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Rapid and Efficient Localization of Depth Electrodes and Cortical Labeling using Free and Open Source Medical Software in Epilepsy Surgery Candidates

1Epilepsy Section, Neurosciences Clinic and Applicated Center, Hospital Ramos Mejia, Universidad de Buenos Aires, Buenos Aires, Argentina.
2Fundación Favaloro, Resonancia Magnética, Neuroimágenes, Buenos Aires, Argentina.
3Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Front Neurosci
Volume Number:
Front Neurosci. 2013 Dec 31;7:260.
PubMed ID:
Epilepsy, electrodes, seeg, MRI, localization
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Generated Citation:
Princich J.P., Wassermann D., Latini F., Oddo S., Blenkmann A.O., Seifer G., Kochen S. Rapid and Efficient Localization of Depth Electrodes and Cortical Labeling using Free and Open Source Medical Software in Epilepsy Surgery Candidates. Front Neurosci. 2013 Dec 31;7:260. PMID: 24427112. PMCID: PMC3876273.
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Depth intracranial electrodes (IEs) placement is one of the most used procedures to identify the epileptogenic zone (EZ) in surgical treatment of drug resistant epilepsy patients, about 20-30% of this population. IEs localization is therefore a critical issue defining the EZ and its relation with eloquent functional areas. That information is then used to target the resective surgery and has great potential to affect outcome. We designed a methodological procedure intended to avoid the need for highly specialized medical resources and reduce time to identify the anatomical location of IEs, during the first instances of intracranial EEG recordings. This workflow is based on established open source software; 3D Slicer and Freesurfer that uses MRI and Post-implant CT fusion for the localization of IEs and its relation with automatic labeled surrounding cortex. To test this hypothesis we assessed the time elapsed between the surgical implantation process and the final anatomical localization of IEs by means of our proposed method compared against traditional visual analysis of raw post-implant imaging in two groups of patients. All IEs were identified in the first 24 H (6-24 H) of implantation using our method in 4 patients of the first group. For the control group; all IEs were identified by experts with an overall time range of 36 h to 3 days using traditional visual analysis. It included (7 patients), 3 patients implanted with IEs and the same 4 patients from the first group. Time to localization was restrained in this group by the specialized personnel and the image quality available. To validate our method; we trained two inexperienced operators to assess the position of IEs contacts on four patients (5 IEs) using the proposed method. We quantified the discrepancies between operators and we also assessed the efficiency of our method to define the EZ comparing the findings against the results of traditional analysis.

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