Predicting calvarial growth in normal and craniosynostotic mice using a computational approach

Area of Science:

• Craniofacial development and computational modeling.
• Biomechanical analysis of skull growth.

Background:

• Postnatal calvarial growth is coordinated by complex biological, chemical, and mechanical signals.
• Craniosynostosis, such as bicoronal suture fusion, requires surgical intervention with potential for re-operation.

Purpose of the Study:

• To evaluate the predictive accuracy of a computational finite element model for calvarial growth.
• To compare skull growth in wild-type (WT) and Fgfr2^C342Y/+ mutant (MT) mice.

Main Methods:

• Morphological studies quantified calvarial growth at postnatal days P3, P10, and P20 in WT and MT mice.
• A finite element model was developed using MicroCT images of P3 skulls to predict P10 calvarial shape.
• Model sensitivity was tested, and predictions were compared with ex vivo data.

Main Results:

• The computational model successfully predicted overall skull growth in both WT and MT mice.
• The model accurately captured the growth differences between WT and MT mice.
• Model predictions aligned with ex vivo experimental data.

Conclusions:

• Computational modeling can accurately predict calvarial growth patterns in mouse models.
• This approach shows potential for translation to human skull growth and craniosynostosis management.
• Improved understanding may reduce re-operations and enhance quality of life for affected children.