To date, much is known about the neural mechanisms underlying working-memory (WM) maintenance and long-term-memory (LTM) encoding. However, these topics have typically been examined in isolation, and little is known about how these processes might interact. Here, we investigated whether EEG oscillations arising specifically during the delay of a delayed matching-to-sample task reflect successful LTM encoding. Given previous findings of increased alpha and theta power with increasing WM load, together with the assumption that successful memory encoding involves processes that are similar to those that are invoked by increasing WM load, alpha and theta power should be higher for subsequently remembered stimuli. Consistent with this assumption, we found stronger alpha power for subsequently remembered stimuli over occipital-to-parietal scalp sites. Furthermore, stronger theta power was found for subsequently remembered stimuli over parietal-to-central electrodes. These results support the idea that alpha and theta oscillations modulate successful LTM encoding.

The roles of theta and alpha oscillations for long-term memory (LTM) retrieval are still under debate. Both are modulated by LTM retrieval demands, but it is unclear what specific LTM functions they are related to. Here, different oscillatory correlates of LTM retrieval could be obtained for theta and alpha with a paradigm that is suited to monitor the activation of a varying number of material-specific LTM representations. Both frequency bands responded parametrically to the number of retrieved items. However, only the alpha effect dissociated topographically for material type, indicating that the activation of material-specific representations became systematically modulated. For theta, this effect was material-unspecific with mid-frontal topography. These results suggest that alpha is functionally related to the activation of stored information, whereas theta is a sign of retrieval-related control processes.

Previous studies have shown that non-invasive stimulation of the dorsolateral prefrontal cortex (DLPFC) could modulate experimentally induced pain and working memory (WM) in healthy subjects. However, the two aspects have never been assessed concomitantly. The present study was set up to investigate the effects of transcranial direct current stimulation (tDCS) of the DLPFC on thermal pain and WM in the same population of healthy volunteers. In a randomized and balanced order of different sessions separated by 1 week, 20 min of 2 mA anodal, cathodal or sham tDCS were applied to the left or right DLPFC in two separate experiments. Twelve healthy volunteers were enrolled for each stimulated hemisphere. Warm and cold detection thresholds, heat and cold pain thresholds as well as heat pain tolerance thresholds were measured before, during and following tDCS. WM was assessed by a 2-back task applied once during cortical stimulation. Anodal tDCS of the right DLPFC led to an increase of tolerance to heat pain. The 2-back task revealed fewer outliers during cathodal tDCS of the left DLPFC. The present data show an involvement of the DLPFC in the processing of pain and WM. There was no correlation between these findings, suggesting that the analgesic effects of cortical stimulation are not associated with cognitive processing. However, this conclusion is difficult to affirm because of some limitations of the study regarding the parameters of stimulation or a ceiling effect of the 2-back task for instance.

A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating ‘functional connectivity’ in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, have somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.

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