Evidence map›Paper›PMID 32086098›Full record

ArticleEpilepsy research2020

The FAST graph: A novel framework for the anatomically-guided visualization and analysis of cortico-cortical evoked potentials.

Kenneth N Taylor, Anand A Joshi, Jian Li, Jorge A Gonzalez-Martinez, Xiaofeng Wang, Richard M Leahy, Dileep R Nair, John C Mosher

Abstract read
In one paragraph

Article in Epilepsy research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kenneth N TaylorEpilepsy Center, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA. Electronic address: taylork2@ccf.org.
Anand A JoshiDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA 90007, USA.
Jian LiDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA 90007, USA.
Jorge A Gonzalez-MartinezEpilepsy Center, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
Xiaofeng WangDepartment of Quantitative Health Sciences, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
Richard M LeahyDepartment of Electrical Engineering, University of Southern California, Los Angeles, CA 90007, USA.
Dileep R NairEpilepsy Center, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
John C MosherDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Funding

Device-Independent Acquisition and Real Time Spatiotemporal Analysis of Clinical Electrophysiology DataU01EB023820 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI MOSHER, JOHN COMPTON, OKADA, YOSHIO · 2017 to 2021
$5.8M
BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG ImagingR01EB026299 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LEAHY, RICHARD M · 2018 to 2025
$5.4M
A Brain Atlas for Mapping Connectivity in Focal EpilepsyR01NS089212 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI LEAHY, RICHARD M, MOSHER, JOHN COMPTON · 2015 to 2019
$2.7M
NIBIB NIH HHS R01 EB026299NIBIB NIH HHS U01 EB023820NINDS NIH HHS R01 NS089212
6 · The paper itself

Abstract

backgroundIntracerebral 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

methodThe 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.

resultsWe 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.

conclusionsThe 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.

Indexed as

AdolescentBrain MappingCerebral CortexChildChild, PreschoolDrug Resistant EpilepsyElectric StimulationElectroencephalographyEpilepsies, PartialEvoked PotentialsFemaleHumansInfantMaleNerve NetNeural PathwaysCortico-cortical evoked potentialsElectrical stimulationElectrocorticographyEpilepsySEEG

Identifiers

PMID32086098
PMCPMC7206791

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.