Integrated datasets of normalized brain with functional localization using intra-operative electrical stimulation
- 37 Downloads
The purpose of this study was to transform brain mapping data into a digitized intra-operative MRI and integrated brain function dataset for predictive glioma surgery considering tumor resection volume, as well as the intra-operative and postoperative complication rates.
Brain function data were transformed into digitized localizations on a normalized brain using a modified electric stimulus probe after brain mapping. This normalized brain image with functional information was then projected onto individual patient’s brain images including predictive brain function data.
Log data were successfully acquired using a medical device integrated into intra-operative MR images, and digitized brain function was converted to a normalized brain data format in 13 cases. For the electrical stimulation positions in which patients showed speech arrest (SA), speech impairment (SI), motor and sensory responses during cortical mapping processes in awake craniotomy, the data were tagged, and the testing task and electric current for the stimulus were recorded. There were 13 SA, 7 SI, 8 motor and 4 sensory responses (32 responses) in total. After evaluation of transformation accuracy in 3 subjects, the first transformation from intra- to pre-operative MRI using non-rigid registration was calculated as 2.6 ± 1.5 and 2.1 ± 0.9 mm, examining neighboring sulci on the electro-stimulator position and the cortex surface near each tumor, respectively; the second transformation from pre-operative to normalized brain was 1.7 ± 0.8 and 1.4 ± 0.5 mm, respectively, representing acceptable accuracy.
This image integration and transformation method for brain normalization should facilitate practical intra-operative brain mapping. In the future, this method may be helpful for pre-operatively or intra-operatively predicting brain function.
KeywordsBrain mapping Digitization Transformation Normalization Predictive glioma surgery
This study was supported by a Grant-in-Aid for Scientific Research (B-JP22300093, C-JP12007086), Core Research for Evolutional Science and Technology (CREST), JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, JSPS KAKENHI Grant-JP15H01128, JP17H05306) and MIC Grant-JP162101001 Strategic Information and Communications R&D Promotion Programme (SCOPE). The authors would like to thank Dr. Etsuko Kobayashi for advisory assistance with precision biomedical engineering and Prof. Akimasa Hirata for advisory assistance with the physics of brain mapping.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving patients were in accordance with the ethical standards of the ethics committee of Tokyo Women’s Medical University and with the 1964 Declaration of Helsinki, as revised in 2013. Each patient provided informed consent before the surgical procedure.
- 1.http://brainvisa.info/ BrainVISA
- 2.Tamura M, Maruyama T, Nitta M, Saito T, Yoshimitsu K, Konishi Y, Okamoto J, Ikuta S, Masamune K, Mangin J-F, Okada Y, Iseki H, Muragaki Y (2015) Preoperative MRI-based delineation of the sulcal and gyral anatomy and its usefulness for glioma resection in neurosurgery. Int J Comput Assist Radiol Surg 10:S91–S92Google Scholar
- 4.Chaichana KL, Jusue-Torres I, Navarro-Ramirez R, Raza SM, Pascual-Gallego M, Ibrahim A, Hernandez-Hermann M, Gomez L, Ye X, Weingart JD, Olivi A, Blakeley J, Gallia GL, Lim M, Brem H, Quinones-Hinojosa A (2014) Establishing percent resection and residual volume thresholds affecting survival and recurrence for patients with newly diagnosed intracranial glioblastoma. Neuro Oncol 16(1):113–122. https://doi.org/10.1093/neuonc/not137 CrossRefGoogle Scholar
- 5.Nitta M, Muragaki Y, Maruyama T, Ikuta S, Komori T, Maebayashi K, Iseki H, Tamura M, Saito T, Okamoto S, Chernov M, Hayashi M, Okada Y (2015) Proposed therapeutic strategy for adult low-grade glioma based on aggressive tumor resection. Neurosurg Focus 38(1):E7. https://doi.org/10.3171/2014.10.FOCUS14651 CrossRefGoogle Scholar
- 9.Muragaki Y, Iseki H, Maruyama T, Tanaka M, Shinohara C, Suzuki T, Yoshimitsu K, Ikuta S, Hayashi M, Chernov M, Hori T, Okada Y, Takakura K (2011) Information-guided surgical management of gliomas using low-field-strength intraoperative MRI. Acta Neurochir Suppl 109:67–72. https://doi.org/10.1007/978-3-211-99651-5_11 CrossRefGoogle Scholar
- 13.Papademetris X, DeLorenzo C, Flossmann S, Neff M, Vives KP, Spencer DD, Staib LH, Duncan JS (2009) From medical image computing to computer-aided intervention: development of a research interface for image-guided navigation. Int J Med Robot 5(2):147–157. https://doi.org/10.1002/rcs.241 CrossRefGoogle Scholar
- 14.http://openigtlink.org OpenIGTLink
- 15.Tokuda J, Fischer GS, Papademetris X, Yaniv Z, Ibanez L, Cheng P, Liu H, Blevins J, Arata J, Golby AJ, Kapur T, Pieper S, Burdette EC, Fichtinger G, Tempany CM, Hata N (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5(4):423–434. https://doi.org/10.1002/rcs.274 CrossRefGoogle Scholar
- 16.https://www.slicer.org 3D Slicer
- 17.http://www.fil.ion.ucl.ac.uk/spm/ Statistical Parametric Mapping (SPM)
- 18.http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009 The McConnell Brain Imaging Centre
- 21.https://itk.org ITK (Insight Segmentation and Registration Toolkit)
- 25.https://doc.cgal.org/latest/Mesh_3/index.html CGAL 4.13–3D Mesh Generation
- 26.Bello L, Gallucci M, Fava M, Carrabba G, Giussani C, Acerbi F, Baratta P, Songa V, Conte V, Branca V, Stocchetti N, Papagno C, Gaini SM (2007) Intraoperative subcortical language tract mapping guides surgical removal of gliomas involving speech areas. Neurosurgery 60(1):67–80. https://doi.org/10.1227/01.neu.0000249206.58601.de (discussion 80-62) CrossRefGoogle Scholar
- 28.Boetto J, Bertram L, Moulinie G, Herbet G, Moritz-Gasser S, Duffau H (2015) Low rate of intraoperative seizures during awake craniotomy in a prospective cohort with 374 supratentorial brain lesions: electrocorticography is not mandatory. World Neurosurg. https://doi.org/10.1016/j.wneu.2015.07.075 Google Scholar
- 37.Corrivetti F, de Schotten MT, Poisson I, Froelich S, Descoteaux M, Rheault F, Mandonnet E (2019) Dissociating motor-speech from lexico-semantic systems in the left frontal lobe: insight from a series of 17 awake intraoperative mappings in glioma patients. Brain Struct Funct. https://doi.org/10.1007/s00429-019-01827-7 Google Scholar
- 38.Ono M (1990) Atlas of the Cerebral Sulci. Thieme, StuttgartGoogle Scholar