Post‐training stimulation of the right dorsolateral prefrontal cortex impairs working memory training performance

Au, Jacky; Katz, Benjamin; Moon, Austin; Talati, Sheebani; Abagis, Tessa; Jonides, John; Jaeggi, Susanne M.

doi:10.1002/jnr.24784

Cited by 10 publications

(11 citation statements)

References 108 publications

(193 reference statements)

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“…reliability (Homayouni et al, 2021), extend previous findings from interventions (Au et al, 2021;Kelly et al, 2021)…”

Section: Ack N Owled G M Entssupporting

confidence: 84%

“…Collectively, the articles in this In Focus issue illuminate the need for studies which aim to determine not only what changes in human cognition and the brain over different timescales, but also how changes in the brain underpin changes in behavior. To do this, it is necessary to determine measures with test–retest reliability (Homayouni et al., 2021), extend previous findings from interventions (Au et al, 2021; Kelly et al., 2021), and engage in longitudinal research despite the challenges. With this knowledge, we may not need to speculate about why certain findings do not replicate.…”

Section: Figurementioning

confidence: 99%

“…Au et al (2021) sought to identify optimal stimulation parameters for pairing noninvasive neurostimulation, here, transcranial direct current stimulation (tDCS), with working memory training in young adults. Specifically, they combined 5 days of working memory training with active or sham (control) tDCS to the right dorsolateral prefrontal cortex (DLPFC) that was applied before, during, or after each training session.…”

Section: Figurementioning

confidence: 99%

“…Structure-function links in brain networks are complex (Suárez et al, 2020), and these results highlight the importance of examining both functional and structural brain measures to achieve a mechanistic understanding of cognitive change. Au et al (2021) sought to identify optimal stimulation parameters for pairing noninvasive neurostimulation, here, transcranial direct current stimulation (tDCS), with working memory training in young adults. Specifically, they combined 5 days of working memory training with active or sham (control) tDCS to the right dorsolateral prefrontal cortex (DLPFC) that was applied before, during, or after each training session.…”

mentioning

confidence: 99%

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Longitudinal indices of human cognition and brain structure

Johnson

Jones

2021

J of Neuroscience Research

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Changes in human cognition, brain structure, and function occur with development and throughout aging. This change occurs across timescales of years, and scientific inquiry into these changes has inspired the neuroscientific fields of development, life span, and aging. Likewise, interventions that remediate or enhance cognitive performance can thwart or truncate the temporal progression by which change might naturally occur, and are drawing increasing interest from basic science, clinical, and commercial audiences alike.

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“…reliability (Homayouni et al, 2021), extend previous findings from interventions (Au et al, 2021;Kelly et al, 2021)…”

Section: Ack N Owled G M Entssupporting

confidence: 84%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

mentioning

confidence: 99%

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Longitudinal indices of human cognition and brain structure

Johnson

Jones

2021

J of Neuroscience Research

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“…see Au et al, 2021). Another experimental design might be an experimental condition (1) in which the experimenters stimulate brain areas expected to be involved in decision-making (perhaps placing stimulation electrodes on the scalp over parietal cortices), and a control condition in which (2) experimenters stimulate brain areas not expected to be involved in decision-making, say over the scalp over the temporal cortex.…”

Section: Experimental Manipulations and Experimental Designmentioning

confidence: 99%

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

Nunez¹,

Vandekerckhove²,

Srinivasan³

2022

Preprint

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We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However most of the motivation and discussion of this paper applies across many modeling procedures and applications.

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