Evidence map›Paper›PMID 35624997›Full record

ReviewBrain sciences2022

Personalized tDCS for Focal Epilepsy-A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data.

Steven Beumer, Paul Boon, Debby C W Klooster, Raymond van Ee, Evelien Carrette, Maarten M Paulides, Rob M C Mestrom

Open access · goldAbstract readReview
In one paragraph

Review in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
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

7 authors at 4 institutions in 2 countries.

Steven BeumerDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-7718-1394
Paul BoonDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-4180-8896
Debby C W KloosterDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-9850-9192
Raymond van EePhilips Research Eindhoven, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.ORCID 0000-0002-1134-8665
Evelien CarretteDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-9736-7401
Maarten M PaulidesDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-5891-2139
Rob M C MestromDepartment of Electrical Engineering, University of Technology Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.ORCID 0000-0002-1971-8691
Ghent University Hospital · BEEindhoven University of Technology · NLErasmus MC Cancer Institute · NLPhilips (Netherlands) · NL

Funding

Health Holland LSHM18064-HSGF
6 · The paper itself

Abstract

Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Through this narrative review, we present a processing workflow to personalize tES for focal epilepsy, for which there is a clear cortical target to stimulate. The workflow utilizes clinical imaging and electroencephalography data and enables us to relate the simulated fields to clinical outcomes. We review and analyze the relevant literature for the processing steps in the workflow, which are the following: tissue segmentation, source localization, and stimulation optimization. In addition, we identify shortcomings and ongoing trends with regard to, for example, segmentation quality and tissue conductivity measurements. The presented processing steps result in personalized tES based on metrics like focality and field strength, which allow for correlation with clinical outcomes.

Indexed as

clinical outcomeforward modelinginverse modelingneurostimulationpersonalizedtranscranial electric stimulation

Identifiers

PMID35624997
PMCPMC9139054
OpenAlexW4229336629

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.