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Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.

Publication ,  Journal Article
Zhan, X; Liu, Y; Cecchi, NJ; Gevaert, O; Zeineh, MM; Grant, GA; Camarillo, DB
Published in: IEEE Trans Biomed Eng
October 2022

OBJECTIVE: Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM). METHODS: We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021). RESULTS: PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy. CONCLUSION: The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. SIGNIFICANCE: The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

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Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

October 2022

Volume

69

Issue

10

Start / End Page

3205 / 3215

Location

United States

Related Subject Headings

  • Principal Component Analysis
  • Humans
  • Head
  • Finite Element Analysis
  • Brain Injuries
  • Brain
  • Biomedical Engineering
  • Biomechanical Phenomena
  • 4603 Computer vision and multimedia computation
  • 4009 Electronics, sensors and digital hardware
 

Citation

APA
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Zhan, X., Liu, Y., Cecchi, N. J., Gevaert, O., Zeineh, M. M., Grant, G. A., & Camarillo, D. B. (2022). Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis. IEEE Trans Biomed Eng, 69(10), 3205–3215. https://doi.org/10.1109/TBME.2022.3163230
Zhan, Xianghao, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, and David B. Camarillo. “Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE Trans Biomed Eng 69, no. 10 (October 2022): 3205–15. https://doi.org/10.1109/TBME.2022.3163230.
Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, et al. Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis. IEEE Trans Biomed Eng. 2022 Oct;69(10):3205–15.
Zhan, Xianghao, et al. “Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE Trans Biomed Eng, vol. 69, no. 10, Oct. 2022, pp. 3205–15. Pubmed, doi:10.1109/TBME.2022.3163230.
Zhan X, Liu Y, Cecchi NJ, Gevaert O, Zeineh MM, Grant GA, Camarillo DB. Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis. IEEE Trans Biomed Eng. 2022 Oct;69(10):3205–3215.

Published In

IEEE Trans Biomed Eng

DOI

EISSN

1558-2531

Publication Date

October 2022

Volume

69

Issue

10

Start / End Page

3205 / 3215

Location

United States

Related Subject Headings

  • Principal Component Analysis
  • Humans
  • Head
  • Finite Element Analysis
  • Brain Injuries
  • Brain
  • Biomedical Engineering
  • Biomechanical Phenomena
  • 4603 Computer vision and multimedia computation
  • 4009 Electronics, sensors and digital hardware