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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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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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, 陳彥, 等. 頭皮腦電圖在迷走神經電刺激治療難治性癲癇研究中的應用. 生物醫學工程學雜志, 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.	屈若為, 王召楠, 王石峰, 等. 基于真實頭模型與多偶極子算法的癲癇致癇灶腦電溯源方法研究. 生物醫學工程學雜志, 2023, 40(2): 272-279.
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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.	朱毓華, 陳怡靜, 葛璟潔, 等. 行為變異型額顳葉癡呆患者腦葡萄糖代謝特征研究. 中國臨床神經科學, 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.	黃保強, 李春勝. 基于癲癇網絡動態重構與虛擬切除的致癇區定位研究. 生物醫學工程學雜志, 2022, 39(6): 1165-1172.
13.	賀文頡, 卜海兵, 童莉, 等. 基于腦網絡連接的實時功能磁共振成像神經反饋技術研究進展. 生物醫學工程學雜志, 2017, 34(3): 456-460.
14.	陳雪嬌. 基于FDG-PET大腦代謝網絡及其魯棒性和模塊化特性研究. 蘭州: 蘭州大學, 2017.
15.	潘婷婷. 基于FDG-PET成像的動物腦代謝網絡分析方法及其應用. 鄭州: 鄭州大學, 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, ?berg 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, 陳彥, 等. 頭皮腦電圖在迷走神經電刺激治療難治性癲癇研究中的應用. 生物醫學工程學雜志, 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. 屈若為, 王召楠, 王石峰, 等. 基于真實頭模型與多偶極子算法的癲癇致癇灶腦電溯源方法研究. 生物醫學工程學雜志, 2023, 40(2): 272-279.
7. 王夏婉. 兒科癲癇患者¹⁸F-FDG PET隨訪及腦代謝網絡評估的研究. 杭州: 浙江大學, 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. 朱毓華, 陳怡靜, 葛璟潔, 等. 行為變異型額顳葉癡呆患者腦葡萄糖代謝特征研究. 中國臨床神經科學, 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. 黃保強, 李春勝. 基于癲癇網絡動態重構與虛擬切除的致癇區定位研究. 生物醫學工程學雜志, 2022, 39(6): 1165-1172.
13. 賀文頡, 卜海兵, 童莉, 等. 基于腦網絡連接的實時功能磁共振成像神經反饋技術研究進展. 生物醫學工程學雜志, 2017, 34(3): 456-460.
14. 陳雪嬌. 基于FDG-PET大腦代謝網絡及其魯棒性和模塊化特性研究. 蘭州: 蘭州大學, 2017.
15. 潘婷婷. 基于FDG-PET成像的動物腦代謝網絡分析方法及其應用. 鄭州: 鄭州大學, 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, ?berg 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