Evidence map›Paper›PMID 34955721›Full record

ArticleFrontiers in neuroscience2021

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes.

Tyler S Davis, Rose M Caston, Brian Philip, Chantel M Charlebois, Daria Nesterovich Anderson, Kurt E Weaver, Elliot H Smith, John D Rolston

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

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  15. Beta bursts mediate amygdala gating of hippocampal emotional encoding.bioRxiv : the preprint server for biology · 2025
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  18. Reading specific memories from human neurons before and after sleep.bioRxiv : the preprint server for biology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Tyler S DavisDepartment of Neurosurgery, University of Utah, Salt Lake City, UT, United States.
Rose M CastonDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.
Brian PhilipDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.
Chantel M CharleboisDepartment of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.
Daria Nesterovich AndersonDepartment of Neurosurgery, University of Utah, Salt Lake City, UT, United States.
Kurt E WeaverDepartment of Radiology, University of Washington, Seattle, WA, United States.
Elliot H SmithDepartment of Neurosurgery, University of Utah, Salt Lake City, UT, United States.
John D RolstonDepartment of Neurosurgery, University of Utah, Salt Lake City, UT, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Indexed as

anatomical localizationelectrocorticography (ECoG)graphical user interface (GUI)intracranial electrode localizationMATLABsoftwarestereotactic electroencephalography (SEEG)

Identifiers

PMID34955721
PMCPMC8695687

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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.