ArticleFrontiers in bioengineering and biotechnology2021
Unraveling the Local Relation Between Tissue Composition and Human Brain Mechanics Through Machine Learning.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Meta-analysis of the make-up and properties of in vitro models of the healthy and diseased blood-brain barrier.Nature biomedical engineering · 2025Pooled it
- Biomimetic scaffolds based on stem cells for neural tissue engineering: a review.Stem cell research & therapy · 2026Review
- Purpose-Adaptable Reinforced 3D Hyaluronic-Acid Based Platform to Study Pathomechanisms of the Central Nervous System.Advanced healthcare materials · 2026Article
- Tunable hydrogel-based micropillar arrays for myelination studies.Nature methods · 2026Article
- Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.Cardiovascular engineering and technology · 2024Review
- The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Amyloid Beta Peptides Lead to Mast Cell Activation in a Novel 3D Hydrogel Model.International journal of molecular sciences · 2023Article
- Review
- Alcohol-Induced Alterations in the Vascular Basement Membrane in theBiomedicines · 2022Article
- Morphological Phenotyping of Organotropic Brain- and Bone-Seeking Triple Negative Metastatic Breast Tumor Cells.Frontiers in cell and developmental biology · 2022Article
- Changes in Brain Neuroimmunology Following Injury and Disease.Frontiers in integrative neuroscience · 2022Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The regional mechanical properties of brain tissue are not only key in the context of brain injury and its vulnerability towards mechanical loads, but also affect the behavior and functionality of brain cells. Due to the extremely soft nature of brain tissue, its mechanical characterization is challenging. The response to loading depends on length and time scales and is characterized by nonlinearity, compression-tension asymmetry, conditioning, and stress relaxation. In addition, the regional heterogeneity-both in mechanics and microstructure-complicates the comprehensive understanding of local tissue properties and its relation to the underlying microstructure. Here, we combine large-strain biomechanical tests with enzyme-linked immunosorbent assays (ELISA) and develop an extended type of constitutive artificial neural networks (CANNs) that can account for viscoelastic effects. We show that our viscoelastic constitutive artificial neural network is able to describe the tissue response in different brain regions and quantify the relevance of different cellular and extracellular components for time-independent (nonlinearity, compression-tension-asymmetry) and time-dependent (hysteresis, conditioning, stress relaxation) tissue mechanics, respectively. Our results suggest that the content of the extracellular matrix protein fibronectin is highly relevant for both the quasi-elastic behavior and viscoelastic effects of brain tissue. While the quasi-elastic response seems to be largely controlled by extracellular matrix proteins from the basement membrane, cellular components have a higher relevance for the viscoelastic response. Our findings advance our understanding of microstructure - mechanics relations in human brain tissue and are valuable to further advance predictive material models for finite element simulations or to design biomaterials for tissue engineering and 3D printing applications.
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