Cellular deconvolution of the brain with topological magnetic resonance image analysis

Magnetic resonance imaging (MRI) is foundational tool in neuroscience, enabling characterization of neuroanatomical markers of disease, behavior, and cognition. However, the precise cellular processes driving the structural and functional readouts provided by MRI remain opaque. Non-invasively assessing cell type, abundance, and location using MRI has the potential to revolutionize both basic science and clinical practice. To this end, we developed SpaTial Representation and Analysis using Topological Architecture (STRATA), an image-based gradient-boosted machine learning framework, which quantifies cell type proportions of neurons, astrocytes, oligodendrocytes, and microglia from MR images. Here we demonstrate and validate STRATA on diverse disease models, species, and regions of interest that together highlight the generalizability of the STRATA framework.

Authors

Institutions

Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2025-12-13
DOI
https://doi.org/10.64898/2025.12.10.693426
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cellular deconvolution of the brain with topological magnetic resonance image analysis

Luis‐Bernardo Vázquez, Tyler K. Ulland, John‐Paul J. Yu, Tracy L. Hagemann et al.
bioRxiv (Cold Spring Harbor Laboratory)
Cell Image Analysis Techniques
article

Cellular deconvolution of the brain with topological magnetic resonance image analysis

Luis‐Bernardo Vázquez, Tyler K. Ulland, John‐Paul J. Yu, Tracy L. Hagemann, Ryan D. Risgaard, Ajay Paul Singh, Doug Dean, José Guerrero-González, André M. M. Sousa, Samuel A. Hurley, Paloma C. Frautschi, Michael B. Fromandi
article en

Abstract

Magnetic resonance imaging (MRI) is foundational tool in neuroscience, enabling characterization of neuroanatomical markers of disease, behavior, and cognition. However, the precise cellular processes driving the structural and functional readouts provided by MRI remain opaque. Non-invasively assessing cell type, abundance, and location using MRI has the potential to revolutionize both basic science and clinical practice. To this end, we developed SpaTial Representation and Analysis using Topological Architecture (STRATA), an image-based gradient-boosted machine learning framework, which quantifies cell type proportions of neurons, astrocytes, oligodendrocytes, and microglia from MR images. Here we demonstrate and validate STRATA on diverse disease models, species, and regions of interest that together highlight the generalizability of the STRATA framework.

bioRxiv (Cold Spring Harbor Laboratory)
University of Wisconsin System (US), University of Wisconsin–Madison (US), Madison Area Technical College (US), Wisconsin Institutes for Discovery (US), Wisconsin Division of Public Health (US)
Openalex Percentile: Top 72%
Cell Image Analysis Techniques
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Cellular deconvolution of the brain with topological magnetic resonance image analysis — Luis‐Bernardo Vázquez, Tyler K. Ulland, et al. · bioRxiv (Cold Spring Harbor Laboratory) (2025) | TGRS Research Map | TGRS