Jende, J. | bioRxiv Science

bioRxiv2025

Voxel-accurate MRI-microscopy correlation enables AI-powered prediction of brain disease states

Magnetic resonance imaging (MRI) is essential for visualizing the healthy and diseased brain, yet the cellular basis of MRI signal and how it changes over time remain poorly understood. Here, we present BRIDGE (Brain Radiological Imaging with Deep-learning based Ground-Truth Exploration), a platform integrating in vivo MRI with in vivo two-photon (2P) and ex vivo super-resolution microscopy using a multi-step, iterative co-registration pipeline. It enables in vivo, longitudinal, and voxel-precise mapping of MRI signals to their cellular origins for the first time. The registered overlay reveals the cellular and anatomical origins of MRI signals and enables training of convolutional neural networks to enhance the effective resolution of MRI. Using BRIDGE, we identified a microenvironmental vessel biomarker for early metastatic colonization in patient-derived xenograft models of brain metastasis. In particular we found that distinct T2*-weighted hypointense lesions correspond to reduced blood flow and erythrostasis in perimetastatic capillaries. In glioma, longitudinal intravital studies further demonstrated direct correlations between non-vasogenic T2-weighted signal changes and patient-dependent tumor growth dynamics. Taken together, BRIDGE advances radiological interpretation by establishing a microscopic ground truth for MRI signatures over time, enabling deep learning-based predictive histology, and providing cellular-level insights into tumor microenvironment features with direct clinical imaging implications. Graphical abstractBRIDGE enables longitudinal voxel-to-voxel correlation and ground truth based automatic segmentation of MR images O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/680637v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@f1f64eorg.highwire.dtl.DTLVardef@1619e3eorg.highwire.dtl.DTLVardef@1dc2e7forg.highwire.dtl.DTLVardef@7097e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2019

Brain tissue properties and morphometry assessment after chronic complete spinal cord injury

There is much controversy about the potential impact of spinal cord injury (SCI) on brains anatomy and function, which is mirrored in the substantial divergence of findings between animal models and human imaging studies. Given recent advances in quantitative magnetic resonance imaging (MRI) we sought to tackle the unresolved question about the link between the presumed injury associated volume differences and underlying brain tissue property changes in a cohort of chronic complete SCI patients. Using the established computational anatomy methods of voxel-based morphometry (VBM) and voxel-based quantification (VBQ) we performed statistical analyses of grey matter volume and parameter maps indicative for brains myelin, iron and free tissue water content in complete SCI patients (n=14) and healthy individuals (n=14). Our whole-brain analysis showed significant white matter volume loss in the rostral and dorsal part of the spinal cord consistent with Wallerian degeneration of proprioceptive axons in the lemniscal tract in SCI subjects, which correlated with spinal cord atrophy assessed with quantification of the spinal cord cross-sectional area at cervical level. The latter finding suggests that Wallerian degeneration of the lemniscal tract represents a main contributor to the observed spinal cord atrophy, which is highly consistent with preclinical ultrastructural/histological evidence of remote changes in the central nervous system secondary to SCI. Structural changes in the brain representing remote changes in the course of chronic SCI could not be confirmed with conventional VBM or VBQ statistical analysis. Whether and how MRI based brain morphometry and brain tissue property analysis will inform clinical decision making and clinical trial outcomes in spinal cord medicine remains to be determined.

neuroscience↗