Kenneth N Taylor1, Anand A Joshi2, Jian Li2, Jorge A Gonzalez-Martinez3, Xiaofeng Wang4, Richard M Leahy2, Dileep R Nair3, John C Mosher5. 1. Epilepsy Center, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA. Electronic address: taylork2@ccf.org. 2. Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90007, USA. 3. Epilepsy Center, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA. 4. Department of Quantitative Health Sciences, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA. 5. Department of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Abstract
BACKGROUND: Intracerebral electroencephalography (iEEG) using stereoelectroencephalography (SEEG) methodology for epilepsy surgery gives rise to complex data sets. The neurophysiological data obtained during the in-patient period includes categorization of the evoked potentials resulting from direct electrical cortical stimulation such as cortico-cortical evoked potentials (CCEPs). These potentials are recorded by hundreds of contacts, making these waveforms difficult to quickly interpret over such high-density arrays that are organized in three dimensional fashion. NEW METHOD: The challenge in analyzing CCEPs data arises not just from the density of the array, but also from the stimulation of a number of different intracerebral sites. A systematic methodology for visualization and analysis of these evoked data is lacking. We describe the process of incorporating anatomical information into the visualizations, which are then compared to more traditional plotting techniques to highlight the usefulness of the new framework. RESULTS: We describe here an innovative framework for sorting, registering, labeling, ordering, and quantifying the functional CCEPs data, using the anatomical labelling of the brain, to provide an informative visualization and summary statistics which we call the "FAST graph" (Functional-Anatomical STacked area graphs). The FAST graph analysis is used to depict the significant CCEPs responses in patient with focal epilepsy. CONCLUSIONS: The novel plotting approach shown here allows us to visualize high-density stimulation data in a single summary plot for subsequent detailed analyses. Improving the visual presentation of complex data sets aides in enhancing the clinical utility of the data.
BACKGROUND:Intracerebral electroencephalography (iEEG) using stereoelectroencephalography (SEEG) methodology for epilepsy surgery gives rise to complex data sets. The neurophysiological data obtained during the in-patient period includes categorization of the evoked potentials resulting from direct electrical cortical stimulation such as cortico-cortical evoked potentials (CCEPs). These potentials are recorded by hundreds of contacts, making these waveforms difficult to quickly interpret over such high-density arrays that are organized in three dimensional fashion. NEW METHOD: The challenge in analyzing CCEPs data arises not just from the density of the array, but also from the stimulation of a number of different intracerebral sites. A systematic methodology for visualization and analysis of these evoked data is lacking. We describe the process of incorporating anatomical information into the visualizations, which are then compared to more traditional plotting techniques to highlight the usefulness of the new framework. RESULTS: We describe here an innovative framework for sorting, registering, labeling, ordering, and quantifying the functional CCEPs data, using the anatomical labelling of the brain, to provide an informative visualization and summary statistics which we call the "FAST graph" (Functional-Anatomical STacked area graphs). The FAST graph analysis is used to depict the significant CCEPs responses in patient with focal epilepsy. CONCLUSIONS: The novel plotting approach shown here allows us to visualize high-density stimulation data in a single summary plot for subsequent detailed analyses. Improving the visual presentation of complex data sets aides in enhancing the clinical utility of the data.
Authors: Riki Matsumoto; Dileep R Nair; Eric LaPresto; William Bingaman; Hiroshi Shibasaki; Hans O Lüders Journal: Brain Date: 2006-10-17 Impact factor: 13.501
Authors: Corey J Keller; Christopher J Honey; Laszlo Entz; Stephan Bickel; David M Groppe; Emilia Toth; Istvan Ulbert; Fred A Lado; Ashesh D Mehta Journal: J Neurosci Date: 2014-07-02 Impact factor: 6.167
Authors: Riki Matsumoto; Dileep R Nair; Eric LaPresto; Imad Najm; William Bingaman; Hiroshi Shibasaki; Hans O Lüders Journal: Brain Date: 2004-07-21 Impact factor: 13.501
Authors: Anand A Joshi; Soyoung Choi; Yijun Liu; Minqi Chong; Gaurav Sonkar; Jorge Gonzalez-Martinez; Dileep Nair; Jessica L Wisnowski; Justin P Haldar; David W Shattuck; Hanna Damasio; Richard M Leahy Journal: J Neurosci Methods Date: 2022-03-17 Impact factor: 2.987