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Gold Standard for Epilepsy/Tumor Surgery Coupled with Deep Learning Offers Independence to a Promising Functional Mapping Modality

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Abstract

RATIONALE: Electrocorticography-based functional language mapping (ECoG-FLM) utilizes an ECoG signal paired with simultaneous language task presentation to create functional maps of the eloquent language cortex in patients selected for resective epilepsy or tumor surgery. At present, the concordance of functional maps derived by ECoG-FLM and electrical cortical stimulation mapping (ESM) remains rather low. This impedes the transition of ECoG-FLM into an independent functional mapping modality. As ESM is considered the gold standard of functional mapping, we aimed to use it in combination with machine learning (ML) approaches (“ESM-ML guide”), to improve the accuracy of ECoG-FLM. METHODS: The ECoG data was collected from 6 patients (29.67 ± 12.5 yrs; 19–52 yrs; 3 males, 3 females). Patient ECoG activity was recorded (g.USBamp, g.tec, Austria) during administration of language tasks. For data analysis: (1) All ECoG sites were divided into ESM positive [ESM(+)] and ESM negative [ESM(−)]; (2) Features of ESM(+) and ESM(−) sites in the ECoG signal were determined by analyzing the signal in the frequency domain; (3) ML classifiers [Random Forest (RF) and Deep Learning (DL)] were trained to identify these features in language-related ECoG activity; (4) The accuracy of the ESM-ML guided classification was compared with the accuracy of the conventional ECoG-FLM. RESULTS: The conventional approach demonstrated: 58% accuracy, 22% sensitivity, and 78% specificity. The “ESM-ML guide” approach with RF classifier demonstrated: 76.2% accuracy, 73.6% sensitivity and 78.78% specificity. The DL classifier achieved the highest performances compared to all others with 83% accuracy, 84% sensitivity and 83% specificity. CONCLUSION: ECoG-FLM accuracy can be improved by using an “ESM-ML guide”, making the use of ECoG-FLM feasible as a stand-alone methodology. The long-term goal is to create a tool-box with “ready to use an ESM-ML guide” algorithm trained to provide high accuracy ECoG-FLM results by classifying between ESM(+) and ESM(−) contacts in prospective sets of language-related ECoG data and, thus, contribute towards improved surgical outcomes.

Keywords

Brain computer interface (BCI) Deep learning (DL) Electrocorticography (ECoG) Epilepsy surgery Functional brain mapping High-gamma mapping Machine learning (ML) Passive mapping Language mapping 

Notes

Acknowledgements

The authors acknowledge The Central Florida Health Research (CFHR) grant (PIs: Drs. M. Korostenskaja and U. Bagci) for supporting this study. Dr. Korostenskaja also would like to express her gratitude to Drs. G. Schalk and W. Wang for their feed-back/criticism/contribution to the development of this project’s idea for submission to NIH.

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Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Functional Brain Mapping and Brain Computer Interface LabOrlandoUSA
  2. 2.MEG Center, Florida Hospital for ChildrenOrlandoUSA
  3. 3.Florida Epilepsy Center, Florida HospitalOrlandoUSA
  4. 4.Center for Research in Computer Vision, University of Central FloridaOrlandoUSA
  5. 5.g.tec Medical Engineering GmbHSchiedlbergAustria
  6. 6.EECS DepartmentUniversity of CincinnatiCincinnatiUSA
  7. 7.Xiang Research Lab, Division of NeurologyCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  8. 8.Harvard Medical SchoolBeth Israel Deaconess Medical CenterBostonUSA

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