机器学习揭示了大脑年龄与力学之间的相关性。

IF 9.4 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Mayra Hoppstädter , Kevin Linka , Ellen Kuhl , Marion Schmicke , Markus Böl
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引用次数: 0

摘要

我们的大脑在其整个生命周期中会发生重大的微观和宏观变化。因此,了解衰老对大脑机械特性的影响对于开发精确的个性化模拟和诊断工具至关重要。在这里,我们系统探究了 439 个脑组织样本在不同解剖区域、不同轴突方向、五个年龄组的拉伸和压缩下的力学行为。我们使用贝叶斯统计来描述脑年龄与力学特性之间的关系,并量化不确定性。根据各向同性的奥格登模型和各向异性的加瑟-奥格登-霍尔扎普费尔模型的实验数据和材料参数,我们的结果揭示了猪脑整个生命周期中年龄与力学之间的非线性关系。三岁时,拉伸和压缩剪切模量均达到峰值,拉伸为 0.4-1.0 千帕,压缩为 0.16-0.32 千帕。各向异性在六个月时最为明显,随后逐渐减弱。这些结果是了解脑组织机械特性随年龄变化的重要一步,为更准确、更逼真的脑模拟计算提供了科学依据。意义声明:本文研究了基于不同变形模式、解剖区域和轴突方向的脑组织随年龄变化的力学特性。我们使用层次贝叶斯建模来确定各向同性和各向异性的材料参数。研究揭示了大脑生命周期中剪切模量、各向异性程度和拉伸-压缩不对称之间的非线性关系。通过证明这些关系的非线性,该研究填补了当前研究中的一个重要知识空白。这项工作是准确描述大脑衰老与力学性能之间复杂关系的基础性一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning reveals correlations between brain age and mechanics

Machine learning reveals correlations between brain age and mechanics
Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and diagnostic tools. Here we systematically probed the mechanical behavior of n=439 brain tissue samples in tension and compression, in different anatomical regions, for different axon orientations, across five age groups. We used Bayesian statistics to characterize the relation between brain age and mechanical properties and quantify uncertainties. Our results, based on our experimental data and material parameters for the isotropic Ogden and the anisotropic Gasser-Ogden-Holzapfel models, reveal a non-linear relationship between age and mechanics across the life cycle of the porcine brain. Both tensile and compressive shear moduli reached peak values ranging from 0.4–1.0 kPa in tension to 0.16–0.32 kPa in compression at three years of age. Anisotropy was most pronounced at six months, and then decreased. These results represent an important step in understanding age-dependent changes in the mechanical properties of brain tissue and provide the scientific basis for more accurate and realistic computational brain simulations.

Statement of significance

In this paper, we investigate the age-dependent mechanical properties of brain tissue based on different deformation modes, anatomical regions, and axon orientations. Hierarchical Bayesian modeling was used to identify isotropic and anisotropic material parameters. The study reveals a nonlinear relationship between shear modulus, degree of anisotropy, and tension-compression asymmetry over the life cycle of the brain. By demonstrating the non-linearity of these relationships, the study fills a significant knowledge gap in current research. This work is a fundamental step in accurately characterizing the complex relationship between brain aging and mechanical properties.
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来源期刊
Acta Biomaterialia
Acta Biomaterialia 工程技术-材料科学:生物材料
CiteScore
16.80
自引率
3.10%
发文量
776
审稿时长
30 days
期刊介绍: Acta Biomaterialia is a monthly peer-reviewed scientific journal published by Elsevier. The journal was established in January 2005. The editor-in-chief is W.R. Wagner (University of Pittsburgh). The journal covers research in biomaterials science, including the interrelationship of biomaterial structure and function from macroscale to nanoscale. Topical coverage includes biomedical and biocompatible materials.
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