Evidence map›Paper›PMID 31689695›Full record

ArticleJournal of neural engineering2020

Enhanced tES and tDCS computational models by meninges emulation.

Jimmy Jiang, Dennis Q Truong, Zeinab Esmaeilpour, Yu Huang, Bashar W Badran, Marom Bikson

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 10% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

  1. Pooled it
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  9. Quasistatic approximation in neuromodulation.Journal of neural engineering · 2024
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  13. Electric-field-based dosing for TMS.Imaging neuroscience (Cambridge, Mass.) · 2024
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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

6 authors at 2 institutions in 1 country.

Jimmy JiangDepartment of Biomedical Engineering, Neural Engineering Laboratory, City College of New York of the City University of New York, New York, NY 10031, United States of America.
Dennis Q TruongDepartment of Biomedical Engineering, Neural Engineering Laboratory, City College of New York of the City University of New York, New York, NY 10031, United States of America.ORCID 0000-0003-2906-3892
Zeinab EsmaeilpourDepartment of Biomedical Engineering, Neural Engineering Laboratory, City College of New York of the City University of New York, New York, NY 10031, United States of America.
Yu HuangDepartment of Biomedical Engineering, Neural Engineering Laboratory, City College of New York of the City University of New York, New York, NY 10031, United States of America.ORCID 0000-0003-4178-0739
Bashar W BadranDepartment of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC 29425, United States of America.
Marom BiksonDepartment of Biomedical Engineering, Neural Engineering Laboratory, City College of New York of the City University of New York, New York, NY 10031, United States of America.
City College of New York · USMedical University of South Carolina · US

Funding

Treating Depression and Enhancing Locomotor Recovery Post-strokeP20GM109040 · NIGMS · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI KAUTZ, STEVEN A. · 2014 to 2024
$23.5M
Techniques DevelopmentP2CHD086844 · NICHD · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI KAUTZ, STEVEN A. · 2015 to 2024
$12.7M
NICHD NIH HHS P2C HD086844NIGMS NIH HHS P20 GM109040
6 · The paper itself

Abstract

objectiveUnderstanding how current reaches the brain during transcranial electrical stimulation (tES) underpins efforts to rationalize outcomes and optimize interventions. To this end, computational models of current flow relate applied dose to brain electric field. Conventional tES modeling considers distinct tissues like scalp, skull, cerebrospinal fluid (CSF), gray matter and white matter. The properties of highly conductive CSF are especially important. However, modeling the space between skull and brain as entirely CSF is not an accurate representation of anatomy. The space conventionally modeled as CSF is approximately half meninges (dura, arachnoid, and pia) with lower conductivity. However, the resolution required to describe individual meningeal layers is computationally restrictive in an MRI-derived head model. Emulating the effect of meninges through CSF conductivity modification could improve accuracy with minimal cost. APPROACH: Models with meningeal layers were developed in a concentric sphere head model. Then, in a model with only CSF between skull and brain, CSF conductivity was optimized to emulate the effect of meningeal layers on cortical electric field for multiple electrode positions. This emulated conductivity was applied to MRI-derived models. MAIN

resultsCompared to a model with conventional CSF conductivity (1.65 S m SIGNIFICANCE: Conventional tES models have been validated using intracranial recording. Residual errors may nonetheless impact model utility. Because CSF is so conductive to current flow, misrepresentation of the skull-brain interface as entirely CSF is not realistic for tES modeling. Updating the conventional model with a CSF conductivity emulating the effect of the meninges enhances modeling accuracy without increasing model complexity. This allows existing modeling pipelines to be leveraged with a simple conductivity change. Using 0.85 S m

Indexed as

Finite Element AnalysisModels, NeurologicalHumansMagnetic Resonance ImagingMeningesSkullTranscranial Direct Current Stimulation

Identifiers

PMID31689695
PMCPMC7254922
OpenAlexW2984950090

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.