The ability to hold information in working memory (WM) is fundamental for cognition. Contrary to the longstanding view that WM depends on sustained, elevated activity, we present evidence suggesting that information can be held in WM via “activity-silent” synaptic mechanisms. Using machine learning to decode brain activity patterns, we show that the active representation of an item in WM drops to baseline when attention shifts away. A targeted pulse of transcranial magnetic stimulation produces a brief reemergence of the item in concurrently measured brain activity. This reactivation effect only occurs and influences memory performance when the item is potentially relevant later in the trial, suggesting that the representation is dynamic and modifiable via cognitive control. The results support a Synaptic Theory of Working Memory.

The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research.

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Recent studies suggest that working memory training may benefit older adults; however, findings regarding training and transfer effects are mixed. The current study aimed to investigate the effects of a process-based training intervention in a diverse sample of older adults and explored possible moderators of training and transfer effects. For that purpose, 80 older adults (65-95 years) were assigned either to a training group that worked on visuospatial, verbal, and executive working memory tasks for 9 sessions over 3 weeks or to a control group. Performance on trained and transfer tasks was assessed in all participants before and after the training period, as well as at a 9-month follow-up. Analyses revealed significant training effects in all 3 training tasks in trained participants relative to controls, as well as near transfer to a verbal working memory task and far transfer to a fluid intelligence task. Encouragingly, all training effects and the transfer effect to verbal working memory were stable at the 9-month follow-up session. Further analyses revealed that training gains were predicted by baseline performance in training tasks and (to a lesser degree) by age. Gains in transfer tasks were predicted by age and by the amount of improvement in the trained tasks. These findings suggest that cognitive plasticity is preserved over a large range of old age and that even a rather short training regime can lead to (partly specific) training and transfer effects. However, baseline performance, age, and training gains moderate the amount of plasticity.