In the past decade, several studies have examined the effects of transcranial direct current stimulation (tDCS) on long-term episodic memory formation and retrieval. These studies yielded conflicting ...results, likely due to differences in stimulation parameters, experimental design and outcome measures.
In this work we aimed to assess the robustness of tDCS effects on long-term episodic memory using a meta-analytical approach.
We conducted four meta-analyses to analyse the effects of anodal and cathodal tDCS on memory accuracy and response times. We also used a moderator analysis to examine whether the size of tDCS effects varied as a function of specific stimulation parameters and experimental conditions.
Although all selected studies reported a significant effect of tDCS in at least one condition in the published paper, the results of the four meta-analyses showed only statistically non-significant close-to-zero effects. A moderator analysis suggested that for anodal tDCS, the duration of the stimulation and the task used to probe memory moderated the effectiveness of tDCS. For cathodal tDCS, site of stimulation was a significant moderator, although this result was based on only a few observations.
To warrant theoretical advancement and practical implications, more rigorous research is needed to fully understand whether tDCS reliably modulates episodic memory, and the specific circumstances under which this modulation does, and does not, occur.
•We conducted four meta-analyses to assess the effects of tDCS on episodic memory.•We examined the effects of anodal and cathodal tDCS.•We found no effects of tDCS on episodic memory accuracy or response times.•Specific stimulation parameters moderated the effects of tDCS.
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.
Alzheimer's disease (AD) is a complex neurodegenerative disease with no existing treatment leading to full recovery. The blood-brain barrier (BBB) breakdown usually precedes the advent of first ...symptoms in AD and accompanies the progression of the disease. At the same time deliberate BBB opening may be beneficial for drug delivery in AD. Non-invasive brain stimulation (NIBS) techniques, primarily transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), have shown multiple evidence of being able to alleviate symptoms of AD. Currently, TMS/tDCS mechanisms are mostly investigated in terms of their neuronal effects, while their possible non-neuronal effects, including mitigation of the BBB disruption, are less studied. We argue that studies of TMS/tDCS effects on the BBB in AD are necessary to boost the effectiveness of neuromodulation in AD. Moreover, such studies are important considering the safety issues of TMS/tDCS use in the advanced AD stages when the BBB is usually dramatically deteriorated. Here, we elucidate the evidence of NIBS-induced BBB opening and closing in various models from in vitro to humans, and highlight its importance in AD.
Recent evidence suggests that brain activity following the offset of a stimulus during encoding contributes to long-term memory formation, however the exact mechanisms underlying offset-related ...encoding are still unclear.
Here, in three repetitive transcranial magnetic stimulation studies (rTMS) we investigated offset-related activity in the left ventrolateral prefrontal cortex (VLPFC). rTMS was administered at different points in time around stimulus offset while participants encoded visually-presented words or pairs of words. The analyses focused on the effects of the stimulation on subsequent memory performance.
rTMS administered at the offset of the stimuli, but not during online encoding, disrupted subsequent memory performance. In Experiment 1 we found that rTMS specifically disrupted encoding mechanisms initiated by the offset of the stimuli rather than general, post-stimulus processes. Experiment 2 showed that this effect was not dependent upon rTMS-induced somatosensory effects. In a third rTMS experiment we further demonstrated a robust decline in associative memory performance when the stimulation was delivered at the offset of the word pairs, suggesting that offset-related encoding may contribute to the binding of information into an episodic memory trace.
The offset of the stimulus may represent an event boundary that promotes the reinstatement of the previously experienced event and episodic binding.
•The administration of rTMS over the left VLPFC at the offset of to-be-remembered words impairs later memory for those words.•Offset-related brain activity is crucial for long-term memory formation.•Offset-related encoding may contribute to the binding of information into an episodic memory trace.
With the urgent need for new medical approaches due to increased bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have been considered as potential treatments for infections. ...Experiments indicate that combinations of several types of AMPs might be even more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacterial resistance. The molecular mechanisms of AMP–AMP synergistic antimicrobial activity, however, remain not well understood. Here, we present a theoretical approach that allows us to relate the physicochemical properties of AMPs and their antimicrobial cooperativity. It utilizes correlation and bioinformatics analysis. A concept of physicochemical similarity is introduced, and it is found that less similar AMPs with respect to certain physicochemical properties lead to greater synergy because of their complementary antibacterial actions. The analysis of correlations between the similarity and the antimicrobial properties allows us to effectively separate synergistic from nonsynergistic AMP pairs. Our theoretical approach can be used for the rational design of more effective AMP combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems.
There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent ...years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.
Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing ...drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical–chemical arguments to explain these observations are presented.
The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with ...other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is impossible to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets disqualifies the application of traditional machine-learning methods or makes the results unreliable. Here, we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria’s response to untested AMPs based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link prediction approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to propose new combinations that are likely to be effective.
Antimicrobial peptides (AMPs), or defence peptides, are compounds naturally produced during immune responses of living organisms against bacterial infections that are currently actively considered as ...promising alternatives to antibiotics. Recent experimental studies uncovered that in many situations, combinations of different AMPs are much more successful in eliminating the bacterial pathogens than single peptide species. However, the microscopic origin of such synergistic activities remains not fully understood. We present and investigate a possible mechanism of synergy between AMPs. It is based on the idea that due to inter-molecular interactions, the presence of an AMP of one type stimulates the association of an AMP of another type, and this accelerates the overall association to the membrane, eventually killing the bacteria. This approach allows us to fully quantify the synergistic activities of AMPs, and it is successfully applied for several experimental systems. It is found that strong cooperativity can be achieved for relatively weak inter-molecular interactions, suggesting that the application of combinations of AMPs can be further rationally optimized to make it a powerful antibacterial treatment.
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