Cellular deconvolution of the brain with topological magnetic resonance image analysis

Vazquez, L. A.; Fromandi, M. B.; Hagemann, T. L.; Risgaard, R. D.; Gonzalez, J. M. G.; Singh, A. P.; Frautschi, P.; Hurley, S. A.; Sousa, A. M. M.; Dean, D. C.; Ulland, T. K.; Yu, J.-P. J.

doi:10.64898/2025.12.10.693426

bioRxiv · 10.64898/2025.12.10.693426

Cellular deconvolution of the brain with topological magnetic resonance image analysis

Vazquez, L. A.·Fromandi, M. B.·Hagemann, T. L.·Risgaard, R. D.·Gonzalez, J. M. G.·Singh, A. P.·Frautschi, P.·Hurley, S. A.·Sousa, A. M. M.·Dean, D. C.·Ulland, T. K.·Yu, J.-P. J.

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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.

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Vazquez, L. A., Fromandi, M. B., Hagemann, T. L., Risgaard, R. D., Gonzalez, J. M. G., Singh, A. P., Frautschi, P., Hurley, S. A., Sousa, A. M. M., Dean, D. C., Ulland, T. K., Yu, J.-P. J.. 2025-12-13. Cellular deconvolution of the brain with topological magnetic resonance image analysis. https://doi.org/10.64898/2025.12.10.693426

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