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      Source imaging of seizure onset predicts surgical outcome in pediatric epilepsy

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          Abstract

          Objective:

          To assess whether ictal electric source imaging (ESI) on low-density scalp EEG can approximate the seizure onset zone (SOZ) location and predict surgical outcome in children with refractory epilepsy undergoing surgery.

          Methods:

          We examined 35 children with refractory epilepsy. We dichotomized surgical outcome into seizure- and non-seizure-free. We identified ictal onsets recorded with scalp and intracranial EEG and localized them using equivalent current dipoles and standardized low-resolution magnetic tomography (sLORETA). We estimated the localization accuracy of scalp EEG as distance of scalp dipoles from intracranial dipoles. We also calculated the distances of scalp dipoles from resection, as well as their resection percentage and compared between seizure-free and non-seizure-free patients. We built receiver operating characteristic curves to test whether resection percentage predicted outcome.

          Results:

          Resection distance was lower in seizure-free patients for both dipoles (p = 0.006) and sLORETA (p = 0.04). Resection percentage predicted outcome with a sensitivity of 57.1% (95% CI, 34–78.2%), a specificity of 85.7% (95% CI, 57.2–98.2%) and an accuracy of 68.6% (95% CI, 50.7–83.5%) ( p = 0.01).

          Conclusion:

          Ictal ESI performed on low-density scalp EEG can delineate the SOZ and predict outcome.

          Significance:

          Such an application may increase the number of children who are referred for epilepsy surgery and improve their outcome.

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          Most cited references55

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          Cortical surface-based analysis. I. Segmentation and surface reconstruction.

          Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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            Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

            Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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              Is Open Access

              Brainstorm: A User-Friendly Application for MEG/EEG Analysis

              Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
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                Author and article information

                Journal
                100883319
                21365
                Clin Neurophysiol
                Clin Neurophysiol
                Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
                1388-2457
                1872-8952
                6 May 2021
                28 April 2021
                July 2021
                01 July 2021
                : 132
                : 7
                : 1622-1635
                Affiliations
                [a ]Laboratory of Children’s Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
                [b ]Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy
                [c ]The Hillingdon Hospital NHS Foundation Trust, London, UK
                [d ]Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
                [e ]Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
                [f ]Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
                [g ]School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX, USA
                [h ]Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
                Author notes

                CRediT authorship contribution statement

                Lorenzo Ricci: Conceptualization, Methodology, Writing - original draft, Visualization. Eleonora Tamilia: Methodology, Validation, Writing - review & editing, Supervision. Michel Alhilani: Data curation. Aliza Alter: Validation. M. Scott Perry: Writing - review & editing. Joseph R Madsen: Resources. Jurriaan M Peters: Validation, Resources. Phillip L Pearl: Resources. Christos Papadelis: Conceptualization, Methodology, Validation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision.

                [* ]Corresponding author at: Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, 1500 Cooper St., Fort Worth, TX 76104, USA. christos.papadelis@ 123456cookchildrens.org (C. Papadelis).
                Article
                NIHMS1700107
                10.1016/j.clinph.2021.03.043
                8202024
                34034087
                3e531b36-dac6-4f01-8ef7-d4d9bc3ef0fb

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Categories
                Article

                Neurology
                electroencephalography,pediatric epilepsy,source localization
                Neurology
                electroencephalography, pediatric epilepsy, source localization

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