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Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning
We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex (VLPFC) and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725363/ https://www.ncbi.nlm.nih.gov/pubmed/33296363 http://dx.doi.org/10.1371/journal.pone.0235179 |
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author | Petrovskaya, Aleksandra Kirillov, Bogdan Asmolova, Anastasiya Galli, Giulia Feurra, Matteo Medvedeva, Angela |
author_facet | Petrovskaya, Aleksandra Kirillov, Bogdan Asmolova, Anastasiya Galli, Giulia Feurra, Matteo Medvedeva, Angela |
author_sort | Petrovskaya, Aleksandra |
collection | PubMed |
description | We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex (VLPFC) and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal cortex (DLPFC). In total, we analyzed data from 97 participants after exclusions. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models. |
format | Online Article Text |
id | pubmed-7725363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77253632020-12-16 Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning Petrovskaya, Aleksandra Kirillov, Bogdan Asmolova, Anastasiya Galli, Giulia Feurra, Matteo Medvedeva, Angela PLoS One Research Article We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex (VLPFC) and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral prefrontal cortex (DLPFC). In total, we analyzed data from 97 participants after exclusions. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models. Public Library of Science 2020-12-09 /pmc/articles/PMC7725363/ /pubmed/33296363 http://dx.doi.org/10.1371/journal.pone.0235179 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Petrovskaya, Aleksandra Kirillov, Bogdan Asmolova, Anastasiya Galli, Giulia Feurra, Matteo Medvedeva, Angela Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title | Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title_full | Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title_fullStr | Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title_full_unstemmed | Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title_short | Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
title_sort | examining the effects of transcranial direct current stimulation on human episodic memory with machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725363/ https://www.ncbi.nlm.nih.gov/pubmed/33296363 http://dx.doi.org/10.1371/journal.pone.0235179 |
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