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iELVis: an open source MATLAB toolbox for localizing and visualizing human intracranial electrode data

Groppe, David M
Bickel, Stephan
Dykstra, Andrew R
Wang, Xiuyuan
Mercier, Manuel R
Lado, Fred A
Mehta, Ashesh D
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Published in Journal of neuroscience methods. 2017, vol. 281, p. 40-48
Abstract Intracranial electrical recordings (iEEG) and brain stimulation (iEBS) are invaluable human neuroscience methodologies. However, the value of such data is often unrealized as many laboratories lack tools for localizing electrodes relative to anatomy. To remedy this, we have developed a MATLAB toolbox for intracranial electrode localization and visualization, iELVis. NEW METHOD: iELVis uses existing tools (BioImage Suite, FSL, and FreeSurfer) for preimplant magnetic resonance imaging (MRI) segmentation, neuroimaging coregistration, and manual identification of electrodes in postimplant neuroimaging. Subsequently, iELVis implements methods for correcting electrode locations for postimplant brain shift with millimeter-scale accuracy and provides interactive visualization on 3D surfaces or in 2D slices with optional functional neuroimaging overlays. iELVis also localizes electrodes relative to FreeSurfer-based atlases and can combine data across subjects via the FreeSurfer average brain.
Keywords AlgorithmsAtlases as TopicBrain/diagnostic imaging/physiology/surgeryElectrocorticography/instrumentation/methodsElectrodesImplantedHumansImagingThree-DimensionalMagnetic Resonance Imaging/methodsMotionNeuroimaging/methodsPattern RecognitionAutomated/methodsPostoperative PeriodPreoperative PeriodSoftware
PMID: 28192130
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Article (Published version) (3.5 MB) - document accessible for UNIGE members only Limited access to UNIGE
Research groups Groupe Anne-Lise Giraud (939)
Human Neuron Lab (1034)
Swiss National Science Foundation: P300P3_148388
Swiss National Science Foundation: PBGEP3_139829
(ISO format)
GROPPE, David M et al. iELVis: an open source MATLAB toolbox for localizing and visualizing human intracranial electrode data. In: Journal of Neuroscience Methods, 2017, vol. 281, p. 40-48. doi: 10.1016/j.jneumeth.2017.01.022 https://archive-ouverte.unige.ch/unige:127609

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Deposited on : 2019-12-09

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