medical Center

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Publications (6)

SPIE Journal Paper | 17 April 2024 Open Access

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Michael Kim, Chenyu Gao, Leon Cai, Qi Yang, Nancy Newlin, Karthik Ramadass, Angela Jefferson, Derek Archer, Niranjana Shashikumar, Kimberly Pechman, Katherine Gifford, Timothy Hohman, Lori Beason-Held, Susan Resnick, Stefan Winzeck, Kurt Schilling, Panpan Zhang, Daniel Moyer, Bennett Landman

JMI, Vol. 11, Issue 02, 024011, (April 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.024011

KEYWORDS: Silver, Diffusion tensor imaging, Error analysis, Diffusion, Statistical analysis, Biological samples, Reliability, Data modeling, Magnetic resonance imaging, Voxels

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Proceedings Article | 2 April 2024 Poster + Paper

Predicting age from white matter diffusivity with residual learning

Chenyu Gao, Michael Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy Newlin, Derek Archer, Angela Jefferson, Warren Taylor, Brian Boyd, Lori Beason-Held, Susan Resnick, Yuankai Huo, Katherine Van Schaik, Kurt Schilling, Daniel Moyer, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129262I (2024) https://doi.org/10.1117/12.3006525

KEYWORDS: Brain, Diffusion tensor imaging, Data modeling, White matter, Neuroimaging, Deep learning, Alzheimer disease, Convolutional neural networks, Computer vision technology, Image registration

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Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI’s advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are nonlinearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Proceedings Article | 2 April 2024 Poster + Paper

Evaluation of mean shift, ComBat, and CycleGAN for harmonizing brain connectivity matrices across sites

Hanliang Xu, Nancy Newlin, Michael Kim, Chenyu Gao, Praitayini Kanakaraj, Aravind Krishnan, Lucas Remedios, Nazirah Mohd Khairi, Kimberly Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Ivana Isgum, Yuankai Huo, Daniel Moyer, Kurt Schilling, Bennett Landman

Proceedings Volume 12926, 129261X (2024) https://doi.org/10.1117/12.3005563

KEYWORDS: Matrices, Brain, Scanners, Alzheimer disease, Biological research, Anatomy

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Proceedings Article | 2 April 2024 Poster + Paper

Learning site-invariant features of connectomes to harmonize complex network measures

Nancy Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, Daniel Moyer

Proceedings Volume 12930, 129302E (2024) https://doi.org/10.1117/12.3009645

KEYWORDS: Education and training, Brain, Scanners, Diffusion, Alzheimer disease, Diffusion magnetic resonance imaging, Data modeling, Biological imaging, Matrices, Magnetic resonance imaging

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Proceedings Article | 2 April 2024 Presentation + Paper

Investigating interactions between subcortical structure, fMRI vigilance signals, and cognition in healthy and pathological aging

Kate Wang, Sarah Goodale, Derek Doss, Deepak Gupta, Katherine Gifford, Kimberly Pechman, Timothy Hohman, Dario Englot, Angela Jefferson, Catie Chang

Proceedings Volume 12930, 129300Q (2024) https://doi.org/10.1117/12.3000729

KEYWORDS: Functional magnetic resonance imaging, Brain, Cognition, Neuroimaging

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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.

Showing 5 of 6 publications

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