Superviscous properties of the in vivo brain at large scales.

Abstract There is growing awareness that brain mechanical properties are important for neural development and health. However, published values of brain stiffness differ by orders of magnitude between static measurements and in vivo magnetic resonance elastography (MRE), which covers a dynamic range over several frequency decades. We here show that there is no fundamental disparity between static mechanical tests and in vivo MRE when considering large-scale properties which encompass the entire brain including fluid filled compartments. Using gradient echo real-time MRE we investigated the viscoelastic dispersion of the human brain in, so far, unexplored dynamic ranges from intrinsic brain pulsations at 1Hz to ultralow-frequency vibrations at 5, 6.25, 7.8 and 10Hz to the normal frequency range of MRE of 40Hz. Surprisingly, we observed variations in brain stiffness over more than two orders of magnitude, suggesting that the in vivo human brain is superviscous on large scales with very low shear modulus of 42±13 Pa and relatively high viscosity of 6.6±0.3 Pa∙s according to the two-parameter solid model. Our data shed light on the crucial role of fluid compartments including blood vessels and cerebrospinal fluid (CSF) for whole brain properties and provide, for the first time, an explanation for the variability of the mechanical brain responses to manual palpation, local indentation, and high-dynamic tissue stimulation as used in elastography. Statement of Significance: Gradient echo steady-state MRE of the human brain allows the characterization of in vivo brain stiffness across large scales and wide dynamic ranges, bridging for the first time in vivo with ex vivo testing methods of the brain. With this technique we show that there is no fundamental disparity between ex vivo and in vivo data when considering large-scale properties of the entire brain. Instead, the superviscous nature of brain tissue, as quantified for the first time herein, explains why so different values of brain stiffness are obtained at different length scales and dynamic ranges. Our technique has great potential as a diagnostic modality since it is sensitive to low-dynamic interactions and thus provides a potential marker for perfusion-related neurological disorders.