Back

The peculiar property of pia mater on the prediction of acute subdural hematoma

Li, C.; Kleiven, S.; Zhou, Z.

2026-06-29 biophysics
10.64898/2026.06.24.733734 bioRxiv
Show abstract

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
5266 papers in training set
Top 8%
19.3%
2
Journal of Biomechanical Engineering
20 papers in training set
Top 0.1%
15.7%
3
Bioengineering
29 papers in training set
Top 0.1%
7.0%
4
Scientific Reports
3612 papers in training set
Top 12%
6.5%
5
Journal of the Mechanical Behavior of Biomedical Materials
24 papers in training set
Top 0.1%
6.5%
50% of probability mass above
6
Biomechanics and Modeling in Mechanobiology
29 papers in training set
Top 0.1%
4.2%
7
Computer Methods in Biomechanics and Biomedical Engineering
10 papers in training set
Top 0.1%
3.7%
8
Annals of Biomedical Engineering
37 papers in training set
Top 0.2%
3.4%
9
Clinical Neurophysiology
56 papers in training set
Top 0.3%
2.9%
10
Journal of Neurotrauma
31 papers in training set
Top 0.2%
2.5%
11
Biophysical Journal
631 papers in training set
Top 2%
2.5%
12
Journal of Biomechanics
64 papers in training set
Top 0.4%
2.2%
13
eLife
5828 papers in training set
Top 43%
2.2%
14
Frontiers in Bioengineering and Biotechnology
98 papers in training set
Top 1.0%
1.8%
15
Fluids and Barriers of the CNS
28 papers in training set
Top 0.2%
1.8%
16
International Journal for Numerical Methods in Biomedical Engineering
14 papers in training set
Top 0.2%
1.0%
17
Acta Biomaterialia
92 papers in training set
Top 1.0%
0.9%
18
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
15 papers in training set
Top 0.3%
0.6%
19
European Journal of Neuroscience
189 papers in training set
Top 4%
0.6%
20
Journal of The Royal Society Interface
235 papers in training set
Top 4%
0.6%
21
Frontiers in Neuroscience
256 papers in training set
Top 7%
0.6%
22
Brain Sciences
55 papers in training set
Top 2%
0.5%
23
PeerJ
308 papers in training set
Top 14%
0.5%
24
Biochemistry and Biophysics Reports
30 papers in training set
Top 2%
0.5%