Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

Aleksandra Petrovskaya; Bogdan Kirillov; Anastasiya Asmolova; Giulia Galli; Matteo Feurra; Angela Medvedeva

doi:10.1371/journal.pone.0235179

Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

PLoS One. 2020 Dec 9;15(12):e0235179. doi: 10.1371/journal.pone.0235179. eCollection 2020.

Authors

Aleksandra Petrovskaya^{1

2}, Bogdan Kirillov³, Anastasiya Asmolova^{1

2}, Giulia Galli⁴, Matteo Feurra^{1

2}, Angela Medvedeva⁵

Affiliations

¹ Psychology Department, National Research University Higher School of Economics, Moscow, Russian Federation.
² Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation.
³ Center of Life Sciences, Skolkovo Institute of Science and Technology, Skolkovo, Russia.
⁴ Department of Psychology, Kingston University, Kingston Upon Thames, United Kingdom.
⁵ Vivian L. Smith Department of Neurosurgery, University of Texas Medical School at Houston, Houston, Texas, United States of America.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Brain / physiology
Cognition / physiology*
Female
Humans
Machine Learning
Male
Memory / physiology*
Memory, Episodic
Memory, Short-Term / physiology
Neuropsychological Tests
Prefrontal Cortex / physiology
Recognition, Psychology / physiology
Transcranial Direct Current Stimulation / methods*
Young Adult

Grants and funding

The article was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5-100’. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.