Abstract
Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation used for improving cognitive functions via delivering weak electrical stimulation with a certain frequency. This systematic review and meta-analysis investigated the effects of tACS protocols on cognitive functions in healthy young adults. We identified 56 qualified studies that compared cognitive functions between tACS and sham control groups, as indicated by cognitive performances and cognition-related reaction time. Moderator variable analyses specified effect size according to (a) timing of tACS, (b) frequency band of simulation, (c) targeted brain region, and (b) cognitive domain, respectively. Random-effects model meta-analysis revealed small positive effects of tACS protocols on cognitive performances. The moderator variable analyses found significant effects for online-tACS with theta frequency band, online-tACS with gamma frequency band, and offline-tACS with theta frequency band. Moreover, cognitive performances were improved in online- and offline-tACS with theta frequency band on either prefrontal and posterior parietal cortical regions, and further both online- and offline-tACS with theta frequency band enhanced executive function. Online-tACS with gamma frequency band on posterior parietal cortex was effective for improving cognitive performances, and the cognitive improvements appeared in executive function and perceptual-motor function. These findings suggested that tACS protocols with specific timing and frequency band may effectively improve cognitive performances.
Similar content being viewed by others
Introduction
Cognitive processes are related to exchanging neuronal signals in a specific manner across widely distributed brain regions1,2. Given that a large number of cortical and sub-cortical regions are functionally interconnected, altered neural activation patterns in specific brain area simultaneously influence neural activations in other brain region3,4. Specifically, the temporal synchronization of rhythmic oscillations across key brain regions may be crucial neurophysiological mechanism for mediating functional neural networks contributing to information processing and communications5,6,7,8,9. For example, the signal synchronization across pre-synaptic spikes within sending neuron populations in one or several cortical regions may effectively drive activities of post-synaptic neuronal populations in receiving regions10,11,12. Interestingly, synchronized oscillations of neuronal populations in a certain frequency band may be associated with advanced cognitive functions13,14.
Previous studies raised a possibility that neural oscillations at specific frequency band predominantly appears in various cognitive processes15,16. For example, increased synchronized oscillations at the theta frequency band (4–7 Hz) may be associated with improved executive function / complex attention and learning and memory17,18. Specifically, a classical animal study that used the electrocorticogram reported greater neural oscillations at the theta frequency band in rat hippocampal pyramidal neurons during spatial navigation tasks19. Moreover, theta rhythmic neural oscillations were observed in the human prefrontal cortex (PFC) while remembering a list of items20,21. Greater neural synchronization in brain at the alpha frequency band (8–12 Hz) may be related to executive function and complex attention22. Several electrophysiological studies evidence higher alpha rhythmic neural synchronization across PFC and parietal cortical areas while generating creative ideas23,24, and further these oscillation patterns was linked to improved inhibitory functions25,26. In addition, greater brain oscillation at the beta frequency band (13–30 Hz) presumably improved the executive function / complex attention27,28. Specifically, beta frequency power in PFC and primary motor cortex (M1) increased during preparatory and inhibitory phases for the movement execution, whereas beta frequency power decreased after the movement execution29,30,31. Presumably, neural oscillation patterns at the gamma frequency band (31–139 Hz) influenced the executive function, complex attention, and social cognition16. Gamma waves emerge in the animal parietal and frontal regions during attentive behavioral states such as a cat observing prey in a room32, and further were activated while integrating sensory information33,34. Taken together, modulating the synchronization of brain oscillations at a specific frequency band may effectively facilitate improvement in various cognitive functions.
Transcranial alternating current stimulation (tACS), one of the non-invasive brain stimulation technique, has been developed to modulate brain oscillations at certain frequency band for enhancing either cognitive or motor functions35,36,37. tACS protocols use weak sinusoidal oscillating electrical currents into the scalp to temporarily synchronize the neural firing timing38,39. Thus, the rhythmically reversed electron flow potentially interacts with endogenous oscillations in the brain40,41 as previous electroencephalogram (EEG) studies suggested entrained endogenous brain oscillations and external currents37,42,43. Interestingly, recent literature review studies raised a possibility of positive effects of tACS protocols on cognitive functions38,44. Moreover, some prior studies suggested that timing of tACS protocols (e.g., tACS protocols during cognitive tasks: online stimulation and tACS protocols before cognitive tasks: offline stimulation) may induce different effects on cognitive functions45,46. For example, online-tACS protocols may facilitate higher entrainment between ongoing neural oscillation and external electrical oscillations40, whereas offline-tACS protocols may cause longer lasting after-effects presumably contributing to network changes related to neural plasticity47. Thus, determining potential treatment effects of tACS interventions based on different stimulation timing can provide meaningful information on identifying optimal stimulation protocols facilitating cognitive functions.
The purpose of this systematic review and meta-analysis was to investigate the effect of tACS protocols on cognitive functions in healthy young adults. Previous studies suggested that the existence of speed and accuracy trade-off in cognitive processes that hasty responses are error-prone whereas careful decisions take more time48,49. Further, different brain involvements were observed between cognitive functions estimated by speed and accuracy, respectively50. Thus, we focused on two types of cognitive function variables including cognitive performance and cognition-related reaction time to examine potential altered cognitive functions between active tACS protocols and sham stimulation. In addition, we compared potential different effects of timing of tACS protocols (i.e., online versus offline stimulation) on cognitive function, and further determined whether specific frequency bands for tACS protocols (i.e., delta vs. theta vs. alpha vs. beta vs. gamma vs. ripple) alter cognitive function improvements40,45. For each frequency band of online- and offline-tACS protocols, we additionally examined specific treatment effects on cognitive functions based on different targeted brain regions and cognitive domains, respectively51.
Results
Study identification
Our initial search found 573 potential studies from the PubMed, 26 potential articles from the Web of Science, and 43 articles from other resources, and we removed 14 duplicated articles. In addition, we excluded 572 articles (i.e., 30 review articles, three case articles, and 539 studies irrelevant to our topic). Finally, the remaining 56 studies that examined potential effects of tACS on cognitive functions using either cognitive performance or cognition-related reaction time variables qualified for this meta-analysis45,46,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105. The PRISMA flow diagram illustrating our study identification procedure is shown in Fig. 1.
The flowchart shows the study identification procedure.
Participant characteristics
Fifty-six total qualified studies in this meta-analysis included 1797 healthy young adults without any neurological and psychological deficits (a range of mean age = 18.0–33.0 years and a range of female proportion = 52.8–100%). Nine studies were randomized controlled trials, and 46 studies used a crossover design. One study used both designs for each experiment65. Table 1 shows specific detailed demographic information on the participants.
tACS protocols and potential side effects
For improving cognitive functions, the qualified studies used tACS protocols stimulating regions of (a) prefrontal cortex (PFC) including primary motor cortex, dorsolateral-prefrontal cortex, inferior frontal gyrus, and frontal region = 21 studies, (b) posterior parietal cortex (PPC) including posterior occipital cortex and parietal cortex = 19 studies, (c) temporal cortex (TC) including fusiform cortex and temporal region = seven studies, and (d) multiple regions (Multi) such as targeting multiple regions across PFC, PPC, and TC = eight studies. One study focused on two different regions including PPC and TC, respectively78. For the timing of tACS protocols, 38 studies used tACS protocols during cognitive tasks (i.e., online-tACS), and 12 studies applied tACS protocols prior to executing cognitive tasks (i.e., offline-tACS). Six studies examined both timings of tACS protocols, respectively46,55,58,65,86,96. Twenty-eight out of 56 total studies administered only tACS protocols, whereas the remaining 28 studies applied tACS protocols with additional task-related trainings (e.g., brief training phase, discrimination task, familiarization session, language assessment, training visual associative memory task, and word-pair learning). Forty-eight studies administered a single session of tACS protocols, whereas eight studies applied multiple sessions of tACS protocols (i.e., 2–4 sessions).
The specific parameters of tACS protocols used for the qualified studies were: (a) stimulation intensity = 0.7–3 mA, (b) electrode area = 1.2–35 cm2, (c) current density = 0.02–0.83 mA/cm2, (d) density charge = 0.24–16.8 C/cm2, and (e) session duration = 2 s–48 min. Specific frequency bands for tACS protocols included: (a) delta band (1–3 Hz) = four studies, (b) theta band (4–7 Hz) = 30 studies, (c) alpha band (8–12 Hz) = 19 studies, (d) beta band (13–30 Hz) = seven studies, (e) gamma band (31–139 Hz) = 24 studies, and (f) ripple band (140 Hz) = one study. Specific details on tACS protocols are shown in Table 2.
Regarding the potential side effects of tACS protocols, 11 studies confirmed that participants did not experience any side effects. Twenty-three studies reported that some participants experienced side effects: (a) discomfort = three studies, (b) itching = 10 studies, (c) mild headache = four studies, (d) tingling = 11 studies, (e) tiredness = three studies, (f) phosphene (flickering) = eight studies, (g) attention difficulties = six studies, (h) dizziness = one study, (i) pain (e.g., pinch, burning, heat, shock-like sensations, pricking) = six studies, and (j) other side effects = three studies (e.g., fatigue, tiring, and anxiety)54,55,56,57,63,64,66,67,70,73,75,76,77,80,82,83,85,87,88,92,93,102,104. In the 34 studies, ~46.2% of participants (i.e., number of participants from studies that reported the presence of side effects / total number of participants from studies that reported presence or absence of side effects × 100) may experience potential side effects of tACS protocols (Supplementary Table 1). However, the remaining 22 studies failed to mention whether participants experienced side effects.
Cognitive function assessments
Thirty-eight out of 56 qualified studies reported cognitive performance variables and six studies showed cognition-related reaction time variables. The remaining twelve studies reported both cognitive performance and reaction time variables. Taken together, 50 out of 56 qualified studies reported cognitive performance variable comparisons and 18 out of 56 qualified studies reported cognition-related reaction time variable comparisons (Table 3).
For cognitive performance variables, specific measurements were: (a) accuracy = six studies, (b) correctness (e.g., correctly recalled words, correct response, and correct associative memory) = eight studies, (c) creativity index: two studies, (d) d-prime = three studies, (e) number of errors = four studies, (f) scores (e.g., digit span forward scores, memory capability scores, fluid intelligence scores, and correctly answered scores) = eight studies, and (g) others (e.g., average probability, behavioral adaptation, d-index, false-choice trial, hit ratio, laterality index, memory performance, motion dominance index, number of adjusted pumps, Pashler’s K, performance change rate, recognition, and updating gain) = 19 studies.
In this study, specific cognitive domains included: (a) perceptual-motor function (e.g., visual detection, Cambridge face perception task, and face and scene task) = 12 studies, (b) learning and memory (e.g., memory recognition task, word-pair learning task, and language learning task) = nine studies, (c) executive function / complex attention (e.g., n-back task, digit span task, and change detection task): = 34 studies, and (d) language (e.g., phoneme-categorization task): = one study.
Specific comparisons for meta-analysis
For meta-analysis procedures, we acquired specific comparisons from each included study because of different experiments, timing (i.e., online and offline), and frequency bands (i.e., delta, theta, alpha, beta, gamma, and ripple) of tACS protocols. Twenty-eight out of 50 studies that used cognitive performance variables reported one comparison, and 22 studies reported multiple comparisons (i.e., 13 studies reported two comparisons, two studies reported three comparisons, five studies reported four comparisons, one study reported five comparisons, and one study showed eight comparisons). For 18 studies that used cognition-related reaction time variables, nine studies reported one comparison and nine studies reported multiple comparisons (i.e., six studies reported two comparisons, one study reported three comparisons, and two studies reported four comparisons). Taken together, the meta-analysis focused on 93 total cognitive performance variable comparisons from the 50 studies and 32 total cognition-related reaction time variable comparisons from the 18 studies.
Methodological quality assessments
The Cochrane risk of bias assessment showed three potential methodological concerns including (a) randomized process, (b) deviations from intended interventions, and (c) measurements of the outcome. Especially, 23 included studies failed to either mention a specific randomization process or randomly assign the tACS conditions, and 41 out of 56 studies did not mention the blinding of experimenters or assessors. However, we confirmed that the current meta-analysis showed a low level of risk bias in (a) timing of identification or recruitment of participants, (b) missing outcome data, and (c) selection of the reported result domains (Fig. 2).
The Cochrane risk of bias assessment reveals potential methodological concerns.
Meta-analytic findings on cognitive performance
The random-effects meta-analysis on 93 total comparisons from the 50 studies identified a significant low overall effect of tACS protocols on cognitive performance improvements (SMD = 0.161; SE = 0.027; 95% CI = 0.109–0.214; Z = 6.038; P < 0.001). The heterogeneity tests revealed lower level of variability across the 93 comparisons (Q-statistics = 130.256 and P = 0.005; I2 = 29.4%), and the publication bias was the relatively asymmetrical distribution of individual effect sizes: (1) a revised funnel plot with 7 imputed values (Supplementary Fig. 1) and (2) Egger’s regression intercept (β0) = 1.57 and P = 0.001.
The first moderator variable analysis for comparing the effects of online-tACS versus offline-tACS on changes in cognitive performance showed significant treatment effects: (a) 71 online-tACS comparisons from the 38 studies: SMD = 0.168; SE = 0.033; 95% CI = 0.104–0.233; Z = 5.138; P < 0.001; Q-statistics = 118.535 with P < 0.001; I2 = 41.0% (Fig. 3) and (b) 22 offline-tACS comparisons from the 17 studies: SMD = 0.153; SE = 0.049; 95% CI = 0.056–0.250; Z = 3.092; P = 0.002; Q-statistics = 11.715 with P = 0.947; I2 = 0.0% (Fig. 4). These findings indicate that tACS protocols showed significant improvements in cognitive performance regardless of stimulation timing.
Meta-analytic findings show potential effects of online-tACS protocols on changes in cognitive performances.
Meta-analytic findings show potential effects of offline-tACS protocols on changes in cognitive performances.
For online-tACS comparisons, the second moderator variable analysis for comparing the effects of different frequency bands (i.e., delta vs. theta vs. alpha vs. beta vs. gamma) of tACS protocols showed significant positive effects of theta and gamma frequency bands on cognitive performance: (a) 26 theta frequency band comparisons from the 22 studies: SMD = 0.247; SE = 0.069; 95% CI = 0.111–0.383; Z = 3.556; P < 0.001; Q-statistics = 62.079 with P < 0.001; I2 = 59.7% and (b) 21 gamma frequency band comparisons from the 18 studies: SMD = 0.175; SE = 0.049; 95% CI = 0.078–0.272; Z = 3.547; P < 0.001; Q-statistics = 23.442 with P = 0.268; I2 = 14.7% (Fig. 5).
Meta-analytic findings show potential effects of online-tACS protocols with theta and gamma frequency bands on changes in cognitive performances.
However, the analyses revealed no significant effects of delta, alpha, and beta frequency bands on cognitive performance improvements: (a) four delta frequency band comparisons from the three studies: SMD = 0.178; SE = 0.106; 95% CI = −0.030–0.387; Z = 1.675; P = 0.094; Q-statistics = 3.525 with P = 0.318; I2 = 14.9%, (b) 15 alpha frequency band comparisons from the 14 studies: SMD = 0.044; SE = 0.052; 95% CI = −0.058–0.146; Z = 0.854; P = 0.393; Q-statistics = 14.800 with P = 0.392; I2 = 5.4%, and (c) five beta frequency band comparisons from the five studies: SMD = 0.185; SE = 0.136; 95% CI = −0.081–0.450; Z = 1.363; P = 0.173; Q-statistics = 8.516 with P = 0.074; I2 = 53.0% (Fig. 6).
Meta-analytic findings show no significant effects of online-tACS protocols with delta, alpha, and beta frequency bands on changes in cognitive performances.
For the comparisons of each frequency band of online-tACS protocols, the third moderator variable analysis examined specific changes in cognitive performances among different targeted brain regions, respectively. The analysis revealed significant positive effects for the following conditions: (a) 12 PFC in the theta frequency band comparisons from 10 studies: SMD = 0.389; SE = 0.122; 95% CI = 0.149–0.629; Z = 3.180; P = 0.001; Q-statistics = 37.850 with P < 0.001; I2 = 70.9%, (b) 10 PPC in the theta frequency band comparisons from eight studies: SMD = 0.206; SE = 0.078; 95% CI = 0.052–0.359; Z = 2.627; P = 0.009; Q-statistics = 10.930 with P = 0.281; I2 = 17.658%, and (c) five PPC in the gamma frequency band comparisons from four studies: SMD = 0.243; SE = 0.120; 95% CI = 0.007–0.479; Z = 2.018; P = 0.044; Q-statistics = 1.747 with P = 0.782; I2 = 0.0% (Fig. 7). We found no significant changes in cognitive performance variables for the remaining conditions (Supplementary Table 2).
Meta-analytic findings show potential effects of online-tACS protocols with theta frequency band on PFC and PPC and online-tACS protocols with gamma frequency band on PFC.
For the comparisons of each frequency band of online-tACS protocols, the fourth moderator variable analysis investigated different changes in cognitive performances based on cognitive domains, respectively. The analysis revealed significant positive effects for the following conditions: (a) 17 executive function / complex attention in the theta frequency band comparisons from 14 studies: SMD = 0.325; SE = 0.102; 95% CI = 0.125–0.526; Z = 3.180; P = 0.001; Q-statistics = 54.206 with P = 0.001; I2 = 70.5%, (b) 15 executive function / complex attention in the gamma frequency band comparisons from 13 studies: SMD = 0.154; SE = 0.060; 95% CI = 0.036– 0.271; Z = 2.564; P = 0.010; Q-statistics = 19.114 with P = 0.161; I2 = 26.8%, and (c) six perceptual-motor function in the gamma frequency band comparisons from five studies: SMD = 0.264; SE = 0.100; 95% CI = 0.068–0.460; Z = 2.635; P = 0.008; Q-statistics = 3.198 with P = 0.669; I2 = 0.0% (Fig. 8). We found no significant changes in cognitive performance variables for the remaining conditions (Supplementary Table 3).
Meta-analytic findings show potential effects of online-tACS protocols with theta frequency band on changes in executive function / complex attention and online-tACS protocols with gamma frequency band on changes in executive function / complex attention and perceptual-motor function.
For offline-tACS comparisons, the moderator variable analysis on 12 theta frequency band comparisons from the 10 studies showed significant positive effects on cognitive performance: SMD = 0.221; SE = 0.067; 95% CI = 0.090–0.352; Z = 3.298; P = 0.001; Q-statistics = 7.183 with P = 0.784; I2 = 0.0% (Fig. 9). However, the analyses showed no significant effects on alpha, beta, gamma, and ripple frequency bands on cognitive performance improvements: (a) four alpha frequency band comparisons form the four studies: SMD = 0.044; SE = 0.112; 95% CI = −0.175–0.264; Z = 0.394; P = 0.693; Q-statistics = 0.667 with P = 0.881; I2 = 0.0% and (b) four gamma frequency band comparisons from the four studies: SMD = 0.060; SE = 0.125; 95% CI = −0.185–0.305; Z = 0.482; P = 0.630; Q-statistics = 1.107 with P = 0.775; I2 = 0.0%.
Meta-analytic findings show potential effects of offline-tACS protocols with theta frequency band on changes in cognitive performances.
For the comparisons of each frequency band of offline-tACS protocols, the third moderator variable analysis that examined specific changes in cognitive performances among different targeted brain regions revealed significant positive effect for the following condition: five PFC in the theta frequency band comparisons from three studies: SMD = 0.204; SE = 0.092; 95% CI = 0.023– 0.384; Z = 2.211; P = 0.027; Q-statistics = 2.009 with P = 0.734; I2 = 0.0% (Fig. 10). The fourth moderator variable analysis that investigated potential different treatment effects based on cognitive domains showed significant positive effect for the following condition: nine executive function / complex attention in the theta frequency band comparisons from seven studies: SMD = 0.204; SE = 0.075; 95% CI = 0.056–0.351; Z = 2.711; P = 0.007; Q-statistics = 4.855 with P = 0.773; I2 = 0.0% (Fig. 10). We found no significant changes in cognitive performance variables for the remaining conditions (Supplementary Tables 4 and 5).
Meta-analytic findings show potential effects of offline-tACS protocols with theta frequency band on PFC and executive function / complex attention.
Meta-analytic findings on cognition-related reaction time
The random-effects model meta-analysis on 32 total comparisons from the 18 studies failed to demonstrate a significant effect of tACS protocols on the reaction time (SMD = 0.062; SE = 0.060; 95% CI = −0.055–0.180; Z = 1.038; P = 0.229) with moderate heterogeneity levels of variability across the 32 comparisons (Q-statistics = 66.181 and P < 0.001; I2 = 53.2%), and publication bias was the relatively symmetrical distribution of individual effect size: (1) a revised funnel plot with 8 imputed values and (2) Egger’s regression intercept (β0) = 0.92 and P = 0.465 (Supplementary Fig. 2).
The moderator variable analysis for comparing effects of online-tACS versus offline-tACS on changes in cognition-related reaction time revealed no significant treatment effects: (a) 26 online-tACS comparisons from the 15 studies: SMD = 0.043; SE = 0.072; 95% CI = −0.098–0.184; Z = 0.595; P = 0.552; Q-statistics = 61.730 with P < 0.001; I2 = 59.5% and (b) six offline-tACS comparisons from the four studies: SMD = 0.148; SE = 0.091; 95% CI = −0.031–0.327; Z = 1.625; P = 0.104; Q-statistics = 3.058 with P = 0.691; I2 = 0.0% (Fig. 11).
Meta-analytic findings show no significant effects of online- and offline-tACS protocols on changes in cognition-related reaction time.
For online-tACS comparisons, moderator variable analysis comparing the effects of different frequency bands (i.e., delta vs. theta vs. alpha vs. beta vs. gamma) of tACS protocols failed to show any significant positive effects on cognition-related reaction time (Supplementary Fig. 3): (a) eight theta frequency band comparisons from the eight studies: SMD = 0.029; SE = 0.189; 95% CI = −0.342–0.399; Z = −0.151; P = 0.880; Q-statistics = 32.153 with P < 0.001; I2 = 78.2%, (b) six alpha frequency band comparisons from the six studies: SMD = 0.020; SE = 0.090; 95% CI = −0.155–0.196; Z = −0.228; P = 0.820; Q-statistics = 3.266 with P = 0.659; I2 = 0.0%, (c) two beta frequency band comparisons from the two studies: SMD = 0.175; SE = 0.175; 95% CI = −0.169–0.518; Z = 0.995; P = 0.320; Q-statistics = 0.006 with P = 0.937; I2 = 0.0%, and (d) nine gamma frequency band comparison from the eight studies: SMD = 0.077; SE = 0.131; 95% CI = −0.180–0.333; Z = 0.587; P = 0.557; Q-statistics = 24.787 with P = 0.002; I2 = 67.7%. For the comparisons of each frequency band of online-tACS protocols, the third (different targeted brain regions) and fourth (different cognitive domains) moderator variable analyses identified no significant effects on cognition-related reaction time (Supplementary Tables 6 and 7).
For offline-tACS comparisons, moderator variable analysis comparing the effects of the different frequency band (i.e., delta vs. theta vs. alpha vs. beta vs. gamma) of tACS protocols failed to show any significant positive effects on cognition-related reaction time (Supplementary Fig. 4): two gamma frequency band comparison from the two studies: SMD = 0.104; SE = 0.170; 95% CI = −0.229–0.437; Z = 0.613; P = 0.540; Q-statistics = 0.161 with P = 0.668; I2 = 0.0%. For the comparisons of each frequency band of offline-tACS protocols, the third (different targeted brain regions) and fourth (different cognitive domains) moderator variable analyses identified no significant effects on cognition-related reaction time (Supplementary Tables 8 and 9).
Discussion
The current systematic review and meta-analysis investigated the effects of specific tACS protocols on cognitive functions in healthy young adults. We identified 56 total studies that examined potential effects of tACS on cognitive functions using either cognitive performance or cognition-related reaction time variables. Fifty out of 56 qualified studies reported cognitive performance variable comparisons and 18 out of 56 qualified studies reported cognition-related reaction time variable comparisons. Ninety-three total comparisons from the 50 qualified studies indicated small positive overall effects on cognitive performances after active tACS protocols than sham control stimulation. Moreover, the moderator variable analyses revealed that both online- and offline-tACS protocols significantly improved cognitive performances, and further these cognitive performance improvements were observed in three specific frequency bands of tACS protocols including (a) online-tACS with theta frequency band, (b) online-tACS with gamma frequency band, and (c) offline-tACS with theta frequency band. Additional moderator analyses found that cognitive performances were improved in online-tACS with theta frequency band on PFC and PPC and online-tACS with gamma frequency band on PPC. For offline-tACS protocols, stimulation with theta frequency band on PFC significantly improved cognitive performances. Finally, online-tACS with theta frequency band significantly improved executive function and online-tACS with gamma frequency band enhanced executive function and perceptual-motor function. Offline-tACS with theta frequency band significantly improved executive function. However, we found that all specific tACS protocols failed to show any significant reduction of cognitive-related reaction time.
Our meta-analytic findings indicated that tACS protocols improved task performances in various cognitive tasks. These findings support the argument from a recent systematic review study that tACS protocols may be advantageous for improving various cognitive domains such as working memory, executive function, and declarative memory40. tACS protocols may induce the synchronization of neural firing timing in the cortical regions by applying low-intensity sinusoidal oscillating electrical stimulation into the scalp106,107. The synchronized neural firing timing in a specific brain region may contribute to improvement in cognitive functions via enhancing information-processing and memory-encoding functions56,108,109. However, we failed to identify a significant reduction of cognitive-related reaction time in healthy young adults. Previous studies suggested that decreased reaction time may be related to greater firing rate of cortical neurons110,111. In fact, greater brain activation appeared in the pre-supplementary cortex (pre-SMA)112, dorsolateral-prefrontal cortex (DLPFC)48,113, and striatum of the basal ganglia while performing faster motor actions48,110. For example, applying transcranial direct current stimulation (tDCS) significantly reduced reaction time during various cognitive tasks in healthy younger and older adults114,115 because of potential effects of tDCS on increased neural firing rate116,117. Potentially, given that tACS protocols may modulate the neural firing timing rather than neural firing rate in the targeted cortical regions106, the application of tACS may be more beneficial for improving cognitive performances (e.g., task accuracy).
The first moderator variable analysis revealed significant improvements in cognitive performances for both online- and offline-tACS conditions. Previous studies reported that applying online-tACS protocols effectively increased the synchronization between external (i.e., electrical stimulation) and internal oscillations (i.e., neural activation) in the targeted brain areas38,40,54. For example, when online-tACS with alpha frequency range was applied in awake non-human primates118 and human parieto-occipital cortex37, the neuron spike timing was significantly synchronized in the alpha frequency band. In addition, the neural synchronization in the occipital lobe after online-tACS protocols improved the perception of healthy younger adults60,92. The benefits of offline-tACS protocols on cognitive performances indicated potential after-effects that may be related to long-term potentiation (LTP) indicating increased synaptic strengthening119,120,121. Further, greater activation of N-methyl-D-aspartic (NMDA) receptors may be associated with the induction of LTP plasticity122. Interestingly, a prior study showed that offline-tACS protocols may cause LTP plasticity via facilitating the NMDA receptors activity in M1, because admistration of the NMDA blocker dextromethorphan diminished the effect123. Overall, the positive effects of both online- and offline-tACS protocols on cognitive performances support a proposition that tACS protocols may be effective for improving cognitive processing via either neural synchronization or LTP plasticity37,124.
Common findings from the second moderator variable analysis included that tACS protocols with theta frequency band significantly improved cognitive performances for both online and offline conditions. Further, additional moderator variable findings suggested that both online- and offline-tACS protocols with theta frequency band on either PFC or PPC enhanced cognitive performances. Specifically, we observed significant improvements in executive function after tACS protocols with theta frequency band. These findings are in line with previous findings that tACS protocols with theta frequency band was beneficial for improving various cognitive functions40. Specifically, tACS protocols with theta frequency band increased neural activations across the right temporal, dorsolateral-prefrontal, and frontal cortex during information encoding and retrieval processes125,126. Interestingly, brain oscillation patterns in frontal and posterior parietal regions were higher activated at theta frequency band when performing the cognitive tasks52,127, and further improvements in cognitive functions appeared with increased functional connectivity between long-distance cortical regions128,129,130. Recent functional magnetic resonance imaging studies additionally evidenced that theta-tACS protocols modulated neural connections of the hippocampal–cortical network70,131. These findings suggested that applying tACS protocols with theta frequency band may facilitate neural pathways within cortical regions and between cortical and sub-cortical regions contributing to improved cognitive functions.
Moreover, online-tACS protocols with gamma frequency band showed beneficial effects on cognitive performances. Interestingly, the additional moderator variable analyses demonstrated potential treatment effects of online-tACS with gamma frequency band stimulating PPC on cognitive performances, and the cognitive improvements appeared in executive function and perceptual-motor function. Previous studies suggested that rapid cortical oscillations at the gamma frequency band contributed to improved cognitive processes132,133,134. For instance, the gamma neural oscillations were observed in the medial visual cortex and anterior insula while showing better visual perception and decision-making abilities135,136. Moreover, applying gamma tACS protocols showed various cognitive improvements such as faster and accurate auditory and visual perceptions and memory performances40,59,137. Brain oscillations at the gamma frequency band may be activated via the reciprocal connection between GABAergic activity of interneurons and activity of glutamatergic pyramidal neurons138,139,140. Presumably, the gamma frequency synchronization facilitated by tACS protocols may allow precisely and flexibly transfer the neural information between the targeted brain areas16,130,141,142.
Although the current meta-analytic findings reveal significant positive effects of tACS protocols (i.e., theta and gamma frequency bands) on cognitive performances, the levels of cognitive improvements are relatively small (effect size range from 0.175 to 0.247). Recent findings suggested a proposition that applying tACS protocols using theta-gamma phase-amplitude coupling (PAC) can effectively modulate cognitive functions143. According to the cross-frequency coupling phenomenon144,145, cognitive function may improve when low-frequency brain oscillations reflecting information processing across largely distributed brain areas are coupled with high-frequency brain oscillations representing information processing in local brain regions146,147,148. The PAC is one of cross-frequency coupling phenomena representing that the low-frequency phase modulates the high-frequency amplitude149,150,151. Several findings posited that inducing theta-gamma PAC by delivering simultaneous theta and gamma frequency tACS over multifocal areas may facilitate neural interactions between the cortical and sub-cortical regions contributing to cognitive improvements152,153,154. In fact, applying co-stimulation protocols with theta and gamma frequency bands to the prefrontal cortex significantly improved working memory functions143. Moreover, theta-gamma cross-frequency coupling is important for various cognitive functions such as visual information processing and working memory154,155. These findings suggest that tACS protocols with co-stimulation at theta and gamma frequency bands may be a viable option to increase cognitive improvements by inducing theta-gamma PAC that potentially reinforces neural communications across brain regions138.
Despite quantitative findings indicating potential effective tACS protocols for cognitive functions in healthy younger adults, these are some limitations. First, given that the current meta-analysis focused on altered cognitive functions in healthy younger adults, the relatively small effects of tACS protocols may be influenced by a ceiling effect55. Thus, future studies need to quantity beneficial effects of tACS protocols on cognitive functions for participants with cognitive impairments (e.g., older adults and patients with neurological diseases). Second, 20 studies in this meta-analysis reported potential side effects after tACS protocols. Tingling and itching are frequently observed after transcranial electrical stimulation156. In particular, tACS may cause phosphenes in which artificial light flashing or shimmering affects visual perceptions and concentration. Phosphenes often appeared when either tACS applied adjacent to occipital cortex or the stimulation intensity greater than 1.5 mA provided157. To minimize these potential side effects, providing individualized current intensity thresholds and electrode montage positions should be considered in future studies.
The current systematic review and meta-analysis revealed that applying tACS protocols significantly improved cognitive performances in healthy younger adults. Moreover, moderator variable analyses found the positive effects on cognitive performances for both online- and offline-tACS conditions. Specifically, significant improved cognitive performances after tACS protocols were observed in following frequency bands: (a) online-tACS with theta frequency band, (b) online-tACS with gamma frequency band, and (c) offline-tACS with theta frequency band. Further, cognitive performances were improved in online- and offline-tACS with theta frequency band on either PFC or PPC, and further both online- and offline-tACS with theta frequency band enhanced executive function. Online-tACS with gamma frequency band on PPC was effective for improving cognitive performances, and the cognitive improvements appeared in executive function and perceptual-motor function. These meta-analytic findings suggest that applying specific tACS protocols can facilitate improvements in various cognitive performances for healthy young adults. Importantly, previous studies revealed that the changes in PAC characteristics caused by decreased theta frequency band may be related to cognitive impairments in older adults as well as patients with neurologic diseases such as schizophrenia, Alzheimer’s disease, and epilepsy150,158,159. Thus, future studies should investigate whether tACS protocols with co-stimulation at theta and gamma frequency bands are beneficial for improving cognitive functions in older adults and patients with neurological diseases.
Methods
Literature search and study inclusion
Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement160, we conducted a systematic review and meta-analysis. The computerized literature search from November 15, 2020 to October 29, 2021 identified potential studies via PubMed and Web of Science. We used the following keywords: (tACS or transcranial alternating current stimulation) and (reaction time or response time or RT or cognitive or cognition or cognitive performance or cognitive function) and (healthy and adults). The inclusion criteria for this meta-analysis were: (a) studies recruiting cognitively healthy young adults, (b) studies performed quantitative evaluation on either cognitive performance or cognition-related reaction time, (c) studies included sham stimulation controls, and (d) studies with a randomized control trial or crossover design. We excluded review articles, case studies, animal studies, and articles that were not related to our main topic (e.g., elderly population, participants with specific disorder, and no tACS effects reported).
Cognitive function outcome measures
To investigate changes in cognitive function after tACS protocols, we focused on two primary outcome measures including (a) cognitive performance variable (i.e., accuracy, precision, correct response, error rated, score, and hit rated) and (b) cognition-related reaction time variable (i.e., time interval between stimuli and the completion of the cognitive task). To examine the effects of tACS on specific cognitive domains, we categorized cognitive functions into five components51,114,161: (a) perceptual-motor function (e.g., visual perception and perceptual-motor integration), (b) learning and memory (e.g., free recall, recognition, long-term memory, and implicit learning), (c) executive function / complex attention (e.g., planning, decision-making, working memory, selective attention, and inhibition), (d) language (e.g., object naming, fluency, and receptive language), and (e) social cognition (e.g., recognition of emotions, theory of mind, and insight).
Meta-analytic approaches for data synthesis
Using the meta-analysis software (Comprehensive Meta-Analysis software ver. 3.2, Englewood, NJ, USA), we performed all meta-analysis procedures. The effect sizes for the parallel group studies were quantified by the difference in task performance and reaction time between the active tACS and sham control groups at the post-test using standardized mean difference (SMD) with a 95% confidence interval (CI)162. Consistent with previous suggestions162,163,164,165, we used a paired analysis for crossover studies to calculate the SMD (e.g., values of sample size and mean difference with P-values or sample size and mean difference with standard error). This approach may correctly report clinically important heterogeneity in the meta-analysis while including crossover trials into a meta-analysis114,165. More positive values of SMD denoted greater positive effects on cognitive functions after active tACS than the sham control stimulation. Finally, all effect size calculations were based on the random-effects meta-analysis models because of the conventional assumption that individual studies have various experiment characteristics (e.g., participants and experimental protocols)166.
To estimate the heterogeneity levels across multiple comparisons, we conducted the Higgins and Green I2 test that demonstrates the percentage of heterogeneity between 0 to 100%167. The heterogeneity levels with 25, 50, and 75% of I2 indicate low, moderate, and high variability across studies, respectively168. In addition, we used Cochran’s Q and P-value, the heterogeneity significance test based on the chi-squared distribution. A P-value less than 0.05 for the Q-statistic indicates significant levels of heterogeneity between studies162. To quantify potential publication bias, we applied two methods. First, an original funnel plot and a revised funnel plot after the trim and fill technique were compared as a visual estimation of the changes in the overall effect sizes169. When no values overlapped between the original overall effect size and corrected overall effect size, a significant publication bias may exist. Second, we conducted Egger’s regression test providing the degree of asymmetry for the funnel plot by quantifying the intercept in the regression of standard normal deviates against precision170. The P-value for the intercept (β0) less than 0.05 implicates a significant publication bias across the comparisons.
To specify the effects of various tACS protocols on cognitive function, we performed moderator variable analyses. The first moderator variable analysis estimated different timing of tACS protocols: (a) online-tACS (i.e., applied tACS protocols during cognitive tasks) and (b) offline-tACS (i.e., using tACS protocols before executing cognitive tasks). In the second moderator variable analysis, we determined whether the effect sizes of specific frequency bands for tACS protocols were different: (a) delta band (1–3 Hz), (b) theta band (4–7 Hz), (c) alpha band (8–12 Hz), (d) beta band (13–30 Hz), (e) gamma band (31–139 Hz), and (f) ripple band (140 Hz). The third moderator variable analysis examined the potential effects of targeted brain regions for tACS protocols on cognitive functions: (a) PFC, (b) PPC, (c) TC, and (d) Multi. The fourth moderator variable analysis investigated the effects of tACS protocols on different cognitive domains: (a) perceptual-motor function, (b) learning and memory (c) executive function / complex attention, (d) language, and (e) social cognition.
Methodological quality assessment
Two authors (TLL and HAL) independently conducted the methodological quality of the included studies in the current meta-analysis using version 2 of a revised Cochrane risk of bias tool171. The assessment tool consists of six questionnaire domains: (a) randomization process, (b) timing of identification or recruitment of participants, (c) deviations from intended interventions, (d) missing outcome data, (e) measurement of the outcome, and (f) selection of the reported result. The methodological quality questionnaire can be evaluated on three levels: (a) low risk of bias, (b) high-risk bias, and (c) some concern.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
All data generated or analyzed during this study are included in this manuscript.
References
Crick, F. & Koch, C. A framework for consciousness. Nat. Neurosci. 6, 119–126 (2003).
Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134 (2012).
Horwitz, B., McIntosh, A. R., Haxby, J. V. & Grady, C. L. Network analysis of brain cognitive function using metabolic and blood flow data. Behav. Brain Res. 66, 187–193 (1995).
Tononi, G., Sporns, O. & Edelman, G. M. Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cereb. Cortex 2, 310–335 (1992).
Uhlhaas, P. et al. Neural synchrony in cortical networks: history, concept and current status. Front. Integr. Neurosci. 3, 17 (2009).
Varela, F., Lachaux, J. P., Rodriguez, E. & Martinerie, J. The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239 (2001).
Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).
Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2006).
Atasoy, S., Donnelly, I. & Pearson, J. Human brain networks function in connectome-specific harmonic waves. Nat. Commun. 7, 10340 (2016).
Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001).
Abeles, M. Role of the cortical neuron: integrator or coincidence detector? Isr. J. Med. 18, 83–92 (1982).
König, P., Engel, A. K. & Singer, W. Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci. 19, 130–137 (1996).
Grover, S., Nguyen, J. A. & Reinhart, R. M. G. Synchronizing brain rhythms to improve cognition. Annu. Rev. Med. 72, 29–43 (2021).
Schutter, D. J. & Wischnewski, M. A meta-analytic study of exogenous oscillatory electric potentials in neuroenhancement. Neuropsychologia 86, 110–118 (2016).
Thut, G., Miniussi, C. & Gross, J. The functional importance of rhythmic activity in the brain. Curr. Biol. 22, R658–R663 (2012).
Fries, P. Rhythms for cognition: communication through coherence. Neuron 88, 220–235 (2015).
Hasselmo, M. E. What is the function of hippocampal theta rhythm?-Linking behavioral data to phasic properties of field potential and unit recording data. Hippocampus 15, 936–949 (2005).
Wang, X. J. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev. 90, 1195–1268 (2010).
O’Keefe, J. & Conway, D. H. Hippocampal place units in the freely moving rat: why they fire where they fire. Exp. Brain Res. 31, 573–590 (1978).
Raghavachari, S. et al. Gating of human theta oscillations by a working memory task. J. Neurosci. 21, 3175–3183 (2001).
Meltzer, J. A. et al. Effects of working memory load on oscillatory power in human intracranial EEG. Cereb. Cortex 18, 1843–1855 (2008).
Kim, N. Y., Wittenberg, E. & Nam, C. S. Behavioral and neural correlates of executive function: interplay between inhibition and updating processes. Front. Neurosci. 11, 378 (2017).
Fink, A., Benedek, M., Grabner, R. H., Staudt, B. & Neubauer, A. C. Creativity meets neuroscience: experimental tasks for the neuroscientific study of creative thinking. Methods 42, 68–76 (2007).
Benedek, M., Franz, F., Heene, M. & Neubauer, A. C. Differential effects of cognitive inhibition and intelligence on creativity. Pers. Individ. Dif. 53–334, 480–485 (2012).
Borghini, G. et al. Alpha oscillations are causally linked to inhibitory abilities in ageing. J. Neurosci. 38, 4418–4429 (2018).
Haegens, S., Nácher, V., Luna, R., Romo, R. & Jensen, O. α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl Acad. Sci. USA 108, 19377–19382 (2011).
Engel, A. K. & Fries, P. Beta-band oscillations–signalling the status quo? Curr. Opin. Neurobiol. 20, 156–165 (2010).
Schmidt, R. et al. Beta oscillations in working memory, executive control of movement and thought, and sensorimotor function. J. Neurosci. 39, 8231–8238 (2019).
Swann, N. et al. Intracranial EEG reveals a time- and frequency-specific role for the right inferior frontal gyrus and primary motor cortex in stopping initiated responses. J. Neurosci. 29, 12675–12685 (2009).
Sanes, J. N. & Donoghue, J. P. Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proc. Natl Acad. Sci. USA 90, 4470–4474 (1993).
Murthy, V. N. & Fetz, E. E. Oscillatory activity in sensorimotor cortex of awake monkeys: synchronization of local field potentials and relation to behavior. J. Neurophysiol. 76, 3949–3967 (1996).
Bouyer, J. J., Montaron, M. F. & Rougeul, A. Fast fronto-parietal rhythms during combined focused attentive behaviour and immobility in cat: cortical and thalamic localizations. Electroencephalogr. Clin. Neurophysiol. 51, 244–252 (1981).
Gray, C. M. Synchronous oscillations in neuronal systems: mechanisms and functions. J. Comput. Neurosci. 1, 11–38 (1994).
Singer, W. & Gray, C. M. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995).
Antal, A. & Herrmann, C. S. Transcranial alternating current and random noise stimulation: possible mechanisms. Neural Plast. 2016, 3616807 (2016).
Schutter, D. J. Syncing your brain: electric currents to enhance cognition. Trends Cogn. Sci. 18, 331–333 (2014).
Helfrich, R. F. et al. Entrainment of brain oscillations by transcranial alternating current stimulation. Curr. Biol. 24, 333–339 (2014).
Tavakoli, A. V. & Yun, K. Transcranial alternating current stimulation (tACS) mechanisms and protocols. Front. Cell. Neurosci. 11, 214 (2017).
Kasten, F. H. & Herrmann, C. S. Transcranial alternating current stimulation (tACS) enhances mental rotation performance during and after stimulation. Front. Hum. Neurosci. 11, 2 (2017).
Klink, K., Paßmann, S., Kasten, F. H. & Peter, J. The modulation of cognitive performance with transcranial alternating current stimulation: a systematic review of frequency-specific effects. Brain Sci. 10, 932 (2020).
Fröhlich, F. & McCormick, D. A. Endogenous electric fields may guide neocortical network activity. Neuron 67, 129–143 (2010).
Helfrich, R. F. et al. Selective modulation of interhemispheric functional connectivity by HD-tACS shapes perception. PLoS Biol. 12, e1002031 (2014).
Wischnewski, M., Schutter, D. & Nitsche, M. A. Effects of beta-tACS on corticospinal excitability: a meta-analysis. Brain Stimul. 12, 1381–1389 (2019).
Fröhlich, F., Sellers, K. K. & Cordle, A. L. Targeting the neurophysiology of cognitive systems with transcranial alternating current stimulation. Expert. Rev. Neurother. 15, 145–167 (2015).
Feurra, M., Galli, G., Pavone, E. F., Rossi, A. & Rossi, S. Frequency-specific insight into short-term memory capacity. J. Neurophysiol. 116, 153–158 (2016).
Vosskuhl, J., Huster, R. J. & Herrmann, C. S. Increase in short-term memory capacity induced by down-regulating individual theta frequency via transcranial alternating current stimulation. Front. Hum. Neurosci. 9, 257 (2015).
Bland, N. S. & Sale, M. V. Current challenges: the ups and downs of tACS. Exp. Brain Res. 237, 3071–3088 (2019).
van Veen, V., Krug, M. K. & Carter, C. S. The neural and computational basis of controlled speed-accuracy tradeoff during task performance. J. Cogn. Neurosci. 20, 1952–1965 (2008).
Spieser, L., Servant, M., Hasbroucq, T. & Burle, B. Beyond decision! Motor contribution to speed-accuracy trade-off in decision-making. Psychon. Bull. Rev. 24, 950–956 (2017).
Standage, D., Blohm, G. & Dorris, M. C. On the neural implementation of the speed-accuracy trade-off. Front. Neurosci. 8, 236 (2014).
Sachdev, P. S. et al. Classifying neurocognitive disorders: the DSM-5 approach. Nat. Rev. Neurol. 10, 634–642 (2014).
Alekseichuk, I., Pabel, S. C., Antal, A. & Paulus, W. Intrahemispheric theta rhythm desynchronization impairs working memory. Restor. Neurol. Neurosci. 35, 147–158 (2017).
Ambrus, G. G. et al. Bi-frontal transcranial alternating current stimulation in the ripple range reduced overnight forgetting. Front. Cell. Neurosci. 9, 374 (2015).
Antonenko, D., Faxel, M., Grittner, U., Lavidor, M. & Flöel, A. Effects of transcranial alternating current stimulation on cognitive functions in healthy young and older adults. Neural Plast. 2016, 4274127 (2016).
Brauer, H., Kadish, N. E., Pedersen, A., Siniatchkin, M. & Moliadze, V. No modulatory effects when stimulating the right inferior frontal gyrus with continuous 6 Hz tACS and tRNS on response inhibition: a behavioral study. Neural Plast. 2018, 3156796 (2018).
Brignani, D., Ruzzoli, M., Mauri, P. & Miniussi, C. Is transcranial alternating current stimulation effective in modulating brain oscillations? PLoS ONE 8, e56589 (2013).
Fusco, G. et al. Midfrontal theta transcranial alternating current stimulation modulates behavioural adjustment after error execution. Eur. J. Neurosci. 48, 3159–3170 (2018).
Giustiniani, A. et al. Effects of low-gamma tACS on primary motor cortex in implicit motor learning. Behav. Brain Res. 376, 112170 (2019).
Hoy, K. E. et al. The effect of γ-tACS on working memory performance in healthy controls. Brain Cogn. 101, 51–56 (2015).
Janik, A. B., Rezlescu, C. & Banissy, M. J. Enhancing anger perception with transcranial alternating current stimulation induced gamma oscillations. Brain Stimul. 8, 1138–1143 (2015).
Jaušovec, N. & Jaušovec, K. Increasing working memory capacity with theta transcranial alternating current stimulation (tACS). Biol. Psychol. 96, 42–47 (2014).
Jaušovec, N., Jaušovec, K. & Pahor, A. The influence of theta transcranial alternating current stimulation (tACS) on working memory storage and processing functions. Acta Psychol. 146, 1–6 (2014).
Lang, S., Gan, L. S., Alrazi, T. & Monchi, O. Theta band high definition transcranial alternating current stimulation, but not transcranial direct current stimulation, improves associative memory performance. Sci. Rep. 9, 8562 (2019).
Loffler, B. S. et al. Counteracting the slowdown of reaction times in a vigilance experiment with 40-Hz transcranial alternating current stimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 2053–2061 (2018).
Luft, C. D. B., Zioga, I., Thompson, N. M., Banissy, M. J. & Bhattacharya, J. Right temporal alpha oscillations as a neural mechanism for inhibiting obvious associations. Proc. Natl Acad. Sci. USA 115, E12144–E12152 (2018).
Lustenberger, C., Boyle, M. R., Foulser, A. A., Mellin, J. M. & Fröhlich, F. Functional role of frontal alpha oscillations in creativity. Cortex 67, 74–82 (2015).
Marchesotti, S. et al. Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia. PLoS Biol. 18, e3000833 (2020).
Meier, J. et al. Intrinsic 40 Hz-phase asymmetries predict tACS effects during conscious auditory perception. PLoS ONE 14, e0213996 (2019).
Meiron, O. & Lavidor, M. Prefrontal oscillatory stimulation modulates access to cognitive control references in retrospective metacognitive commentary. Clin. Neurophysiol. 125, 77–82 (2014).
Meng, A. et al. Transcranial alternating current stimulation at theta frequency to left parietal cortex impairs associative, but not perceptual, memory encoding. Neurobiol. Learn. Mem. 182, 107444 (2021).
Nomura, T., Asao, A. & Kumasaka, A. Transcranial alternating current stimulation over the prefrontal cortex enhances episodic memory recognition. Exp. Brain Res. 237, 1709–1715 (2019).
Pahor, A. & Jaušovec, N. The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. Int. J. Psychophysiol. 93, 322–331 (2014).
Polanía, R., Nitsche, M. A., Korman, C., Batsikadze, G. & Paulus, W. The importance of timing in segregated theta phase-coupling for cognitive performance. Curr. Biol. 22, 1314–1318 (2012).
Polanía, R., Moisa, M., Opitz, A., Grueschow, M. & Ruff, C. C. The precision of value-based choices depends causally on fronto-parietal phase coupling. Nat. Commun. 6, 8090 (2015).
Pollok, B., Boysen, A. C. & Krause, V. The effect of transcranial alternating current stimulation (tACS) at alpha and beta frequency on motor learning. Behav. Brain Res. 293, 234–240 (2015).
Santarnecchi, E. et al. Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Curr. Biol. 23, 1449–1453 (2013).
Santarnecchi, E. et al. Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex 75, 33–43 (2016).
Santarnecchi, E. et al. Gamma tACS over the temporal lobe increases the occurrence of Eureka! moments. Sci. Rep. 9, 5778 (2019).
Schuhmann, T. et al. Left parietal tACS at alpha frequency induces a shift of visuospatial attention. PloS One 14, e0217729 (2019).
Sela, T., Kilim, A. & Lavidor, M. Transcranial alternating current stimulation increases risk-taking behavior in the balloon analog risk task. Front. Neurosci. 6, 22 (2012).
Violante, I. R. et al. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. ELife 6, e22001 (2017).
Wischnewski, M., Zerr, P. & Schutter, D. Effects of theta transcranial alternating current stimulation over the frontal cortex on reversal learning. Brain Stimul. 9, 705–711 (2016).
Wynn, S. C., Kessels, R. P. C. & Schutter, D. Effects of parietal exogenous oscillatory field potentials on subjectively perceived memory confidence. Neurobiol. Learn. Mem. 168, 107140 (2020).
Alekseichuk, I., Turi, Z., Veit, S. & Paulus, W. Model-driven neuromodulation of the right posterior region promotes encoding of long-term memories. Brain Stimul. 13, 474–483 (2020).
Braun, V., Sokoliuk, R. & Hanslmayr, S. On the effectiveness of event-related beta tACS on episodic memory formation and motor cortex excitability. Brain Stimul. 10, 910–918 (2017).
Deng, Y., Reinhart, R. M., Choi, I. & Shinn-Cunningham, B. G. Causal links between parietal alpha activity and spatial auditory attention. ELife 8, e51184 (2019).
Grabner, R. H., Krenn, J., Fink, A., Arendasy, M. & Benedek, M. Effects of alpha and gamma transcranial alternating current stimulation (tACS) on verbal creativity and intelligence test performance. Neuropsychologia 118, 91–98 (2018).
Gutteling, T. P., Schutter, D. & Medendorp, W. P. Alpha-band transcranial alternating current stimulation modulates precision, but not gain during whole-body spatial updating. Neuropsychologia 106, 52–59 (2017).
Hopfinger, J. B., Parsons, J. & Fröhlich, F. Differential effects of 10-Hz and 40-Hz transcranial alternating current stimulation (tACS) on endogenous versus exogenous attention. Cogn. Neurosci. 8, 102–111 (2017).
Javadi, A. H., Glen, J. C., Halkiopoulos, S., Schulz, M. & Spiers, H. J. Oscillatory reinstatement enhances declarative memory. J. Neurosci. 37, 9939–9944 (2017).
Kasten, F. H., Maess, B. & Herrmann, C. S. Facilitated event-related power modulations during transcranial alternating current stimulation (tACS) revealed by concurrent tACS-MEG. eNeuro 5, 1–15 (2018).
Laczó, B., Antal, A., Niebergall, R., Treue, S. & Paulus, W. Transcranial alternating stimulation in a high gamma frequency range applied over V1 improves contrast perception but does not modulate spatial attention. Brain Stimul. 5, 484–491 (2012).
Moliadze, V. et al. After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions. Brain Stimul. 12, 1464–1474 (2019).
Neubauer, A. C., Wammerl, M., Benedek, M., Jauk, E. & Jaušovec, N. The influence of transcranial alternating current stimulation (tACS) on fluid intelligence: an fMRI study. Pers. Individ. Dif. 118, 50–55 (2017).
Pahor, A. & Jaušovec, N. Making brains run faster: are they becoming smarter? Span. J. Psychol. 19, E88 (2016).
Reinhart, R. M. G. Disruption and rescue of interareal theta phase coupling and adaptive behavior. Proc. Natl Acad. Sci. USA 114, 11542–11547 (2017).
Riecke, L., Sack, A. T. & Schroeder, C. E. Endogenous delta/theta sound-brain phase entrainment accelerates the buildup of auditory streaming. Curr. Biol. 25, 3196–3201 (2015).
Riecke, L., Formisano, E., Sorger, B., Başkent, D. & Gaudrain, E. Neural entrainment to speech modulates speech intelligibility. Curr. Biol. 28, 161–169 (2018).
Strüber, D., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K. & Herrmann, C. S. Antiphasic 40 Hz oscillatory current stimulation affects bistable motion perception. Brain Topogr. 27, 158–171 (2014).
Tseng, P., Chang, Y. T., Chang, C. F., Liang, W. K. & Juan, C. H. The critical role of phase difference in gamma oscillation within the temporoparietal network for binding visual working memory. Sci. Rep. 6, 32138 (2016).
Tseng, P., Iu, K. C. & Juan, C. H. The critical role of phase difference in theta oscillation between bilateral parietal cortices for visuospatial working memory. Sci. Rep. 8, 349 (2018).
Wöstmann, M., Vosskuhl, J., Obleser, J. & Herrmann, C. S. Opposite effects of lateralised transcranial alpha versus gamma stimulation on auditory spatial attention. Brain Stimul. 11, 752–758 (2018).
Zavecz, Z., Horváth, K., Solymosi, P., Janacsek, K. & Nemeth, D. Frontal-midline theta frequency and probabilistic learning: a transcranial alternating current stimulation study. Behav. Brain Res. 393, 112733 (2020).
Zoefel, B., Archer-Boyd, A. & Davis, M. H. Phase entrainment of brain oscillations causally modulates neural responses to intelligible speech. Curr. Biol. 28, 401–408 (2018).
Zoefel, B., Allard, I., Anil, M. & Davis, M. H. Perception of rhythmic speech is modulated by focal bilateral transcranial alternating current stimulation. J. Cogn. Neurosci. 32, 226–240 (2020).
Herrmann, C. S., Rach, S., Neuling, T. & Strüber, D. Transcranial alternating current stimulation: a review of the underlying mechanisms and modulation of cognitive processes. Front. Hum. Neurosci. 7, 279 (2013).
Reato, D., Rahman, A., Bikson, M. & Parra, L. C. Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing. J. Neurosci. 30, 15067–15079 (2010).
Klimesch, W., Doppelmayr, M., Russegger, H. & Pachinger, T. Theta band power in the human scalp EEG and the encoding of new information. Neuroreport 7, 1235–1240 (1996).
Klimesch, W., Doppelmayr, M., Schimke, H. & Ripper, B. Theta synchronization and alpha desynchronization in a memory task. Psychophysiology 34, 169–176 (1997).
Bogacz, R., Wagenmakers, E. J., Forstmann, B. U. & Nieuwenhuis, S. The neural basis of the speed-accuracy tradeoff. Trends Neurosci. 33, 10–16 (2010).
Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed-accuracy tradeoff in macaque area LIP. eLife 3, e02260 (2014).
Forstmann, B. U. et al. Striatum and pre-SMA facilitate decision-making under time pressure. Proc. Natl Acad. Sci. USA 105, 17538–17542 (2008).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Lee, J. H., Lee, T. L. & Kang, N. Transcranial direct current stimulation decreased cognition-related reaction time in older adults: a systematic review and meta-analysis. Ageing Res. Rev. 70, 101377 (2021).
Teo, F., Hoy, K., Daskalakis, Z. & Fitzgerald, P. Investigating the role of current strength in tDCS modulation of working memory performance in healthy controls. Front. Psychiatry 2, 45 (2011).
Francis, J. T., Gluckman, B. J. & Schiff, S. J. Sensitivity of neurons to weak electric fields. J. Neurosci. 23, 7255–7261 (2003).
Bonaiuto, J. J. & Bestmann, S. Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation. Prog. Brain Res. 222, 75–103 (2015).
Johnson, L. et al. Dose-dependent effects of transcranial alternating current stimulation on spike timing in awake nonhuman primates. Sci. Adv. 6, eaaz2747 (2020).
Zaehle, T., Rach, S. & Herrmann, C. S. Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS ONE 5, e13766 (2010).
Veniero, D., Vossen, A., Gross, J. & Thut, G. Lasting EEG/MEG aftereffects of rhythmic transcranial brain stimulation: level of control over oscillatory network activity. Front. Cell. Neurosci. 9, 477 (2015).
Vossen, A., Gross, J. & Thut, G. Alpha power increase after transcranial alternating current stimulation at alpha frequency (α-tACS) reflects plastic changes rather than entrainment. Brain Stimul. 8, 499–508 (2015).
Dan, Y. & Poo, M. M. Spike timing-dependent plasticity of neural circuits. Neuron 44, 23–30 (2004).
Wischnewski, M. et al. NMDA receptor-mediated motor cortex plasticity after 20 Hz transcranial alternating current stimulation. Cereb. Cortex 29, 2924–2931 (2019).
Kasten, F. H., Dowsett, J. & Herrmann, C. S. Sustained aftereffect of α-tACS lasts up to 70 min after stimulation. Front. Hum. Neurosci. 10, 245 (2016).
Sauseng, P., Griesmayr, B., Freunberger, R. & Klimesch, W. Control mechanisms in working memory: a possible function of EEG theta oscillations. Neurosci. Biobehav. Rev. 34, 1015–1022 (2010).
Adelhöfer, N. & Beste, C. Pre-trial theta band activity in the ventromedial prefrontal cortex correlates with inhibition-related theta band activity in the right inferior frontal cortex. NeuroImage 219, 117052 (2020).
Ryu, K., Choi, Y., Kim, J., Kim, Y. & Chio, S. Differential frontal theta activity during cognitive and motor tasks. J. Integr. Neurosci. 15, 295–303 (2016).
Crespo-Garcia, M., Cantero, J. L., Pomyalov, A., Boccaletti, S. & Atienza, M. Functional neural networks underlying semantic encoding of associative memories. NeuroImage 50, 1258–1270 (2010).
Hosseinian, T., Yavari, F., Kuo, M. F., Nitsche, M. A. & Jamil, A. Phase synchronized 6 Hz transcranial electric and magnetic stimulation boosts frontal theta activity and enhances working memory. NeuroImage 245, 118772 (2021).
Jensen, O. & Colgin, L. L. Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci. 11, 267–269 (2007).
Abellaneda-Pérez, K. et al. Differential tDCS and tACS effects on working memory-related neural activity and resting-state connectivity. Front. Neurosci. 13, 1440 (2019).
Jensen, O., Kaiser, J. & Lachaux, J. P. Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 30, 317–324 (2007).
Honkanen, R., Rouhinen, S., Wang, S. H., Palva, J. M. & Palva, S. Gamma oscillations underlie the maintenance of feature-specific information and the contents of visual working memory. Cereb. Cortex 25, 3788–3801 (2015).
Roux, F., Wibral, M., Mohr, H. M., Singer, W. & Uhlhaas, P. J. Gamma-band activity in human prefrontal cortex codes for the number of relevant items maintained in working memory. J. Neurosci. 32, 12411–12420 (2012).
Castelhano, J., Duarte, I. C., Wibral, M., Rodriguez, E. & Castelo-Branco, M. The dual facet of gamma oscillations: separate visual and decision making circuits as revealed by simultaneous EEG/fMRI. Hum. Brain Mapp. 35, 5219–5235 (2014).
Rebola, J., Castelhano, J., Ferreira, C. & Castelo-Branco, M. Functional parcellation of the operculo-insular cortex in perceptual decision making: an fMRI study. Neuropsychologia 50, 3693–3701 (2012).
Baltus, A. & Herrmann, C. S. The importance of individual frequencies of endogenous brain oscillations for auditory cognition—a short review. Brain Res 1640, 243–250 (2016).
Abubaker, M., Al Qasem, W. & Kvašňák, E. Working memory and cross-frequency coupling of neuronal oscillations. Front. Psychol. 12, 756661 (2021).
Buzsáki, G. & Wang, X. J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).
Nowak, M., Zich, C. & Stagg, C. J. Motor cortical gamma oscillations: what have we learnt and where are we headed? Curr. Behav. Neurosci. Rep. 5, 136–142 (2018).
Griffiths, B. J. et al. Directional coupling of slow and fast hippocampal gamma with neocortical alpha/beta oscillations in human episodic memory. Proc. Natl Acad. Sci. USA 116, 21834–21842 (2019).
Wildie, M. & Shanahan, M. Establishing communication between neuronal populations through competitive entrainment. Front. Comput. Neurosci. 5, 62 (2011).
Alekseichuk, I., Turi, Z., Amador de Lara, G., Antal, A. & Paulus, W. Spatial working memory in humans depends on theta and high gamma synchronization in the prefrontal cortex. Curr. Biol. 26, 1513–1521 (2016).
Florin, E. & Baillet, S. The brain’s resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations. NeuroImage 111, 26–35 (2015).
Jirsa, V. & Müller, V. Cross-frequency coupling in real and virtual brain networks. Front. Comput. Neurosci. 7, 78 (2013).
Stankovski, T., Ticcinelli, V., McClintock, P. V. E. & Stefanovska, A. Neural cross-frequency coupling functions. Front. Syst. Neurosci. 11, 33 (2017).
Aru, J. et al. Untangling cross-frequency coupling in neuroscience. Curr. Opin. Neurobiol. 31, 51–61 (2015).
Fujisawa, S. & Buzsáki, G. A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities. Neuron 72, 153–165 (2011).
Canolty, R. T. & Knight, R. T. The functional role of cross-frequency coupling. Trends Cogn. Sci. 14, 506–515 (2010).
Salimpour, Y. & Anderson, W. S. Cross-frequency coupling based neuromodulation for treating neurological disorders. Front. Neurosci. 13, 125 (2019).
Radiske, A. et al. Cross-frequency phase-amplitude coupling between hippocampal theta and gamma oscillations during recall destabilizes memory and renders it susceptible to reconsolidation disruption. J. Neurosci. 40, 6398–6408 (2020).
Zeng, K. et al. Fronto-subthalamic phase synchronization and cross-frequency coupling during conflict processing. NeuroImage 238, 118205 (2021).
Sweeney-Reed, C. M. et al. Corticothalamic phase synchrony and cross-frequency coupling predict human memory formation. ELife 3, e05352 (2014).
Axmacher, N. et al. Cross-frequency coupling supports multi-item working memory in the human hippocampus. Proc. Natl Acad. Sci. USA 107, 3228–3233 (2010).
Meehan, C. E. & Spooner, R. K. Utilizing transcranial alternating current stimulation and functional neuroimaging to investigate human sensory adaptation. J. Neurophysiol. 124, 1010–1012 (2020).
Antal, A. et al. Low intensity transcranial electric stimulation: safety, ethical, legal regulatory and application guidelines. Clin. Neurophysiol. 128, 1774–1809 (2017).
Kanai, R., Chaieb, L., Antal, A., Walsh, V. & Paulus, W. Frequency-dependent electrical stimulation of the visual cortex. Curr. Biol. 18, 1839–1843 (2008).
Goutagny, R. et al. Alterations in hippocampal network oscillations and theta-gamma coupling arise before Aβ overproduction in a mouse model of Alzheimer’s disease. Eur. J. Neurosci. 37, 1896–1902 (2013).
Hirano, S. et al. Phase-amplitude coupling of the electroencephalogram in the auditory cortex in schizophrenia. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 69–76 (2018).
Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open. Med. 3, e123–e130 (2009).
Peterson, D. S., King, L. A., Cohen, R. G. & Horak, F. B. Cognitive contributions to freezing of gait in Parkinson disease: implications for physical rehabilitation. Phys. Ther. 96, 659–670 (2016).
Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. Introduction to Meta‐Analysis (John Wiley & Sons, 2009).
Miller, J. R. et al. A systematic review and meta-analysis of crossover studies comparing physiological, perceptual and performance measures between treadmill and overground running. Sports Med. 49, 763–782 (2019).
Stedman, M. R., Curtin, F., Elbourne, D. R., Kesselheim, A. S. & Brookhart, M. A. Meta-analyses involving cross-over trials: methodological issues. Int. J. Epidemiol. 40, 1732–1734 (2011).
Nasser, M. Cochrane handbook for systematic reviews of interventions. Am. J. Public. Health 110, 753–754 (2020).
Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Res. Synth. Methods 1, 97–111 (2010).
Higgins, J. P. & Thompson, S. G. Quantifying heterogeneity in a meta‐analysis. Stat. Med. 21, 1539–1558 (2002).
Higgins, J. P., Thompson, S. G., Deeks, J. J. & Altman, D. G. Measuring inconsistency in meta-analyses. Br. Med. J. 327, 557–560 (2003).
Duval, S. & Tweedie, R. Trim and fill: a simple funnel‐plot-based method of testing and adjusting for publication bias in meta‐analysis. Biometrics 56, 455–463 (2000).
Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. Br. Med. J. 315, 629–634 (1997).
Cumpston, M. et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane handbook for systematic reviews of interventions. Cochrane Database Syst. Rev. 10, ED000142 (2019).
Acknowledgements
This work was supported by a grant from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF- 2020S1A5A8041203 to N.K.).
Author information
Authors and Affiliations
Contributions
T.L.L. contributed to data collection, statistical analyses, data interpretation, and manuscript drafts. H.L. contributed to data collection, statistical analyses, and manuscript drafts. N.K. conceived and designed the study, conducted data collection, statistical analyses, data interpretation, and manuscript drafts. All authors approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Lee, T.L., Lee, H. & Kang, N. A meta-analysis showing improved cognitive performance in healthy young adults with transcranial alternating current stimulation. npj Sci. Learn. 8, 1 (2023). https://doi.org/10.1038/s41539-022-00152-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41539-022-00152-9
This article is cited by
-
Investigating neuromodulatory effect of transauricular vagus nerve stimulation on resting-state electroencephalography
Biomedical Engineering Letters (2024)
-
Transcranial alternating current stimulation in affecting cognitive impairment in psychiatric disorders: a review
European Archives of Psychiatry and Clinical Neuroscience (2023)
