Evidence map›Paper›PMID 33049412›Full record

Trial reportBrain stimulation

Machine learning and individual variability in electric field characteristics predict tDCS treatment response.

Alejandro Albizu, Ruogu Fang, Aprinda Indahlastari, Andrew O'Shea, Skylar E Stolte, Kyle B See, Emanuel M Boutzoukas, Jessica N Kraft, Nicole R Nissim, Adam J Woods

Abstract readClinical Trial, Phase IIRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 4 pooled it
–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

53 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024
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  14. Neuromodulation and cognition in late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Alejandro AlbizuCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA.
Ruogu FangCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Aprinda IndahlastariCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
Andrew O'SheaCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
Skylar E StolteJ. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Kyle B SeeJ. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Emanuel M BoutzoukasCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
Jessica N KraftCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA.
Nicole R NissimCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA.
Adam J WoodsCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA. Electronic address: ajwoods@phhp.ufl.edu.

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
Research Training in Non-Pharmacological Interventions for Cognition in Aging, MCI, and Alzheimer's DiseaseT32AG020499 · NIA · UNIVERSITY OF FLORIDA · PI MARSISKE, MICHAEL, SMITH, GLENN E. · 2003 to 2024
$3.7M
Breathing Research and Therapeutics (BREATHE)T32HL134621 · NHLBI · UNIVERSITY OF FLORIDA · PI Gordon S. Mitchell · 2017 to 2026
$3.4M
Advanced 3T for Structural and Functional Imaging at University of FloridaS10OD021726 · OD · UNIVERSITY OF FLORIDA · PI LONG, JOANNA R · 2017 to 2017
$2.0M
Clinical and Translational Predoctoral training in Alzheimers Disease and Related DementiasT32AG061892 · NIA · UNIVERSITY OF FLORIDA · PI PARAMITA CHAKRABARTY, JADA M LEWIS · 2018 to 2026
$1.8M
Neuromodulation of Cognition in Older AdultsK01AG050707 · NIA · UNIVERSITY OF FLORIDA · PI WOODS, ADAM J. · 2016 to 2020
$613k
NHLBI NIH HHS T32 HL134621NIA NIH HHS K01 AG050707NIA NIH HHS R01 AG054077NIA NIH HHS T32 AG020499NIA NIH HHS T32 AG061892NIH HHS S10 OD021726
6 · The paper itself

Abstract

backgroundTranscranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Quantification of this variability could enable person-specific optimization of tDCS outcomes. This pilot study used machine learning and MRI-derived electric field models to predict working memory improvements as a proof of concept for precision cognitive intervention.

methodsFourteen healthy older adults received 20 minutes of 2 mA tDCS stimulation (F3/F4) during a two-week cognitive training intervention. Participants performed an N-back working memory task pre-/post-intervention. MRI-derived current models were passed through a linear Support Vector Machine (SVM) learning algorithm to characterize crucial tDCS current components (intensity and direction) that induced working memory improvements in tDCS responders versus non-responders. MAIN

resultsSVM models of tDCS current components had 86% overall accuracy in classifying treatment responders vs. non-responders, with current intensity producing the best overall model differentiating changes in working memory performance. Median current intensity and direction in brain regions near the electrodes were positively related to intervention responses (r=0.811,p<0.001 and r=0.774,p=0.001).

conclusionsThis study provides the first evidence that pattern recognition analyses of MRI-derived tDCS current models can provide individual prognostic classification of tDCS treatment response with 86% accuracy. Individual differences in current intensity and direction play important roles in determining treatment response to tDCS. These findings provide important insights into mechanisms of tDCS response as well as proof of concept for future precision dosing models of tDCS intervention.

Indexed as

IndividualityMachine LearningAgedAged, 80 and overBrainCognitionDouble-Blind MethodFemaleForecastingHumansMagnetic Resonance ImagingMaleMemory, Short-TermPilot ProjectsTranscranial Direct Current StimulationTreatment OutcomeCognitive agingFinite element modelingMachine learningtDCSTranscranial direct current stimulationTreatment response

Identifiers

PMID33049412
PMCPMC7731513

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.