Evidence map›Paper›PMID 37418486›Full record

ArticlePloS one2023

Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes.

Daniel J Soper, Dustine Reich, Alex Ross, Pariya Salami, Sydney S Cash, Ishita Basu, Noam Peled, Angelique C Paulk

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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.

Daniel J SoperDepartment of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.ORCID 0000-0002-8353-0810
Dustine ReichDepartment of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.
Alex RossDepartment of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America.
Pariya SalamiDepartment of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.
Sydney S CashDepartment of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.
Ishita BasuDepartment of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America.
Noam PeledDepartment of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America.
Angelique C PaulkDepartment of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America.

Funding

Neurophysiology of Human Cortical EpilepsyR01NS062092 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2010 to 2021
$4.7M
Multi-Scale Cortical Dynamics in Human EpilepsyR01NS079533 · NINDS · BROWN UNIVERSITY · PI TRUCCOLO, WILSON · 2012 to 2022
$3.4M
Multiscale analysis and modeling of spatiotemporal dynamics in human epilepsyR01NS072023 · NINDS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI EDEN, URI TZVI, KRAMER, MARK ALAN · 2011 to 2015
$1.8M
Seizure focus delineation using spontaneous and stimulus evoked EEG featuresK24NS088568 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2015 to 2019
$923k
Neurophysiology of Human Cortical EpilepsyR56NS062092 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S · 2017 to 2017
$608k
A neural population model-based characterization and modulation of neural oscillations underlying cognitive control in healthy and depressed/anxious human subjectsR21MH127009 · NIMH · UNIVERSITY OF CINCINNATI · PI BASU, ISHITA · 2022 to 2023
$446k
NIMH NIH HHS R21 MH127009NINDS NIH HHS K24 NS088568NINDS NIH HHS R01 NS062092NINDS NIH HHS R01 NS072023NINDS NIH HHS R01 NS079533NINDS NIH HHS R56 NS062092
6 · The paper itself

Abstract

Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.

Indexed as

BrainElectrocorticographyBrain MappingCerebral CortexElectrodesElectrodes, ImplantedElectroencephalographyHumansMagnetic Resonance Imaging

Identifiers

PMID37418486
PMCPMC10328232

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Registered trials

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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.