利用自動病變檢測規劃立體定向腦電圖：可行性回顧性研究_Journal of Epilepsy

Authors：

 WagstylKonrad , AdlerSophie , PimpelBirgit , 郭玉潔　譯 , 慕潔　審

Corresponding?author：

WagstylKonrad, Email: k.wagstyl@ucl.ac.uk

Keywords：

深度學習; 癲癇; 神經影像; 兒科學; 立體定向腦電圖

DOI：

10.7507/2096-0247.20210059

Video：

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本回顧性橫斷面研究評估了將深度學習的難治性癲癇患兒的結構性磁共振成像（MRI）納入到規劃立體定向腦電圖（SEEG）植入的可行性和潛在益處。本研究旨在評估自動病變檢測與 SEEG 檢測出癲癇發作起始區（SOZ）之間的共定位程度。將神經網絡分類器應用于基于皮層 MRI 數據的三個隊列：① 對 34 例局灶性皮質發育不良（FCD）患者的神經網絡進行學習、訓練和交叉驗證；② 對 20 名健康兒童對照者進行特異性評估；③ 對 34 例患兒納入 SEEG 植入計劃的可行性進行了評價。SEEG 電極觸點的坐標與分類器預測的病變進行核驗。臨床神經生理學家鑒定癲癇發作起源和易激惹區的 SEEG 電極觸點位置。若 SOZ 坐標點和分類器預測的病變之間的距離<10 mm 則被認為是共定位的。影像學診斷病灶的分類敏感度為 74%（25/34）。對照組中未檢測到異常（特異性=100%）。在 34 例 SEEG 植入患者中，21 例有局灶性皮層 SOZ，其中 8 例經病理證實為 FCD。分類器正確地檢測了這 8 例 FCD 患者中的 7 例（86%）。組織病理學存在異質性的局灶性皮層病變患者中，62% 的患者分類器輸出結果與 SOZ 之間存在共定位。3 例患者中，電臨床提示為局灶性癲癇，SEEG 上無 SOZ 定位點，但在這些患者中，分類器識別了尚未植入的額外異常點。自動病變檢測與 SEEG 之間的共定位存在高度的一致性。我們已經建立了一個框架，將基于深度學習的 MRI 自動病變檢測納入到 SEEG 植入計劃。我們的發現支持了對自動 MRI 分析的前瞻性評估，以規劃最佳電極植入軌跡方案。

Citation： WagstylKonrad, AdlerSophie, PimpelBirgit, 郭玉潔　譯, 慕潔　審. 利用自動病變檢測規劃立體定向腦電圖：可行性回顧性研究. Journal of Epilepsy, 2021, 7(4): 358-366. doi: 10.7507/2096-0247.20210059 Copy

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21.	Lagarde S, Buzori S, Trebuchon A, et al. The repertoire of seizureonset patterns in human focal epilepsies: determinants and prognosticvalues. Epilepsia, 2019, 60(1): 85-95.
22.	David O, Blauwblomme T, Job AS, et al. Imaging the seizureonset zone with stereo-electroencephalography. Brain, 2011, 134(11): 2898-2911.
23.	Abraham A, Pedregosa F, Eickenberg M, et al. Machine learningfor neuroimaging with scikit-learn. Front Neuroinform, 2014, 8(1): 14.
24.	Allen M, Poggiali D, Whitaker K, et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res, 2019, 4(1): 63.
25.	Adler S, Blackwood M, Northam GB, et al. Multimodal computationalneocortical anatomy in pediatric hippocampal sclerosis. AnnClin Transl Neurol, 2018, 5(5): 1200-1210.
26.	McGovern RA, Ruggieri P, Bulacio J, et al. Risk analysis of hemorrhage in stereo-electroencephalography procedures. Epilepsia, 2019, 60(3): 571-580.
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28.	Galovic M, van Dooren VQH, Postma T, et al. Progressive corticalthinning in patients with focal epilepsy. JAMA Neurol, 2019, 76(10): 1230.
29.	Adler S, Whitaker K, Semmelroch M, et al. Multi-centre EpilepsyLesion Detection (MELD) project: conducting clinical research inan open-science framework. f1000research. 2018.
30.	Rosen BE. Ensemble learning using decorrelated neural networks. Conn Sci, 1996, 8: 373-384.
31.	Cucurull G, Wagstyl K, Casanova A, et al. Convolutional neuralnetworks for mesh-based parcellation of the cerebral cortex. 2018. [cited 2018 Jun 14]. Available from https://openr eview.net/pdf?id=rkKvB Aiiz.

1. Kwan P, Schachter SC, Brodie MJ. Drug-resistant epilepsy. N Engl J Med, 2011, 365: 919-926.
2. Wyllie E. Surgical treatment of epilepsy in children. Pediatr Neurol, 1998, 19(1): 179-188.
3. Gonzalez-Martinez J, Mullin J, Bulacio J, et al. Stereoelectroencephalography in children and adolescents withdifficult-to-localize refractory focal epilepsy. Neurosurgery, 2014, 75(3): 258-268.
4. McGovern RA, Knight EP, Gupta A, et al. Robot-assisted stereoelectroencephalographyin children. J Neurosurg Pediatr, 2018, 23: 288-296.
5. Huppertz H-J, Grimm C, Fauser S, et al. Enhanced visualization ofblurred gray–white matter junctions in focal cortical dysplasia byvoxel-based 3D MRI analysis. Epilepsy Res, 2005, 67(1): 35-50.
6. Wagner J, Weber B, Urbach H, et al. Morphometric MRI analysis improves detection of focal corticaldysplasia type II. Brain, 2011, 134: 2844-2854.
7. Wang ZI, Jones SE, Jaisani Z, et al. Voxel-based morphometricmagnetic resonance imaging (MRI) postprocessing in MRInegativeepilepsies. Ann Neurol, 2015, 77: 1060-1075.
8. Hong SJ, Kim H, Schrader D, Bernasconi N, Bernhardt BC, Bernasconi A. Automated detection of cortical dysplasia type II inMRI-negative epilepsy. Neurology, 2014, 83(1): 48-55.
9. Adler S, Wagstyl K, Gunny R, et al. Novel surface features for automateddetection of focal cortical dysplasias in paediatric epilepsy. Neuroimage Clin, 2017, 14(1): 18-27.
10. Hoyos-Osorio K, álvarez AM, Orozco áA, et al. Clustering-based undersampling to supportautomatic detection of focal cortical dysplasias. In: MendozaM, Velastín S, editors Progress in Pattern Recognition, ImageAnalysis, Computer Vision, and Applications. Cham, Switzerland: Springer, 2017: 298-305.
11. Jin B, Krishnan B, Adler S, et al. Automated detection of focalcortical dysplasia type II with surface-based magnetic resonanceimaging postprocessing and machine learning. Epilepsia, 2018, 59(4): 982-992.
12. Mo JJ, Zhang JG, Li WL, et al. Clinical value of machine learningin the automated detection of focal cortical dysplasia usingquantitative multimodal surface-based features. Front Neurosci, 2018, 12: 1008.
13. Fischl B. FreeSurfer. Neuroimage, 2012, 62(3): 774-781.
14. Greve DN, Van der Haegen L, Cai Q, et al. A surface-based analysisof language lateralization and cortical asymmetry. J Cogn Neurosci, 2013, 25(6): 1477-1492.
15. Sharma JD, Seunarine KK, Tahir MZ, et al. Accuracy ofrobot-assisted versus optical frameless navigated stereoelectroencephalographyelectrode placement in children. J Neurosurg Pediatr, 2019, 23(2): 297-302.
16. Jenkinson M, Bannister P, Brady M, et al. Improved optimizationfor the robust and accurate linear registration and motioncorrection of brain images. Neuroimage, 2002, 17(5): 825-841.
17. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functionaland structural MR image analysis and implementation asFSL. Neuroimage, 2004, 23(Suppl): 208-219.
18. Kikinis R, Pieper SD, Vosburgh KG. 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz FA, ed. Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer, 2014: 277–289.
19. Narizzano M, Arnulfo G, Ricci S, et al. SEEG assistant: a 3DSlicerextension to support epilepsy surgery. BMC Bioinformatics, 2017, 18(1): 124.
20. Perucca P, Dubeau F, Gotman J. Intracranial electroencephalographicseizure-onset patterns: effect of underlying pathology. Brain, 2014, 137(1): 183-196.
21. Lagarde S, Buzori S, Trebuchon A, et al. The repertoire of seizureonset patterns in human focal epilepsies: determinants and prognosticvalues. Epilepsia, 2019, 60(1): 85-95.
22. David O, Blauwblomme T, Job AS, et al. Imaging the seizureonset zone with stereo-electroencephalography. Brain, 2011, 134(11): 2898-2911.
23. Abraham A, Pedregosa F, Eickenberg M, et al. Machine learningfor neuroimaging with scikit-learn. Front Neuroinform, 2014, 8(1): 14.
24. Allen M, Poggiali D, Whitaker K, et al. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res, 2019, 4(1): 63.
25. Adler S, Blackwood M, Northam GB, et al. Multimodal computationalneocortical anatomy in pediatric hippocampal sclerosis. AnnClin Transl Neurol, 2018, 5(5): 1200-1210.
26. McGovern RA, Ruggieri P, Bulacio J, et al. Risk analysis of hemorrhage in stereo-electroencephalography procedures. Epilepsia, 2019, 60(3): 571-580.
27. Adler S, Hong SJ, Liu M, et al. Topographic principles of corticalfluid-attenuated inversion recovery signal in temporal lobe epilepsy. Epilepsia, 2018, 5(5): 627-635.
28. Galovic M, van Dooren VQH, Postma T, et al. Progressive corticalthinning in patients with focal epilepsy. JAMA Neurol, 2019, 76(10): 1230.
29. Adler S, Whitaker K, Semmelroch M, et al. Multi-centre EpilepsyLesion Detection (MELD) project: conducting clinical research inan open-science framework. f1000research. 2018.
30. Rosen BE. Ensemble learning using decorrelated neural networks. Conn Sci, 1996, 8: 373-384.
31. Cucurull G, Wagstyl K, Casanova A, et al. Convolutional neuralnetworks for mesh-based parcellation of the cerebral cortex. 2018. [cited 2018 Jun 14]. Available from https://openr eview.net/pdf?id=rkKvB Aiiz.

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