Fontanel compensation for infant electroencephalography forward modeling method_Journal of Biomedical Engineering

Authors：

ZHANG Ting ^1,2,3 ,  LIU Yan ⁴ , PENG Bo ² , ZHANG Siqi ⁵ , HU Ying ⁶ , ZHONG Weifeng ¹ ,  DAI Yakang ^2,7,8

1. School of Automation, Harbin University of Science and Technology, Harbin 150080, P. R. China;
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China;
3. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China;
4. College of Computer and Control Engineering, Northeast Forestry University, Harbin 150080, P. R. China;
5. Faculty of Psychology, Beijing Normal University, Beijing 100875, P. R. China;
6. School of Biomedical Engineering, University of Science and Technology of China, Hefei 230026, P. R. China;
7. Suzhou Guoke Medical Technology Development (Group) Co. LTD, Suzhou, Jiangsu 215163, P. R. China;
8. Suzhou Guoke Kangcheng Medical Technology Co., LTD, Suzhou, Jiangsu 215163, P. R. China;

Corresponding author：

LIU Yan, Email: yan.liu@nefu.edu.cn; DAI Yakang, Email: daiyk@sibet.ac.cn

Keywords：

Electroencephalography forward modeling; Infant fontanel; Fontanel compensation

DOI：

10.7507/1001-5515.202307003

Video：

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Magnetic resonance imaging (MRI)-based electroencephalography (EEG) forward modeling method has become prevalent in the field of EEG. However, due to the inability to obtain clear images of an infant’s fontanel through MRI, the fontanelle information is often lacking in the EEG forward model, which affects accuracy of modeling in infants. To address this issue, we propose a novel method to achieve fontanel compensation for infant EEG forward modeling method. First, we employed imaging segmentation and meshing to the head MRIs, creating a fontanel-free model. Second, a projection-based surface reconstruction method was proposed, which utilized priori information on fontanel morphology and the fontanel-free head model to reconstruct the two-dimensional measured fontanel into a three-dimensional fontanel model to achieve fontanel-compensation modeling. Finally, we calculated a fontanel compensation-based EEG forward model for infants based on this model. Simulation results, based on a real head model, demonstrated that the compensation of fontanel had a potential to improve EEG forward modeling accuracy, particularly for the sources beneath the fontanel (relative difference measure larger than 0.05). Additional experimental results revealed that the uncertainty of the infant’s skull conductivity had the widest impact range on the neural sources, and the absence of fontanel had the strongest impact on the neural sources below the fontanel. Overall, the proposed fontanel-compensated method showcases the potential to improve the modeling accuracy of EEG forward problem without relying on computed tomography (CT) acquisition, which is more in line with the requirements of practical application scenarios.

Citation： ZHANG Ting, LIU Yan, PENG Bo, ZHANG Siqi, HU Ying, ZHONG Weifeng, DAI Yakang. Fontanel compensation for infant electroencephalography forward modeling method. Journal of Biomedical Engineering, 2024, 41(6): 1085-1094. doi: 10.7507/1001-5515.202307003 Copy

1.	Mosher J C, Leahy R M, Lewis P S. EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng, 1999, 46(3): 245-259.
2.	Michel C M, Murray M M, Lantz G, et al. EEG source imaging. Clin Neurophysiol, 2004, 115(10): 2195-222.
3.	Vorwerk J, Cho J-H, Rampp S, et al. A guideline for head volume conductor modeling in EEG and MEG. NeuroImage, 2014, 100: 590-607.
4.	徐立超, 王仲朋, 许敏鹏, 等. 脑电逆问题在运动康复领域中的应用. 中国生物医学工程学报, 2017, 36(6): 733-740.
5.	胡莹, 刘燕, 程晨晨, 等. 基于自适应时频共空间模式结合卷积神经网络的多任务运动想象脑电分类. 生物医学工程学杂志, 2022, 39(6): 1065-1073, 1081.
6.	Wolters C H, Grasedyck L, Hackbusch W. Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem. Inverse Problems, 2004, 20(4): 1099.
7.	Oostenveld R, Fries P, Maris E, et al. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci, 2010, 2011: 156869.
8.	De Munck J C, Wolters C H, Clerc M. EEG and MEG: forward modeling. Cambridge: Cambridge University Press. 2012: 192-256.
9.	Azizollahi H, Aarabi A, Wallois F. Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates. Hum Brain Mapp, 2016, 37(10): 3604-3622.
10.	Azizollahi H, Aarabi A, Wallois F. Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates. J Neural Eng, 2020, 17(5): 056004.
11.	Roche-Labarbe N, Aarabi A, Kongolo G, et al. High-resolution electroencephalography and source localization in neonates. Hum Brain Mapp, 2008, 29(2): 167-176.
12.	Lew S, Sliva D D, Choe M S, et al. Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model. Neuroimage, 2013, 76: 282-293.
13.	Gargiulo P, Belfiore P, Friðgeirsson E A, et al. The effect of fontanel on scalp EEG potentials in the neonate. Clin Neurophysiol, 2015, 126(9): 1703-1710.
14.	Flemming L, Wang Y, Caprihan A, et al. Evaluation of the distortion of EEG signals caused by a hole in the skull mimicking the fontanel in the skull of human neonates. Clin Neurophysiol, 2005, 116(5): 1141-1152.
15.	李璟, 王琨, 朱善安, 等. 利用有限差分算法研究开颅术对头皮电位分布的影响. 中国生物医学工程学报, 2007, 26(5): 708-712.
16.	Adeyemo A A, Omotade O O. Variation in fontanelle size with gestational age. Early Hum Dev, 1999, 54(3): 207-214.
17.	Kiesler J, Ricer R. The abnormal fontanel. Am Fam Physician, 2003, 67(12): 2547-2552.
18.	Pursiainen S, Lew S, Wolters C H. Forward and inverse effects of the complete electrode model in neonatal EEG. J Neurophysiol, 2017, 117(3): 876-884.
19.	Boran P, Oğuz F, Furman A, et al. Evaluation of fontanel size variation and closure time in children followed up from birth to 24 months. J Neurosurg Pediatr, 2018, 22(3): 323-329.
20.	Oumer M, Guday E, Teklu A, et al. Anterior fontanelle size among term neonates on the first day of life born at University of Gondar Hospital, Northwest Ethiopia. PLoS One, 2018, 13(10): e0202454.
21.	Faix R G. Fontanelle size in black and white term newborn infants. J Pediatr, 1982, 100(2): 304-306.
22.	Omotade O O, Kayode C M, Adeyemo A A. Anterior fontanelle size in Nigerian children. Ann Trop Paediatr, 1995, 15(1): 89-91.
23.	Moffett E, Aldridge K. Size of the anterior fontanelle: Three-dimensional measurement of a key trait in human evolution. Anat Rec, 2014, 297: 234-239.
24.	Medani T, Garcia-Prieto J, Tadel F, et al. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. NeuroImage, 2023, 267: 119851.
25.	Iivanainen J, Mäkinen A J, Zetter R, et al. Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design. NeuroImage, 2021, 245: 118747.
26.	Moridera T, Rashed E, Mizutani S, et al. High-resolution EEG source localization in segmentation-free head models based on finite-difference method and matching pursuit algorithm. Front Neurosci, 2021, 15: 695668.
27.	Wolters C H, Köstler H, Möller C, et al. Numerical mathematics of the subtraction method for the modeling of a current dipole in EEG source reconstruction using finite element head models. SIAM J Sci Comput, 2008, 30(1): 24-45.
28.	Makarov S N, Hamalainen M, Okada Y, et al. Boundary element fast multipole method for enhanced modeling of neurophysiological recordings. IEEE Trans Biomed Eng, 2021, 68(1): 308-318.
29.	Hämäläinen M, Hari R, Ilmoniemi R J, et al. Magnetoencephalography---theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys, 1993, 65(2): 413-497.
30.	Vorwerk J, Oostenveld R, Piastra M, et al. The FieldTrip-SimBio pipeline for EEG forward solutions. Biomed Eng Online, 2018, 17: 37.
31.	Wolters C H, Anwander A, Berti G, et al. Geometry-adapted hexahedral meshes improve accuracy of finite-element-method-based EEG source analysis. IEEE Trans Biomed Eng, 2007, 54(8): 1446-1453.
32.	Esmaeili M, Esmaeili M, Ghane Sharbaf F, et al. Fontanel size from birth to 24 months of age in Iranian children. Iran J Child Neurol, 2015, 9(4): 15-23.
33.	Makarov S N, Golestanirad L, Wartman W A, et al. Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes. J Neural Eng, 2021, 18(4): 0460d4.
34.	Murakami S, Okada Y. Invariance in current dipole moment density across brain structures and species: Physiological constraint for neuroimaging. NeuroImage, 2015, 111: 49-58.
35.	Sundaram P, Nummenmaa A, Wells W, et al. Direct neural current imaging in an intact cerebellum with magnetic resonance imaging. Neuroimage, 2016, 132: 477-490.
36.	Zhang T, Liu Y, Ma E, et al. Flexible-center hat complete electrode model for EEG forward problem. IEEE Trans Biomed Eng, 2024, 71(8): 2287-2299.
37.	Cheng C, Liu Y, You B, et al. Multilevel feature learning method for accurate interictal epileptiform spike detection. IEEE Trans Neural Syst Rehab Eng, 2022, 30(1): 2506-2516.
38.	Cheng C, Zhou Y, You B, et al. Multiview feature fusion representation for interictal epileptiform spikes detection. Int J Neural Syst, 2022, 32(7): 2250014.
39.	Lanfer B, Scherg M, Dannhauer. M, et al. Influences of skull segmentation inaccuracies on EEG source analysis. NeuroImage, 2012, 62(1): 418-431.
40.	Vorwerk J, Engwer C, Pursiainen S, et al. A mixed finite element method to solve the EEG forward problem. IEEE Trans Med Imaging, 2017, 36(4): 930-941.

1. Mosher J C, Leahy R M, Lewis P S. EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng, 1999, 46(3): 245-259.
2. Michel C M, Murray M M, Lantz G, et al. EEG source imaging. Clin Neurophysiol, 2004, 115(10): 2195-222.
3. Vorwerk J, Cho J-H, Rampp S, et al. A guideline for head volume conductor modeling in EEG and MEG. NeuroImage, 2014, 100: 590-607.
4. 徐立超, 王仲朋, 许敏鹏, 等. 脑电逆问题在运动康复领域中的应用. 中国生物医学工程学报, 2017, 36(6): 733-740.
5. 胡莹, 刘燕, 程晨晨, 等. 基于自适应时频共空间模式结合卷积神经网络的多任务运动想象脑电分类. 生物医学工程学杂志, 2022, 39(6): 1065-1073, 1081.
6. Wolters C H, Grasedyck L, Hackbusch W. Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem. Inverse Problems, 2004, 20(4): 1099.
7. Oostenveld R, Fries P, Maris E, et al. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci, 2010, 2011: 156869.
8. De Munck J C, Wolters C H, Clerc M. EEG and MEG: forward modeling. Cambridge: Cambridge University Press. 2012: 192-256.
9. Azizollahi H, Aarabi A, Wallois F. Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates. Hum Brain Mapp, 2016, 37(10): 3604-3622.
10. Azizollahi H, Aarabi A, Wallois F. Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates. J Neural Eng, 2020, 17(5): 056004.
11. Roche-Labarbe N, Aarabi A, Kongolo G, et al. High-resolution electroencephalography and source localization in neonates. Hum Brain Mapp, 2008, 29(2): 167-176.
12. Lew S, Sliva D D, Choe M S, et al. Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model. Neuroimage, 2013, 76: 282-293.
13. Gargiulo P, Belfiore P, Friðgeirsson E A, et al. The effect of fontanel on scalp EEG potentials in the neonate. Clin Neurophysiol, 2015, 126(9): 1703-1710.
14. Flemming L, Wang Y, Caprihan A, et al. Evaluation of the distortion of EEG signals caused by a hole in the skull mimicking the fontanel in the skull of human neonates. Clin Neurophysiol, 2005, 116(5): 1141-1152.
15. 李璟, 王琨, 朱善安, 等. 利用有限差分算法研究开颅术对头皮电位分布的影响. 中国生物医学工程学报, 2007, 26(5): 708-712.
16. Adeyemo A A, Omotade O O. Variation in fontanelle size with gestational age. Early Hum Dev, 1999, 54(3): 207-214.
17. Kiesler J, Ricer R. The abnormal fontanel. Am Fam Physician, 2003, 67(12): 2547-2552.
18. Pursiainen S, Lew S, Wolters C H. Forward and inverse effects of the complete electrode model in neonatal EEG. J Neurophysiol, 2017, 117(3): 876-884.
19. Boran P, Oğuz F, Furman A, et al. Evaluation of fontanel size variation and closure time in children followed up from birth to 24 months. J Neurosurg Pediatr, 2018, 22(3): 323-329.
20. Oumer M, Guday E, Teklu A, et al. Anterior fontanelle size among term neonates on the first day of life born at University of Gondar Hospital, Northwest Ethiopia. PLoS One, 2018, 13(10): e0202454.
21. Faix R G. Fontanelle size in black and white term newborn infants. J Pediatr, 1982, 100(2): 304-306.
22. Omotade O O, Kayode C M, Adeyemo A A. Anterior fontanelle size in Nigerian children. Ann Trop Paediatr, 1995, 15(1): 89-91.
23. Moffett E, Aldridge K. Size of the anterior fontanelle: Three-dimensional measurement of a key trait in human evolution. Anat Rec, 2014, 297: 234-239.
24. Medani T, Garcia-Prieto J, Tadel F, et al. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. NeuroImage, 2023, 267: 119851.
25. Iivanainen J, Mäkinen A J, Zetter R, et al. Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design. NeuroImage, 2021, 245: 118747.
26. Moridera T, Rashed E, Mizutani S, et al. High-resolution EEG source localization in segmentation-free head models based on finite-difference method and matching pursuit algorithm. Front Neurosci, 2021, 15: 695668.
27. Wolters C H, Köstler H, Möller C, et al. Numerical mathematics of the subtraction method for the modeling of a current dipole in EEG source reconstruction using finite element head models. SIAM J Sci Comput, 2008, 30(1): 24-45.
28. Makarov S N, Hamalainen M, Okada Y, et al. Boundary element fast multipole method for enhanced modeling of neurophysiological recordings. IEEE Trans Biomed Eng, 2021, 68(1): 308-318.
29. Hämäläinen M, Hari R, Ilmoniemi R J, et al. Magnetoencephalography---theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys, 1993, 65(2): 413-497.
30. Vorwerk J, Oostenveld R, Piastra M, et al. The FieldTrip-SimBio pipeline for EEG forward solutions. Biomed Eng Online, 2018, 17: 37.
31. Wolters C H, Anwander A, Berti G, et al. Geometry-adapted hexahedral meshes improve accuracy of finite-element-method-based EEG source analysis. IEEE Trans Biomed Eng, 2007, 54(8): 1446-1453.
32. Esmaeili M, Esmaeili M, Ghane Sharbaf F, et al. Fontanel size from birth to 24 months of age in Iranian children. Iran J Child Neurol, 2015, 9(4): 15-23.
33. Makarov S N, Golestanirad L, Wartman W A, et al. Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes. J Neural Eng, 2021, 18(4): 0460d4.
34. Murakami S, Okada Y. Invariance in current dipole moment density across brain structures and species: Physiological constraint for neuroimaging. NeuroImage, 2015, 111: 49-58.
35. Sundaram P, Nummenmaa A, Wells W, et al. Direct neural current imaging in an intact cerebellum with magnetic resonance imaging. Neuroimage, 2016, 132: 477-490.
36. Zhang T, Liu Y, Ma E, et al. Flexible-center hat complete electrode model for EEG forward problem. IEEE Trans Biomed Eng, 2024, 71(8): 2287-2299.
37. Cheng C, Liu Y, You B, et al. Multilevel feature learning method for accurate interictal epileptiform spike detection. IEEE Trans Neural Syst Rehab Eng, 2022, 30(1): 2506-2516.
38. Cheng C, Zhou Y, You B, et al. Multiview feature fusion representation for interictal epileptiform spikes detection. Int J Neural Syst, 2022, 32(7): 2250014.
39. Lanfer B, Scherg M, Dannhauer. M, et al. Influences of skull segmentation inaccuracies on EEG source analysis. NeuroImage, 2012, 62(1): 418-431.
40. Vorwerk J, Engwer C, Pursiainen S, et al. A mixed finite element method to solve the EEG forward problem. IEEE Trans Med Imaging, 2017, 36(4): 930-941.

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