Mr. Timothy J. Hohman Profile

Timothy J. Hohman

at Vanderbilt University Medical Center

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Author

Publications (5)

SPIE Journal Paper | 24 August 2024 Open Access

Field-of-view extension for brain diffusion MRI via deep generative models

Chenyu Gao, Shunxing Bao, Michael Kim, Nancy Newlin, Praitayini Kanakaraj, Tianyuan Yao, Gaurav Rudravaram, Yuankai Huo, Daniel Moyer, Kurt Schilling, Walter Kukull, Arthur Toga, Derek Archer, Timothy Hohman, Bennett Landman, Zhiyuan Li

JMI, Vol. 11, Issue 04, 044008, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044008

KEYWORDS: Brain, Diffusion weighted imaging, Neuroimaging, Diffusion magnetic resonance imaging, Education and training, Alzheimer disease, Diffusion, Data modeling, Anatomy, Medical imaging

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PurposeIn brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data.ApproachWe propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV.ResultsFor evaluating the imputed slices, on the Wisconsin Registry for Alzheimer’s Prevention (WRAP) dataset, the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, and SSIMb1300=0.893; on the National Alzheimer’s Coordinating Center (NACC) dataset, it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, and SSIMb1300=0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p<0.001) on both the WRAP and NACC datasets.ConclusionsResults suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer’s disease.

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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

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 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.

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