arXiv subjects

Ryan P. Cabeen

Publications and source records attributed to Ryan P. Cabeen.

3 recordsLinked to original sources

arXiv2026

General Microstructure Factor Analysis of Diffusion MRI in Gray-Matter Predicts Cognitive Scores

Diffusion magnetic resonance imaging (MRI) has revealed important insights into white matter microstructure, but its application to gray matter remains comparatively less explored. Here, we investigate whether global patterns of gray-matter microstructure can be captured through neurite orientation dispersion and density imaging (NODDI) and whether such patterns are predictive of cognitive performance. Using diffusion MRI and behavioral data from the Human Connectome Project Young Adult study, we derive region averaged NODDI parameters and apply principal component analysis (PCA) to construct general gray-matter microstructure factors. We find that the factor derived from isotropic volume fraction explained substantial inter-individual variability and was significantly correlated with specific cognitive scores collected from the NIH Toolbox. In particular, the isotropic volume fraction factor is linked to reading and vocabulary performance and to cognitive fluidity. Our findings demonstrate that PCA-based global indicators of gray-matter microstructure provide complementary markers of structure-function relationships, extending beyond region-specific analyses. Our results suggest that general microstructure factors may serve as population-level exploratory biomarkers for studying cognition and cortical organization.

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arXiv2023

Computational Image-based Stroke Assessment for Evaluation of Cerebroprotectants with Longitudinal and Multi-site Preclinical MRI

While ischemic stroke is a leading cause of death worldwide, there has been little success translating putative cerebroprotectants from rodent preclinical trials to human patients. We investigated computational image-based assessment tools for practical improvement of the quality, scalability, and outlook for large scale preclinical screening for potential therapeutic interventions in rodent models. We developed, evaluated, and deployed a pipeline for image-based stroke outcome quantification for the Stroke Preclinical Assessment Network (SPAN), a multi-site, multi-arm, multi-stage study evaluating a suite of cerebroprotectant interventions. Our fully automated pipeline combines state-of-the-art algorithmic and data analytic approaches to assess stroke outcomes from multi-parameter MRI data collected longitudinally from a rodent model of middle cerebral artery occlusion (MCAO), including measures of infarct volume, brain atrophy, midline shift, and data quality. We applied our approach to 1,368 scans and report population level results of lesion extent and longitudinal changes from injury. We validated our system by comparison with both manual annotations of coronal MRI slices and tissue sections from the same brain, using crowdsourcing from blinded stroke experts from the network. Our results demonstrate the efficacy and robustness of our image-based stroke assessments. The pipeline may provide a promising resource for ongoing rodent preclinical studies conducted by SPAN and other networks in the future.

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arXiv2022

Evaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging

Rodent stroke models are important for evaluating treatments and understanding the pathophysiology and behavioral changes of brain ischemia, and magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in preclinical studies. Brain extraction is an essential first step in most neuroimaging pipelines; however, it can be challenging in the presence of severe pathology and when dataset quality is highly variable. Convolutional neural networks (CNNs) can improve accuracy and reduce operator time, facilitating high throughput preclinical studies. As part of an ongoing preclinical stroke imaging study, we developed a deep-learning mouse brain extraction tool by using a U-net CNN. While previous studies have evaluated U-net architectures, we sought to evaluate their practical performance across data types. We ask how performance is affected with data across: six imaging centers, two time points after experimental stroke, and across four MRI contrasts. We trained, validated, and tested a typical U-net model on 240 multimodal MRI datasets including quantitative multi-echo T2 and apparent diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a large preclinical stroke database (N=1,368). We describe the design and development of this system, and report our findings linking data characteristics to segmentation performance. We consistently found high accuracy and ability of the U-net architecture to generalize performance in a range of 95-97% accuracy, with only modest reductions in performance based on lower fidelity imaging hardware and brain pathology. This work can help inform the design of future preclinical rodent imaging studies and improve their scalability and reliability.

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