There is a significant interest in characterizing mechanical properties of the brain tissue due to the role of mechanics in neurodevelopment and neurological disorders. Previous Scanning Force Microscopy studies have reported that brain tissue has highly heterogeneous mechanical properties. Yet, it is not known how the structural components of the brain such as neurons, glial cells, their axons and dendrites, and extracellular matrix contribute to these stiffness variations. To investigate the structure-stiffness relation in brain tissue and thus solve this issue, we have employed dynamic indentation-controlled mapping with a spatial resolution of ~50 µm to measure viscoelastic properties of hippocampus and cortex on isolated horizontal mouse brain sections. Nonlinear-viscoelastic nature of the brain tissue was observed by oscillatory ramp testing, where stiffness increased linearly with the strain (strain < 10 %); frequency sweeps revealed frequency-stiffening (1-10 Hz) with the phase delay in the range of 15-30˚. Viscoelasticity maps showed that regions with distinct mechanical properties correspond to morphological layers with the mean storage modulus varying from 779±77 Pa for granular layer to 3260±74 Pa for stratum lacunosum-moleculare (mean ± SE). Density of the nuclei was estimated for the measured regions and was found to negatively correlate with the stiffness, except for alveus, mostly composed of axonal fibers, being significantly softer than all other high-stiffness low-cell-density regions. Taken together, our study shows that our novel indentation method is able to map mechanical differences of the brain at the cellular level, leading to a better understanding of the relation between tissue composition and stiffness.
Nelda Antonovaite, Wytse J. Wadman, Elly M. Hol, and Davide Iannuzzi, "Structure-stiffness relation of the brain tissue determined by oscillatory indentation mapping (Conference Presentation)," Proc. SPIE 10496, Optical Elastography and Tissue Biomechanics V, 1049614 (Presented at SPIE BiOS: January 28, 2018; Published: 15 March 2018); https://doi.org/10.1117/12.2288961.5752217082001.
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