ArticlePLoS computational biology2021
Personalized brain stimulation for effective neurointervention across participants.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Report Approval for Transcranial Electrical Stimulation (RATES): expert recommendation based on a Delphi consensus study.Nature protocols · 2026Review
- Feasibility and Effectiveness of Personalized Home-Based Neurostimulation for Teachers Experiencing Work-Related Rumination.Brain and behavior · 2026Article
- Neuromodulation and cognition in late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy.BME frontiers · 2026Article
- Learning from missteps: Potential of transcranial electrical stimulation in neuropsychological rehabilitation.Journal of neuropsychology · 2025Review
- Personalized home based neurostimulation via AI optimization augments sustained attention.NPJ digital medicine · 2025Article
- Article
- A classification-based generative approach to selective targeting of global slow oscillations during sleep.Frontiers in human neuroscience · 2024Article
- Predicting the phase distribution during multi-channel transcranial alternating current stimulation in silico and in vivo.Computers in biology and medicine · 2023Article
- Article
- Noise improves the association between effects of local stimulation and structural degree of brain networks.PLoS computational biology · 2023Article
- Leveraging technology to personalize cognitive enhancement methods in aging.Nature aging · 2022Article
- Predicting Math Ability Using Working Memory, Number Sense, and Neurophysiology in Children and Adults.Brain sciences · 2022Article
- Personalized Frequency Modulated Transcranial Electrical Stimulation for Associative Memory Enhancement.Brain sciences · 2022Article
- Mathematical Model Insights into EEG Origin under Transcranial Direct Current Stimulation (tDCS) in the Context of Psychosis.Journal of clinical medicine · 2022Article
- Personalized transcranial alternating current stimulation improves sleep quality: Initial findings.Frontiers in human neuroscience · 2022Article
- Non-invasive brain stimulation and neuroenhancement.Clinical neurophysiology practice · 2022Review
- Parametrizing alternating current stimulation for neuromodulation.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants-personalized Bayesian optimization (pBO)-that searches available parameter combinations to optimize an intervention as a function of an individual's ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject's baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.
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Registered trials
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.