Biology subjects

Beason-Held, L. L.

Publications and source records attributed to Beason-Held, L. L..

6 recordsLinked to original sources

bioRxiv2026

Charting Cervical Spinal Cord Morphometry Across the Lifespan

Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. We leveraged 30 population-based brain MRI datasets, aggregating 78,269 scans from 41,042 individuals (ages 0-100) whose imaging protocols included cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right-left (RL/transverse), and shape indices (compression ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.

neuroscience↗

bioRxiv2026

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10-4. Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length. These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.

bioengineering↗

bioRxiv2025

White matter microstructure and macrostructure brain charts across the human lifespan

Normative reference charts are widely used in healthcare, especially for assessing the development of individuals by benchmarking anatomic and physiological features against population trajectories across the lifespan. Recent work has extended this concept to gray matter morphology in the brain, but no such reference framework currently exists for white matter (WM) even though WM constitutes the essential substrate for neuronal communication and large-scale network integration. Here, we present the first comprehensive WM brain charts, which describe how microstructural and macrostructural features of WM evolve across the lifespan, by leveraging over 35,120 diffusion MRI scans from 50 harmonized studies. Using generalized additive models for location, scale, and shape (GAMLSS), we estimate age- and sex-stratified trajectories for 72 individual white matter pathways, quantifying both tract-specific microstructural and morphometric features. We demonstrate that these WM brain charts enable four important applications: (1) defining normative trajectories of WM maturation and decline across distinct pathways, (2) identifying previously uncharacterized developmental milestones and spatial gradients of tract maturation, (3) detecting individualized deviations from normative patterns with clinical relevance across multiple neurological disorders, and (4) facilitating standardized, cross-study centile scoring of new datasets. By establishing a unified, interpretable reference framework for WM structure, these brain charts provide a foundational metric for research and clinical neuroscience. The accompanying open-access trajectories, centile scoring tools, and harmonization methods facilitate precise mapping of WM development, aging, and pathology across diverse populations. We release the brain charts and provide an out-of-sample alignment process as a Docker image: https://zenodo.org/records/17561821.

neuroscience↗

bioRxiv2024

Sex, racial, and APOE-ϵ4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSThe effects of sex, race, and Apolipoprotein E (APOE) - Alzheimers disease (AD) risk factors - on white matter integrity are not well characterized. METHODSDiffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 {+/-} 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, and APOE-{varepsilon}4 carrier status. RESULTSSex differences in FAFWcorr in association and projection tracts, racial differences in FAFWcorr in projection tracts, and APOE-{varepsilon}4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSIONThere are prominent differences in white matter microstructure by sex, race, and APOE- {varepsilon}4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted. HighlightsO_LISex, race, and APOE-{varepsilon}4 carrier status relate to white matter microstructural integrity C_LIO_LIFemales generally have lower FAFWcorr compared to males C_LIO_LINon-Hispanic Black adults generally have lower FAFWcorr than non-Hispanic White adults C_LIO_LIAPOE-{varepsilon}4 carriers tended to have higher FW than non-carriers C_LI Research in Context Systematic ReviewThe authors used PubMed and Google Scholar to review literature that used conventional and free-water (FW)-corrected microstructural metrics to evaluate sex, race, and APOE-{varepsilon}4 differences in white matter microstructure. While studies have previously explored differences by sex and APOE-{varepsilon}4 status, less is known about racial differences and no large-scale FW-corrected analysis has been performed. InterpretationSex and race were more associated with FAFWcorr while APOE-{varepsilon}4 status was associated with FW metrics. Association, projection, limbic, and occipital transcallosal tracts showed the greatest differences. Future DirectionFuture studies to determine the biological and social pathways that lead to sex, racial, and APOE-{varepsilon}4 differences are warranted. Consent StatementAll participants provided informed consent in their respective cohort studies.

neuroscience↗

bioRxiv2020

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

PurposeDiffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. MethodsThe proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. ResultsRaw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. ConclusionThe proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

bioengineering↗

bioRxiv2019

TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter Atlas

Reproducible identification of white matter tracts across subjects is essential for the study of structural connectivity of the human brain. The key challenges are anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the human/manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. The state-of-the-art for white matter atlas is the single population-averaged Johns Hopkins Eve atlas. A critical bottleneck with the Eve atlas framework is that manual labeling time is extensive and peripheral white matter regions are conservatively labeled. In this work, we developed protocols that will facilitate manual virtual dissection of white matter pathways, with the goals to be anatomically accurate, intuitive, reproducible, and act as an initial stage to build an amenable knowledge base of neuroanatomical regions. We analyzed reproducibility of the fiber bundles and variability of human raters using DICE correlation coefficient, intraclass correlation coefficient, and root mean squared error. The protocols at their initial stage have shown promising results on both typical 3T research acquisition Baltimore Longitudinal Study of Aging and high-acquisition quality Human Connectome Project datasets. The TractEM manual labeling protocols allow for reconstruction of reproducible subject-specific fiber bundles across the brain. The protocols and sample results have been made available in open source to improve generalizability and reliability in collaboration.

neuroscience↗