Fractional Anisotropy as a Surrogate Marker of Brain Mechanics

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Authors: Stefan Rampp, Silvia Budday, Nina Reiter, Nicole Tueni, Jan Hinrichsen, Lars Bräuer, Friedrich Paulsen, Oliver Schnell, Guillaume Flé, Frederik Bernd Laun, Arnd Doerfler
Cited by: 1
References: 22

Abstract

Abstract Understanding the mechanical properties of brain tissue may provide crucial insights into brain development, injury, disease and surgical planning. Conventionally, these properties are measured ex vivo or in vivo during surgical procedures, while non-invasive in vivo alternatives are sparse. This study investigates whether fractional anisotropy (FA) derived from diffusion-weighted magnetic resonance imaging can serve as a surrogate marker for brain tissue stiffness in healthy human brains. MRI data were collected from three body donor brains, 28 healthy adults, and a publicly available independent dataset of 26 adults . FA values were compared with mechanical properties from ex vivo mechanical testing of brain tissue. Statistical analysis revealed a strong negative correlation between FA and the mechanical response for small strains expressed as shear modulus of a one-term hyperelastic Ogden model, indicating that higher FA values are associated with lower tissue stiffness. The nonlinearity parameter alpha exhibited a qualitatively similar, but considerably weaker correlation with FA. These findings were consistent across datasets. The findings suggest that FA can be a robust, non-invasive marker for estimating mechanical properties of brain tissue, with potential applications in clinical diagnosis and computational modeling of brain mechanics and the study of brain development. Further research is needed to clarify the relationship in lesional tissues and to optimize clinical utility.

Fields of Study & Subjects

Advanced Neuroimaging Techniques and Applications Ultrasound Imaging and Elastography Automotive and Human Injury Biomechanics

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
13 April 2026
Document type
Preprint