Brain Connectivity Alterations During Sleep By Closed-loop Transcranial Neurostimulation Predict Metamemory Sensitivity Part 3

RESULTS

Behavioral Effects

As reported by Pilly et al. (2020), STAMP stimulation did not lead to a significant improvement in overall memory recall accuracy. However, STAMP stimulation led to improved metamemory sensitivity on Day 3 of the experiment (following Night 2). 

Metamemory sensitivity refers to a person's awareness and understanding of the memory ability he possesses, while memory ability refers to a person's memory ability demonstrated in specific practice. There is a close relationship between the two.

First, metamemory sensitivity directly affects memory performance. If a person believes that he or she is confident about a certain type of memory, he or she will tend to work harder at remembering it and perform better on that type of memory task. In contrast, if a person lacks confidence in their memory abilities, it may cause them to perform less well on memory tasks, thereby reducing their memory ability.

Second, positive metamemory sensitivity helps improve memory. If a person believes that there is room for improvement in their memory ability, they will be more active in training their memory ability, thereby improving the accuracy and persistence of memory. Those who believe that their memory ability has reached its peak are often unable to accept new memory challenges and thus miss the opportunity to improve their memory ability.

Therefore, we need to actively cultivate our meta-memory sensitivity, break the restrictions and settings on our memory abilities, and believe that we can continue to improve in memory tasks. At the same time, we also need to rely on accumulated training and practice to improve our memory ability so that it can be used more effectively in specific practice. Only in this way can we maximize our memory potential and achieve better results. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory, because Cistanche deserticola can also regulate the balance of neurotransmitters, such as increasing the levels of acetylcholine and growth factors. These substances are very important for memory and learning. In addition, Cistanche deserticola can also improve blood flow and promote oxygen delivery, which can ensure that the brain receives sufficient nutrients and energy, thereby improving brain vitality and endurance.

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Specifically, dependent t-tests comparing metamemory sensitivity across conditions revealed that sensitivity in the Tag & Cue condition was significantly greater than both the Tag & No Cue condition [t(23) = 3.51, adjusted p < 0.01, Holm–Bonferroni correction for two comparisons; paired-sample Cohen's d = 0.72] and Sham condition [t(23) = 2.089, adjusted p = 0.048, Holm–Bonferroni correction for two comparisons; paired-sample Cohen's d = 0.43]. 

A significant improvement in metamemory sensitivity was only found on Day 3 of the experiment. Thus, the application of STAMPs during SWOs in the two nights following one-shot viewing led to specific enhancement of metamemory for the episodes that were both tagged and cued.

Global Coherence Changes

As a first pass, we examined overall changes in mean coherence across the scalp due to STAMP stimulation to determine if stimulation led to any measurable changes in connectivity. 

Additionally, we sought to replicate previous work and assess whether stimulation would produce similar changes in coherence as learning during sleep (Mölle et al., 2004). Previous work (Mölle et al., 2004) reported modulation of the delta, spindle, and gamma band coherence during sleep following learning; in contrast, other work (Polanía et al., 2011) demonstrated increased connectivity across all frequency bands following tDCS stimulation. 

Therefore, we hypothesized that STAMP stimulation would increase mean coherence compared to the Sham condition in the delta, spindle, and gamma bands, but examined differences in other bands as well with conservative statistical testing. 

Mean iCoh values in all frequency bands were computed for Active and Sham baseline-corrected data to investigate global connectivity changes, and thus one-tailed t-tests were conducted on mean coherence values in each frequency band and were corrected for using a false discovery rate of p < 0.05 (Benjamini & Hochberg, 1995). 

Coherence maps showing greater connectivity in the Active condition across the scalp were visualized by setting the visualization threshold to the maximum of the Sham condition for each frequency band, essentially subtracting the Sham condition. Stimulation-induced mean coherence changes in different frequency bands are plotted in Figure 3. 

The topography plots display the coherence between pairs of electrodes in the Active condition, thresholded by the coherence in the Sham condition, whereas the boxplots show non-thresholded mean coherence values across the entire scalp. The analysis of overall changes in coherence across the scalp revealed that STAMPs produced significant increases in mean coherence in multiple frequency bands. 

EEG coherence was significantly increased following STAMPs in the theta, alpha, and spindle bands ( p < 0.05) compared to the Sham condition. Mean coherences in the delta, beta, and gamma bands did not significantly differ between the stimulation conditions. Thus, STAMP stimulation-induced changes in brain connectivity in specific frequency bands.

Classification and Behavioral Prediction

Results from the classification analysis are plotted in Figure 4 and outlined in Table 1. Average importance values across folds for Boruta-selected features are plotted on the left, with features consistently considered important highlighted in green (significantly above the average shadow max). Across folds, the radius in the spindle band (12–15 Hz) was consistently selected as the most important feature. 

Other important features included: path length (12–15 Hz) and mean coherence and density (3–8 Hz). ROC curves of classifier performance are plotted on the right. As outlined in Mason and Graham (2002), Mann–Whitney U tests were conducted on each classifier to determine if classifier performance was statistically significant above chance (AUC = 0.5). All tests reported significant p values ( p < 0.001). 

We additionally calculated 95% confidence intervals using a stratified bootstrap resampling approach with 10,000 bootstrap replications, which further demonstrated above-chance classification performance (Carpenter & Bithell, 2000). 

Thus, classification accuracy was above chance, and taking the top variables from classification and regression analyses (path length in 16–30 Hz and radius in 12–15 Hz) led to the best classification performance. 

As a follow-up analysis, the four features selected by the Boruta algorithm for classification were submitted to t-tests, testing the difference in connectivity between the Active and Sham conditions. The resultant p values were corrected with a false discovery rate set at 0.05. 

The corrected p values for 12–15 Hz path length and radius, and 3–8 Hz mean coherence were significant ( p = 0.029); 3–8 Hz density was also significant at an alpha level of significance ( p = 0.05). Thus, connectivity features that were significantly altered following Active stimulation compared to Sham stimulation were found by the feature selection pipeline. Path length and radius features from pre- to poststimulation were significantly decreased in the Active condition compared to Sham, whereas mean coherence and density features were significantly increased compared to Sham. 

Boxplots showing these features for both conditions are presented in Supporting Information Figure S3. Results from the behavioral prediction analysis are plotted in Figure 5 and outlined in Table 2. 

Here, changes in neural measures following stimulation were used as features to predict change in recall test metamemory sensitivity (AUC) for both the Tag & Cue and Sham conditions from Day 2 to Day 3 to identify brain-behavior relationships. 

The importance values from Boruta for graph theoretic features are plotted on the left-, path length in the beta band (16–30 Hz) was considered highly important for predicting changes in metamemory sensitivity, and the only feature selected. The relationship between stimulation-induced change in 16–30 Hz path length and overnight change in AUC performance for both the Active (Tag & Cue) data and Sham conditions are plotted on the right. 

The correlation between change in beta-band path length and change in AUC was significant for the Active condition (r = −0.66, p = 0.003), indicating that individuals with decreased path length following STAMPs tended to show a positive change in memory performance. This relationship was not significant for the Sham condition (r = −0.22, p = 0.39). 

Based on the outcome of these analyses, follow-up linear regressions were run predicting overnight change in metacognitive sensitivity using path length in the 16–30 Hz band for both the Active data and the Sham data. 

For the Active data, 16–30 Hz path length significantly predicted AUC change (t = −3.56, p = 0.003, adj. R2 = 0.41). For the Sham data, 16–30 Hz path length was not a significant predictor (t = −0.88, p = 0.39, adj. R2 = −0.01). Additional analyses to compare the Active and Sham conditions were also performed and are outlined further in the Supporting Information.

DISCUSSION

In the present experiment, participants engaged in an episodic memory task while receiving unique spatiotemporal patterns of transcranial electrical stimulation, and a subset of the patterns were reapplied during up-states of SWOs in subsequent nights to cue reactivation of specific associated memories (see Pilly et al., 2020). 

Here, we report changes in functional brain connectivity induced by these tES patterns during sleep that were predictive of changes in metamemory sensitivity from pre- to post-sleep. 

Using machine learning techniques to identify important features for classifying between Active and Sham stimulation, we found that connectivity features in the theta and spindle bands significantly differed between the two conditions. 

Additionally, we found that changes in path length in the beta band predicted memory sensitivity changes, in that performance increases across the night were related to beta path length decreases following stimulation. 

Taken together, these results contribute to a growing body of research investigating the neural processes underlying memory reactivations and consolidation during sleep and suggest that modulation of path length at specific frequencies during the consolidation process may lead to higher confidence and better decision-making for newly consolidated memories. 

The short bursts of tES, or STAMPs, utilized in the current experiment increased overall scalp coherence in lower frequency bands-namely, in the theta, alpha, and spindle bands, or essentially from 3–15 Hz. Interestingly, changes in spindle-band connectivity during sleep were also found following learning; however, changes in connectivity were not found in the delta or gamma bands following STAMP stimulation, which is somewhat at odds with previous experimental work (Mölle et al., 2004). 

However, the stimulation protocol used here largely differed from previous studies-namely, we used short bursts of spatiotemporally distributed electrical currents that were primarily designed to cue specific memories due to their uniqueness and association with the encoding period, not modulate or entrain specific oscillations. Importantly, activity in the reported frequency bands, particularly the theta and spindle bands, is related to the memory consolidation process (Staresina et al., 2015). 

Thus, STAMPs may have modulated the ongoing processes of memory reactivation or consolidation during sleep, leading to changes in connectivity in the observed frequency bands. Recent work has questioned the effectiveness of tES in significantly affecting behavior (Horvath et al., 2015), as well as in its ability to modulate neural firing patterns or oscillatory activity (Lafon et al., 2017; Vöröslakos et al., 2018). 

However, other studies have demonstrated measurable neural changes in humans and nonhuman primates following tES, even at lower intensity levels (Huang et al., 2017; Krause et al., 2017; Opitz et al., 2016). A particular concern is the ability of tES, specifically alternating current stimulation, to entrain neural oscillations to a specific frequency. 

Here, we do not claim that the reported electrophysiological changes are due to oscillatory entrainment to the short bursts of stimulation, or that we are inducing these changes de novo, but rather that the STAMPs act to boost ongoing memory consolidation processes, which occur through subtle modulations of network-level connectivity. We would argue that the observed connectivity patterns during sleep are natural phenomena that occur and lead to memory consolidation, and STAMP stimulation acted upon these preexisting processes to boost metamemory sensitivity. 

The reported modulation of graph theoretic metrics in the theta and spindle bands following STAMPs is in line with previous research implicating these oscillations in the processes of memory reactivation and consolidation. Targeted memory reactivation during sleep with auditory cues elicits increases in theta and spindle power (Ong et al., 2016; Schreiner et al., 2015; Schreiner & Rasch, 2014). 

Numerous studies have also shown the importance of theta-band activity for memory encoding and retrieval during wake (Backus et al., 2016; Lega et al., 2012; Nyhus & Curran, 2010); in particular, theta oscillations in the hippocampus are critical for episodic memory encoding (Hasselmo et al., 2002; Lin et al., 2017). Similarly, thalamocortical spindles are related to episodic memory (Van Der Helm et al., 2011) and play an important role in the process of memory consolidation, potentially facilitating the transfer of newly encoded information in the medial temporal lobes to stable representations in the cerebral cortex (Sirota et al., 2003; Staresina et al., 2015). 

During sleep, Theta activity may be indicative of the memory reactivation process, whereas spindle activity may reflect the active consolidation of the reactivated memories. Here, STAMPs may have cued reactivation of the associated memories, leading to boosted consolidation of this information. This consolidation led to improved confidence for memories downstream. 

The theta-band graph theoretic measures that were significantly modulated by STAMPs were mean coherence and density. Mean coherence refers to the average coherence across all vertices, while density is calculated as the number of connections in the graph, normalized by the total possible number of connections (Sporns, 2003). 

Changes in these measures suggest that STAMPs increased overall coherence in the theta band between channels, leading to a greater number of edges above the cutoff threshold and thus a higher average connection density. This greater coherence and density may reflect an increase in the exchange of information in the theta band across neural areas following STAMPs. This is in line with previous findings implicating increases in theta coherence between the hippocampus and rhinal cortex (Fell et al., 2003), as well as the frontal cortex (Anderson et al., 2009; Benchenane et al., 2010), as important for learning and memory, and potentially critical for memory reactivations (Carr et al., 2011). 

The spindle-band graph theoretic measures that significantly differed following STAMP stimulation were path length and radius. These measures are related-radius refers to the minimum eccentricity of the graph and path length refers to the graph's average eccentricity, where the eccentricity of a node is defined as the maximum distance to any other node (Sporns, 2003). 

The path length is often considered a measure of network integration or efficiency of information transfer, with smaller path lengths reflecting greater efficiency (Bullmore & Sporns, 2009; Reijneveld et al., 2007). 

Here, STAMPs led to an average decrease in path length and radius in the spindle band, meaning the average and minimum eccentricity were decreased, and the efficiency of information transfer was increased. 

This may reflect a reorganization of the network toward a small-world network (Watts & Strogatz, 1998), a near-optimal structure between a perfectly ordered and randomly organized system, allowing for greater flexibility and synchronization of neural activity (Barahona & Pecora, 2002; Bassett & Bullmore, 2006; Masuda & Aihara, 2004). Changes in "small-worldness" are often measured as changes in path length without concomitant changes in local clustering, or cluster coefficient.

Thus, the observed change in path length may suggest boosted synchronization between brain areas, leading to more efficient information transfer by spindles. Note that effects in the theta band were more closely related to overall changes in mean coherence-essentially, changes in the magnitude of connectivity as opposed to the efficiency. 

While the data here is unable to establish a causal link between these changes, a hypothetical relationship is that STAMPs caused greater connectivity in the theta band (Fell et al., 2003), and Spindle information transfer efficiency, measured as path length, was ramped up to compensate for this increase, leading to increased memory reactivation. 

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