Evidence map›Paper›PMID 36603745›Full record

ArticleNeuroImage2023

Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM).

Dezhi Wang, Nahian I Hasan, Moritz Dannhauer, Abdulkadir C Yucel, Luis J Gomez

Open access · goldAbstract read
In one paragraph

Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.7field-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

7 citing papers in PubMed, 22 citations in OpenAlex.

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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

5 authors at 3 institutions in 3 countries.

Dezhi WangElmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USA. Electronic address: wang5355@purdue.edu.
Nahian I HasanElmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USA. Electronic address: hasan34@purdue.edu.
Moritz DannhauerNational Institute of Mental Health (NIMH), National Institute of Health (NIH), 6001 Executive Boulevard, Bethesda, 20892, MD, USA. Electronic address: moritz.dannhauer@nih.gov.
Abdulkadir C YucelDepartment of Electrical and Computer Engineering, Nanyang Technological University (NTU), 50 Nanyang Ave, 639798, Singapore. Electronic address: acyucel@ntu.edu.sg.
Luis J GomezElmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USA. Electronic address: ljgomez@purdue.edu.
Purdue University West Lafayette · USNanyang Technological University · SGNational Institute of Mental Health · US

Funding

Non-invasive Neuromodulation Unit (NNU)ZIAMH002955 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI ZARATE, CARLOS · 2016 to 2025
$40.3M
Accurate and reliable computational dosimetry and targeting for transcranial magnetic stimulationR00MH120046 · NIMH · PURDUE UNIVERSITY · PI GOMEZ, LUIS JAVIER · 2020 to 2022
$676k
Intramural NIH HHS ZIA MH002955NIMH NIH HHS R00 MH120046
6 · The paper itself

Abstract

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error.

Indexed as

BrainTranscranial Magnetic StimulationHeadHumansMagnetic Resonance ImagingRadiometryAdaptive cross approximation (ACA)Fast simulationLow rank approximationMotor mappingTranscranial magnetic stimulation (TMS)

Identifiers

PMID36603745
PMCPMC11658687
OpenAlexW4313478869

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.