Evidence map›Paper›PMID 37279860›Full record

ArticleBrain stimulation

Machine-learning defined precision tDCS for improving cognitive function.

Alejandro Albizu, Aprinda Indahlastari, Ziqian Huang, Jori Waner, Skylar E Stolte, Ruogu Fang, Adam J Woods

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain stimulation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 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

7 authors at 2 institutions in 1 country.

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.
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.
Ziqian HuangCenter for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
Jori WanerCenter 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 StolteCenter 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.
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; Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA. Electronic address: ruogu.fang@ufl.edu.
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.
University of Florida · USUniversity of Florida Health · 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
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
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
Advanced 3T for Structural and Functional Imaging at University of FloridaS10OD021726 · OD · UNIVERSITY OF FLORIDA · PI LONG, JOANNA R · 2017 to 2017
$2.0M
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 AG071469NIA NIH HHS T32 AG020499NIH HHS S10 OD021726
6 · The paper itself

Abstract

backgroundTranscranial direct current stimulation (tDCS) paired with cognitive training (CT) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that the level of benefit from tDCS paired with CT varies from person to person, likely due to individual differences in neuroanatomical structure.

objectiveThe current study aims to develop a method to objectively optimize and personalize current dosage to maximize the functional gains of non-invasive brain stimulation.

methodsA support vector machine (SVM) model was trained to predict treatment response based on computational models of current density in a sample dataset (n = 14). Feature weights of the deployed SVM were used in a weighted Gaussian Mixture Model (GMM) to maximize the likelihood of converting tDCS non-responders to responders by finding the most optimum electrode montage and applied current intensity (optimized models).

resultsCurrent distributions optimized by the proposed SVM-GMM model demonstrated 93% voxel-wise coherence within target brain regions between the originally non-responders and responders. The optimized current distribution in original non-responders was 3.38 standard deviations closer to the current dose of responders compared to the pre-optimized models. Optimized models also achieved an average treatment response likelihood and normalized mutual information of 99.993% and 91.21%, respectively. Following tDCS dose optimization, the SVM model successfully predicted all tDCS non-responders with optimized doses as responders.

conclusionsThe results of this study serve as a foundation for a custom dose optimization strategy towards precision medicine in tDCS to improve outcomes in cognitive decline remediation for older adults.

Indexed as

Neurodegenerative DiseasesTranscranial Direct Current StimulationAgedBrainCognitionElectrodesHumansAgingFinite element modelMachine-learningMRIPrecision medicinetES

Identifiers

PMID37279860
PMCPMC11080612
OpenAlexW4379364778

What OpenQuestion holds

Textmetadata
LicenceTDM
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.