Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on <sup>18</sup>F-FDG PET_Journal of Biomedical Engineering

Authors：

JI Xuan ¹ , QU Ruowei ^2,3 , WANG Zhaonan ¹ , WANG Shifeng ⁴ ,  XU Guizhi ^1,2

1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China;
3. School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, P. R. China;
4. Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, P. R. China;

Corresponding author：

XU Guizhi, Email: gzxu@hebut.edu.cn

Keywords：

Positron emission tomography; Standardized uptake value ratio; Epilepsy; Brain metabolic network; Network characteristic

DOI：

10.7507/1001-5515.202312025

Video：

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Abstract Full text Figures/Tables Video References Cited by

The establishment of brain metabolic network is based on ¹⁸fluoro-deoxyglucose positron emission computed tomography (¹⁸F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to ¹⁸F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on ¹⁸F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Citation： JI Xuan, QU Ruowei, WANG Zhaonan, WANG Shifeng, XU Guizhi. Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on ¹⁸F-FDG PET. Journal of Biomedical Engineering, 2024, 41(4): 708-714. doi: 10.7507/1001-5515.202312025 Copy

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3.	Mesraoua B, Deleu D, Hassan A H, et al. Dramatic outcomes in epilepsy: depression, suicide, injuries, and mortality. Current Medical Research and Opinion, 2020, 36(9): 1473-1480.
4.	覃小雅, 袁媛, 陈彦, 等. 头皮脑电图在迷走神经电刺激治疗难治性癫痫研究中的应用. 生物医学工程学杂志, 2020, 37(4): 699-707.
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13.	贺文颉, 卜海兵, 童莉, 等. 基于脑网络连接的实时功能磁共振成像神经反馈技术研究进展. 生物医学工程学杂志, 2017, 34(3): 456-460.
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15.	潘婷婷. 基于FDG-PET成像的动物脑代谢网络分析方法及其应用. 郑州: 郑州大学, 2022.
16.	Yakushev I, Chételat G, Fischer F U, et al. Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults. Neuroimage, 2013, 79: 184-190.
17.	Deco G, Jirsa V K, McIntosh A R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci, 2011, 12(1): 43-56.
18.	Zhao B, McGonigal A, Hu W, et al. Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG. Epilepsia, 2023, 64(3): 667-677.
19.	Shan Y, Zhou H C, Shang K, et al. Functional connectivity alterations based on hypometabolic region may predict clinical prognosis of temporal lobe epilepsy: a simultaneous ¹⁸F-FDG PET/fMRI study. Biology. 2022, 11(8): 1178.
20.	Pagani M, Giuliani A, Öberg J, et al. Progressive disintegration of brain networking from normal aging to Alzheimer disease: analysis of independent component of ¹⁸F-FDG-PET data. J Nucl Med, 2017, 58(7): 1132-1139.
21.	Lagarde S, Boucekine M, McGonigal A, et al. Relationship between PET metabolism and SEEG epileptogenicity in focal lesional epilepsy. Eur J Nucl Med Mol Imaging, 2020, 47(13): 3130-3142.
22.	Toussaint P J, Perlbarg V, Bellec P, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. Neuroimage, 2012, 63(2): 936-946.
23.	Wang J, Jin C, Zhou J, et al. PET molecular imaging for pathophysiological visualization in Alzheimer's disease. European Journal of Nuclear Medicine and Molecular Imaging, 2023, 50(3): 765-783.

1. Wang G, Wang D, Du C, et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(12): 2711-2720.
2. Sumsky S L, Santaniello S. Decision support system for seizure onset zone localization based on channel ranking and high-frequency EEG activity. IEEE J Biomed Health Inform, 2019, 23(4): 1535-1545.
3. Mesraoua B, Deleu D, Hassan A H, et al. Dramatic outcomes in epilepsy: depression, suicide, injuries, and mortality. Current Medical Research and Opinion, 2020, 36(9): 1473-1480.
4. 覃小雅, 袁媛, 陈彦, 等. 头皮脑电图在迷走神经电刺激治疗难治性癫痫研究中的应用. 生物医学工程学杂志, 2020, 37(4): 699-707.
5. Chu C J. High density EEG-what do we have to lose?. Clinical Neurophysiology, 2015, 126(3): 433-434.
6. 屈若为, 王召楠, 王石峰, 等. 基于真实头模型与多偶极子算法的癫痫致痫灶脑电溯源方法研究. 生物医学工程学杂志, 2023, 40(2): 272-279.
7. 王夏婉. 儿科癫痫患者¹⁸F-FDG PET随访及脑代谢网络评估的研究. 杭州: 浙江大学, 2021.
8. Dong L, Wang P, Bin Y, et al. Local multimodal serial analysis for fusing EEG-fMRI: a new method to study familial cortical myoclonic tremor and epilepsy. IEEE Transactions on Autonomous Mental Development, 2015, 7(4): 311-319.
9. Zhang Q, Liao Y, Wang X, et al. A deep learning framework for ¹⁸F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy. European Journal of Nuclear Medicine and Molecular Imaging, 2021, 48(8): 2476-2485.
10. 朱毓华, 陈怡静, 葛璟洁, 等. 行为变异型额颞叶痴呆患者脑葡萄糖代谢特征研究. 中国临床神经科学, 2020, 28(3): 259-265.
11. Morbelli S, Drzezga A, Perneczky R, et al. Resting metabolic connectivity in prodromal Alzheimer’s disease. A European Alzheimer Disease Consortium (EADC) project . Neurobiol Aging, 2012, 33(11): 2533-2550.
12. 黄保强, 李春胜. 基于癫痫网络动态重构与虚拟切除的致痫区定位研究. 生物医学工程学杂志, 2022, 39(6): 1165-1172.
13. 贺文颉, 卜海兵, 童莉, 等. 基于脑网络连接的实时功能磁共振成像神经反馈技术研究进展. 生物医学工程学杂志, 2017, 34(3): 456-460.
14. 陈雪娇. 基于FDG-PET大脑代谢网络及其鲁棒性和模块化特性研究. 兰州: 兰州大学, 2017.
15. 潘婷婷. 基于FDG-PET成像的动物脑代谢网络分析方法及其应用. 郑州: 郑州大学, 2022.
16. Yakushev I, Chételat G, Fischer F U, et al. Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults. Neuroimage, 2013, 79: 184-190.
17. Deco G, Jirsa V K, McIntosh A R. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci, 2011, 12(1): 43-56.
18. Zhao B, McGonigal A, Hu W, et al. Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG. Epilepsia, 2023, 64(3): 667-677.
19. Shan Y, Zhou H C, Shang K, et al. Functional connectivity alterations based on hypometabolic region may predict clinical prognosis of temporal lobe epilepsy: a simultaneous ¹⁸F-FDG PET/fMRI study. Biology. 2022, 11(8): 1178.
20. Pagani M, Giuliani A, Öberg J, et al. Progressive disintegration of brain networking from normal aging to Alzheimer disease: analysis of independent component of ¹⁸F-FDG-PET data. J Nucl Med, 2017, 58(7): 1132-1139.
21. Lagarde S, Boucekine M, McGonigal A, et al. Relationship between PET metabolism and SEEG epileptogenicity in focal lesional epilepsy. Eur J Nucl Med Mol Imaging, 2020, 47(13): 3130-3142.
22. Toussaint P J, Perlbarg V, Bellec P, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. Neuroimage, 2012, 63(2): 936-946.
23. Wang J, Jin C, Zhou J, et al. PET molecular imaging for pathophysiological visualization in Alzheimer's disease. European Journal of Nuclear Medicine and Molecular Imaging, 2023, 50(3): 765-783.

Journal of Biomedical Engineering

Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on ¹⁸F-FDG PET

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Journal of Biomedical Engineering

Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET

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Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on ¹⁸F-FDG PET