Evidence map›Paper›PMID 38077189›Full record

ArticleFrontiers in human neuroscience2023

Impact of electrode selection on modeling tDCS in the aging brain.

Aprinda Indahlastari, Ayden L Dunn, Samantha Pedersen, Jessica N Kraft, Shizu Someya, Alejandro Albizu, Adam J Woods

Open access · goldAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
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 2 institutions in 1 country.

Aprinda IndahlastariCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Ayden L DunnCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Samantha PedersenCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Jessica N KraftCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Shizu SomeyaCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Alejandro AlbizuCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
Adam J WoodsCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.
University of Florida Health · USUniversity of Florida · US

Funding

Augmenting Cognitive Training in Older Adults - The ACT GrantR01AG054077 · NIA · UNIVERSITY OF FLORIDA · PI COHEN, RONALD A, MARSISKE, MICHAEL · 2016 to 2020
$6.9M
Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT studyRF1AG071469 · NIA · UNIVERSITY OF FLORIDA · PI FANG, RUOGU, WOODS, ADAM J. · 2021 to 2021
$2.2M
Neuromodulation of Cognition in Older AdultsK01AG050707 · NIA · UNIVERSITY OF FLORIDA · PI WOODS, ADAM J. · 2016 to 2020
$613k
NIA NIH HHS K01 AG050707NIA NIH HHS R01 AG054077NIA NIH HHS RF1 AG071469
6 · The paper itself

Abstract

Background: Person-specific computational models can estimate transcranial direct current stimulation (tDCS) current dose delivered to the brain and predict treatment response. Artificially created electrode models derived from virtual 10-20 EEG measurements are typically included in these models as current injection and removal sites. The present study directly compares current flow models generated via artificially placed electrodes ("artificial" electrode models) against those generated using real electrodes acquired from structural MRI scans ("real" electrode models) of older adults. Methods: A total of 16 individualized head models were derived from cognitively healthy older adults (mean age = 71.8 years) who participated in an in-scanner tDCS study with an F3-F4 montage. Visible tDCS electrodes captured within the MRI scans were segmented to create the "real" electrode model. In contrast, the "artificial" electrodes were generated in ROAST. Percentage differences in current density were computed in selected regions of interest (ROIs) as examples of stimulation targets within an F3-F4 montage. Main results: We found significant inverse correlations ( Conclusions: This study demonstrates potential discrepancies in generated current density models using real versus artificial electrode placement when applying tDCS to an older adult cohort. Our findings strongly suggest that future tDCS clinical work should consider closely monitoring and rigorously documenting electrode location during stimulation to model tDCS montages as closely as possible to actual placement. Detailed physical electrode location data may provide more precise information and thus produce more robust tDCS modeling results.

Indexed as

cognitive agingcomputational modelelectrodesfinite element methodnon-invasive brain stimulationtDCS

Identifiers

PMID38077189
PMCPMC10704166
OpenAlexW4388997274

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.