Evidence map›Paper›PMID 40313662›Full record

ArticleArXiv2025

Mechanical Characterization of Brain Tissue: Experimental Techniques, Human Testing Considerations, and Perspectives.

Jixin Hou, Kun Jiang, Arunachalam Ramanathan, Abhishek Saji Kumar, Wei Zhang, Lin Zhao, Taotao Wu, Ramana Pidaparti, Dajiang Zhu, Gang Li and 5 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Jixin HouSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Kun JiangSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Arunachalam RamanathanSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Abhishek Saji KumarDepartment of Material Science, School for Engineering of Matter, Transport and Energy, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85287, USA.
Wei ZhangSchool of Computer and Cyber Sciences, Augusta University, GA 30602, USA.
Lin ZhaoSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Taotao WuSchool of Chemical, Materials, and Biomedical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Ramana PidapartiSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Dajiang ZhuDepartment of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA.
Gang LiDepartment of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, NC 27599, USA.
Kenan SongSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Tianming LiuSchool of Computing, The University of Georgia, Athens, GA 30602, USA.
Mir Jalil RazaviDepartment of Mechanical Engineering, SUNY Binghamton University, Binghamton, NY 13902, USA.
Ellen KuhlDepartment of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.
Xianqiao WangSchool of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.

Funding

Continued Development of Infant Neuroimaging Analysis ToolsR01EB037388 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Gang Li, Li Wang · 2025 to 2026
$1.2M
SCH: Using Data-Driven Computational Biomechanics to Disentangle Brain Structural Commonality, Variability, and Abnormality in ASDR01NS135574 · NINDS · UNIVERSITY OF GEORGIA · PI Xianqiao Wang · 2023 to 2026
$1.1M
NIBIB NIH HHS R01 EB037388NINDS NIH HHS R01 NS135574
6 · The paper itself

Abstract

Understanding the mechanical behavior of brain tissue is crucial for advancing both fundamental neuroscience and clinical applications. Yet, accurately measuring these properties remains challenging due to the brain's unique mechanical attributes and complex anatomical structures. This review provides a comprehensive overview of commonly used techniques for characterizing brain tissue mechanical properties, covering both invasive methods-such as atomic force microscopy, indentation, axial mechanical testing, and oscillatory shear testing-and noninvasive approaches like magnetic resonance elastography and ultrasound elastography. Each technique is evaluated in terms of working principles, applicability, representative studies, and experimental limitations. We further summarize existing publications that have used these techniques to measure human brain tissue mechanical properties. With a primary focus on invasive studies, we systematically compare their sample preparation, testing conditions, reported mechanical parameters, and modeling strategies. Key sensitivity factors influencing testing outcomes (e.g., sample size, anatomical location, strain rate, temperature, conditioning, and post-mortem interval) are also discussed. Additionally, selected noninvasive studies are reviewed to assess their potential for

Indexed as

Brain tissue mechanical characterizationHuman brain tissue mechanical propertiesInvasive testingNoninvasive elastography

Identifiers

PMID40313662
PMCPMC12045392

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.