Elemental and Structural Composition underlying Brain MRI

ERC (European Research Council)HORIZON-ERCID: 101169897
EC Contribution
€20,000
Consortium Size
1 orgs
Start Year
2025
Summary

Magnetic Resonance Imaging (MRI) is widely established, with 50,000 scanners worldwide and more than 100 million diagnostic scans conducted yearly. Myelin acts as the electrical insulation of neuronal fibers and is essential for motor, sensory, and cognitive functions and is composed of several lipids and proteins arranged in a specific geometry. However, myelin loss occurs regionally in inflammatory diseases like multiple sclerosis, myelitis, and optic neuritis, and it also serves as a biomarker of aging.Despite the availability of various MRI-based approaches for detecting myelin, there remains a significant gap in systematically validated knowledge regarding how myelin's lipids, proteins, and structural variations influence MRI signal generation. Furthermore, there is no gold standard for assessing the constituents of myelin, and conclusions are primarily based on MRI of formalin-fixed tissue.Quantitative post-mortem MRI of unfixed human brains in situ, combined with subsequent mass spectrometry imaging (LA-ICP-TOF-MS, MALDI-MSI), small-angle X-ray scattering (SAXS), and microscopy (CARS, TEM, IHC), will enable histologically enriched modelling of tissue and the simulation of fundamental MRI parameters (relaxometry, susceptibility, and diffusion) to reveal causal relationships with chemical elements, matrix composition, structure, and common molecules of myelin, neural fiber orientation, and biometals. In addition to 20 control brains, 10 brains from deceased individuals with multiple sclerosis will be recruited to broaden the spectrum of myelination under investigation.This research addresses the fundamental biophysical mechanisms underlying MRI of myelin and chemical elements, which currently lack validation. We will establish a publicly available elemental and histochemical atlas of the human brain and identify how histological features causally contribute to quantitative MRI parameters.

Consortium (1)