Evidence map›Paper›PMID 38351938›Full record

ArticleArXiv2024

Quasistatic approximation in neuromodulation.

Boshuo Wang, Angel V Peterchev, Gabriel Gaugain, Risto J Ilmoniemi, Warren M Grill, Marom Bikson, Denys Nikolayev

Abstract readPreprint
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Boshuo Wang
Angel V Peterchev
Gabriel Gaugain
Risto J Ilmoniemi
Warren M Grill
Marom Bikson
Denys Nikolayev

Funding

G-RISE: Graduate Research Initiative for Student Advancement at The City College of New YorkT32GM136499 · NIGMS · CITY COLLEGE OF NEW YORK · PI RUTH E. STARK · 2020 to 2026
$4.9M
Engineering the Neuronal Response to Electrical MicrostimulationU01NS126052 · NINDS · UNIVERSITY OF FLORIDA · PI ORAZEM, MARK E., OTTO, KEVIN J. · 2022 to 2025
$4.3M
Biology and Biophysics of the Cortical Response to Transcranial Magnetic StimulationR01NS117405 · NINDS · DUKE UNIVERSITY · PI PETERCHEV, ANGEL V, SOMMER, MARC A · 2020 to 2024
$3.3M
NeuroSimNIBS: Integrated electric field and neuronal response modeling for transcranial electric and magnetic stimulationR01MH128422 · NIMH · DUKE UNIVERSITY · PI Warren M. Grill, Angel V Peterchev · 2022 to 2026
$2.7M
Open-source computational modeling of Spinal Cord Stimulation (SCS) to enhance dissemination of 1R01NS112996R01NS112996 · NINDS · CITY COLLEGE OF NEW YORK · PI BIKSON, MAROM · 2020 to 2024
$2.0M
Optimized Electrical Block of Peripheral NervesR01NS126376 · NINDS · DUKE UNIVERSITY · PI Warren M. Grill · 2023 to 2026
$2.0M
The coupled vascular hypothesis for transcranial direct current stimulation (tDCS)R01NS101362 · NINDS · CITY COLLEGE OF NEW YORK · PI BIKSON, MAROM · 2017 to 2021
$1.7M
"Bridges to the Baccalaureate Research Training Program at LaGuardia Community College"T34GM137858 · NIGMS · LAGUARDIA COMMUNITY COLLEGE · PI BIKSON, MAROM, ORTIZ, BENJAMIN D., · 2020 to 2024
$1.5M
Non-Invasive Vagal Nerve Stimulation in Patients with Opioid Use Disorders UG3DA048502 · NIDA · EMORY UNIVERSITY · PI BREMNER, JAMES DOUGLAS, INAN, OMER TOLGA · 2020 to 2021
$844k
NIDA NIH HHS UG3 DA048502NIGMS NIH HHS T32 GM136499NIGMS NIH HHS T34 GM137858NIMH NIH HHS R01 MH128422NINDS NIH HHS R01 NS101362NINDS NIH HHS R01 NS112996NINDS NIH HHS R01 NS117405NINDS NIH HHS R01 NS126376NINDS NIH HHS U01 NS126052
6 · The paper itself

Abstract

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

Identifiers

PMID38351938
PMCPMC10862934

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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.