Biology subjects

Cutting, L.

Publications and source records attributed to Cutting, L..

8 recordsLinked to original sources

bioRxiv2026

Automated Segmentation of Brainstem and Subcortical White Matter: Mapping the Deep Tegmental Core with BundleParc

Diffusion MRI enables noninvasive mapping of human white matter pathways, but automated segmentation methods have largely focused on large association, projection, and commissural bundles. Brainstem and subcortical pathways supporting basal ganglia, cerebellar, limbic/reward, sensory, and homeostatic functions remain underrepresented in large-scale connectomic analyses. To address this gap, we adapted BundleParc, a recently introduced bundle-parcellation architecture, into an automated pipeline for direct segmentation and along-tract parcellation of 97 subcortical and brainstem white matter pathways. The model was trained on a curated reference dataset derived from Human Connectome Project diffusion MRI using anatomy-guided tractography, explicit inclusion and exclusion criteria, automated outlier filtering, and manual quality assurance. Operating directly on native-space fiber orientation distributions, the algorithm successfully recovers these intricate anatomical trajectories and ordered parcellations. We show the model generalizes to diverse external datasets spanning development, aging, and neurodegenerative disease cohorts, maintaining robust performance across variations in spatial resolution and angular sampling. The released container, trained model, population atlas, reference streamlines, and quality assurance outputs provide a resource for studying deep brainstem and subcortical pathways in development, aging, disease, and neuromodulation-relevant anatomy.

neuroscience↗

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

Lifespan Trajectories of Asymmetry in White Matter Tracts

Asymmetry in white matter is believed to give rise to the brains capacity for specialized processing and is involved in the lateralization of various cognitive processes, such as language and visuo-spatial reasoning. Although studies of white matter asymmetry have been previously documented, they have often been constrained by limited age ranges, sample sizes, or the scope of the tracts and structural features examined. While normative lifespan charts for brain structures are emerging, comprehensive charts detailing white matter asymmetries across numerous pathways and diverse structural measures have been notably absent. This study addresses this gap by leveraging a large-scale dataset of 35,120 typically developing and aging individuals, ranging from 0 to 100 years of age, from 50 primary neuroimaging studies. We generated comprehensive lifespan trajectories for 30 lateralized association and projection white matter tracts, examining 6 distinct microstructural and macrostructural features of these pathways. Our findings reveal that: (1) asymmetries are widespread across the brains white matter and are present in all 30 pathways; (2) for a given pathway, the degree and direction of asymmetry differ between features of tissue microstructure and pathway macrostructure; (3) asymmetries vary across and within pathway types (association and projection tracts); and (4) these asymmetries are not static, following unique trajectories across the lifespan, with distinct changes during development, and a general trend of becoming more asymmetric with increasing age (particularly in later adulthood) across pathways. This study represents the most extensive characterization of white matter asymmetry across the lifespan to date, charting how lateralization patterns emerge, mature, and change throughout life. It provides a foundational resource for understanding the principles of white matter organization from early to late life, its relation to functional specialization and inter-individual variability, and offers a key reference for interpreting deviations during healthy development and aging as well as those associated with clinical populations.

neuroscience↗

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↗

bioRxiv2023

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

bioengineering↗

bioRxiv2020

Pandora: 4-D white matter bundle population-based atlasesderived from diffusion MRI fiber tractography

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

neuroscience↗

bioRxiv2020

Registration-free Distortion Correction of Diffusion Weighted MRI

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

bioengineering↗