Electrophysiological and neuromodulatory correlates of waiting impulsivity

Universidade do Minho Escola de Psicologia Augusto José Martins Mendes julho de 2022 Electrophysiological and neuromodulatory correlates of waiting impulsivity Augusto José Martins Mendes Electrophysiological and neuromodulatory correlates of waiting impulsivity UMinho|2022 Augusto José Martins Mendes julho de 2022 Electrophysiological and neuromodulatory correlates of waiting impulsivity Trabalho efetuado sob a orientação da Professora Doutora Sandra Conceição Ribeiro de Carvalho do Professor Doutor António Jorge da Costa Leite e da Professora Doutora Adriana da Conceição Soares Sampaio Tese de Doutoramento Doutoramento em Psicologia Básica Universidade do Minho Escola de Psicologia ii DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ iii Agradecimentos Aos meus orientadores, Professora Sandra Carvalho e Professor Jorge Leite que acreditaram no meu trabalho desde o início e permitiram-me crescer a nível académico e pessoal. Estou francamente agradecido por me abrirem as portas da investigação e desse modo possibilitarem eu fazer o que mais gosto e sempre gostei. Aos meus colegas de doutoramento, especialmente ao Alberto, Diogo e Dani, que foram meus companheiros de licenciatura, mestrado e doutoramento numa aventura que começou em 2011 na mesma Escola de Psicologia. A eles agradeço-lhes por todos os momentos de companheirismo, amizade e ajuda. A todos os colegas, ex-colegas e investigadores do Laboratório de Neurociência Psicológicas que me ajudaram no meu percurso desde o início do meu mestrado, em particular à Professora Adriana Sampaio que assumiu co-orientação no final do meu doutoramento e ao Diogo Branco por ter sido um parceiro sempre disponível para ajudar. Ao Centro de Investigação de Psicologia (CIPsi) por ter acolhido a minha aprendizagem e trabalho, à Fundação para a Ciência e Tecnologia (FCT) pelo apoio financeiro que permitiu a conclusão deste projeto1 e à Fulbright por ter apoiado a minha ida para Boston (EUA). Ao Professor Felipe Fregni e toda a equipa do Spaulding Neuromodulation Center por me terem proporcionado uma experiência encantadora a nível académico e pessoal. Aos meus amigos por toda a amizade repleta de momentos incríveis ao longo destes anos. O vosso companheirismo ajudou-me a encarar esta longa caminhada com mais ânimo e humor. À minha mãe, ao meu pai, e ao meu irmão por estarem sempre ao meu lado. Sem eles não seria possível chegar aqui. À Inês, o amor da minha vida, por tudo que ela significa para mim e para a minha vida. __________________ 1 This work was funded by the Portuguese Foundation for Science and Technology (FCT, Portugal) and the Portuguese Ministry of Science, Technology and Higher Education through national funds, and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653)., with the doctoral Grant reference PD/BD/142820/2018. iv Statement of Integrity I hereby declare have conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. v Correlatos eletrofisiológicos e neuromodulatórios da impulsividade de espera Resumo A impulsividade de espera é a capacidade de esperar para realizar uma ação associada a uma recompensa. Uma maneira de estudar a dinâmica cerebral durante os processos de impulsividade é analisando os potenciais relacionados a eventos (PRE) através da atividade contínua do EEG, como a P3 (ou P300). Assim, o primeiro estudo desta tese teve como objetivo avaliar se a técnica de estimulação transcraniana por corrente contínua (ETCC) era capaz de modular a amplitude e a latência de P3 durante tarefas cognitivas. Uma meta-análise com 23 estudos mostrou que a ETCC frontal aumentou a amplitude do P3 parietal durante as tarefas oddball e n-back . Este estudo sugeriu que a P3 eliciada em áreas parietais pode ser modulada através da aplicação de ETCC em áreas frontais. O segundo estudo pretendeu validar o efeito anterior na amplitude de P3 e na atividade oscilatória inerente durante a impulsividade de espera. Assim, 40 participantes realizaram duas sessões separadas de ETCC ativa e simulada sobre a circunvolução frontal inferior direita (CFId) durante um paradigma de respostas prematuras. Os resultados mostraram um efeito diferencial em comparação com o estudo 1, ou seja, a ETCC frontal diminuiu a amplitude do P3-alvo e a potência delta inerente em comparação com a ETCC simulada. Da mesma forma, a ETCC ativa também reduziu a amplitude do P3also (ou seja, outro PRE induzido durante a impulsividade de espera), mas sem nenhum efeito significativo na atividade oscilatória evocada. Por fim, considerando os resultados divergentes anteriores, o estudo 3 pretendeu avaliar a viabilidade de individualizar a estimulação transcraniana por corrente alternada (ETCA) ao componente P3 endógeno. Para isso, uma configuração de ETCA-EEG permitindo a sincronização da frequência e fase de ETCA com P3 foi aplicada a 12 voluntários saudáveis durante duas sessões (ativo vs sham). A sessão de tACS ativa revelou um aumento significativo na amplitude do alvo-P3 em comparação com o sham, embora não tenham sido observadas diferenças significativas no poder oscilatório. Este mostrou que a P3 é um marcador útil para combinar com diferentes técnicas de estimulação elétrica transcraniana (EET). Os resultados sugerem um efeito diferencial da ETCC no componente P3 e na atividade oscilatória inerente, dependendo dos requisitos da tarefa (por exemplo, processamento “frio” versus “quente”). Por outro lado, a sincronização da ETCA com a atividade endógena levou a um aumento da amplitude da P3. No geral, este trabalho melhorou a nossa compreensão sobre como as intervenções EET indexadas a marcadores de EEG podem ser úteis para modular a impulsividade de espera. Keywords: Delta; Impulsividade de Espera; P3; tDCS; tACS vi Electrophysiological and neuromodulatory correlates of waiting impulsivity Abstract Waiting impulsivity is the ability to wait to perform a rewarded action. One way to study the brain dynamics during impulsive processes is by analyzing the event-related potentials in the ongoing EEGactivity, such as the P3 (or P300). For that, the first study of this thesis pretended to evaluate if transcranial Direct Current Stimulation (tDCS) technique was capable to modulate P3 amplitude and latency during cognitive tasks. A meta-analysis with 23 studies has shown that frontal tDCS increased the parietal P3 amplitude during oddball and n-back tasks. This study suggested that P3 elicited in parietal areas can be modulated through the application of tDCS in frontal areas. At next, the second study pretended to validate the previous effect in P3 amplitude and inherent oscillatory activity during waiting impulsivity. Hence, 40 participants performed two separate sessions of active and sham tDCS over the right Inferior Frontal Gyrus (rIFG) during a premature response paradigm. Results have shown a differential effect in comparison with study 1, namely, frontal tDCS decreased the target-P3 amplitude and inherent delta power in comparison with sham . Likewise, active tDCS also reduced cue-P3 amplitude (i.e., another ERP elicited during waiting impulsivity), but without any significant effect in the evoked-oscillatory activity. At last, considering the previous divergent results, study 3 pretended to evaluate the feasibility of individualizing transcranial Alternating Current Stimulation (tACS) to the endogenous P3. For that, a tACSEEG setup allowing the synchronization of the frequency and phase of tACS with P3 was applied to 12 healthy volunteers during two sessions (active vs sham ). The active tACS session revealed a significant increase in target-P3 amplitude in comparison with sham , although no significant differences were observed in the oscillatory power. The current work showed that P3 is a useful marker to combine with different transcranial Electric Stimulation techniques (tES). These findings suggest a differential effect of tDCS in P3 component and inherent oscillatory activity depending on the task requirements (e.g., cold vs hot processing). On the other hand, the synchronization of tACS with the endogenous activity led to an enhancement of P3 amplitude. Overall, this work improved our understanding about how tES interventions indexed to EEG markers can be useful to modulate waiting impulsivity. Keywords: Delta; P3; tDCS; tACS; Waiting Impulsivity vii Table of Contents Agradecimentos .................................................................................................................................. iii Statement of Integrity ......................................................................................................................... iv Resumo............................................................................................................................................... v Abstract.............................................................................................................................................. vi Table of Contents .............................................................................................................................. vii Abbreviations ..................................................................................................................................... xii List of Figures ................................................................................................................................... xiv List of Tables .................................................................................................................................... xvii CHAPTER 1 ........................................................................................................................................ 1 1.1. Waiting Impulsivity .................................................................................................................. 2 1.2. P3 & Delta/Theta .................................................................................................................... 4 1.3. Transcranial Electrical Stimulation Techniques: tDCS and tACS ................................................ 6 1.4. Objectives and Hypotheses ...................................................................................................... 9 1.5. References ............................................................................................................................ 10 CHAPTER 2 ...................................................................................................................................... 19 2.1. Abstract ................................................................................................................................ 21 2.2. Introduction .......................................................................................................................... 22 2.3. Methods ................................................................................................................................ 25 2.3.1 Literature Search and Study Selection ............................................................................. 25 2.3.2 Data extraction ................................................................................................................ 26 2.3.3. Statistical analysis .......................................................................................................... 26 2.3.3.1. Pooled effect estimates and subgroup analysis. ....................................................... 27 2.3.3.2. Influential analysis. ................................................................................................. 27 2.3.3.3. Moderator analysis.................................................................................................. 27 xiv List of Figures Chapter 1: General Introduction: Figure 1. Current field depending on the phase of the tACS sinusoidal waveform Chapter 2: Modulation of the cognitive event-related potential P3 by transcranial Direct Current Stimulation: systematic review and meta-analysis: Figure 2. PRISMA flow diagram Figure 3. Forest plot with pooled effect estimate and subgroup analysis concerning the cathodal stimulation on frontal P3 amplitude during oddball Figure 4. Forest plot with pooled effect estimate and subgroup analysis concerning the anodal stimulation on parietal P3 amplitude during oddball Figure 5. Forest plot with pooled effect estimate and subgroup analysis concerning the anodal stimulation on parietal P3 amplitude during n-back Figure 6. Overall effects observed on the meta-analysis of P3 amplitude and latency in each section Figure SM.1. Traffic light plots with risk of bias assessment in oddball Figure SM.2. Traffic light plots with risk of bias assessment in n-back Figure SM.3. Traffic light plots with risk of bias assessment in GNG Figure SM.4. Traffic light plots with risk of bias assessment in Emotional Processing Figure SM.5. Forest plot with pooled effect estimates and subgroup analysis in frontal P3 amplitude during oddball in anodal stimulation Figure SM.6. Forest plot with pooled effect estimates and subgroup analysis in parietal P3 amplitude during oddball in cathodal stimulation Figure SM.7. Forest plot with pooled effect estimates and subgroup analysis in frontal P3 amplitude during n-back in anodal stimulation Figure SM.8. Forest plot with pooled effect estimates and subgroup analysis in frontal No-Go P3 amplitude during GNG in anodal stimulation Figure SM.9. Forest plot with pooled effect estimates and subgroup analysis in parietal No-Go P3 amplitude during GNG in anodal stimulation xv Figure SM.10. Forest plot with pooled effect estimates and subgroup analysis in parietal Go P3 amplitude during GNG in anodal stimulation Figure SM.11. Forest plot with pooled effect estimates and subgroup analysis in frontal P3 amplitude during Emotional Processing in anodal stimulation Figure SM.12. Forest plot with pooled effect estimates and subgroup analysis in parietal P3 amplitude during Emotional Processing in anodal stimulation (only offline comparisons) Figure SM.13. Funnel plots in the analysis with at least 10 comparisons Chapter 3: Transcranial Direct Current Stimulation decreases P3 amplitude and inherent delta activity during a waiting impulsivity paradigm Figure 7. Overview of the experimental task and the tailored reward/punishment system Figure 8. Correlation between the number of premature responses and the average of the release time in the baseline block Figure 9. Grand average event-related target-P3 with topographical maps in the time-window of interest and ERO results at Pz electrode between both tDCS conditions Figure 10. Grand average event-related cue-P3 with topographical maps in the time-window of interest and ERO results at Pz electrode between both tDCS conditions Figure SM.14. ITPC results of cue-P3 at Pz electrode in the time-window of interest between both tDCS conditions Figure SM.15. ITPC results of target-P3 at Pz electrode in the time-window of interest between both tDCS conditions Chapter 4: Tailoring transcranial alternating current stimulation based on endogenous event-related P3 to modulate premature responses: a feasibility study Figure 11. Overview of the study design with two distinct sessions (A) and the experimental task to evaluate premature responses (B). The experimental setup with temporal and frequency synchronization between P3 and tACS (C). At last, tACS electrodes montage with the electric field map (D) Figure 12. Results from EEG analysis of target-P3 at Pz electrode (A), namely the event-related potentials (B) in the time-window of interest, event-related oscillations (C), and power spectral density (D) in Pre and Post-tACS block in both sessions xvi Figure 13. Results from EEG analysis of cue-P3 at Pz electrode (A), namely the event-related potentials (B) in the time-window of interest, event-related oscillations (C), and power spectral density (D) in Pre and Post-tACS block in both sessions Figure 14. Frameworks about mechanisms of action of tACS that might explain the decrease in evokeddelta after tACS, namely the anti-phasic effect (A) and the spike-timing dependent plasticity (STDP) (B) Figure SM.16. Examples of synchronization between the tACS peak and P3 latency in CPRT when P3 latency is higher than the time of 1 cycle + π/2 from tACS frequency Figure SM.17. Examples of synchronization between the tACS peak and P3 latency in CPRT when P3 latency is lower than the time of 1 cycle + π/2 from tACS frequency Figure SM.18. The tailored reward/punishment system from the CPRT Figure SM.19. The additional analysis extracted the ERO power from Participant 1 (A), from the 12 participants, and the table comprises all the values of the windows (C) xvii List of Tables Chapter 1: General Introduction Table 1. Computerized tasks to measure the different subprocesses within impulsivity Chapter 2: Modulation of the cognitive event-related potential P3 by transcranial Direct Current Stimulation: systematic review and meta-analysis Table SM.1. Combinations of the descriptors used in search strategy Table SM.2. Training of reviewers before titles and abstract screening Table SM.3. References of studies included in quantitative synthesis Table SM.4. Labels of the comparisons in each study Table SM.5. Number of studies and comparisons per cognitive task and tDCS-polarity Table SM.6. Number of studies and comparisons per cognitive task and tDCS-location Table SM.7. Summary of Findings Table SM.8. Characteristics of the studies considering the cognitive task that elicited P3 Table SM.9. Characteristics of the cognitive task and EEG P3 analysis Chapter 3: Transcranial Direct Current Stimulation decreases P3 amplitude and inherent delta activity during a waiting impulsivity paradigm Table 2. Descriptive and inferential statistics for each behavioral outcome Table 3. Descriptive and inferential statistics for each EEG outcome Table SM.10. VAS of the side effects associated with tDCS in both sessions Table SM.11. Blinding of tDCS in both sessions Chapter 4: Tailoring transcranial alternating current stimulation based on endogenous event-related P3 to modulate premature responses: a feasibility study Table 4. Descriptive and inferential statistics in ERP, ERO, and PSD analysis for target-P3 Table 5. Descriptive and inferential statistics in ERP, ERO, and PSD analysis for cue-P3 Table 6. Descriptive and inferential statistics for CPRT outcomes Table SM.12. Sociodemographic, impulsivity, and clinical information xviii Table SM.13. Blinding of tDCS in both sessions Table SM.14. EEG online analysis results and parameters of reward/punishment system Table SM.15. Total number of epochs in the different steps of the preprocessing of EEG files xix Thesis Overview The current thesis pretends to address the electrophysiological and neuromodulatory correlates of waiting impulsivity. This is of particular interest given that exacerbated waiting impulsivity has been observed in several clinical conditions (e.g., addiction and Attention-deficit/Hyperactivity disorders). Likewise, deficits in the ability to wait are associated with abnormal patterns in electrophysiological (EEG) activity such as in the P3 component. Therefore, taking into consideration the association between the P3, impulsive processes, and clinical symptomatology, the current thesis pretended to address how the transcranial Electrical Stimulation (tES) techniques might impact EEG markers of waiting impulsivity. Therefore, in order to address our goal, the current chapter introduces the theoretical concepts and revision of recent studies about waiting impulsivity, EEG markers (i.e., P3 and delta/theta oscillations), and tES techniques (i.e., tDCS and tACS). The Chapters 2, 3, and 4 comprise the studies conducted during this thesis. Specifically, the first study (Chapter 2) pretends to evaluate the transcranial Direct Current Stimulation (tDCS) effects in the P3 amplitude and latency during oddball, n-back, Go/NoGo, and emotional processing. At next, in Chapter 3, the previous tDCS effects are tested in light of a premature response paradigm. The fourth chapter aims the optimization of the effects detected in the previous chapters through the application of transcranial Alternating Current Stimulation (tACS) based on the endogenous activity of each participant. At last, the findings observed in the three studies of the thesis are discussed accordingly to the most recent literature of the field in Chapter 5. Additionally, in the fifth chapter limitations and future directions are discussed, as well a brief conclusion of the present work. Overall, the chapters in this thesis pretend to study the modulatory effects of tES techniques in electrophysiological surrogate markers of cognitive processing, specifically the P3 and underlying EROs. Our findings will allow a better understanding of the use of tES techniques in cognition and behavior, while the neuronal activity behind those modulations is unveiled. Therefore, our studies can provide important insight into applied research with clinical populations associated with abnormalities in EEG activity. 1 CHAPTER 1 General Introduction CHAPTER 1 2 1.1. Waiting Impulsivity Waiting impulsivity is the ability underlying waiting for gratification or withholding from performing an action (Robbins & Dalley, 2017). This ability has been thought to be a predictor of the development of addiction (Belin et al., 2008), as well a consequence of drug consumption (Voon et al., 2014). Furthermore, the inability to wait may end up in more proneness for premature responses, which have been shown to be increased in several clinical populations, such as Attention-deficit/Hyperactivity disorder (ADHD; Van Dessel et al., 2018), alcohol use disorder (Morris et al., 2016), binge drinkers (Sanchez-Roige et al., 2014), methamphetamine use disorder and recreational cannabis users (Voon et al., 2014). In order to assess waiting impulsivity, two main methods have been suggested as ways of measuring it, namely the impulsive choice by the delay discounting and the impulsive action by premature responses (Robbins & Dalley, 2017). The former implies a choice between smaller-and-immediate or larger-and-delayed rewards, whilst the latter relies on the ability of holding on before performing an action associated with a reward (Dalley et al., 2011). Both processes are dissociable at behavioral and neuronal levels, given that impulsive action is related to response inhibition, whilst impulsive choice is mostly ruled by reward processing (Reynolds et al., 2006). This translates in waiting impulsivity to rely on ‘cold’ processes with less influence of affective and cognitive processing (i.e., premature response) and ‘hot’ processes with affective charge implying limbic circuitries (i.e., delay discounting) (Dalley & Robbins, 2017; Winstanley et al., 2006). On the other hand, both processes seem to be partially overlapped at a behavioral and neuronal level in animal studies. Specifically, a study with rodents showed that premature responses and delay discounting were strongly associated (Robinson et al., 2009). However, this association was not observed in studies with humans, thus suggesting different neuronal circuitries between premature responding and delay discounting (Voon et al., 2014). Regarding the neuronal substrates, subjects that prefer larger-anddelayed rewards show higher activations in the ventral striatum, mesial prefrontal cortex (MPFC), and posterior cingulate cortical (PCC) (Ballard & Knutson, 2009). On the other hand, premature responding have shown an inverse relation with activations in the ventral striatum, ventromedial prefrontal cortex and subthalamic nucleus (Morris et al., 2016). Hence, ventral striatum is suggested to be a crucial subcortical region in both impulsive choice and action from waiting impulsivity (Dalley et al., 2011). Moreover, proactive inhibitory processes were also suggested to play an important role in preventing premature responses (Los, 2013; Voon, 2014). This notion is explained by the fact that CHAPTER 1 3 proactive inhibition requires an action stoppage before the execution of response, whilst reactive inhibition requires that an already ongoing action to be stopped (Aron, 2011). Although both processes rely on the rIFG, they share different neuronal circuits. Specifically, proactive inhibition is associated with an indirect pathway between rIFG and striatum, whereas reactive has been related with a hyperdirect pathway from the rIFG towards the subthalamic nucleus (Aron, 2011; Jahfari et al., 2011). Likewise, there is some evidence of different cortico-striatal substrates between the act of waiting and stopping. Specifically, waiting relies on neuronal areas that are crucial in reward processing, such as the ventral striatum (i.e., nucleus accumbens) and ventromedial prefrontal cortex, whereas stopping is associated with motor areas, namely, dorsal striatum (i.e., caudate nucleus and the putamen), preSupplementary Motor Area and right Inferior Frontal Gyrus (rIFG) (Dalley et al., 2011). Nonetheless, proactive inhibition and premature responding rely on the subcortical relay structure subthalamic nucleus (STN) involved in motor control (Ballanger et al., 2009; Morris et al., 2016). Overall, premature responding is evaluated as an impulsive action within the waiting impulsivity realm. The waiting impulsive action is suggested to share common features with other impulsive subprocesses. For instance, impulsive action can also be evaluated as a stopping act, and waiting impulsivity can be also evaluated from the perspective of impulsive choice (see Table 1). Nevertheless, the inter-dependency between these impulsive subprocesses is still not clear, and as such further evidence is required. In this sense, the use of electroencephalography and transcranial electrical stimulation can prove to be invaluable in this pursuit of further evidence. CHAPTER 1 4 Table 1 Computerized tasks to measure the different subprocesses within impulsivity. Table based on Robbins and Dalley (2017) 1.2. P3 & Delta/Theta Event-related potentials (ERP) are a widely used technique to measure the brain response towards a specific stimulus (Luck & Kappenman, 2011). Since the advent of cognitive sciences, ERPs have been of utmost importance because they allowed the understanding of mental operations underlying a specific output (Sutton et al., 1965). ERPs are measured by averaging the electrical activity collected by the electroencephalogram (EEG) during cognitive tasks. This allow for several cognitive processes to be studied, based on different waveforms, latencies and even in different brain regions. For instance, early ERPs in primary cortices are associated with sensory processing, whilst late ERPs in frontoparietal regions are implied in cognitive functioning (Herrmann & Knight, 2001). One of the cognitive ERPs is the P3 (or P300), a positive waveform that peaks between 250 – 600 ms after the onset of a stimulus at centroparietal regions (Polich, 2007). This waveform is elicited Waiting/Stopping Choice/Action Task Reference Waiting Impulsivity Impulsive Choice Delay Discounting task Ballard & Knutson, 2009 Waiting Impulsivity Impulsive Action 4/5-Choice Serial Reaction Time task Morris et al., 2016, SanchezRoige et al., 2014, Voon et al., 2014 Stopping Impulsivity Impulsive Action Go/No-Go task Los, 2013 Stop-Signal Reaction Time task Morris et al., 2016, SanchezRoige et al., 2014, CHAPTER 1 11 https://doi.org/10.1016/J.NEUROIMAGE.2008.11.004 Başar, E. (1998). Brain function and oscillations: volume I: brain oscillations. Principles and approaches . https://books.google.com/books?hl=ptPT&lr=&id=cajoCAAAQBAJ&oi=fnd&pg=PA1&ots=8GzPs6U2LW&sig=cDpdkrkNqycUGFSIr8xVS33daw Batsikadze, G., Moliadze, V., Paulus, W., Kuo, M.-F., & Nitsche, M. A. (2013). Partially non-linear stimulation intensity-dependent effects of direct current stimulation on motor cortex excitability in humans. The Journal of Physiology , 591 (7), 1987–2000. https://doi.org/10.1113/jphysiol.2012.249730 Belin, D., Mar, A. C., Dalley, J. W., Robbins, T. W., & Everitt, B. J. (2008). High impulsivity predicts the switch to compulsive cocaine-taking. Science , 320 , 1352–1355. https://doi.org/10.1126/science.1158136 Benwell, C. S. Y., Learmonth, G., Miniussi, C., Harvey, M., & Thut, G. (2015). Non-linear effects of transcranial direct current stimulation as a function of individual baseline performance: Evidence from biparietal tDCS influence on lateralized attention bias. Cortex , 69 , 152–165. https://doi.org/10.1016/J.CORTEX.2015.05.007 Bernat, E. M., Malone, S. M., Williams, W. J., Patrick, C. J., & Iacono, W. G. (2007). Decomposing delta, theta, and alpha time-frequency ERP activity from a visual oddball task using PCA. International Journal of Psychophysiology , 64 (1), 62–74. https://doi.org/10.1016/j.ijpsycho.2006.07.015 Bortoletto, M., Pellicciari, M. C., Rodella, C., & Miniussi, C. (2015). The Interaction With Task-induced Activity is More Important Than Polarization: A tDCS Study. Brain Stimulation , 8 (2), 269–276. https://doi.org/10.1016/J.BRS.2014.11.006 Broyd, S. J., Richards, H. J., Helps, S. K., Chronaki, G., Bamford, S., & Sonuga-Barke, E. J. S. (2012). An electrophysiological monetary incentive delay (e-MID) task: A way to decompose the different components of neural response to positive and negative monetary reinforcement. Journal of Neuroscience Methods , 209 (1), 40–49. https://doi.org/10.1016/j.jneumeth.2012.05.015 Brunoni, A. R., & Vanderhasselt, M. A. (2014). Working memory improvement with non-invasive brain stimulation of the dorsolateral prefrontal cortex: A systematic review and meta-analysis. Brain and Cognition , 86 (1), 1–9. https://doi.org/10.1016/j.bandc.2014.01.008 Burke, J. F., Zaghlou, K. A., Jacobs, J., Williams, R. B., Sperling, M. R., Sharan, A. D., & Kahana, M. J. (2013). Synchronous and Asynchronous Theta and Gamma Activity during Episodic Memory Formation. Journal of Neuroscience , 33 (1), 292–304. https://doi.org/10.1523/JNEUROSCI.2057-12.2013 Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience , 14 (5), 365–376. https://doi.org/10.1038/nrn3475 Buzsáki, G., & Draguhn, A. (2004). Neuronal olscillations in cortical networks. Science , 304 (5679), 1926–1929. https://doi.org/10.1126/science.1099745 Coffman, B. A., Clark, V. P., & Parasuraman, R. (2014). Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. In CHAPTER 1 12 NeuroImage (Vol. 85, pp. 895–908). Academic Press Inc. https://doi.org/10.1016/j.neuroimage.2013.07.083 Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, Compulsivity, and Top-Down Cognitive Control. Neuron , 69 (4), 680–694. https://doi.org/10.1016/j.neuron.2011.01.020 Dalley, J. W., & Robbins, T. W. (2017). Fractionating impulsivity: Neuropsychiatric implications. Nature Reviews Neuroscience , 18 (3), 158–171. https://doi.org/10.1038/nrn.2017.8 Demiralp, T., Ademoglu, A., Istefanopulos, Y., Başar-Eroglu, C., & Başar, E. (2001). Wavelet analysis of oddball P300. International Journal of Psychophysiology , 39 (2–3), 221–227. https://doi.org/10.1016/S0167-8760(00)00143-4 Desmedt, J. E., & Debecker, J. (1979). Wave form and neural mechanism of the decision P350 elicited without pre-stimulus CNV or readiness potential in random sequences of near-threshold auditory clicks and finger stimuli. Electroencephalography and Clinical Neurophysiology , 47 (6), 648–670. https://doi.org/10.1016/0013-4694(79)90293-1 Donchin, E., & Coles, M. G. H. (1988). Is the P300 component a manifestation of context updating? Behavioral and Brain Sciences , 11 (3), 357–374. https://doi.org/10.1017/S0140525X00058027 Donoghue, T., Schaworonkow, N., & Voytek, B. (2021). Methodological considerations for studying neural oscillations. European Journal of Neuroscience . https://doi.org/10.1111/EJN.15361 Dupuy, F. E., Clarke, A. R., Barry, R. J., Selikowitz, M., & McCarthy, R. (2014). EEG and electrodermal activity in girls with Attention-Deficit/Hyperactivity Disorder. Clinical Neurophysiology , 125 (3), 491–499. https://doi.org/10.1016/J.CLINPH.2013.09.007 Ergen, M., Marbach, S., Brand, A., Başar-Eroǧlu, C., & Demiralp, T. (2008). P3 and delta band responses in visual oddball paradigm in schizophrenia. Neuroscience Letters , 440 (3), 304–308. https://doi.org/10.1016/J.NEULET.2008.05.054 Fertonani, A., & Miniussi, C. (2017). Transcranial electrical stimulation: What we know and do not know about mechanisms. Neuroscientist , 23 (2), 109–123. https://doi.org/10.1177/1073858416631966 Frohlich, F., & Townsend, L. (2021). Closed-Loop Transcranial Alternating Current Stimulation: Towards Personalized Non-invasive Brain Stimulation for the Treatment of Psychiatric Illnesses. Current Behavioral Neuroscience Reports , 8 (2), 51–57. https://doi.org/10.1007/S40473-021-002278/FIGURES/1 Fuentemilla, L., Marco-Pallarés, J., & Grau, C. (2006). Modulation of spectral power and of phase resetting of EEG contributes differentially to the generation of auditory event-related potentials. NeuroImage , 30 (3), 909–916. https://doi.org/10.1016/J.NEUROIMAGE.2005.10.036 Fuentes-Claramonte, P., Ávila, C., Rodríguez-Pujadas, A., Costumero, V., Ventura-Campos, N., Bustamante, J. C., Rosell-Negre, P., & Barrós-Loscertales, A. (2016). Inferior frontal cortex activity is modulated by reward sensitivity and performance variability. Biological Psychology , 114 , 127– 137. https://doi.org/10.1016/J.BIOPSYCHO.2016.01.001 Gill, J., Shah-Basak, P. P., & Hamilton, R. (2015). It’s the Thought That Counts: Examining the Taskdependent Effects of Transcranial Direct Current Stimulation on Executive Function. Brain Stimulation , 8 (2), 253–259. https://doi.org/10.1016/J.BRS.2014.10.018 CHAPTER 1 13 Glazer, J. E., Kelley, N. J., Pornpattananangkul, N., Mittal, V. A., & Nusslock, R. (2018). Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing. In International Journal of Psychophysiology (Vol. 132, pp. 184–202). Elsevier B.V. https://doi.org/10.1016/j.ijpsycho.2018.02.002 Goldsworthy, M. R., & Hordacre, B. (2017). Dose dependency of transcranial direct current stimulation: implications for neuroplasticity induction in health and disease. Journal of Physiology , 595 (11), 3265–3266. https://doi.org/10.1113/JP274089 Gu, R., Jiang, Y., Kiser, S., Black, C. L., Broster, L. S., Luo, Y. jia, & Kelly, T. H. (2017). Impulsive personality dimensions are associated with altered behavioral performance and neural responses in the monetary incentive delay task. Neuropsychologia , 103 , 59–68. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2017.07.013 Güntekin, B., & Başar, E. (2010). A new interpretation of P300 responses upon analysis of coherences. Cognitive Neurodynamics , 4 (2), 107–118. https://doi.org/10.1007/s11571-010-9106-0 Hamidovic, A., & Wang, Y. (2019). The P300 in alcohol use disorder: A meta-analysis and metaregression. Progress in Neuro-Psychopharmacology and Biological Psychiatry , 95 , 109716. https://doi.org/10.1016/j.pnpbp.2019.109716 Harper, J., Malone, S. M., & Bernat, E. M. (2014). Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task. Clinical Neurophysiology , 125 (1), 124–132. https://doi.org/10.1016/J.CLINPH.2013.06.025 Helfrich, R. F., Schneider, T. R., Rach, S., Trautmann-Lengsfeld, S. A., Engel, A. K., & Herrmann, C. S. (2014). Entrainment of Brain Oscillations by Transcranial Alternating Current Stimulation. Current Biology , 24 (3), 333–339. https://doi.org/10.1016/J.CUB.2013.12.041 Herrmann, C. S., & Knight, R. T. (2001). Mechanisms of human attention: event-related potentials and oscillations. Neuroscience & Biobehavioral Reviews , 25 (6), 465–476. https://doi.org/10.1016/S0149-7634(01)00027-6 Herrmann, C. S., Rach, S., Vosskuhl, J., & Strüber, D. (2014). Time-frequency analysis of event-related potentials: A brief tutorial. Brain Topography , 27 (4), 438–450. https://doi.org/10.1007/S10548013-0327-5/FIGURES/6 Huster, R. J., Enriquez-Geppert, S., Lavallee, C. F., Falkenstein, M., & Herrmann, C. S. (2013). Electroencephalography of response inhibition tasks: Functional networks and cognitive contributions. In International Journal of Psychophysiology (Vol. 87, Issue 3, pp. 217–233). Elsevier. https://doi.org/10.1016/j.ijpsycho.2012.08.001 Huster, R. J., Messel, M. S., Thunberg, C., & Raud, L. (2020). The P300 as marker of inhibitory control – Fact or fiction? Cortex , 132 , 334–348. https://doi.org/10.1016/j.cortex.2020.05.021 Jackson, M. P., Rahman, A., Lafon, B., Kronberg, G., Ling, D., Parra, L. C., & Bikson, M. (2016). Animal models of transcranial direct current stimulation: Methods and mechanisms. Clinical Neurophysiology , 127 (11), 3425–3454. https://doi.org/10.1016/J.CLINPH.2016.08.016 Jahfari, S., Waldorp, L., van den Wildenberg, W. P. M., Scholte, H. S., Ridderinkhof, K. R., & Forstmann, B. U. (2011). Effective connectivity reveals important roles for both the hyperdirect (frontosubthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition. Journal of Neuroscience , 31 (18), 6891–6899. CHAPTER 1 14 https://doi.org/10.1523/JNEUROSCI.5253-10.2011 Johnson, J. D., Allana, T. N., Medlin, M. D., Harris, E. W., & Karl, A. (2013). Meta-analytic review of P3 components in posttraumatic stress disorder and their clinical utility. In Clinical EEG and Neuroscience (Vol. 44, Issue 2, pp. 112–134). SAGE PublicationsSage CA: Los Angeles, CA. https://doi.org/10.1177/1550059412469742 Jones, K. A., Porjesz, B., Chorlian, D., Rangaswamy, M., Kamarajan, C., Padmanabhapillai, A., Stimus, A., & Begleiter, H. (2006). S-transform time-frequency analysis of P300 reveals deficits in individuals diagnosed with alcoholism. Clinical Neurophysiology , 117 (10), 2128–2143. https://doi.org/10.1016/j.clinph.2006.02.028 Jones, S. R. (2016). When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning. Current Opinion in Neurobiology , 40 , 72–80. https://doi.org/10.1016/J.CONB.2016.06.010 Kaiser, A., Aggensteiner, P. M., Baumeister, S., Holz, N. E., Banaschewski, T., & Brandeis, D. (2020). Earlier versus later cognitive event-related potentials (ERPs) in attention-deficit/hyperactivity disorder (ADHD): A meta-analysis. In Neuroscience and Biobehavioral Reviews (Vol. 112, pp. 117–134). Elsevier Ltd. https://doi.org/10.1016/j.neubiorev.2020.01.019 Kanai, R., Chaieb, L., Antal, A., Walsh, V., & Paulus, W. (2008). Frequency-Dependent Electrical Stimulation of the Visual Cortex. Current Biology , 18 (23), 1839–1843. https://doi.org/10.1016/j.cub.2008.10.027 Klimesch, W., Sauseng, P., Hanslmayr, S., Gruber, W., & Freunberger, R. (2007). Event-related phase reorganization may explain evoked neural dynamics. Neuroscience & Biobehavioral Reviews , 31 (7), 1003–1016. https://doi.org/10.1016/J.NEUBIOREV.2007.03.005 Klink, K., Paßmann, S., Kasten, F. H., & Peter, J. (2020). The Modulation of Cognitive Performance with Transcranial Alternating Current Stimulation: A Systematic Review of Frequency-Specific Effects. Brain Sciences 2020, Vol. 10, Page 932 , 10 (12), 932. https://doi.org/10.3390/BRAINSCI10120932 Kok, A. (2001). On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology , 38 (3), S0048577201990559. https://doi.org/10.1017/S0048577201990559 Kutchko, K. M., & Fröhlich, F. (2013). Emergence of Metastable State Dynamics in Interconnected Cortical Networks with Propagation Delays. PLOS Computational Biology , 9 (10), e1003304. https://doi.org/10.1371/JOURNAL.PCBI.1003304 Leite, J., Morales-Quezada, L., Carvalho, S., Thibaut, A., Doruk, D., Chen, C.-F., Schachter, S. C., Rotenberg, A., & Fregni, F. (2017). Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System. International Journal of Neural Systems , 27 (6), 1750026. https://doi.org/https://doi.org/10.1142/S0129065717500265 Los, S. A. (2013). The role of response inhibition in temporal preparation: Evidence from a go/no-go task. Cognition , 129 (2), 328–344. https://doi.org/10.1016/J.COGNITION.2013.07.013 Luck, S. J., & Kappenman, E. S. (2011). The Oxford Handbook of Event-Related Potential Components . Oxford University Press. https://books.google.pt/books?hl=en&lr=&id=gItoAgAAQBAJ&oi=fnd&pg=PP1&dq=event+related+ potentials+handbook&ots=B1KRAMNkRJ&sig=sQ_ldZ3Gkqdeahn6xD2qFA184Dk&redir_esc=y#v= CHAPTER 1 15 onepage&q=event related potentials handbook&f=false Luck, S. J., Woodman, G. F., & Vogel, E. K. (2000). Event-related potential studies of attention. Trends in Cognitive Sciences , 4 (11), 432–440. https://doi.org/10.1016/S1364-6613(00)01545-X Marshall, L., Helgadóttir, H., Mölle, M., & Born, J. (2006). Boosting slow oscillations during sleep potentiates memory. Nature 2006 444:7119 , 444 (7119), 610–613. https://doi.org/10.1038/nature05278 Min, B. K., Busch, N. A., Debener, S., Kranczioch, C., Hanslmayr, S., Engel, A. K., & Herrmann, C. S. (2007). The best of both worlds: Phase-reset of human EEG alpha activity and additive power contribute to ERP generation. International Journal of Psychophysiology , 65 (1), 58–68. https://doi.org/10.1016/j.ijpsycho.2007.03.002 Mishra, J., Martínez, A., Schroeder, C. E., & Hillyard, S. A. (2012). Spatial attention boosts short-latency neural responses in human visual cortex. NeuroImage , 59 (2), 1968–1978. https://doi.org/10.1016/J.NEUROIMAGE.2011.09.028 Monte-Silva, K., Kuo, M. F., Hessenthaler, S., Fresnoza, S., Liebetanz, D., Paulus, W., & Nitsche, M. A. (2013). Induction of late LTP-like plasticity in the human motor cortex by repeated non-invasive brain stimulation. Brain Stimulation , 6 (3), 424–432. https://doi.org/10.1016/j.brs.2012.04.011 Morris, L. S., Kundu, P., Baek, K., Irvine, M. A., Mechelmans, D. J., Wood, J., Harrison, N. A., Robbins, T. W., Bullmore, E. T., & Voon, V. (2016). Jumping the gun: Mapping neural correlates of waiting impulsivity and relevance across alcohol misuse. Biological Psychiatry , 79 (6), 499–507. https://doi.org/10.1016/j.biopsych.2015.06.009 Nikolin, S., Martin, D., Loo, C. K., & Boonstra, T. W. (2018). Effects of TDCS dosage on working memory in healthy participants. Brain Stimulation , 11 (3), 518–527. https://doi.org/10.1016/j.brs.2018.01.003 Nitsche, M. A., & Paulus, W. (2000). Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. The Journal of Physiology , 527 (Pt 3), 633. https://doi.org/10.1111/J.1469-7793.2000.T01-1-00633.X Nitsche, M. a, Cohen, L., Wassermann, E. M., Priori, A., Lang, N., Antal, A., Paulus, W., Hummel, F., Boggio, P. S., Fregni, F., & Pascual-Leone, A. (2008). Transcranial direct current stimulation: State of the art 2008. Brain Stimulation , 1 (3), 206–223. https://doi.org/10.1016/j.brs.2008.06.004 Novak, K. D., & Foti, D. (2015). Teasing apart the anticipatory and consummatory processing of monetary incentives: An event-related potential study of reward dynamics. Psychophysiology , 52 (11), 1470–1482. https://doi.org/10.1111/PSYP.12504 Pahor, A., & Jaušovec, N. (2018). The effects of theta and gamma tacs on working memory and electrophysiology. Frontiers in Human Neuroscience , 11 , 651. https://doi.org/10.3389/fnhum.2017.00651 Pfabigan, D. M., Seidel, E. M., Sladky, R., Hahn, A., Paul, K., Grahl, A., Küblböck, M., Kraus, C., Hummer, A., Kranz, G. S., Windischberger, C., Lanzenberger, R., & Lamm, C. (2014). P300 amplitude variation is related to ventral striatum BOLD response during gain and loss anticipation: An EEG and fMRI experiment. NeuroImage , 96 , 12–21. https://doi.org/10.1016/J.NEUROIMAGE.2014.03.077 CHAPTER 1 16 Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. In Clinical Neurophysiology (Vol. 118, Issue 10, pp. 2128–2148). Elsevier. https://doi.org/10.1016/j.clinph.2007.04.019 Popp, F., Dallmer-Zerbe, I., Philipsen, A., & Herrmann, C. S. (2019). Challenges of P300 modulation using Transcranial Alternating Current Stimulation (tACS). Frontiers in Psychology , 10 (MAR), 476. https://doi.org/10.3389/fpsyg.2019.00476 Pornpattananangkul, N., & Nusslock, R. (2016). Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards. Neuropsychologia , 91 , 141– 162. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2016.07.037 Rahn, E., & Basar, E. (1993). Enhancement of Visual Evoked Potentials by Stimulation During Low Prestimulus Eeg Stages. International Journal of Neuroscience , 72 (1–2), 123–136. https://doi.org/10.3109/00207459308991629 Riddle, J., & Frohlich, F. (2021). Targeting neural oscillations with transcranial alternating current stimulation. Brain Research , 1765 , 147491. https://doi.org/10.1016/J.BRAINRES.2021.147491 Robbins, T. W., & Dalley, J. W. (2017). Dissecting impulsivity: Brain mechanisms and neuropsychiatric implications. In Nebraska Symposium on Motivation (pp. 201–226). https://doi.org/10.1007/978-3-319-51721-6_7 Robinson, E. S. J., Eagle, D. M., Economidou, D., Theobald, D. E. H., Mar, A. C., Murphy, E. R., Robbins, T. W., & Dalley, J. W. (2009). Behavioural characterisation of high impulsivity on the 5choice serial reaction time task: Specific deficits in ‘waiting’ versus ‘stopping.’ Behavioural Brain Research , 196 (2), 310–316. https://doi.org/10.1016/J.BBR.2008.09.021 Saletu, B., Anderer, P., & Saletu-Zyhlarz, G. M. (2010). EEG topography and tomography (LORETA) in diagnosis and pharmacotherapy of depression. Clinical EEG and Neuroscience , 41 (4), 203–210. https://doi.org/10.1177/155005941004100407 Sanchez-Roige, S., Baro, V., Trick, L., Peña-Oliver, Y., Stephens, D. N., & Duka, T. (2014). Exaggerated waiting impulsivity associated with human binge drinking, and high alcohol consumption in mice. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology , 39 (13), 2919–2927. https://doi.org/10.1038/npp.2014.151 Sauseng, P., Klimesch, W., Gruber, W. R., Hanslmayr, S., Freunberger, R., & Doppelmayr, M. (2007). Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion. Neuroscience , 146 (4), 1435–1444. https://doi.org/10.1016/J.NEUROSCIENCE.2007.03.014 Schroeder, C. E., Steinschneider, M., Javitt, D. C., Tenke, C. E., Givre, S. J., Mehta, A. D., Simpson, G. V., Arezzo, J. C., & Vaughan, H. G. (1995). Localization of ERP generators and identification of underlying neural processes. Electroencephalography and Clinical Neurophysiology. Supplement , 44 , 55–75. https://europepmc.org/article/med/7649056 Schroeder, P. A., Schwippel, T., Wolz, I., & Svaldi, J. (2020). Meta-analysis of the effects of transcranial direct current stimulation on inhibitory control. In Brain Stimulation (Vol. 13, Issue 5, pp. 1159– 1167). Elsevier Inc. https://doi.org/10.1016/j.brs.2020.05.006 Schürmann, M., Başar-Eroglu, C., Kolev, V., & Başar, E. (1995). A new metric for analyzing single-trial event-related potentials (ERPs): application to human visual P300 delta response. Neuroscience CHAPTER 1 17 Letters , 197 (3), 167–170. https://doi.org/10.1016/0304-3940(95)11912-G Schutter, D. J. L. G., & Wischnewski, M. (2016). A meta-analytic study of exogenous oscillatory electric potentials in neuroenhancement. Neuropsychologia , 86 , 110–118. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2016.04.011 Stagg, C. J., & Nitsche, M. A. (2011). Physiological basis of transcranial direct current stimulation. In Neuroscientist (Vol. 17, Issue 1, pp. 37–53). Neuroscientist. https://doi.org/10.1177/1073858410386614 Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965). Evoked-potential correlates of stimulus uncertainty. Science , 150 (3700), 1187–1188. https://doi.org/10.1126/science.150.3700.1187 Van Dessel, J., Morsink, S., Van der Oord, S., Lemiere, J., Moerkerke, M., Grandelis, M., Sonuga-Barke, E., & Danckaerts, M. (2018). Waiting impulsivity: a distinctive feature of ADHD neuropsychology? Child Neuropsychology , 1–8. https://doi.org/10.1080/09297049.2018.1441819 van Ede, F., Quinn, A. J., Woolrich, M. W., & Nobre, A. C. (2018). Neural Oscillations: Sustained Rhythms or Transient Burst-Events? Trends in Neurosciences , 41 (7), 415–417. https://doi.org/10.1016/J.TINS.2018.04.004 Verleger, R. (2020). Effects of relevance and response frequency on P3b amplitudes: Review of findings and comparison of hypotheses about the process reflected by P3b. Psychophysiology , 57 (7), e13542. https://doi.org/10.1111/psyp.13542 Verleger, R., Baur, N., Metzner, M. F., & Śmigasiewicz, K. (2014). The hard oddball: Effects of difficult response selection on stimulus-related P3 and on response-related negative potentials. Psychophysiology , 51 (11), 1089–1100. https://doi.org/10.1111/PSYP.12262 Vignapiano, A., Mucci, A., Ford, J., Montefusco, V., Plescia, G. M., Bucci, P., & Galderisi, S. (2016). Reward anticipation and trait anhedonia: An electrophysiological investigation in subjects with schizophrenia. Clinical Neurophysiology , 127 (4), 2149–2160. https://doi.org/10.1016/J.CLINPH.2016.01.006 Vogeti, S., Boetzel, C., & Herrmann, C. S. (2022). Entrainment and Spike-Timing Dependent Plasticity – A Review of Proposed Mechanisms of Transcranial Alternating Current Stimulation. Frontiers in Systems Neuroscience , 16 , 19. https://doi.org/10.3389/FNSYS.2022.827353/BIBTEX Voon, V. (2014). Models of Impulsivity with a Focus on Waiting Impulsivity: Translational Potential for Neuropsychiatric Disorders. Current Addiction Reports , 1 (4), 281–288. https://doi.org/10.1007/s40429-014-0036-5 Voon, V., Irvine, M. A., Derbyshire, K., Worbe, Y., Lange, I., Abbott, S., Morein-Zamir, S., Dudley, R., Caprioli, D., Harrison, N. A., Wood, J., Dalley, J. W., Bullmore, E. T., Grant, J. E., & Robbins, T. W. (2014). Measuring “waiting” impulsivity in substance addictions and binge eating disorder in a novel analogue of rodent serial reaction time task. Biological Psychiatry , 75 (2), 148–155. https://doi.org/10.1016/j.biopsych.2013.05.013 Voss, U., Holzmann, R., Hobson, A., Paulus, W., Koppehele-Gossel, J., Klimke, A., & Nitsche, M. A. (2014). Induction of self awareness in dreams through frontal low current stimulation of gamma activity. Nature Neuroscience 2014 17:6 , 17 (6), 810–812. https://doi.org/10.1038/nn.3719 Vossen, A., Gross, J., & Thut, G. (2015). Alpha Power Increase After Transcranial Alternating Current Stimulation at Alpha Frequency (α-tACS) Reflects Plastic Changes Rather Than Entrainment. Brain CHAPTER 1 18 Stimulation , 8 (3), 499–508. https://doi.org/10.1016/J.BRS.2014.12.004 Winstanley, C. A., Eagle, D. M., & Robbins, T. W. (2006). Behavioral models of impulsivity in relation to ADHD: Translation between clinical and preclinical studies. Clinical Psychology Review , 26 (4), 379–395. https://doi.org/10.1016/j.cpr.2006.01.001 Wischnewski, M., & Schutter, D. J. L. G. (2017). After-effects of transcranial alternating current stimulation on evoked delta and theta power. Clinical Neurophysiology , 128 (11), 2227–2232. https://doi.org/10.1016/J.CLINPH.2017.08.029 Wischnewski, M., Zerr, P., & Schutter, D. J. L. G. (2016). Effects of Theta Transcranial Alternating Current Stimulation Over the Frontal Cortex on Reversal Learning. Brain Stimulation , 9 (5), 705– 711. https://doi.org/10.1016/J.BRS.2016.04.011 Yener, G. G., Güntekin, B., Örken, D. N., Tülay, E., Forta, H., & Başar, E. (2012). Auditory delta eventrelated oscillatory responses are decreased in Alzheimer’s disease. Behavioural Neurology , 25 (1), 3–11. https://doi.org/10.3233/BEN-2012-0344 Yener, G. G., Kurt, P., Emek-Savaş, D. D., Güntekin, B., & Başar, E. (2013). Reduced Visual EventRelated Delta Oscillatory Responses in Amnestic Mild Cognitive Impairment. Journal of Alzheimer’s Disease , 37 (4), 759–767. https://doi.org/10.3233/JAD-130569 Zaehle, T., Rach, S., & Herrmann, C. S. (2010). Transcranial Alternating Current Stimulation Enhances Individual Alpha Activity in Human EEG. PLOS ONE , 5 (11), e13766. https://doi.org/10.1371/JOURNAL.PONE.0013766 Zhang, Y., Li, Q., Wang, Z., Liu, X., & Zheng, Y. (2017). Temporal dynamics of reward anticipation in the human brain. Biological Psychology , 128 , 89–97. https://doi.org/10.1016/J.BIOPSYCHO.2017.07.011 19 CHAPTER 2 Modulation of the cognitive event-related potential P3 by transcranial Direct Current Stimulation: systematic review and meta-analysis CHAPTER 2 20 The work presented in Chapter 2 was published in Neuroscience & Biobehavioral Reviews. The referencing style used in this Chapter is in accordance with the published paper, while the other Chapters are in APA 7th Edition. Reference: Mendes, A. J., Pacheco-Barrios, K., Lema, A., Gonçalves, Ó. F., Fregni, F., Leite, J., & Carvalho, S. (2021). Modulation of the cognitive event-related potential P3 by transcranial direct current stimulation: Systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews , 132 , 894-907. https://doi.org/10.1016/j.neubiorev.2021.11.002 CHAPTER 2 27 2.3.3.1. Pooled effect estimates and subgroup analysis. A random-effect model was performed due to the expected high level of heterogeneity, assuming that the true effect size among the studies might not be identical (Borenstein et al., 2010). The effect size was calculated by subtracting sham P3 values from the active tDCS condition measured during/after tDCS. The standard mean difference (SMD) between both tDCS conditions, namely the effect size of the intervention relatively to its variability, was calculated following the unbiased method of Hedges’ g (Hedges, 1981). Thus, the pooled effect estimates were analyzed independently for anodal and cathodal tDCS due to its potential antagonistic effects (Cochrane, 2019). The subgroup analysis were performed accordingly to the tDCS polarity and brain region of stimulation (e.g., left dorsolateral prefrontal cortex - lDLPFC, right Inferior Frontal Gyrus - rIFG). These analyses were performed only when there were the effect estimates from at least two studies. Furthermore, the I2 index was performed to assess heterogeneity (Higgins and Thompson, 2002). 2.3.3.2. Influential analysis. The influential analysis was performed using the leave-one-out method. This technique allows the recalculation of the estimates of the meta-analysis by removing one study per recalculation in a total of N-1 times (Viechtbauer and Cheung, 2010). This sensitivity analysis tests the robustness of the detected effects by observing the influence of each comparison in the significant findings. 2.3.3.3. Moderator analysis. The moderator analysis was completed using a univariate regression model. The meta-regression comprised the following moderators: brain region and hemisphere of stimulation, tDCS parameters (i.e., intensity, density, duration), number of sessions (i.e., single or multi-session) population (i.e., healthy and clinical), response requirement, target probability, timing (online/offline), and study design. Nonetheless, not all moderators have been included in every moderator analysis because it was dependent on the heterogeneity of the studies analyzed in each subsection. For instance, if all the studies from a sub-analysis have the same tDCS intensity parameter except in one comparison, this variable was not analyzed. Moreover, the meta-regression was not performed if there were less than 10 comparisons (Thompson and Higgins, 2002). CHAPTER 2 28 2.3.3.4. Publication bias. The publication bias was analyzed through funnel plots and Egger’s regression test for the asymmetry (Egger et al., 1997). The p-value and the test-statistics (i.e., zvalue) from Egger’s test were considered to evaluate potential asymmetries. The methods to detect publication bias test the differences between studies, which implies that only one comparison per study must be included. Nonetheless, in this study, all the comparisons were included due to the low number of studies but with a high number of comparisons. Therefore, these analyses were only performed when there were at least 10 comparisons (Sterne et al., 2011). 2.3.4. Risk of Bias The risk of bias was assessed using the Cochrane Collaboration’s risk of bias tool (Higgins et al., 2011). Each study was classified as “high risk”, “low risk” or “unclear” in seven criteria, namely (1) random sequence generation, (2) allocation concealment, (3) selective reporting, (4) other sources of bias, (5) participants and (6) raters blinding, and (7) lack of outcome data. The traffic light graphs were plotted using the robvis package in R (McGuinness & Higgins, 2020; robvis Version 0.3.0, released on 22-11-2019). 2.3.5. Evidence certainty assessment We assessed the certainty of our pooled estimates applying the grading of recommendation, assessment, development, and evaluation (GRADE) approach (Balshem et al., 2011). This assessment is based on five domains: study limitations (i.e., risk of bias of the studies included), imprecision (i.e., sample sizes and confidence intervals (CI)), indirectness (generalizability), inconsistency (heterogeneity), and publication bias as stated in the GRADE handbook (Schünemann et al., 2013). The certainty of the evidence was characterized as high, moderate, low, or very low and was described in the Summary of findings table to present the most relevant pooled estimates. We used the web-based platform GRADE online tool (http://gradepro.org). 2.4. Results A total of 23 studies were included, specifically, 4 with GNG, 7 with n-back, 10 with oddball, and 4 with emotional processing. There was one study that evaluated P3 on GNG and in an oddball paradigm and two that used emotional-charge stimuli in the GNG task. Therefore, these studies were included in two sections of analyses accordingly to their characteristics. Two studies that analyzed P3 in an auditory oddball and in GNG were not included because they only reported the data from the electrode site Cz. CHAPTER 2 29 Furthermore, six studies did not report sufficient information to estimate the effect size and the corresponding author did not reply to the request via e-mail. We excluded five studies evaluating P3 on other cognitive tasks, such as flanker task, recognition task, naming task, and a decision-making paradigm (Figure 2). The results of the study characteristics, pooled effect estimates, and subgroup analysis are divided into four main analyses, namely in GNG, n-back, oddball, and emotional processing. Finally, we presented the moderator and influential analysis, the publication bias, risk of bias, and evidence certainty assessment. Figure 2. PRISMA flow diagram (*one study analyzed P3 on a GNG task and oddball paradigm; **two studies were included in GNG and emotional processing analysis because P3 was evaluated in an emotional GNG). CHAPTER 2 30 2.4.1. Oddball 2.4.1.1. Study Characteristics. Thirteen studies met the eligibility criteria. However, two studies were excluded because, in one, no relevant data was available directly from the article, and another study only analyzed the P3 in the Cz electrode. Therefore, 11 studies (with 22 comparisons) with a total of 236 participants were analyzed (see Table SM.8 in Supplementary Materials). Seven (out of 11) studies (with 16 comparisons) analyzed the P3 amplitude in the frontal region and nine studies (with 16 comparisons) in the parietal area. In the frontal P3 assessment, the anodal stimulation was performed in seven studies (with 10 comparisons and the cathodal in five studies, with eight comparisons). Considering the studies that analyzed parietal P3 amplitude, all the studies (nine studies with 11 comparisons) studied the effect of anodal stimulation, whilst four of them (with five comparisons) also tested the effects of cathodal tDCS. In line with other tasks, the P3 latency was less frequently analyzed, specifically four studies (with five comparisons) tested anodal and cathodal tDCS in frontal P3, while two studies (with four comparisons) explored the anodal effect and one study (with two comparisons) on parietal P3 (see Table SM.5 in Supplementary Materials). Taking into account the brain region of tDCS, most of the studies targeted to the lDLPFC (six studies with 16 comparisons), others the cerebellum (two studies with six comparisons), rIFG (one study and one comparison), supraorbital area (one study and two comparisons) and motor cortex (one study and one comparison) (see Table SM.6 in Supplementary Materials). Most of the studies performed tDCS before the assessment of P3 (nine studies), whilst only one did it during tDCS and another one assessed the after effects of tDCS on the P3 component. Additionally, the oddball tasks were mostly designed using auditory stimuli (nine out of 11 studies) and only two studies used visual cues (i.e., one employed letters and numbers and another one used human faces). Finally, six studies (with 10 comparisons) explored P3 in healthy subjects and five studies (with eight comparisons) in a clinical population (i.e., three in people with schizophrenia, one in people with multiple sclerosis and Alzheimer’s disease). CHAPTER 2 31 2.4.1.2. Pooled effect estimates and subgroup analysis. The pooled effect estimated from the seven studies (with 10 comparisons) that analyzed anodal tDCS on the frontal P3 amplitude did not present significant heterogeneity (p = 0.685, I2 = 2.429). Moreover, this set of studies did not show a significant effect on frontal P3 amplitude (p = 0.576, SMD = -0.062, 95% CI [-0.28 0.16]). Further subgroup analysis did not show significant heterogeneity in the studies applying anodal stimulation on cerebellum (p = 0.928, I2 = 0), neither on the lDLPFC (p = 0.385, I2 = 22.448). Nonetheless, both subgroup analysis revealed a non-significant effect of stimulation on the frontal P3 amplitude, namely when using anodal tDCS over the cerebellum (p = 0.668, SMD = -0.104, 95% CI [-0.58 0.37]) or over the lDLPFC (p = 0.839, SMD = -0.029, 95% CI [-0.31 0.25]). Additionally, five studies (with six comparisons) that analyzed the effect of cathodal tDCS in frontal P3 amplitude did not reveal significant heterogeneity (p = 0.18, I2 = 22.141) and showed that cathodal tDCS significantly decreased frontal P3 amplitude (p = 0.017, SMD = -0.404, 95% CI [-0.73 -0.07]) (Figure 3). The subsequent subgroups analysis regarding brain region stimulation did not show significant result in heterogeneity test for cathodal tDCS over the cerebellum (p = 0.109, I2 = 54.612) or over the lDLPFC (p = 0.215, I2 = 38.57). Cathodal stimulation over the cerebellum did not show a significant effect on frontal P3 amplitude (p = 0.383, SMD = -0.325, 95% CI [-1.05 0.41]), whilst a non-significant trend was showed when the cathodal tDCS was delivered over the lDLPFC (p = 0.076, SMD = -0.42, 95% CI [-0.88 0.04]). For frontal P3 Figure 3. Forest plot with pooled effect estimate and subgroup analysis concerning the cathodal stimulation on frontal P3 amplitude during oddball. CHAPTER 2 32 latency, the four studies (with five comparisons), in which anodal stimulation was applied, were significantly heterogeneous ( p < 0.001, I2 = 92.818). However no significant effects of anodal tDCS in frontal P3 latency were found ( p = 0.47, SMD = 0.493, 95% CI [-0.84 1.83]). Furthermore, the heterogeneity test in the subgroup analysis revealed a non-significant heterogeneity in anodal cerebellar tDCS ( p = 0.79, I2 = 0), but a significant heterogeneity in the studies applying anodal stimulation over the lDLPFC ( p < 0.001, I2 = 97.817). The subgroup analysis probing the effects of anodal tDCS in the frontal P3 latency was non-significant, regardless of the stimulation site ( p = 0.937, SMD = 0.019, 95% CI [-0.46 0.5] for cerebellum) and ( p = 0.518, SMD = 1.221, 95% CI [-0.31 4.92] for the lDLPFC). Concerning the same for studies, but for the cathodal stimulation comparisons (five comparisons), a significant heterogeneity was revealed ( p < 0.001, I2 = 91.403. However, there were no significant effects of cathodal tDCS on the frontal P3 latency ( p = 0.172, SMD = 0.843, 95% CI [-0.37 2.05]). Subgroup analysis suggested significant heterogeneity in cathodal cerebellar ( p < 0.001, I2 = 93.107)) and in the lDLPFC tDCS( p = 0.006, I2 = 86.523). In line with the pooled effect estimate analysis, both subgroups showed no significant effect on the frontal P3 latency, namely with cerebellar ( p = 0.283, SMD = 1.17, 95% CI [- 0.97 3.31]) or the lDLPFC tDCS ( p = 0.465, SMD = 0.492, 95% CI [-0.88 1.81]). For probing the effects of anodal tDCS in the parietal P3 amplitude, nine studies (with 11 comparisons) were retrieved. There was a non-significant trend regarding heterogeneity ( p = 0.058, I2 = 42.218) and there was no significant anodal tDCS effect ( p = 0.596, SMD = 0.081, 95% CI [-0.22 0.38]). Moreover, subgroup analysis did not present significant heterogeneity in studies with anodal tDCS over the cerebellum ( p = 0.68, I2 = 0) or the lDLPFC ( p = 0.338, I2 = 11.264). No significant effect of anodal cerebellar tDCS on parietal P3 amplitude was shown ( p = 0.984, SMD = 0.005, 95% CI [-0.47 0.48]), however there was a significant effect of anodal stimulation over the lDLFPC ( p = 0.018, SMD = 0.4, 95% CI [0.07 0.73]). The anodal tDCS over the lDLPFC increased the frontal P3 amplitude in comparison with the sham condition (Figure 4). The subgroup analysis of anodal tDCS over supraorbital, rIFG or M1 are not reported because they were comprised by only one study. For cathodal tDCS, the four studies (with five comparisons) did not show significant heterogeneity ( p = 0.857, I2 = 0) and there was no significant effect of cathodal tDCS in the parietal P3 amplitude ( p = 0.837, SMD = -0.036, 95% CI [-0.38 0.31]). The subgroup analysis with the cathodal cerebellar tDCS comparisons neither reveal heterogeneity ( p = 0.578, I2 = 0), nor significant effect of cathodal tDCS ( p = 0.78, SMD = -0.068, 95% CI [-0.55 0.41]). The subgroup analysis of cathodal CHAPTER 2 33 tDCS over the supraorbital region, or the lDLPFC is not reported because there is only one study targeting those regions. Finally, the pooled effect estimate and subgroup analysis was not performed in the parietal P3 latency during oddball due to the lack of data. 2.4.2. N-back tasks Figure 4. Forest plot with pooled effect estimate and subgroup analysis concerning the anodal stimulation on parietal P3 amplitude during oddball. CHAPTER 2 34 2.4.2.1. Study Characteristics. A total of eight studies met the inclusion criteria, but one of them did not report the required data and the corresponding author did not reply to the data request. So, seven studies (with 20 comparisons) comprising 132 participants were analyzed (see Table SM.8 in Supplementary Materials). Most of the studies (six out of seven with 18 comparisons) analyzed the P3 amplitude in the frontal region, whilst four studies (with 11 comparisons) also assessed it on the parietal area. Concerning anodal polarity, six of them (with 15 comparisons) tested the frontal P3, whilst only four (with eight comparisons) tested anodal tDCS in parietal P3. On the other hand, two of them (with three comparisons) also tested the cathodal stimulation effect in frontal and parietal P3 amplitude. Regarding the P3 latency, only two studies (with seven comparisons) analyzed P3 in the frontal region, and one study (with two comparisons) analyzed P3 in the parietal area (see Table SM.5 in Supplementary Materials). Every study explored the effect of tDCS on frontal areas, namely over the lDLPFC (five studies out of seven with 15 comparisons assessed frontal P3; and two studies with six comparisons in total, assessed parietal P3) and rIFG (two studies out of seven with three comparisons assessed frontal P3 and five comparisons in parietal P3) (see Table SM.6 in Supplementary Materials). All the studies performed tDCS before assessing the P3 component. Moreover, the study population was different between the included studies, comprising healthy adults (five studies with 15 comparisons), healthy elderly (one study with two comparisons), patients with Alzheimer disease (one study with two comparisons) and children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD; one study with two comparisons). Finally, the P3 was assessed in 0-back and 1-back (three studies with five comparisons), 2-back (five studies with eight comparisons), and 3-back (four studies with seven comparisons). 2.4.2.2. Pooled effect estimates and subgroup analysis. The pooled effect estimates of the six studies (and 15 comparisons) with anodal tDCS that incorporated frontal P3 amplitude in their analysis revealed a significant heterogeneity ( p < 0.001, I2 = 70.497). Considering all the studies, there were no differences between anodal and sham tDCS in frontal P3 amplitude ( p = 0.959, SMD = 0.008, 95% CI [-0.31 0.33]). The studies with anodal tDCS over the lDLPFC presented a significant heterogeneity ( p = 0.03, I2 = 47.561). Furthermore, there were no significant effects of anodal tDCS over the lDLPFC in frontal P3 amplitude ( p = 0.169, SMD = 0.202, 95% CI [-0.09 0.49]). The subgroup analysis of anodal tDCS over rIFG is not reported because only one study analyzed the frontal P3 amplitude. Additionally, both studies that explored the effects of cathodal tDCS over the frontal P3 amplitude did not present significant heterogeneity ( p = 0.368, I2 = 2.664), but no significant effects were detected ( p = 0.888, SMD CHAPTER 2 35 = -0.032, 95% CI [-0.48 0.41]). Finally, regarding the frontal P3 latency, the two studies (with seven comparisons) did not reveal significant heterogeneity ( p = 0.936, I2 = 0) and no significant effects of anodal tDCS on frontal P3 latency were found ( p = 0.201, SMD = -0.173, 95% CI [-0.44 0.09]). Regarding the P3 evaluated in the parietal region, the four studies (with 8 comparisons) with anodal tDCS analysis did not reveal a significant heterogeneity ( p = 0.49, I2 = 64.786) and there was a significant effect of tDCS on parietal P3 amplitude ( p = 0.001, SMD = 0.477, 95% CI [0.2 0.76]). In fact, there is an increase in the P3 amplitude during the performance of the n-back tasks after anodal tDCS (Figure 5). Subgroup analysis performed in the studies that applied anodal tDCS over rIFG did not reveal a significant heterogeneity level ( p = 0.572, I2 = 0) and showed even a significant larger positive mean estimated effect size ( p < 0.001, SMD = 0.669, 95% CI [0.31 1.03]). Additionally, subgroup analysis on studies with anodal stimulation over the lDLPFC did not present significant heterogeneity ( p = 0.672, I2 = 47.561), but also did not reveal a significant effect estimate ( p = 0.398, SMD = 0.19, 95% CI [-0.25 0.63]). Concerning both studies assessing cathodal tDCS effect, neither significant results in terms of heterogeneity between comparisons was found ( p = 0.891, I2 = 2.664), nor a significant effect on parietal Figure 5. Forest plot with pooled effect estimate and subgroup analysis concerning the anodal stimulation on parietal P3 amplitude during n-back. CHAPTER 2 36 P3 amplitude ( p = 0.939, SMD = -0.017, 95% CI [-0.46 0.42]). Finally, only one study assessed the parietal P3 latency in n-back tasks, which did not allow the analysis to be performed. 2.4.3. Go/No-Go task 2.4.3.1. Study Characteristics. Six studies were eligible according to the aforementioned criteria, nonetheless, in two of them it was not possible to extract the required information and the corresponding author did not reply to our requests. Therefore, four studies (with seven comparisons) comprising 120 participants were analyzed (see Table SM.8 in Supplementary Materials). All the studies used anodal tDCS. Every study analyzed the No-Go P3 amplitude, while only two (out of five) analyzed the No-Go latency. Nonetheless, two of them evaluated the No-Go P3 in the frontal region, whilst the other two in the parietal. Regarding the Go-P3, data was extracted from parietal electrodes, and amplitude was assessed in two studies, and latency in one of them. Most of the studies applied tDCS over frontal areas (three out of four), whilst only one applied tDCS over the motor cortex. Considering the studies of frontal tDCS, the targeted regions were the rIFG, and the right and left DLPFC (see Table SM.6 in Supplementary Materials). All the studies applied tDCS before the EEG recording. Moreover, every study included a sample of healthy adults, but one study included an additional sample of binge drinkers (BDs) and another a sample of elderly subjects. Finally, two studies used emotional-charged stimuli in the GNG, namely alcohol-related and food-related pictures. 2.4.3.2. Pooled effect estimates and subgroup analysis. The pooled effect estimates of the two studies (with one comparison each) which analyzed the No-Go P3 amplitude in frontal areas showed a non-significant trend in heterogeneity ( p = 0.087, I2 = 65.884). No effect of tDCS was revealed in the frontal No-Go P3 ( p = 0.866, SMD = -0.086, 95% CI [-1.09 0.92]). On the other hand, two studies (with five comparisons) analyzed the No-Go P3 in parietal electrodes and they did not reveal a significant heterogeneity ( p = 0.702, I2 = 0). Furthermore, there was no significant effect of anodal tDCS on parietal No-Go P3 ( p = 0.574, SMD = 0.08, 95% CI [-0.2 0.36]). The No-Go P3 latency was only analyzed in parietal region and the two studies (with five comparisons) did not reveal a significant heterogeneity ( p = 0.818, I2 = 0). Moreover, there was no significant effect of anodal tDCS on parietal No-Go P3 latency ( p = 0.854, SMD = 0.026, 95% CI [-0.25 0.3]). Additionally, the two studies (with three comparisons) that assessed the parietal P3 amplitude did not reveal significant heterogeneity ( p = 0.336, I2 = 20.313), but also no significant effect of anodal tDCS effect ( p = 0.793, SMD = -0.061, 95% CI [-0.51 0.39]) was detected. Subgroup analysis were not performed due to the lack of data (see Table SM.5 in Supplementary Materials). CHAPTER 2 43 2.5.2. N-back tasks tDCS modulated differently the P3 amplitude during n-back task in parietal regions, however no effects were found on frontal regions. P3 amplitude increased after active tDCS over frontal areas in comparison with sham (SMD = 0.33), especially when tDCS was applied over the rIFG (SMD = 0.67). The significant effect estimate of frontal tDCS was observed in healthy and clinical populations, specifically Alzheimer’s disease and ADHD (see Table SM.8 in Supplementary Materials). This is consistent with what has been found after programs of cognitive training with WM exercises (O’Brien et al., 2013; Tusch et al., 2016). Hence, an enhanced parietal P3 amplitude might be related to better WM processing, as some studies suggest this correlation (e.g., Cespón et al., 2017). This finding is in line with Polich (2007) about the role of P3b in the updating WM, given that behavioral performance increase in n-back were associated with the parietal P3 and not in frontal P3 that is more related to attentional processes. The P3 dynamics observed within the frontoparietal network during WM tasks after tDCS might be explained by the efficiency of the neuronal processing (Neubauer and Fink, 2009). The optimal cognitive functioning relies on the efficiency of broader neuronal networks, instead of the overactivation of frontal regions. In fact, subjects with higher levels of intelligence present less cortical activation in frontal areas during WM task with moderate difficulty (Nussbaumer et al., 2015). Likewise, elderly performing a WM task shown a larger frontal P3 amplitude and a smaller in parietal region in comparison with young adults, suggesting an ineffective distribution of neuronal resources (Saliasi et al., 2013). On the other hand, the opposite pattern is observed on young adults with better WM skills than elderly, namely a larger P3 amplitude in parietal region and a reduced amplitude in frontal areas (Cespón et al., 2017; van Dinteren et al., 2014). Hence, although the large spatial resolution of EEG might difficult the interpretation about the source of the evoked potential, the increase of parietal P3 amplitude after anodal frontal tDCS might indicate the activation of a broader network involved in the WM processing (i.e., attentional allocation in the frontal regions and categorization of task-relevant events in parietal). Therefore, studies aiming at the enhancement of WM processing using NIBS techniques targeted the frontoparietal network, given its role in the access, maintenance and manipulation of information. A recent study found a coupling between the phase of frontal midline theta rhythm and gamma oscillatory amplitude on the parietal region during a visuospatial WM task (Berger et al., 2019). In fact, changes on phase-amplitude coupling between frontal theta and parietal gamma activity were observed after four sessions of WM cognitive training coupled with tDCS over frontal and parietal regions associated with WM improvements (Jones et al., 2020). Another study, in which intermittent Theta Burst Stimulation (iTBS) was delivered to the lDLPFC, resulted in an improvement in WM skills coupled with stronger connectivity CHAPTER 2 44 of frontoparietal theta and an enhancement of parietal gamma activity (Hoy et al., 2016). A similar effect was found in a study testing tDCS over the lDLPFC in patients with schizophrenia, who shown behavioral gains and an increased synchronization of gamma activity during the task (Hoy et al., 2015). Thus, gamma oscillations assume an important role in WM processes, which intriguingly is co-occurring with the P3 component, in parietal regions after task-relevant stimuli, although both markers might index different mental events (Pitts et al., 2014). More recently, Riddle and colleagues (2020) explored theta and alpha oscillations using repetitive Transcranial Magnetic Stimulation (rTMS) in the frontal and parietal regions respectively to improve WM processing through an optimal engagement and disengagement of neuronal resources. Results have shown that both entrainments enhanced WM abilities, specifically frontal theta entrainment improved the prioritization of information, whilst parietal alpha assisted the inhibition of irrelevant information (Riddle et al., 2020). These studies propose an inter-dependency between both cortical areas for successful access and maintenance of task-relevant information that can be modulated by NIBS. In line with these findings, the current meta-analysis shows a modulation within the frontoparietal network through an increase in parietal P3 amplitude after the application of tDCS over frontal areas. In fact, a comparable effect was already found using neuroimaging techniques in the frontoparietal network after tDCS over the lDLPFC (Keeser et al., 2011). Moreover, enhanced parietal P3 amplitude might be related with gamma synchronization in parietal areas, given that both are enhanced by NIBS and related to successful WM processing (Berger et al., 2019; Hoy et al., 2016). 2.5.3. Go/No-Go task tDCS did not show any significant change in P3 amplitude and latency during the no-go and go trials, even though a recent meta-analysis suggested a moderate significant effect of tDCS on the behavioral outcomes of inhibitory response tasks (Schroeder et al., 2020). This subsection of analysis included a very low number of studies and with large heterogeneity among them. For instance, two studies (with four comparisons) assessed the no-go P3 in parietal areas, whereas two other studies (with two comparisons) assessed the effects of no-go trials in the frontal region. This variability led us to analyze the no-go P3 in frontal and parietal areas independently, which resulted in a very low number of comparisons per analysis, which decreased the power of the analysis. The P3 elicited during no-go trials is thought to be generated in fronto-medial areas and it is highly associated with delta band processes (Huster et al., 2013). The frontal delta activity has been associated with the motivational salience of stimuli, which suggests its importance in the attentional CHAPTER 2 45 processes required towards a no-go trial (Knyazev, 2012). Likewise, these electrophysiological markers were also observed during the oddball paradigm, namely the enhancement in the delta band, suggesting similar mental operations related to information processing between both cognitive tasks (Bernat et al., 2007; Demiralp et al., 2001). In fact, delta activity has been associated with other cognitive functions such as attention, perception, and decision-making. Moreover, changes in delta activity have also been associated with several clinical conditions with cognitive deficits, such as, mild cognitive impairment, Alzheimer’s disease or schizophrenia, in which delta band activity is decreased (Güntekin and Başar, 2016). Additionally, the frontal midline theta, discussed in the previous cognitive tasks, should be considered as well during response inhibition tasks (Miller et al., 2015). Thus, considering these electrophysiological features and its topography, the no-go P3 is thought to be a variant of the P3a component (Polich, 2007). So, the absence of tDCS effect in no-go P3 is in line with the oddball and nback findings, given that in these cognitive tasks it was not found any modulation on frontal P3. On the other hand, the go-P3 follows a more posterior topography in comparison with the P3 elicited in no-go trials, which suggests similarities with the P3b component (Huster et al., 2013). Nonetheless, the parietal go-P3 amplitude was not modulated by tDCS, although it is important to highlight the scarceness of data to make this claim (i.e., two studies with a total of three comparisons). These findings should be cautiously interpreted accordingly to recent models of inhibitory control. Specifically, inhibition is a broad concept that can be divided into several subtypes, such as the proactive and reactive processes. The proactive inhibition aims the inhibition a forthcoming response (i.e., GNG task), whilst reactive inhibition is dependent on an external cue (i.e., SSRT task; Aron, 2011). The rIFG assumes an important role in both processes, although proactive inhibition is related to an indirect pathway that connects the rIFG with the striatum, whilst reactive inhibition has been associated with a hyperdirect pathway from rIFG to subthalamic nucleus (Jahfari et al., 2011). Therefore, anodal tDCS over the rIFG might modulate differently both subtypes of response inhibition. In fact, a recent meta-analysis showed that tDCS enhanced inhibitory control only in the SSRT task (and a marginally significant in GNG) and the effect estimate was larger in the anodal stimulation over rIFG in comparison with other cortical areas (Schroeder et al., 2020). Nevertheless, the effect of anodal tDCS over the rIFG in P3 during GNG were analyzed in only one study out of the four. 2.5.4. Emotional Processing The tDCS did not affect the P3 amplitude and latency after the presentation of emotionally laden stimuli. This subsection aimed to study the emotional processing that occurred during tasks using CHAPTER 2 46 affective-charged stimuli (e.g., food, drugs). Specifically, the frontal P3 component related to orienting and attentional allocations was suggested to be an endogenous marker of stimulus-reactivity. For instance, subjects with patterns of heavy drinking in a social context showed a larger frontal P3 amplitude after the visualization of alcohol-related pictures in comparison with neutral pictures (Herrmann et al., 2001). Therefore, given that the DLPFC assumes an important role in top-down cognitive control, it has been hypothesized that tDCS over that area could reduce the reactivity to salient stimuli (Lapenta et al., 2014; Nakamura-Palacios et al., 2012). Nonetheless, although several studies showed that anodal tDCS over the lDLPFC decreased (Den Uyl et al., 2015) or increased craving levels (Carvalho et al., 2019), the current meta-analysis did not show any modulation of this tDCS montage in frontal or parietal P3 amplitude or latency after affective stimuli. In line with the previous findings from GNG tasks, this analysis was comprised of a reduced number of studies that share important differences among them. First, the study population was different in the four studies, namely Binge drinkers, people suffering from alcohol use disorder, with crack/cocaine addiction, and healthy controls. This might be a potential confounder in the present meta-analysis, given that the pooled effects were observed on distinct effects of craving and consumption pattern (den Uyl et al., 2018; Den Uyl et al., 2015). Second, two studies analyzed the P3 component in a cue-reactivity task, whilst the other two in a GNG with emotional stimulus. Although the analysis included only the P3 evaluated after the affective stimulus, in the cue-reactivity task participants were only instructed to observe the picture and in the GNG they were required to press a button (or not) depending on the type of trial. Therefore, the cognitive operations required during the GNG task might difficult the interpretation of P3 as a marker of cue-reactivity, especially because task dependent effects of tDCS have been shown. Overall, the tDCS effect on cue-reactivity P3 still needs further clarification due to the heterogeneous and small set of studies analyzed. The P3 related to emotional processing might be dependent on specificities of the population (e.g., BDs vs alcoholics) and also on the experimental task (e.g., observation vs press a button). 2.5.5. Future Directions The effects of tDCS on the brain during cognitive processing are still unclear (Chan et al., 2021). The current study showed how tDCS can modulate the cognitive P3 in distinct contexts, but the underlying neurophysiological mechanisms are still unclear. For a better understanding, it is important to test how tDCS can influence the connectivity within frontoparietal network during cognitive processing. In particular, the frontal theta activity is a common marker observed in several cognitive processes that rely CHAPTER 2 47 on the PFC and has been associated with the synchronization of other task-related regions (Cohen, 2014). Although recent studies have approached the tDCS effect on frontal theta within the frontoparietal network in resting-state (Jones et al., 2020), the dynamics during cognitive functioning are not fully understood yet. Furthermore, the neurotransmitters dynamics are also an important component to understand the cognitive processing and are strongly associated with the elicitation of P3 (Polich, 2007). Specifically, the phasic activity of the LC-NE has been implicated in the P3 generation along frontoparietal areas (Nieuwenhuis et al., 2005). Several physiological changes related with the LC-NE system occur in parallel with the P3 elicitation, such as an increase in pupil diameter or heart rate (Nieuwenhuis et al., 2011). Nonetheless, there is a lack of evidence regarding the tDCS impact on norepinephrine release observed on these autonomic components during P3 response. Moreover, despite the fact that recent meta-analysis suggests that tDCS impacts cognitive function as assessed by behavior (Brunoni and Vanderhasselt, 2014; Schroeder et al., 2020), it is also true that tDCS affects EEG activity per se. Even though ERPs are very specific, it is not possible from the present results to state that the effects of tDCS on P3 are due to changes in cognition, or in the underlying brain activity. However, this does not change the potential value of using biomarkers to direct interventions, especially because they are highly correlated with cognitive function, and as such may prove to be very useful to understand the mechanisms underlying tDCS effects, or to guide interventions, for instance using closed loop systems (Leite et al., 2017). Correlation between the modulatory effects of tDCS on P3 and direct changes in cognition should be further explored with behavioral data analysis. Finally, the current meta-analysis shows the low number of studies testing the tDCS effect in P3 during GNG task or emotional processing. Even in oddball and n-back task analysis, the set of included studies share a reduced sample sizes and the methodological flaws should be addressed in future studies. 2.5.6. Limitations The low number of studies in some subsections did not allow all the intended analysis, such as the publication bias and the meta-regression analysis. For instance, the meta-regression was only performed in parietal and frontal P3 amplitude after anodal stimulation during oddball and the parietal P3 amplitude after anodal tDCS in the n-back task (see Table SM.5 in Supplementary Materials). Also, the set of studies included share high variability among them (e.g., tDCS intensity, duration), which can difficult the evaluation of the impact of different parameters of tDCS in P3. In addition, the current study explored the post-tDCS P3 assessments rather than the difference between baseline and post-intervention, due to the fact that eight of the studies did not assess P3 CHAPTER 2 48 component before the application of tDCS. If differences towards baseline were to be probed, these studies would ultimately be excluded, further decreasing the statistical power to draw conclusions. Nonetheless, controlling for different baseline levels would be important for an improved analysis of the effects of tDCS on P3, as tDCS effects are dependent on the baseline neuronal state (Dubreuil-Vall et al., 2019; Li et al., 2019). Furthermore, the current meta-analysis included healthy and clinical population, which might increase the variability among results. Although heterogeneity tests and meta-regression did not suggest a differential effect of tDCS on P3 regarding study population, this should be addressed in future studies. Lastly, neuroimaging data suggest high levels of interindividual variability of the effects of active tDCS when comparing to sham (Wörsching et al., 2017). To the best of our knowledge, no similar study was performed using EEG, nonetheless, the available data from behavioral performance, suggests a nonlinear effect of tDCS, which is dependent on multiple factors (e.g., individual differences, baseline, task, intensity, duration, electrode placement, and size). Despite these differences, most of the studies included in this meta-analysis are crossovers (16 out of 23), which might mitigate differences in the tDCS effect between individuals. 2.6. Conclusion This meta-analysis suggests the usefulness of P3 component to study the neurophysiological effects of tDCS during cognition. Specifically, the current study has shown that tDCS over frontal areas had an impact in P3 amplitude assessed in parietal regions during oddball and n-back tasks (Figure 6). Nonetheless, these effects must be cautiously interpreted due to the low number of studies in this analysis, the low-to-moderate certainty of evidence, and the heterogeneity among them (e.g., study population). Additionally, no tDCS effect was detected in P3 evaluated in the GNG task, after emotionally charged stimulus, or in latency. Even so, the low number of analyzed comparisons and the small sample sizes included in these subsections might undermine the statistical power (Button et al., 2013). Our findings suggest the broad spatial resolution of tDCS impact, given that the changes were not observed in the brain region of stimulation, but in the task-related brain network. In particular, the connectivity within the frontoparietal network might assume an important role in the neurophysiological effects of frontal anodal tDCS during oddball and n-back tasks, mostly via theta band (Gulbinaite et al., 2014). The frontal midline theta has an important role in several cognitive tasks and it has been associated with the synchronization of other task-relevant brain regions (Cohen, 2014), which can be a mediator of the frontal tDCS impact in other areas (i.e., parietal region). In line with this hypothesis, recent CHAPTER 2 49 evidence demonstrated that NIBS techniques are able to modulate not only the cognitive functioning but also its electrophysiological markers in a spatially distributed manner (Hoy et al., 2015; Jones et al., 2020). Therefore, those neuromodulatory effects observed in the oscillatory synchronization might cooccur in parallel with the modulation of P3, namely the increase of parietal P3 amplitude after the application of anodal tDCS over frontal areas. CHAPTER 2 50 Figure 6. Overall effects observed on the meta-analysis of P3 amplitude and latency in each section. CHAPTER 2 51 2.7. References Adelhöfer, N., Mückschel, M., Teufert, B., Ziemssen, T., Beste, C., 2019. Anodal tDCS affects neuromodulatory effects of the norepinephrine system on superior frontal theta activity during response inhibition. Brain Struct. Funct. 224, 1291–1300. https://doi.org/10.1007/s00429-01901839-3 Aron, A.R., 2011. From reactive to proactive and selective control: Developing a richer model for stopping inappropriate responses. Biol. Psychiatry 69, e55–e68. https://doi.org/10.1016/j.biopsych.2010.07.024 Aston-Jones, G., Chiang, C., Alexinsky, T., 1991. Discharge of noradrenergic locus coeruleus neurons in behaving rats and monkeys suggests a role in vigilance, in: Progress in Brain Research. Elsevier, pp. 501–520. https://doi.org/10.1016/S0079-6123(08)63830-3 Balshem, H., Helfand, M., Schünemann, H.J., Oxman, A.D., Kunz, R., Brozek, J., Vist, G.E., Falck-Ytter, Y., Meerpohl, J., Norris, S., Guyatt, G.H., 2011. GRADE guidelines: 3. Rating the quality of evidence. J. Clin. Epidemiol. 64, 401–406. https://doi.org/10.1016/j.jclinepi.2010.07.015 Batsikadze, G., Moliadze, V., Paulus, W., Kuo, M.-F., Nitsche, M.A., 2013. Partially non-linear stimulation intensity-dependent effects of direct current stimulation on motor cortex excitability in humans. J. Physiol. 591, 1987–2000. https://doi.org/10.1113/jphysiol.2012.249730 Berger, B., Griesmayr, B., Minarik, T., Biel, A.L., Pinal, D., Sterr, A., Sauseng, P., 2019. Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory. Nat. Commun. 10, 1–11. https://doi.org/10.1038/s41467-019-12057-0 Bernat, E.M., Malone, S.M., Williams, W.J., Patrick, C.J., Iacono, W.G., 2007. Decomposing delta, theta, and alpha time-frequency ERP activity from a visual oddball task using PCA. Int. J. Psychophysiol. 64, 62–74. https://doi.org/10.1016/j.ijpsycho.2006.07.015 Boggio, P.S., Zaghi, S., Fregni, F., 2009. Modulation of emotions associated with images of human pain using anodal transcranial direct current stimulation (tDCS). Neuropsychologia 47, 212–217. https://doi.org/10.1016/j.neuropsychologia.2008.07.022 Borenstein, M., Hedges, L. V., Higgins, J.P.T., Rothstein, H.R., 2010. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111. https://doi.org/10.1002/jrsm.12 Brunoni, A.R., Vanderhasselt, M.A., 2014. Working memory improvement with non-invasive brain stimulation of the dorsolateral prefrontal cortex: A systematic review and meta-analysis. Brain Cogn. 86, 1–9. https://doi.org/10.1016/j.bandc.2014.01.008 Button, K.S., Ioannidis, J.P.A., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S.J., Munafò, M.R., 2013. Power failure: Why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376. https://doi.org/10.1038/nrn3475 Carvalho, S., Leite, J., Galdo-Álvarez, S., Gonçalves, Ó.F., 2011. Psychophysiological Correlates of Sexually and Non-Sexually Motivated Attention to Film Clips in a Workload Task. PLoS One 6, e29530. https://doi.org/10.1371/journal.pone.0029530 CHAPTER 2 52 Carvalho, S., Sampaio, A., Mendes, A.J., Lema, A., Vieira, D., Gonçalves, Ó.F., Leite, J., 2019. Polarity Specific Effects of Cross-Hemispheric tDCS Coupled With Approach-Avoidance Training on Chocolate Craving. Front. Pharmacol. 9, 1500. https://doi.org/10.3389/fphar.2018.01500 Cavanagh, J.F., Zambrano-Vazquez, L., Allen, J.J.B., 2012. Theta lingua franca: A common mid-frontal substrate for action monitoring processes. Psychophysiology 49, 220–238. https://doi.org/10.1111/j.1469-8986.2011.01293.x Cespón, J., Rodella, C., Rossini, P.M., Miniussi, C., Pellicciari, M.C., 2017. Anodal transcranial direct current stimulation promotes frontal compensatory mechanisms in healthy elderly subjects. Front. Aging Neurosci. 9, 420. https://doi.org/10.3389/fnagi.2017.00420 Chan, M.M.Y., Yau, S.S.Y., Han, Y.M.Y., 2021. The neurobiology of prefrontal transcranial direct current stimulation (tDCS) in promoting brain plasticity: A systematic review and meta-analyses of human and rodent studies. Neurosci. Biobehav. Rev. https://doi.org/10.1016/j.neubiorev.2021.02.035 Cochrane, 2019. Cochrane Handbook for Systematic Reviews of Interventions, Cochrane Handbook for Systematic Reviews of Interventions. Wiley. https://doi.org/10.1002/9781119536604 Coffman, B.A., Clark, V.P., Parasuraman, R., 2014. Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. Neuroimage. https://doi.org/10.1016/j.neuroimage.2013.07.083 Coffman, B.A., Trumbo, M.C., Clark, V.P., 2012. Enhancement of object detection with transcranial direct current stimulation is associated with increased attention. BMC Neurosci. 13, 1–8. https://doi.org/10.1186/1471-2202-13-108 Cohen, M.X., 2014. A neural microcircuit for cognitive conflict detection and signaling. Trends Neurosci. https://doi.org/10.1016/j.tins.2014.06.004 Cooper, P.S., Darriba, Á., Karayanidis, F., Barceló, F., 2016. Contextually sensitive power changes across multiple frequency bands underpin cognitive control. Neuroimage 132, 499–511. https://doi.org/10.1016/j.neuroimage.2016.03.010 Demiralp, T., Ademoglu, A., Istefanopulos, Y., Başar-Eroglu, C., Başar, E., 2001. Wavelet analysis of oddball P300. Int. J. Psychophysiol. 39, 221–227. https://doi.org/10.1016/S01678760(00)00143-4 den Uyl, T.E., Gladwin, T.E., Lindenmeyer, J., Wiers, R.W., 2018. A Clinical Trial with Combined Transcranial Direct Current Stimulation and Attentional Bias Modification in Alcohol-Dependent Patients. Alcohol. Clin. Exp. Res. 42, 1961–1969. https://doi.org/10.1111/acer.13841 Den Uyl, T.E., Gladwin, T.E., Wiers, R.W., 2015. Transcranial direct current stimulation, implicit alcohol associations and craving. Biol. Psychol. 105, 37–42. https://doi.org/10.1016/j.biopsycho.2014.12.004 Dubreuil-Vall, L., Chau, P., Ruffini, G., Widge, A.S., Camprodon, J.A., 2019. tDCS to the left DLPFC modulates cognitive and physiological correlates of executive function in a state-dependent manner. Brain Stimul. 12, 1456–1463. https://doi.org/10.1016/j.brs.2019.06.006 Dunning, J.P., Parvaz, M.A., Hajcak, G., Maloney, T., Alia-Klein, N., Woicik, P.A., Telang, F., Wang, G.-J., Volkow, N.D., Goldstein, R.Z., 2011. Motivated attention to cocaine and emotional cues in abstinent and current cocaine users - an ERP study. Eur. J. Neurosci. 33, 1716–1723. https://doi.org/10.1111/j.1460-9568.2011.07663.x CHAPTER 2 59 2.8. Supplementary Materials Table SM.1 Combinations of the Descriptors Used in our Search strategy Clinical question MEDLINE PubMed 3/11/2020 #1 (Electroencephalography[Mesh] OR EEG*[TIAB] OR Electroencephalogram*[TIAB] OR "event-related potential*"[tiab] OR "event related potential*"[tiab] OR "evoked potential*"[tiab] OR "event related desynchronization"[tiab] OR "event related synchronization"[tiab] OR "event-related desynchronization"[tiab] OR "event-related synchronization"[tiab] OR "brain wave*"[tiab]) #2 ("Transcranial Direct Current Stimulation"[Mesh] OR "brain polarization"[tiab] OR "noninvasive brain stimulation"[tiab] OR "non-invasive brain stimulation"[tiab] OR "noninvasive brain stimulation"[tiab] OR "transcranial direct current stimulation"[tiab] OR "trans cranial direct current stimulation"[tiab] OR neuromodulation[tiab] OR NIBS[tiab] OR TDCS[tiab] OR "transcranial electrical stimulation"[tiab] OR "transcranial stimulation"[tiab] OR "Transcranial Magnetic Stimulation"[Mesh] OR "transcranial magnetic stimulation"[tiab] OR TMS[tiab] OR rTMS[tiab] OR "motor cortex stimulation"[tiab] OR MCS[tiab] OR "cranial electrotherapy stimulation"[tiab] OR CES[tiab]) #3 ("Animals"[Mesh] NOT "Humans"[Mesh]) #4 #1 AND #2 NOT #3 CHAPTER 2 60 The Cochrane Library 3/11/2020 #1 (MeSH descriptor: [Electroencephalography] explode all trees OR EEG:ti,ab OR Electroencephalogram*:ti,ab OR “event-related potential*”:ti,ab OR “event related potential*”:ti,ab OR “evoked potential*”:ti,ab OR “event related desynchronization”:ti,ab OR “event related synchronization”:ti,ab OR “event-related desynchronization”:ti,ab OR “event-related synchronization”:ti,ab OR “brain wave*”:ti,ab) # 2 (MeSH descriptor: [Transcranial Direct Current Stimulation] explode all trees OR "brain polarization":ti,ab OR "noninvasive brain stimulation":ti,ab OR "non-invasive brain stimulation":ti,ab OR "noninvasive brain stimulation":ti,ab OR "transcranial direct current stimulation":ti,ab OR "trans cranial direct current stimulation":ti,ab OR neuromodulation:ti,ab OR NIBS:ti,ab OR TDCS:ti,ab OR "transcranial electrical stimulation":ti,ab OR "transcranial stimulation":ti,ab OR "Transcranial Magnetic Stimulation"[Mesh] OR "transcranial magnetic stimulation":ti,ab OR TMS:ti,ab OR rTMS:ti,ab OR "motor cortex stimulation":ti,ab OR MCS:ti,ab OR "cranial electrotherapy stimulation":ti,ab OR CES:ti,ab) #3 (MeSH descriptor: [animals] explode all trees NOT MeSH descriptor: [humans] explode all trees) #4 #1 AND #2 NOT #3 EMBASE 3/11/2020 #1 (Electroencephalography /exp OR EEG:ab,ti OR Electroencephalogram*:ab,ti OR ‘event-related potential’:ab,ti OR ‘event-related potentials’:ab,ti OR ‘evoked potential’:ab,ti CHAPTER 2 61 OR ‘evoked potentials’:ab,ti OR ‘event-related desynchronization’:ab,ti OR ‘event-related synchronization’:ab,ti OR ‘brain waves’:ab,ti OR ‘brain oscillation’:ab,ti OR ‘brain oscillations’:ab,ti OR ‘brain wave’:ab,ti) #2 (‘Transcranial Direct Current Stimulation’/exp OR ‘brain polarization’:ab,ti OR ‘noninvasive brain stimulation’:ab,ti OR ‘noninvasive brain stimulation’:ab,ti OR ‘non-invasive brain stimulation’:ab,ti OR ‘transcranial direct current stimulation’:ab,ti OR ‘trans cranial direct current stimulation’:ab,ti OR neuromodulation:ab,ti OR NIBS:ab,ti OR TDCS:ab,ti OR ‘transcranial electrical stimulation’:ab,ti OR ‘transcranial stimulation’:ab,ti OR ‘Transcranial Magnetic Stimulation’/exp OR ‘transcranial magnetic stimulation’:ab,ti OR TMS:ab,ti OR rTMS:ab,ti OR ‘motor cortex stimulation’:ab,ti OR MCS:ab,ti OR ‘cranial electrotherapy stimulation’:ab,ti OR CES:ab,ti) #3 ([animals]/lim NOT [humans]/lim) #4 #1 AND #2 NOT #3 CHAPTER 2 62 Web of Science 3/11/2020 TS=(((Electroencephalography OR EEG OR Electroencephalogram* OR "event-related potential*" OR "event-related potential" OR "event-related potentials" OR "evoked potential" OR "evoked potentials"OR "event-related desynchronization" OR "event-related synchronization" OR "brain wave" OR "brain waves" OR "brain oscillation" OR "brain oscillations") AND ("Transcranial Direct Current Stimulation" OR "brain polarization" OR "noninvasive brain stimulation" OR "non-invasive brain stimulation" OR "noninvasive brain stimulation" OR "transcranial direct current stimulation" OR "trans cranial direct current stimulation" OR neuromodulation OR NIBS OR TDCS OR "transcranial electrical stimulation" OR "transcranial stimulation" OR "Transcranial Magnetic Stimulation" OR "transcranial magnetic stimulation" OR TMS OR rTMS OR "motor cortex stimulation" OR MCS OR "cranial electrotherapy stimulation" OR CES)) NOT ("Animals" NOT "Humans")) Scopus 3/11/2020 TITLE-ABS-KEY ( ( Electroencephalography OR EEG OR. Electroencephalogram* OR "event-related potential*" OR "event related potential*" OR "evoked potential*" OR "event related desynchronization" OR "event related synchronization" OR "event-related desynchronization" OR "event0-related synchronization" OR "brain wave*") AND ( "Transcranial Direct Current Stimulation" OR "brain polarization" OR "noninvasive brain stimulation" OR "noninvasive brain stimulation" OR "noninvasive brain stimulation" OR "transcranial direct current stimulation" OR "transcranial direct current stimulation" OR neuromodulation OR nibs OR tdcs OR "transcranial electrical stimulation" OR "transcranial stimulation" OR "Transcranial Magnetic Stimulation" OR "transcranial magnetic stimulation" OR CHAPTER 2 63 tms OR rtms OR "motor cortex stimulation" OR mcs OR "cranial electrotherapy stimulation" OR ces ) ) Table SM.2 Training of reviewers before titles and abstract screening Formulas: • Reviewer agreement: (included by both raters + excluded by both raters)/total • Chance of inclusion: (Number of included studies/Total, for rater1) * (Number of included studies /Total for rater 2) • Chance of exclusion: (Number of excluded studies/Total, for rater1) * (Number of excluded studies /Total for rater 2) • Chance agreement: Chance Yes * Chance No • Kappa: (Agreement - Chance agreement) / 1 - Chance agreement On May 2020, we selected a random sample of pre-selected studies from the screening first stage (see section 2.1.). One-hundred references were randomly selected served for training and standardization purposes between the reviewers. The agreement between the reviewers was high (> 0.9). Training 1 (Agreement: 0.98; Kappa: 0.93) Reviewer 1 (KP-B) Included Excluded Reviewer Included 18 0 18 2 (AJM) Excluded 2 80 82 20 80 100 Training 2 (Agreement: 0.91; Kappa: 0.88) Reviewer 1 (KP-B) Included Excluded Reviewer Included 11 0 11 3 (RM) Excluded 9 80 89 20 80 100 CHAPTER 2 64 Training 3 (Agreement: 0.93; Kappa: 0.89) Reviewer 1 (KP-B) Included Excluded Reviewer Included 13 0 13 4 (SGL) Excluded 7 80 87 20 80 100 Table SM.3 References of studies included in quantitative synthesis Studies with Asterisk (*) are present in two subsections i. Oddball paradigms *Campanella, S., Schroder, E., Monnart, A., Vanderhasselt, M. A., Duprat, R., Rabijns, M., Kornreich, C., Verbanck, P., & Baeken, C. (2017). Transcranial Direct Current Stimulation over the Right Frontal Inferior Cortex Decreases Neural Activity Needed to Achieve Inhibition: A Double-Blind ERP Study in a Male Population. Clinical EEG and Neuroscience, 48 (3), 176–188. https://doi.org/10.1177/1550059416645977 Dunn, W., Rassovsky, Y., Wynn, J. K., Wu, A. D., Iacoboni, M., Hellemann, G., & Green, M. F. (2016). Modulation of neurophysiological auditory processing measures by bilateral transcranial direct current stimulation in schizophrenia. Schizophrenia Research, 174 (1–3), 189–191. https://doi.org/10.1016/j.schres.2016.04.021 Fiene, M., Rufener, K. S., Kuehne, M., Matzke, M., Heinze, H. J., & Zaehle, T. (2018). Electrophysiological and behavioral effects of frontal transcranial direct current stimulation on cognitive fatigue in multiple sclerosis. Journal of Neurology, 265 (3), 607–617. https://doi.org/10.1007/s00415-0188754-6 Izzidien, A., Ramaraju, S., Roula, M. A., & Mccarthy, P. W. (2016). Effect of Anodal-tDCS on Event-Related Potentials: A Controlled Study. BioMed Research International , 2016. https://doi.org/10.1155/2016/1584947 Khedr, E. M., Gamal, N. F. El, El-Fetoh, N. A., Khalifa, H., Ahmed, E. M., Ali, A. M., Noaman, M., El-Baki, A. A., & Karim, A. A. (2014). A Double-Blind Randomized Clinical Trial on the Efficacy of Cortical Direct Current Stimulation for the Treatment of Alzheimer’s Disease. Frontiers in Aging Neuroscience, 6 , 275. https://doi.org/10.3389/fnagi.2014.00275 Knechtel, L., Schall, U., Cooper, G., Ramadan, S., Stanwell, P., Jolly, T., & Thienel, R. (2014A). Transcranial direct current stimulation of prefrontal cortex: An auditory event-related potential and CHAPTER 2 65 proton magnetic resonance spectroscopy study. Neurology Psychiatry and Brain Research , 20 (4), 96–101. https://doi.org/10.1016/j.npbr.2014.06.001 Knechtel, L., Thienel, R., Cooper, G., Case, V., & Schall, U. (2014B). Transcranial direct current stimulation of prefrontal cortex: An auditory event-related potential study in schizophrenia. Neurology Psychiatry and Brain Research, 20 (4), 102–106. https://doi.org/10.1016/j.npbr.2014.10.002 Mannarelli, D., Pauletti, C., de Lucia, M. C., Delle Chiaie, R., Bersani, F. S., Spagnoli, F., Minichino, A., Currà, A., Trompetto, C., & Fattapposta, F. (2016). Effects of cerebellar transcranial direct current stimulation on attentional processing of the stimulus: Evidence from an event-related potentials study. Neuropsychologia, 84 , 127–135. https://doi.org/10.1016/j.neuropsychologia.2016.02.002 Rassovsky, Y., Dunn, W., Wynn, J. K., Wu, A. D., Iacoboni, M., Hellemann, G., & Green, M. F. (2018). Single transcranial direct current stimulation in schizophrenia: Randomized, cross-over study of neurocognition, social cognition, ERPs, and side effects. PLOS ONE, 13 (5), e0197023. https://doi.org/10.1371/journal.pone.0197023 Ruggiero, F., Ferrucci, R., Bocci, T., Nigro, M., Vergari, M., Marceglia, S., Barbieri, S., & Priori, A. (2019). Spino-cerebellar tDCS modulates N100 components of the P300 event related potential. Neuropsychologia , 135 , 107231. https://doi.org/10.1016/j.neuropsychologia.2019.107231 Weigl, M., Mecklinger, A., & Rosburg, T. (2016). Transcranial direct current stimulation over the left dorsolateral prefrontal cortex modulates auditory mismatch negativity. Clinical Neurophysiology , 127 (5), 2263–2272. https://doi.org/10.1016/j.clinph.2016.01.024 ii. N-back task Breitling, C., Zaehle, T., Dannhauer, M., Tegelbeckers, J., Flechtner, H. H., & Krauel, K. (2020). Comparison between conventional and HD-tDCS of the right inferior frontal gyrus in children and adolescents with ADHD. Clinical Neurophysiology, 131 (5), 1146–1154. https://doi.org/10.1016/j.clinph.2019.12.412 Cespón, J., Rodella, C., Miniussi, C., & Pellicciari, M. C. (2019). Behavioural and electrophysiological modulations induced by transcranial direct current stimulation in healthy elderly and Alzheimer’s disease patients: A pilot study. Clinical Neurophysiology, 130 (11), 2038–2052. https://doi.org/10.1016/j.clinph.2019.08.016 Cespón, Jesús, Rodella, C., Rossini, P. M., Miniussi, C., & Pellicciari, M. C. (2017). Anodal transcranial direct current stimulation promotes frontal compensatory mechanisms in healthy elderly subjects. Frontiers in Aging Neuroscience, 9 , 420. https://doi.org/10.3389/fnagi.2017.00420 CHAPTER 2 66 Hill, A. T., Rogasch, N. C., Fitzgerald, P. B., & Hoy, K. E. (2019). Impact of concurrent task performance on transcranial direct current stimulation (tDCS)-Induced changes in cortical physiology and working memory. Cortex, 113 , 37–57. https://doi.org/10.1016/j.cortex.2018.11.022 Hunter, M. A., Lieberman, G., Coffman, B. A., Trumbo, M. C., Armenta, M. L., Robinson, C. S. H., Bezdek, M. A., O’Sickey, A. J., Jones, A. P., Romero, V., Elkin-Frankston, S., Gaurino, S., Eusebi, L., Schumacher, E. H., Witkiewitz, K., & Clark, V. P. (2018). Mindfulness-based training with transcranial direct current stimulation modulates neuronal resource allocation in working memory: A randomized pilot study with a nonequivalent control group. Heliyon, 4 (7), e00685. https://doi.org/10.1016/j.heliyon.2018.e00685 Keeser, D., Meindl, T., Bor, J., Palm, U., Pogarell, O., Mulert, C., Brunelin, J., Möller, H. J., Reiser, M., & Padberg, F. (2011). Prefrontal transcranial direct current stimulation changes connectivity of restingstate networks during fMRI. Journal of Neuroscience, 31 (43), 15284–15293. https://doi.org/10.1523/JNEUROSCI.0542-11.2011 Nikolin, S., Martin, D., Loo, C. K., & Boonstra, T. W. (2018). Effects of TDCS dosage on working memory in healthy participants. Brain Stimulation, 11 (3), 518–527. https://doi.org/10.1016/j.brs.2018.01.003 iii. Go/No-Go task *Campanella, S., Schroder, E., Monnart, A., Vanderhasselt, M. A., Duprat, R., Rabijns, M., Kornreich, C., Verbanck, P., & Baeken, C. (2017). Transcranial Direct Current Stimulation over the Right Frontal Inferior Cortex Decreases Neural Activity Needed to Achieve Inhibition: A Double-Blind ERP Study in a Male Population. Clinical EEG and Neuroscience, 48 (3), 176–188. https://doi.org/10.1177/1550059416645977 Conley, A. C., Fulham, W. R., Marquez, J. L., Parsons, M. W., & Karayanidis, F. (2016). No Effect of Anodal Transcranial Direct Current Stimulation Over the Motor Cortex on Response-Related ERPs during a Conflict Task. Frontiers in Human Neuroscience, 10 , 13. https://doi.org/10.3389/fnhum.2016.00384 *Dormal, V., Lannoy, S., Bollen, Z., D’Hondt, F., & Maurage, P. (2020). Can we boost attention and inhibition in binge drinking? Electrophysiological impact of neurocognitive stimulation. Psychopharmacology, 237 (5), 1493–1505. https://doi.org/10.1007/s00213-020-05475-2 *Lapenta, O. M., Sierve, K. Di, de Macedo, E. C., Fregni, F., & Boggio, P. S. (2014). Transcranial direct current stimulation modulates ERP-indexed inhibitory control and reduces food consumption. Appetite, 83 , 42–48. https://doi.org/10.1016/j.appet.2014.08.005 CHAPTER 2 67 iv. Emotional Processing Conti, C. L., Moscon, J. A., Fregni, F., Nitsche, M. A., & Nakamura-Palacios, E. M. (2014). Cognitive related electrophysiological changes induced by non-invasive cortical electrical stimulation in crackcocaine addiction. The International Journal of Neuropsychopharmacology, 17 (09), 1465–1475. https://doi.org/10.1017/S1461145714000522 *Dormal, V., Lannoy, S., Bollen, Z., D’Hondt, F., & Maurage, P. (2020). Can we boost attention and inhibition in binge drinking? Electrophysiological impact of neurocognitive stimulation. Psychopharmacology, 237 (5), 1493–1505. https://doi.org/10.1007/s00213-020-05475-2 *Lapenta, O. M., Sierve, K. Di, de Macedo, E. C., Fregni, F., & Boggio, P. S. (2014). Transcranial direct current stimulation modulates ERP-indexed inhibitory control and reduces food consumption. Appetite, 83 , 42–48. https://doi.org/10.1016/j.appet.2014.08.005 Nakamura-Palacios, E. M., de Almeida Benevides, M. C., da Penha Zago-Gomes, M., de Oliveira, R. W. D., de Vasconcellos, V. F., de Castro, L. N. P., da Silva, M. C., Ramos, P. A., & Fregni, F. (2012). Auditory event-related potentials (P3) and cognitive changes induced by frontal direct current stimulation in alcoholics according to Lesch alcoholism typology. The International Journal of Neuropsychopharmacology, 15 (05), 601–616. https://doi.org/10.1017/S1461145711001040 CHAPTER 2 68 Table SM.4 Labels of the comparisons in each study * Labelled as 1 and 2 because the frontal P3 was elicited using deviations on the frequency (a) and duration (b) of the target. a b c d Oddball Dunn (2016) anodal cathodal - - Fiene (2018) online offline - - Khedr (2014) anodal cathodal - - Knechtel (2014A) frequency* duration* - - Knechtel (2014B) frequency* duration* - - Rassovsky (2018) anodal cathodal - - Ruggiero (2019) anodal cathodal - - Weigl (2016) anodal cathodal - - N-back Breitling (2020) conventional HD-tDCS - - Cespón (2017) anodal in young anodal in elderly cathodal in young cathodal in elderly Cespón (2019) anodal cathodal - - Hill (2019) only HD-tDCS 2-back only HD-tDCS 3-back HD-tDCS w/ task 2-back HD-tDCS w/ task 3-back Hunter (2018) 1-back 2-back 3-back - Keeser (2019) 0-back 1-back 2-back - Nikolin (2018) 1mA 0.034mA - - GNG Conley (2016) young adults elderly w/ tDCS over dominant M1 elderly w/ tDCS over non-dominant M1 - Dormal (2020) binge drinkers healthy - - Emotional Processing Nakamura-Palacios (2012) offline online - - Dormal (2020) binge drinkers in no-go healthy in no-go binge drinkers in go healthy in go CHAPTER 2 75 Figure SM.4. Traffic light plots with risk of bias assessment in emotional processing tasks. Figure SM.5. Forest plot with pooled effect estimates and subgroup analysis for frontal P3 amplitude during oddball in anodal stimulation. CHAPTER 2 76 Figure SM.6. Forest plot with pooled effect estimates and subgroup analysis for parietal P3 amplitude during oddball in cathodal stimulation. Figure SM.7. Forest plot with pooled effect estimates and subgroup analysis for frontal P3 amplitude during n-back in anodal stimulation. CHAPTER 2 77 Figure SM.8. Forest plot with pooled effect estimates and subgroup analysis for frontal No-Go P3 amplitude during GNG in anodal stimulation . Figure SM.9. Forest plot with pooled effect estimates and subgroup analysis for parietal No-Go P3 amplitude during GNG in anodal stimulation. CHAPTER 2 78 Figure SM.10. Forest plot with pooled effect estimates and subgroup analysis for parietal Go P3 amplitude during GNG in anodal stimulation. Figure SM.11. Forest plot with pooled effect estimates and subgroup analysis for frontal P3 amplitude during Emotional Processing in anodal stimulation. CHAPTER 2 79 Figure SM.12. Forest plot with pooled effect estimates and subgroup analysis for parietal P3 amplitude during Emotional Processing in anodal stimulation (only offline comparisons). CHAPTER 2 80 Table SM.8. Characteristics of the studies considering the cognitive task that elicited P3 * Studies with anodal and cathodal tDCS comparisons ** Studies present in two subsections. First author (Year) tDCS group Control group Study Design tDCS intensity (mA) tDCS density (mA/cm2) Duration (min) Active Electrode Return Electrode Electrode Size (cm2) Timing Number of Sessions Population Oddball studies (n = 11) Campanella (2017) ** 15 16 Betweensubjects 2.0 0.08 20 crossing point between T4-Fz and F8-Cz superior region of the trapezius muscle 25 Offline 1 Healthy Dunn (2016) 12 12 Betweensubjects 1.0 0.028 20 Fp1 & Fp2 right upper arm 35 Offline 1 Schizophrenia Fiene (2018) * 15 15 Within-subjects 1.5 0.06 ~27.29 F3 right shoulder 25 (active) & 35 (return) Both 1 Multiple Sclerosis Izzidien (2016) 10 10 Within-subjects 1.5 0.06 15 C3 right supraorbital area 25 Online 1 Healthy Khedr (2014) * 11 (Anodal) & 12 (Cathodal) 11 Between-subjects 2.0 0.083 25 F3 right supraorbital area 24 (active) & 100 (return) Offline 10 Alzheimer Knechtel (2014A) 16 16 Within-subjects 2.0 0.057 20 F3 right supraorbital region 35 Offline 1 Healthy Knechtel (2014B) 18 18 Within-subjects 2.0 0.057 20 F3 right supraorbital region 35 Offline 1 Schizophrenia Mannarelli (2016) * 15 15 Within-subjects 2.0 0.08 20 left cerebellar cortex left deltoid muscle 25 Online 1 Healthy Rassovsky (2018) * 37 37 Within-subjects 2.0 0.057 20 F3 right supraorbital region 35 Offline 1 Schizophrenia Ruggiero (2019) * 9 9 Between-subjects 2.0 0.082 20 median line of cerebellum thoracic spinal cord 35 (active) & 80 (return) Offline 1 Healthy Weigl (2016) * 18 18 Within-subjects 1.0 0.029 15 F3 right supraorbital region 35 Offline 1 Healthy N-back studies (n = 7) Breitiling (2020) * 10 10 Within-subjects 0.5 (HD-tDCS) & 1.0 (conventional) 0.029 (conventional) 20 F8 left supraorbital region (conventional) 0.79 (HD-tDCS) & 35 (conventional Offline 3 ADHD Cespón (2019) * 12 12 Within-subjects 1.5 0.09 13 F3 right shoulder 16 (active) & 50 (return) Offline 1 Alzheimer Cespón (2017) * 14 14 Within-subjects 1.5 0.09 13 F3 right shoulder 16 (active) & 50 (return) Offline 1 Healthy (young & elderly) Hill (2019) 20 20 Within-subjects 1.5 0.477 (anode) and 0.119 (each cathode) 15 F3 Fp1, Fz, C3 and F7 (HDtDCS) 3.14 (HD-tDCS) Offline 1 Healthy Hunter (2018) 16 13 Between-subjects 2.0 0.181 30 F10 left lateral upper bicep muscle 11 Offline ~7 Healthy Keeser (2011) 10 10 Within-subjects 2.0 0.057 20 F3 right supraorbital region 35 Offline 1 Healthy Nikolin (2018) 18 19 Between-subjects 1.0 & 0.034 0.062 & 0.002 15 F3 F4 16 Offline 1 Healthy Go/No-Go studies (n = 4) Campanella (2017) ** 15 16 Betweensubjects 2.0 0.08 20 crossing point between T4-Fz and F8-Cz (rIFG) superior region of the trapezius muscle 25 Offline 1 Healthy Conley (2016) 23 (healthy) & 37 (elderly) 23 (healthy) & 37 (elderly) Within-subjects 1.0 0.029 20 C3 (C4 in one subgroup) right supraorbital region 35 Offline 1 Healthy (young & elderly) Dormal (2020) ** 20 20 Within-subjects 1.5 0.043 20 F3 right supraorbital region 35 Offline 1 Healthy & BDs Lapenta (2014) ** 9 9 Within-subjects 2.0 0.057 20 F4 F3 35 Offline 1 Healthy Emotional Processing (n = 4) Conti (2014) 6 3 Between-subjects 2.0 0.057 20 F4 F3 35 Both 5 Crack/Cocaine addiction Dormal (2020) ** 20 20 Within-subjects 1.5 0.043 20 F3 right supraorbital region 35 Offline 1 Healthy & BDs Lapenta (2014) ** 9 9 Within-subjects 2.0 0.057 20 F4 F3 35 Offline 1 Healthy Nakamura-Palacios (2012) 49 49 Within-subjects 1.0 0.029 10 F3 contralateral supradeltoid 35 Both 1 Alcoholics CHAPTER 2 81 Table SM.9. Characteristics of the cognitive task and EEG P3 analysis. * Studies with anodal and cathodal tDCS comparisons ** Studies present in two subsections. ***Frontal cluster: F4, F8, AF8, FC6, F3, F7, AF7, FC5; Parietal cluster: P4, P8, PO8, CP6, P3, P7, PO7, CP5; CRP: Cue-Reactivity Paradigm First author (Year) Cognitive Task Response Requirement Stimuli Modality Target Probability (%) Frontal P3 Electrode Parietal P3 Electrode Frontal P3 Time Window (ms) Parietal P3 Time Window (ms) Oddball studies (n = 11) Campanella (2017) ** Oddball Press button Faces 30 n.a. Pz n.a. 300 - 580 Dunn (2016) Oddball Press button Tones 12 n.a. Pz n.a. 290 - 400 Fiene (2018) * Oddball Press button Tones 20 n.a. Pz n.a. 250 - 450 Izzidien (2016) Oddball speller Observation Letters and Numbers n.a. n.a. Pz n.a. 270 - 400 Khedr (2014) * Oddball Press button Tones 20 Fz n.a. 250 – 500 n.a. Knechtel (2014A) Oddball Press button Tones 10 Fz Pz 240 – 450 174 - 470 Knechtel (2014B) Oddball Press button Tones 10 Fz Pz 240 – 450 174 - 470 Mannarelli (2016) * Oddball Count the targets Tones 10 Fz Pz 250 – 500 250 - 500 Rassovsky (2018) * Oddball Press button Tones 12 n.a. Pz n.a. 300-600 Ruggiero (2019) * Oddball Count the targets Tones 25 Fz Pz 250 – 500 250 - 500 Weigl (2016) * Oddball Press button Tones 10 Fz Pz 260 – 360 280 - 380 N-back studies (n = 7) Breitiling (2020) * 2-back Press button Letters 21 n.a. P4 & P8 n.a. 300 – 450 Cespón (2019) * 1-back Press button Letters 25 Frontal cluster*** Parietal cluster*** 400 – 700 400 – 700 Cespón (2017) * 2- & 3-back Press button Letters 25 Frontal cluster*** Parietal cluster*** 350 – 450 350 – 450 Hill (2019) 2- & 3-back Press button Letters 25 F1 n.a. 300 – 430 n.a. Hunter (2018) 1-, 2- & 3-back Press button Letters 33 Fz Pz 300 – 650 300 - 650 Keeser (2011) 0-, 1- & 2-back Press button Numbers ? Fz - 260 – 400 n.a. Nikolin (2018) 3-back Press button Letters ? Fz - 220 – 420 n.a. Go/No-Go studies (n = 4) Campanella (2017) ** GNG Withhold Letters 30 Fz n.a. 300 – 580 n.a. Conley (2016) GNG Withhold Symbols 30 n.a. Pz n.a. 200–500 (young adults); 250–650 (elderly) Dormal (2020) ** GNG Withhold Alcohol-related pictures 33 n.a. Pz, P3 & P4 n.a. 300 - 500 Lapenta (2014) ** GNG Withhold Food pictures 50 Frontal cluster Parietal cluster 350 – 500 350 - 500 Emotional Processing (n = 4) Conti (2014) CRP Observation Crack-related pictures 50 Frontal cluster n.a. 350 – 600 n.a. Dormal (2020) ** GNG Withhold Alcohol-related pictures 33 n.a. Pz, P3 & P4 n.a. 300 - 500 Lapenta (2014) ** GNG Withhold Food pictures 50 Frontal cluster Parietal cluster 350 – 500 350 - 500 Nakamura-Palacios (2012) CRP Listening Alcohol-related sounds 100 Fz Pz 250 – 400 250 – 400 CHAPTER 2 82 Figure SM.13. Funnel plots in the analysis with at least 10 comparisons. 83 CHAPTER 3 Transcranial Direct Current Stimulation decreases P3 amplitude and inherent delta activity during a waiting impulsivity paradigm CHAPTER 3 84 3.1. Abstract Background: The inability to wait for a target before initiating an action (i.e., waiting impulsivity) is one of the main features of addictive behaviors. Current interventions for addiction, such as transcranial Direct Current Stimulation (tDCS) have been suggested to improve this inability. Nonetheless, whereas there is consensus about the role of prefrontal cortex on impulsive behavior, the effects of tDCS in waiting impulsivity and underlying electrophysiological (EEG) markers are still not clear. Objective: The study aimed to evaluate the effects of neuromodulation over the right inferior frontal gyrus on behavior and EEG markers of reward anticipation (i.e., cue and target-P3 and underlying delta/theta power) during a task designed to elicit premature responses. Methods: Forty healthy subjects participated in two experimental sessions, where they received active and sham tDCS over the right Inferior Frontal Gyrus (rIFG) with a current intensity of 2 mA for 20 minutes. Participants were asked to perform a premature responding task, while they were receiving tDCS. EEG recording was performed throughout the stimulation period. Participants also performed two control tasks to evaluate transfer effects for delay discounting and motor inhibition abilities. Results: Active tDCS decreased the cue-P3 and target-P3 amplitudes, as well as delta power during target-P3. While no tDCS effects were found for motor inhibition, active tDCS increased the discounting of future rewards in small values when compared to sham. Conclusion: These findings suggest a tDCS-induced modulation of the P3 component and underlying oscillatory activity during waiting impulsivity. Moreover, this modulation was also associated with changes in terms of discounting of future rewards. Thus, the current study suggests the usefulness of tDCS in impulsive processes modulation, namely in terms of reward processing and changes in terms of P3 amplitude and inherent delta power. Keywords: Waiting Impulsivity; Premature responses; tDCS; rIFG; P3; Delta; Theta. CHAPTER 3 91 3.3.4. Control Assignments 3.3.4.1. Stop-Signal Reaction Time Task. The SSRTT is designed to assess the inhibitory control abilities and the version used in the study followed the latest guidelines for appropriate structure of the task (Verbruggen et al., 2019). For this purpose, participants were instructed to respond accordingly to the orientation of an arrow. Specifically, if an arrow was pointing to the left, the participant should press the left button (“Z”); on the other hand, when the arrow was pointing to the right, the participant the participant should press the right button (“M”). Moreover, for stop trials, a red frame around the arrow (i.e., stop-signal) was shown on the screen, which informed participants to withhold any response. The stop-signal appeared after the arrow was displayed with a delay adjusted for each participant following a staircase-tracking algorithm (Band & van Boxtel, 1999). The Stop-Signal Delay (SSD) was set to 250ms at the beginning of each block, which was adjusted after an unsuccessful inhibition (-25 ms) and a successful inhibition (+25 ms). The maximum SSD was 400 ms and the minimum was 0 ms. The goal of this adjustment was to guarantee a p(response|stop-signal) of 0.5. Therefore, participants were instructed to be as faster and accurate as possible, even though they should not wait for the stop-signal to control for the speed-accuracy tradeoff. The task started with a training block of 24 trials, followed by four experimental blocks with 64 trials each. In each block, there were 75% of go trials (i.e., 48 trials) and 25% of stop trials (i.e., 16 trials). However, the first 6 trials of a block were always a go trial. The task had a total duration of approximately 8 minutes. The outliers were identified following the lenient criteria from Congdon and colleagues (Congdon et al., 2012). The outcomes included in the statistical analysis were accuracy and RT of go trials, p(respond|signal), SSD, and the stop-signal reaction time (SSRT). CHAPTER 3 92 3.3.4.2. 27-item Monetary Choice Questionnaires. The MCQ-27 was administered to evaluate the discount rating (i.e., if the participant prefers smaller but immediate rewards, instead of larger but delayed rewards). The questionnaire is composed of 27 questions with two possible answers, namely smaller and immediate or a larger, however, delayed reward (Kirby et al., 1999). The outcomes of the questionnaire are the overall, small, medium, and large k. The overall k represents the steepness of the discounting for all the monetary values (i.e., takes into consideration all the items from the questionnaire), while the small, medium, and large k are specific to the corresponding amounts (i.e., takes into consideration 9 items from the questionnaire per amount). The discount rates (i.e., k) were calculated in an Excel-based spreadsheet scoring tool (Kaplan et al., 2016). 3.3.5. Transcranial Direct Current Stimulation Participants received both active and sham tDCS in distinct sessions through a Starstim R20 (Neuroelectrics, Barcelona, Spain). For the active tDCS condition, an electric current of 2 mA of intensity for 20 min (with 15 second of ramp up and ramp down) was applied while the participant performed the CPRT. Sham procedure was similar to the active stimulation, however with only a 45 sec duration (with 15 seconds of ramp up and ramp down). 25 cm2 round saline-soaked electrode sponges (~radius of 3 cm, current density: 0.08 mA/cm2) were placed over F8 (active electrode) and posterior to the left mastoid (return electrode) (Breitling et al., 2016). This tDCS montage was chosen, because according to computer modeling, the current densities are higher over the inferior frontal (Breitling et al., 2016). Moreover, the placement of the return electrode on the left mastoid may prevent potential dual effects from other relevant brain areas (Leite et al., 2018). 3.3.6. Electrophysiological acquisition and data analysis Online EEG data was collected with a Starstim R20 (Neuroelectrics, Barcelona, Spain) using 18 scalps electrodes and one earlobe electrode. Electrophysiological data was offline preprocessed and analyzed using EEGLAB (Delorme & Makeig, 2004). Data was sampled at a rate of 500 Hz and FIR filtered with a bandpass between 0.5 and 40 Hz. The DC offset was removed as the line noise using a notch filter (i.e., 50 Hz). The artifacts in the continuous data were corrected and noisy channels removed using the Artifact Subspace Reconstruction in the clean_rawdata function. The parameters for the identification of noisy channels were the following: flatline with a maximum duration of 5 seconds and correlation between channels below 0.7. EEG data was re-referenced without the pre-identified noisy channels and the rejected channels were interpolated using the spherical spline method (Perrin et al., 1989). An CHAPTER 3 93 average of 1.85 channels were rejected (SD = 1.09) in datasets from the active sessions and 1.78 (SD = 0.92) from the sham ones. The continuous data was segmented in epochs with a total length of four seconds (i.e., 2000 ms prior and post-stimulus onset) centered in the target and cue. The epochs containing premature responses in the 1000ms post-cue onset were excluded. Moreover, due to the time window chosen for the cue-P3, only epochs with at least 800 ms of cue-target interval were selected. The epochs, in which the EEG signal surpassed ±150 µV at non-frontal electrodes were rejected. All the epochs were visually inspected and manually removed in the plot window if artifacts were present. At last, an independent component analysis (ICA) was performed to detect and remove muscle and eye movement artifacts using the ICLabel (Pion-Tonachini et al., 2019). Seven participants were excluded from the EEG analysis due to: the saturation of the signal during the anodal tDCS session (4 participants), the number of EEG epochs in any condition was lower than 20 trials (2 participants), and one outlier with the difference of P3 amplitude between active and sham condition higher than 3 SD from the mean. CHAPTER 3 94 3.3.6.1. Event-Related Potentials. The ERPs analyzed were the cue-P3 and target-P3 in the Pz electrode. The target-P3 and cue-P3 epochs were baselined to the 200 ms pre-stimulus interval. The time-windows selected for each ERP were based on the study by Broyd and colleagues (2012), and the data was averaged following these time-windows: target-P3 was the average amplitude between 250 and 450 ms and the cue-P3 was between 350 and 600 ms. 3.3.6.2. Event-Related Oscillations. The ERO power was analyzed using the Event-Related Spectral Perturbation (ERSP) in EEGLAB function newtimef() (Delorme & Makeig, 2004). An additional analysis was performed regarding the ERO phase, specifically the magnitude of Inter-Trial Phase Coherence (ITPC) (see in Supplementary Materials). For that, a time-frequency decomposition using 3 cycle Morlet wavelets with a frequency resolution of 0.25Hz and temporal resolution of 8 ms was applied. The analyzed frequencies ranged from 1.5Hz to 20Hz for the cue and target epochs. The ERSP baseline normalization followed an unbiased single-trial baseline correction (i.e., full-epoch length single-trial corrections) to minimize the sensitivity to noisy trials (Grandchamp & Delorme, 2011). Therefore, at first, the average activity from the full-epoch length (i.e., µV) was subtracted to each epoch. Subsequently, the spectral power was averaged considering all the trials according to the baseline window (i.e., 1000 ms pre-cue or target). Finally, following the additive model (Grandchamp & Delorme, 2011; Gyurkovics et al., 2021), each epoch was normalized by the previously calculated power spectral average . The ERSP was averaged for delta (1.5 – 4 Hz) and theta (4 – 7 Hz) bands (Demiralp et al., 2001) following the same electrode (i.e., Pz) and time-windows (i.e., target-P3: 250 – 450 ms; cue-P3: 350 – 600 ms) from the ERP analysis (Broyd et al., 2012). 3.3.7. Statistical analysis The analysis focused on the difference between the active and sham stimulation conditions. Therefore, paired t-tests were performed when the difference between both conditions followed the normal distribution, as assessed by the Shapiro-Wilk test. If there was no normal distribution, Wilcoxon signed rank test on paired samples were performed instead. Holm-Bonferroni correction was also performed in each section of the statistical analysis for multiple comparisons. At last, to probe the association between the number of premature responses and the average release time on the Baseline block, Pearson correlations were performed to evaluate the association between the reward/punishment system in the number of premature responses. The statistical analysis was performed in R (R Development Core Team, 2018; Version 4.0.3). CHAPTER 3 95 3.4. Results 3.4.1. Behavioral analysis 3.4.1.1. Cued Premature Response Task. No significant effects of tDCS were observed in premature responses (t(35) = -0.79, p = 0.438), monetary amount earned (t(37) = 0.78, p = 0.438), and release time (t(37) = -0.87, p = 0.438) (Table 2). However, there was a significant correlation between the number of premature responses and the average release time for the baseline block (R = -0.25, p = 0.028), suggesting that the reward/punishment system was associated with the posterior number of premature responses (Figure 8). 3.4.1.2. Stop Signal Reaction Time Task. The t-tests did not reveal any significant effect, namely in terms of Go trials accuracy (t(30) = -2.11, p = 0.215), Go trials response time (t(30) = 1.09, p = 0.707), p(respond|signal) (t(30) = -0.38, p = 0.71), SSD (t(30) = -0.52, p = 0.71), or SSRT (t(30) = 0.73, p = 0.71) (Table 2). Figure 8 Correlation between the number of premature responses and the average release time in the baseline block. CHAPTER 3 96 3.4.1.3. 27-item Monetary Choice Questionnaires. There was a significant effect in the Small k (V(39) = 199, p = 0.016), suggesting a higher k in small amounts of money for the active tDCS session, when comparing to sham. However, no other were differences due to tDCS were found for the 27-MCQ, namely Overall k (V(39) = 344, p = 0.12), Medium k (V(39) = 134, p = 0.12), and Large k (V(39) = 223, p = 0.12) (Table 2) . 3.4.2. EEG analysis 3.4.2.1. Event-related Potentials. The Wilcoxon signed rank test revealed a significant difference between active and sham session for the target-P3 amplitude (V(32) = 106, p = 0.003) and the cue-P3 (V(32) = 142, p = 0.036). The target and cue-P3 amplitude was significantly lower in active session, when comparing to sham (Figure 9.B and 10.B; Table 3). 3.4.2.2. Event-related Oscillations. During the target-P3 time-window, the event-related synchronization in delta band during target-P3 was significantly higher during sham when comparing with active tDCS (t(32) = -2.29, p = 0.03) (Figure 9.C and 10.C). However no differences were found for theta band power (t(32) = -0.66, p = 0.805). Regarding the cue-P3 time-window, t-tests did not reveal any significant effect in terms of ERO, namely for delta (t(32) = -0.24, p = 0.847) and theta bands (t(32) = - 0.19, p = 0.847) (Table 3). CHAPTER 3 97 Table 2 Descriptive (mean and SD) and inferential statistics (degrees of freedom, t or V, Hedges’ g, p-value, and adjusted p-value) for each behavioral outcome tDCS df Adjusted pvalue (BH) Active Sham t / V* g p-value Cued Premature Response Task Premature Responses 27.78 (14.65) 29.75 (15.77) 35 -0.79 0.13 0.432 0.438 Monetary Gain/Loss 41.82 (66.10) 31.50 (70.73) 37 0.78 0.15 0.438 0.438 Release time (ms) 240.97 (34.19) 244.89 (42.14) 37 -0.87 0.1 0.388 0.438 Stop-Signal Reaction Time Task Accuracy Go trials 97.1 (2) 96.2 (3) 30 2.11 0.35 0.043 0.215 RT Go trials 456.49 (70.32) 443.34 (82.68) 30 1.09 0.17 0.283 0.707 p(respond|signal) 44 (10) 45 (10) 30 -0.38 0.1 0.710 0.71 SSD 207.71 (55.89) 212.25 (51.45) 30 -0.52 0.08 0.605 0.71 SSRT 223.77 (46.38) 215.85 (61.63) 30 0.73 0.15 0.471 0.71 Monetary Choice Questionnaire - 27 Overall k 0.016 (0.02) 0.015 (0.03) 39 344* 0.04 0.061 0.12 Small k 0.033 (0.04) 0.024 (0.04) 39 199* 0.23 0.004 0.016 Medium k 0.016 (0.02) 0.015 (0.03) 39 134* 0.04 0.120 0.12 Large k 0.012 (0.02) 0.010 (0.03) 39 223* 0.09 0.106 0.12 CHAPTER 3 98 Figure 9 Grand average event-related target-P3 at Pz electrode (B) with topographical maps in the time-window of interest (represented in the gray area and dashed lines: 250 – 450 ms), and ERO results at Pz electrode (C) between both tDCS conditions. Figure 10 Grand average event-related cue-P3 at Pz electrode (B) with topographical maps in the time-window of interest (represented in the gray area and dashed lines: 350 – 600 ms), and ERO results at Pz electrode (C) between active and sham tDCS. CHAPTER 3 99 Table 3 Descriptive (mean and SD) and inferential statistics (degrees of freedom, t or V, Hedges’ g, p-value, and adjusted p-value) for each EEG outcome tDCS Active Sham df t / V* g p-value Adjusted pvalue (BH) Targe tP3 (250 – 450 ms ) ERP (µV) 0.99 (5.33) 3.58 (3.14) 32 106* 0.59 0.001 0.003 Delta (dB) 2.49 (3.18) 5.01 (6.51) 32 -2.29 0.49 0.015 0.03 Theta (dB) 3.28 (3.44) 3.77 (4.72) 32 -0.66 0.12 0.805 0.805 Cue -P3 (300 – 650 ms ) ERP (µV) 0.38 (1.97) 1.39 (1.65) 32 142* 0.56 0.012 0.036 Delta (dB) -0.17 (1.06) -0.11 (1.04) 32 -0.24 0.03 0.808 0.847 Theta (dB) -0.12 (0.91) -0.07 (1.17) 32 -0.19 0.05 0.847 0.847 CHAPTER 3 100 3.5. Discussion The current study shows that tDCS over the right inferior frontal gyrus is able to modulate the P3 component and underlying oscillatory activity during a waiting impulsivity task (CPRT). Namely, active tDCS induced a decrease of target and cue-P3 amplitudes. Moreover, the reduction in target-P3 amplitude during active stimulation was combined with a simultaneous reduction in terms of delta power for the same time-window. Regarding behavioral analysis, there was a significantly higher k in the small amounts condition after active tDCS in comparison with sham, thus suggesting a preference for small immediate rewards instead of larger delayed in active tDCS session. However, no modulatory effects of tDCS over rIFG were found in terms of waiting impulsivity and inhibitory control measures (i.e., CPRT and SSRTT respectively). 3.5.1. Electrophysiological correlates In the present study, anodal tDCS over the rIFG decreased the target-P3 amplitude and underlying oscillatory activity (namely delta power) during a waiting impulsivity task. However, there is a growing body of evidence suggesting that anodal tDCS over frontal areas, is able to increase the P3 amplitude in tasks involving attentional and working memory processes (Mendes et al., 2022). However, the effects of tDCS over frontal regions in the P3 amplitude during inhibitory control paradigms are mixed. While some studies report a decreased (Cunillera et al., 2016; Verveer et al., 2020) P3 amplitude following tDCS, others report an increased P3 amplitude following tDCS (Lapenta et al., 2014). Thus, the differential effects of tDCS on P3 may be related to the functional role of each cognitive task and its underlying neuronal substrates (Mendes et al., 2022). Furthermore, these findings underpin the relationship between P3 and the delta/theta power at the same time-window observed in several cognitive tasks (Demiralp et al., 2001; Harper et al., 2014) (Inter-Trial Phase Coherence (ITPC) analysis and additional discussion about cue-P3 in Supplementary Materials). In fact, a recent study showed an enhancement of P3 amplitude during a visual oddball paradigm after the entrainment of delta/theta frequency bands through the application of transcranial Alternating Current Stimulation (tACS) (Dallmer-Zerbe et al., 2020). However, theta activity was not modulated concurrently to both cue and target-P3 amplitudes. Surprisingly, it was delta power that was modulated. This may show a potential inter-dependency between both electrophysiological markers and its importance during impulsive behaviors. For instance, a study showed decreased impulsive eating CHAPTER 3 107 Congdon, E., Mumford, J. A., Cohen, J. R., Galvan, A., Canli, T., & Poldrack, R. A. (2012). Measurement and reliability of response inhibition. Frontiers in Psychology , 3 (FEB), 37. https://doi.org/10.3389/FPSYG.2012.00037/BIBTEX Cunillera, T., Brignani, D., Cucurell, D., Fuentemilla, L., & Miniussi, C. (2016). The right inferior frontal cortex in response inhibition: A tDCS–ERP co-registration study. NeuroImage , 140 , 66–75. https://doi.org/10.1016/j.neuroimage.2015.11.044 Cunillera, T., Fuentemilla, L., Brignani, D., Cucurell, D., & Miniussi, C. (2014). A simultaneous modulation of reactive and proactive inhibition processes by anodal tDCS on the right inferior frontal cortex. PLoS ONE , 9 (11), e113537. https://doi.org/10.1371/journal.pone.0113537 Dalley, J. W., & Ersche, K. D. (2019). Neural circuitry and mechanisms of waiting impulsivity: relevance to addiction. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences , 374 (1766), 20180145. https://doi.org/10.1098/rstb.2018.0145 Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, Compulsivity, and Top-Down Cognitive Control. Neuron , 69 (4), 680–694. https://doi.org/10.1016/j.neuron.2011.01.020 Dalley, J. W., & Robbins, T. W. (2017). Fractionating impulsivity: Neuropsychiatric implications. Nature Reviews Neuroscience , 18 (3), 158–171. https://doi.org/10.1038/nrn.2017.8 Dallmer-Zerbe, I., Popp, F., Lam, A. P., Philipsen, A., & Herrmann, C. S. (2020). Transcranial Alternating Current Stimulation (tACS) as a Tool to Modulate P300 Amplitude in Attention Deficit Hyperactivity Disorder (ADHD): Preliminary Findings. Brain Topography , 33 (2), 191–207. https://doi.org/10.1007/s10548-020-00752-x Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods , 134 (1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Demiralp, T., Ademoglu, A., Istefanopulos, Y., Başar-Eroglu, C., & Başar, E. (2001). Wavelet analysis of oddball P300. International Journal of Psychophysiology , 39 (2–3), 221–227. https://doi.org/10.1016/S0167-8760(00)00143-4 Fertonani, A., & Miniussi, C. (2017). Transcranial electrical stimulation: What we know and do not know about mechanisms. Neuroscientist , 23 (2), 109–123. https://doi.org/10.1177/1073858416631966 Fonteneau, C., Redoute, J., Haesebaert, F., Le Bars, D., Costes, N., Suaud-Chagny, M.-F., & Brunelin, J. (2018). Frontal Transcranial Direct Current Stimulation Induces Dopamine Release in the Ventral Striatum in Human. Cerebral Cortex , 28 (7), 2636–2646. https://doi.org/10.1093/cercor/bhy093 Fukai, M., Bunai, T., Hirosawa, T., Kikuchi, M., Ito, S., Minabe, Y., & Ouchi, Y. (2019). Endogenous dopamine release under transcranial direct-current stimulation governs enhanced attention: a study with positron emission tomography. Translational Psychiatry , 9 (1), 1–10. https://doi.org/10.1038/s41398-019-0443-4 Glazer, J. E., Kelley, N. J., Pornpattananangkul, N., Mittal, V. A., & Nusslock, R. (2018). Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing. In International Journal of Psychophysiology (Vol. 132, pp. 184–202). Elsevier B.V. https://doi.org/10.1016/j.ijpsycho.2018.02.002 CHAPTER 3 108 Grandchamp, R., & Delorme, A. (2011). Single-trial normalization for event-related spectral decomposition reduces sensitivity to noisy trials. Frontiers in Psychology , 2 (SEP), 236. https://doi.org/10.3389/FPSYG.2011.00236/BIBTEX Guleken, Z., Sutcubasi, B., & Metin, B. (2021). The cognitive dynamics of small-sooner over large-later preferences during temporal discounting task through event-related oscillations (EROs). Neuropsychologia , 162 , 108046. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2021.108046 Güntekin, B., & Başar, E. (2016). Review of evoked and event-related delta responses in the human brain. In International Journal of Psychophysiology (Vol. 103, pp. 43–52). Elsevier B.V. https://doi.org/10.1016/j.ijpsycho.2015.02.001 Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani, M., & Gratton, G. (2021). The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale. NeuroImage , 237 , 118192. https://doi.org/10.1016/J.NEUROIMAGE.2021.118192 Hamidovic, A., & Wang, Y. (2019). The P300 in alcohol use disorder: A meta-analysis and metaregression. Progress in Neuro-Psychopharmacology and Biological Psychiatry , 95 , 109716. https://doi.org/10.1016/j.pnpbp.2019.109716 Harper, J., Malone, S. M., & Bernat, E. M. (2014). Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task. Clinical Neurophysiology , 125 (1), 124–132. https://doi.org/10.1016/J.CLINPH.2013.06.025 Herrmann, C. S., Rach, S., Vosskuhl, J., & Strüber, D. (2014). Time-frequency analysis of event-related potentials: A brief tutorial. Brain Topography , 27 (4), 438–450. https://doi.org/10.1007/S10548013-0327-5/FIGURES/6 Hildebrand, M. (2015). The Psychometric Properties of the Drug Use Disorders Identification Test (DUDIT): A Review of Recent Research. Journal of Substance Abuse Treatment , 53 , 52–59. https://doi.org/10.1016/J.JSAT.2015.01.008 Jahfari, S., Waldorp, L., van den Wildenberg, W. P. M., Scholte, H. S., Ridderinkhof, K. R., & Forstmann, B. U. (2011). Effective connectivity reveals important roles for both the hyperdirect (frontosubthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition. Journal of Neuroscience , 31 (18), 6891–6899. https://doi.org/10.1523/JNEUROSCI.5253-10.2011 Kaiser, A., Aggensteiner, P. M., Baumeister, S., Holz, N. E., Banaschewski, T., & Brandeis, D. (2020). Earlier versus later cognitive event-related potentials (ERPs) in attention-deficit/hyperactivity disorder (ADHD): A meta-analysis. In Neuroscience and Biobehavioral Reviews (Vol. 112, pp. 117–134). Elsevier Ltd. https://doi.org/10.1016/j.neubiorev.2020.01.019 Kaplan, B. A., Amlung, M., Reed, D. D., Jarmolowicz, D. P., McKerchar, T. L., & Lemley, S. M. (2016). Automating Scoring of Delay Discounting for the 21and 27-Item Monetary Choice Questionnaires. Behavior Analyst , 39 (2), 293–304. https://doi.org/10.1007/S40614-016-0070-9/FIGURES/3 Kirby, K. N., Petry, N. M., & Bickel, W. K. (1999). Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls. Journal of Experimental Psychology: General , 128 (1), 78. https://doi.org/10.1037/0096-3445.128.1.78 CHAPTER 3 109 Lapenta, O. M., Marques, L. M., Rego, G. G., Comfort, W. E., & Boggio, P. S. (2018). TDCS in Addiction and Impulse Control Disorders. Journal of ECT , 34 (3), 182–192. https://doi.org/10.1097/YCT.0000000000000541 Lapenta, O. M., Sierve, K. Di, de Macedo, E. C., Fregni, F., & Boggio, P. S. (2014). Transcranial direct current stimulation modulates ERP-indexed inhibitory control and reduces food consumption. Appetite , 83 , 42–48. https://doi.org/10.1016/j.appet.2014.08.005 Leite, J., Gonçalves, Ó. F., Pereira, P., Khadka, N., Bikson, M., Fregni, F., & Carvalho, S. (2018). The differential effects of unihemispheric and bihemispheric tDCS over the inferior frontal gyrus on proactive control. Neuroscience Research , 130 , 39–45. https://doi.org/10.1016/j.neures.2017.08.005 Leite, J., Morales-Quezada, L., Carvalho, S., Thibaut, A., Doruk, D., Chen, C.-F., Schachter, S. C., Rotenberg, A., & Fregni, F. (2017). Surface EEG-Transcranial Direct Current Stimulation (tDCS) Closed-Loop System. International Journal of Neural Systems , 27 (6), 1750026. https://doi.org/https://doi.org/10.1142/S0129065717500265 Mayer, J. T., Chopard, G., Nicolier, M., Gabriel, D., Masse, C., Giustiniani, J., Vandel, P., Haffen, E., & Bennabi, D. (2020). Can transcranial direct current stimulation (tDCS) improve impulsivity in healthy and psychiatric adult populations? A systematic review. In Progress in NeuroPsychopharmacology and Biological Psychiatry (Vol. 98). Elsevier Inc. https://doi.org/10.1016/j.pnpbp.2019.109814 Mendes, A. J., Pacheco-Barrios, K., Lema, A., Gonçalves, Ó. F., Fregni, F., Leite, J., & Carvalho, S. (2022). Modulation of the cognitive event-related potential P3 by transcranial direct current stimulation: Systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews , 132 , 894–907. https://doi.org/10.1016/J.NEUBIOREV.2021.11.002 Morris, L. S., Kundu, P., Baek, K., Irvine, M. A., Mechelmans, D. J., Wood, J., Harrison, N. A., Robbins, T. W., Bullmore, E. T., & Voon, V. (2016). Jumping the gun: Mapping neural correlates of waiting impulsivity and relevance across alcohol misuse. Biological Psychiatry , 79 (6), 499–507. https://doi.org/10.1016/j.biopsych.2015.06.009 Novak, K. D., & Foti, D. (2015). Teasing apart the anticipatory and consummatory processing of monetary incentives: An event-related potential study of reward dynamics. Psychophysiology , 52 (11), 1470–1482. https://doi.org/10.1111/PSYP.12504 Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology , 72 (2), 184–187. https://doi.org/10.1016/0013-4694(89)90180-6 Pfabigan, D. M., Seidel, E. M., Sladky, R., Hahn, A., Paul, K., Grahl, A., Küblböck, M., Kraus, C., Hummer, A., Kranz, G. S., Windischberger, C., Lanzenberger, R., & Lamm, C. (2014). P300 amplitude variation is related to ventral striatum BOLD response during gain and loss anticipation: An EEG and fMRI experiment. NeuroImage , 96 , 12–21. https://doi.org/10.1016/J.NEUROIMAGE.2014.03.077 Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). The ICLabel dataset of electroencephalographic (EEG) independent component (IC) features. Data in Brief , 25 , 104101. https://doi.org/10.1016/J.DIB.2019.104101 CHAPTER 3 110 Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. In Clinical Neurophysiology (Vol. 118, Issue 10, pp. 2128–2148). Elsevier. https://doi.org/10.1016/j.clinph.2007.04.019 Pornpattananangkul, N., & Nusslock, R. (2016). Willing to wait: Elevated reward-processing EEG activity associated with a greater preference for larger-but-delayed rewards. Neuropsychologia , 91 , 141– 162. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2016.07.037 Reynolds, B., Ortengren, A., Richards, J. B., & de Wit, H. (2006). Dimensions of impulsive behavior: Personality and behavioral measures. Personality and Individual Differences , 40 (2), 305–315. https://doi.org/10.1016/j.paid.2005.03.024 Robbins, T. W., & Dalley, J. W. (2017). Dissecting impulsivity: Brain mechanisms and neuropsychiatric implications. In Nebraska Symposium on Motivation (pp. 201–226). https://doi.org/10.1007/978-3-319-51721-6_7 Saunders, J. B., Aasland, O. G., Babor, T. F., De La Fente, J. R., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption‐II. Addiction , 88 (66), 791–804. https://doi.org/10.1111/j.1360-0443.1993.tb02093.x Schroeder, P. A., Schwippel, T., Wolz, I., & Svaldi, J. (2020). Meta-analysis of the effects of transcranial direct current stimulation on inhibitory control. In Brain Stimulation (Vol. 13, Issue 5, pp. 1159– 1167). Elsevier Inc. https://doi.org/10.1016/j.brs.2020.05.006 Terenzi, D., Catalan, M., Polverino, P., Bertolotti, C., Manganotti, P., Rumiati, R. I., & Aiello, M. (2021). Effects of tDCS on reward responsiveness and valuation in Parkinson’s patients with impulse control disorders. Journal of Neurology . https://doi.org/10.1007/S00415-021-10733-0 Verbruggen, F., Aron, A. R., Band, G. P. H., Beste, C., Bissett, P. G., Brockett, A. T., Brown, J. W., Chamberlain, S. R., Chambers, C. D., Colonius, H., Colzato, L. S., Corneil, B. D., Coxon, J. P., Dupuis, A., Eagle, D. M., Garavan, H., Greenhouse, I., Heathcote, A., Huster, R. J., … Boehler, C. N. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. ELife , 8 . https://doi.org/10.7554/eLife.46323 Verveer, I., Remmerswaal, D., van der Veen, F. M., & Franken, I. H. A. (2020). Long-term tDCS effects on neurophysiological measures of cognitive control in tobacco smokers. Biological Psychology , 156 , 107962. https://doi.org/10.1016/J.BIOPSYCHO.2020.107962 Vignapiano, A., Mucci, A., Ford, J., Montefusco, V., Plescia, G. M., Bucci, P., & Galderisi, S. (2016). Reward anticipation and trait anhedonia: An electrophysiological investigation in subjects with schizophrenia. Clinical Neurophysiology , 127 (4), 2149–2160. https://doi.org/10.1016/J.CLINPH.2016.01.006 Voon, V. (2014). Models of Impulsivity with a Focus on Waiting Impulsivity: Translational Potential for Neuropsychiatric Disorders. Current Addiction Reports , 1 (4), 281–288. https://doi.org/10.1007/s40429-014-0036-5 Voon, V., Chang-Webb, Y. C., Morris, L. S., Cooper, E., Sethi, A., Baek, K., Grant, J., Robbins, T. W., & Harrison, N. A. (2016). Waiting impulsivity: The influence of acute methylphenidate and feedback. International Journal of Neuropsychopharmacology , 19 (1), 1–10. https://doi.org/10.1093/ijnp/pyv074 Voon, V., Irvine, M. A., Derbyshire, K., Worbe, Y., Lange, I., Abbott, S., Morein-Zamir, S., Dudley, R., CHAPTER 3 111 Caprioli, D., Harrison, N. A., Wood, J., Dalley, J. W., Bullmore, E. T., Grant, J. E., & Robbins, T. W. (2014). Measuring “waiting” impulsivity in substance addictions and binge eating disorder in a novel analogue of rodent serial reaction time task. Biological Psychiatry , 75 (2), 148–155. https://doi.org/10.1016/j.biopsych.2013.05.013 Weafer, J., Crane, N. A., Gorka, S. M., Phan, K. L., & de Wit, H. (2019). Neural correlates of inhibition and reward are negatively associated. NeuroImage , 196 (April), 188–194. https://doi.org/10.1016/j.neuroimage.2019.04.021 Wu, H., Miller, K. J., Blumenfeld, Z., Williams, N. R., Ravikumar, V. K., Lee, K. E., Kakusa, B., Sacchet, M. D., Wintermark, M., Christoffel, D. J., Rutt, B. K., Bronte-Stewart, H., Knutson, B., Malenka, R. C., & Halpern, C. H. (2018). Closing the loop on impulsivity via nucleus accumbens delta-band activity in mice and man. Proceedings of the National Academy of Sciences of the United States of America , 115 (1), 192–197. https://doi.org/10.1073/pnas.1712214114 CHAPTER 3 112 3.8. Supplementary Materials Table SM.10 VAS of the side effects associated with tDCS in both sessions (Mean and SD). Active tDCS Sham tDCS Pre Post Difference Pre Post Difference Visual Analogue Scale (VAS) Fatigue 2.55 (1.89) 3.24 (1.89) 0.73 (1.52) 3.13 (2.29) 3.71 (2.29) 0.53 (1.52) Anxiety 1.45 (1.53) 0.86 (1.5) -0.65 (1.25) 1.71 (1.95) 1.13 (1.68) -0.58 (1.59) Sadness 0.68 (1.08) 0.43 (0.88) -0.25 (0.78) 0.89 (1.31) 0.63 (1.01) -0.25 (0.71) Agitation 1.55 (1.89) 1.41 (2.06) -0.18 (1.74) 1.97 (2.26) 1.76 (2.09) -0.2 (1.76) Sleepiness 2.13 (2.08) 2.68 (2.46) 0.68 (2.23) 2.13 (2.63) 2.97 (2.65) 0.75 (2.53) Itching 0.21 (0.99) 1.7 (2.34) 1.38 (1.98) 0.13 (1.06) 0.79 (1.33) 0.48 (1.6) Headache 0.47 (1.15) 0.73 (1.26) 0.23 (0.77) 0.47 (0.88) 0.74 (1.2) 0.25 (0.74) Another type of pain 0.29 (1.01) 0.32 (0.86) 0.03 (0.62) 0.24 (0.97) 0.24 (0.86) 0 (0.32) Tingling 0 (0) 0.92 (1.89) 0.85 (1.87) 0.03 (0.16) 0.47 (1.11) 0.43 (1.11) Metallic taste 0 (0) 0.03 (0.16) 0.03 (0.16) 0.08 (0.47) 0.16 (0.48) 0.08 (0.42) CHAPTER 3 113 Table SM.11 Blinding of tDCS in both sessions. Participant Active Session Sham Session Guess Confidence Guess Confidence 1 Active 1 NK 4 2 Active 3 Sham 3 3 Active 1 Active 1 4 Active 4 Sham 2 5 NK - Active 2 6 Active 3 NK - 7 Active 1 Sham 1 8 Active 2 Active 2 9 Active 2 Missing Data 10 Active 3 Sham 3 11 Active 3 NK - 12 Sham 2 Sham 4 13 Sham 2 Active 2 14 Sham 3 Active 4 15 Active 4 Active 4 16 Active 3 Sham 4 17 Active 4 Active 4 18 Active 3 Active 3 19 Active 2 Active 2 20 Active 4 Sham 2 21 NK - Sham 1 22 NK - Active 2 23 Sham 2 Active 3 24 Sham 1 Active 2 25 Sham 2 Active 2 26 Sham 2 Sham 2 27 Active 2 Active 3 28 Sham 1 Sham 2 29 Sham 2 Active 3 30 Active 3 Sham 3 31 Sham 1 Sham 4 32 Active 1 Sham 2 33 Active 3 Active 3 34 Active 3 Active 3 35 NK - NK - 36 NK - Sham 4 37 Active 4 Sham 3 38 Sham 1 Active 1 39 NK - NK - 40 Active 2 Sham 2 Correct Guess 57.5 41.03 Wrong Guess 27.5 46.15 NK 15 12.82 CHAPTER 3 114 Inter-Trial Phase Coherence (ITPC) The inter-trial phase coherence (ITPC) was calculated given that the delta activity during cue-P3 was not reduced during active tDCS, as opposed to the target-P3. The ITC of delta band during the timewindow of cue-P3 was marginally significant higher in sham in comparison with active tDCS (t(32) = - 1.74, p = 0.092; Figure S1). Likewise, this statistically significant effect in delta ITC was also observed in target-P3 (t(32) = -2.34, p = 0.026; Figure S2). This suggests that active tDCS increases the variability of the phase of delta activity within trials when compared to sham. However, the relation between tDCS and ITC is still not clear, one study has shown a synchronization of theta phase after frontal tDCS (Reinhart et al., 2015), whilst another study did not show any tDCS modulation in ITPC (Miyagishi et al., 2018). The literature is scarce about tDCS effects in ITC, nonetheless, the current findings are in line with the notion that the power and phase of EROs are distinct physiological processes (Burke et al., 2013; Buzsáki & Draguhn, 2004). Specifically, the decrease of cue-P3 amplitude during tDCS might be explained by higher variability in the delta phase caused by tDCS, instead of the decrease in the delta evoked-power (together with phase variability) as occurred in target-P3. Figure SM.14 The ITPC results of cue-P3 at Pz electrode in the time-window of interest (dashed lines: 350 – 600 ms) between both tDCS conditions. CHAPTER 3 115 Figure SM.15 The ITPC results of target-P3 at Pz electrode in the time-window of interest (dashed lines: 250 – 450 ms) between both tDCS conditions. Supplemental References Burke, J. F., Zaghlou, K. A., Jacobs, J., Williams, R. B., Sperling, M. R., Sharan, A. D., & Kahana, M. J. (2013). Synchronous and Asynchronous Theta and Gamma Activity during Episodic Memory Formation. Journal of Neuroscience , 33 (1), 292–304. https://doi.org/10.1523/JNEUROSCI.2057-12.2013 Buzsáki, G., & Draguhn, A. (2004). Neuronal olscillations in cortical networks. Science , 304 (5679), 1926–1929. https://doi.org/10.1126/science.1099745 Miyagishi, Y., Ikeda, T., Takahashi, T., Kudo, K., Morise, H., Minabe, Y., & Kikuchi, M. (2018). Gammaband auditory steady-state response after frontal tDCS: A double-blind, randomized, crossover study. PLOS ONE , 13 (2), e0193422. https://doi.org/10.1371/JOURNAL.PONE.0193422 Reinhart, R. M. G., Zhu, J., Park, S., & Woodman, G. F. (2015). Synchronizing theta oscillations with direct-current stimulation strengthens adaptive control in the human brain. Proceedings of the National Academy of Sciences of the United States of America , 112 (30), 9448–9453. https://doi.org/10.1073/PNAS.1504196112/-/DCSUPPLEMENTAL 116 CHAPTER 4 Tailoring transcranial alternating current stimulation based on endogenous event-related P3 to modulate premature responses: a feasibility study CHAPTER 4 123 Figure 11 Overview of the study design with two distinct sessions (A) and the experimental task to evaluate premature responses (B). The temporal adjustment synchronizing the P3 latency calculated in the baseline block with tACS peak was accomplished with the inclusion of a tailored ‘waiting’ period before the display of the target in the CPRT (C). On the other hand, the frequency of tACS was calculated based on the P3 ERO, which was the frequency with the maximum dB value within the P3 time-window (C). At last, tACS electrodes were placed in two electrode clusters (i.e., P3 and P4 & Fp1 and Fp2) that were interchangeably anode and cathodes. The electric field map computed in the NIC 2.0 software (Neuroelectrics, Barcelona, Spain) represents the voltage topography distribution when P3 and P4 electrodes deliver cathodal stimulation (D).electrodes deliver anodal stimulation and Fp1 and Fp2 CHAPTER 4 124 4.3.5.1. Event-Related Potential: P3. The online analysis calculated the target-P3 latency considering the peak on the time window between 250 and 600 ms in the Pz electrode, based in the previous study (Mendes, Galdo-Álvarez, et al., 2022). The target-P3 latency was estimated to allow the conclusion of the online analysis (see ERO subsection). The offline analysis focused on cue and target-P3 amplitude in Pz. The P3 amplitude was calculated with previous time-windows used in literature (Broyd et al., 2012; Mendes, Galdo-Álvarez, et al., 2022), specifically the average amplitude between 250 and 450 ms for the target-P3 and between 350 and 600 ms for the cue-P3. 4.3.5.2. Event-Related Oscillations. The ERO analysis was performed with the EEGLAB function newtimef() (Delorme & Makeig, 2004). Specifically, 3 cycle Morlet wavelets were used for the time-frequency decomposition (i.e., frequency resolution = 0.25 Hz; temporal resolution = 8 ms). The baseline normalization was chosen considering the additive model through an unbiased single-trial normalization method (Grandchamp & Delorme, 2011). The normalization method subtracted firstly each epoch by the average activity of the whole epoch. Then, the dB conversion was performed in each epoch considering the baseline period of 1000 ms before the target-onset. In the online analysis, based on Dallmer-Zerbe and colleagues (Dallmer-Zerbe et al., 2020), the dB from the Pz electrode was averaged around ±150 ms the P3 latency for each participants and the maximum value was identified as the P3 ERO and consequently the stimulation frequency to apply (Broyd et al., 2012) (see Figure 11.C). On the other hand, in the offline analysis, the delta (1.5 – 4 Hz) and theta (4 – 7 Hz) bands were averaged in Pz and time-window of cue and target-P3 mentioned before. 4.3.5.3. Power Spectral Analysis. The power spectral density (PSD) was analyzed using the function spectopo() from EEGLAB toolbox using the fast Fourier Transform (FFT) (Delorme & Makeig, 2004). The delta and theta band power were estimated for Pz through the Welch method with a Hamming window (i.e., window length: 500 points; FFT length: 500 points). Moreover, it was set an overlap of 20% of the sampling rate (i.e., 100 points) to control the leakage effect caused by epoching. The PSD was performed independently for cue and target-P3 epochs. CHAPTER 4 125 4.3.5.4. Additional Analysis. We also performed an EEG analysis tailored to each participant based on Dallmer-Zerbe and colleagues (Dallmer-Zerbe et al., 2020). Specifically, three analyses were done: (i) adjusting to the P3 latency of each participant, (ii) adjusting to the tACS frequency applied to each participant, and (iii) adjusting simultaneously to the P3 latency and tACS frequency. Regarding the temporal adjustment (i), it was analyzed the ERP and ERO with the same procedures, excepting that the time-windows analyzed was the average around ±150 ms to the P3 latency calculated in the online analysis. The temporal adjustment analysis was only performed in target-P3 epochs given that the P3 latency was estimated on target-P3. Likewise, the frequency adjustment (ii) followed the same procedures in ERO and PSD analysis, but, instead of averaging dB in delta and theta bands, it was averaged the dB around ±3 Hz to the tACS frequency of each participant. At last, the adjustment to the time-window and frequency (iii) was a combination of both analyses explained before. 4.3.6. Sample size calculation The sample size calculation was based in an effect estimate of 1.2 observed in the preliminary within-subject study testing tACS in P3 amplitude (Dallmer-Zerbe et al., 2020). Therefore, the sample size was estimated to find a within-group effect between active and sham tACS in P3 amplitude with a statistical power of 95% and alpha level of 5%. A total of 10 subjects were calculated, but we added 2 subjects to the estimation considering potential differences in relation with the aforementioned study. Thus, the final sample size for this feasibility study was 12 participants. The calculation was performed in G*Power version 3.1.9.7 (Faul et al., 2009). 4.3.7. Statistical analysis The absolute change was calculated between pre and post-tACS for each session (i.e., active and sham). For that, the values that were observed in the block before tACS were subtracted from the values obtained after tACS. This estimation was performed in every EEG and behavioral outcome. We followed a similar procedure as Dallmer-Zerbe and colleagues (Dallmer-Zerbe et al., 2020). We chose the absolute change because relative change may lead to huge variability when there are very low values in the pretACS block. The absolute change observed in active and sham session were tested using paired t-tests if the difference between both tACS conditions followed normality according to the Shapiro-Wilk test. Otherwise, non-parametric analysis was performed, specifically the Wilcoxon signed rank test. The statistics were analyzed in R (R Development Core Team, 2018; R Version 4.0.3). CHAPTER 4 126 4.4. Results 4.4.1. Event-related Potentials: P3 The nonparametric analysis revealed a significant difference between the absolute change between active and sham session in target-P3 amplitude (V(10) = 63, p = 0.005), whilst no significant effect were detected in cue-P3 (t(10) = -0.11, p = 0.912). In the additional analysis considering the temporal adjustment around P3 latency to each participant (i), it was also revealed a significant effect in target-P3 amplitude between active and sham tACS (t(10) = 2.84, p = 0.017). The absolute change of target-P3 amplitude was higher in the active session in comparison with sham in both analysis (Figure 12.B; Table 4). 4.4.2. Event-related Oscillations No significant effects between both sessions were observed in evoked-delta (t(10) = 1.55, p = 0.153) and evoked-theta (t(10) = 0.26, p = 0.797) during target-P3 (Figure 12.C; Table 4). On the other hand, paired t-tests revealed a marginal significant effect in delta activity during cue-P3 (t(10) = -2.06, p = 0.067), but no significant effect was observed in theta (t(10) = -1.54, p = 0.154) (Figure 13.C; Table 5). Furthermore, the additional analysis of the temporal adjustment (i) did not reveal significant differences in delta (t(10) = 1.59, p = 0.142) and in theta (t(10) = 0.79, p = 0.448) between sessions in target-P3. In the frequency adjustment analysis (ii), no significant differences were detected in the adjusted ERO from target-P3 (t(10) = 0.98, p = 0.349), but, it was revealed a significant effect between both tACS session in the adjusted ERO from cue-P3 (t(10) = -2.07, p = 0.032). The active tACS session showed a significant decrease in the adjusted ERO in comparison with sham. At last, the temporal and frequency analysis performed in target-P3 (iii) did not reveal a significant effect on adjusted ERO between active and sham (t(10) = 1.11, p = 0.294). 4.4.3. Power Spectral Analysis No differences were found in the absolute change between active and sham session in the delta (V(10) = 37, p = 0.765) and theta band (V(10) = 45, p = 0.32) in target-P3 epochs (Figure 12.D; Table 4). Likewise, no significant differences in spectral power of cue-P3 epochs for delta (t(10) = 0.04, p = 0.972) and theta (t(10) = 0.46, p = 0.658) (Figure 13.D; Table 5) were found. At last, regarding the frequency adjustment analysis (ii), there weren´t also significant differences between both sessions in target-P3 (t(10) = 0.73, p = 0.484) and in cue-P3 epochs (t(10) = 0.61, p = 0.556). CHAPTER 4 127 Figure 12 Results from EEG analysis of target-P3 at Pz electrode (A), namely the event-related potentials (B) in the time-window of interest (represented in the gray area and dashed lines: 250 – 450 ms), event-related oscillations (C), and power spectral density (D) in Pre and Post-tACS block in both sessions. Figure 13 Results from EEG analysis of cue-P3 at Pz electrode (A), namely the event-related potentials (B) in the time-window of interest (represented in the gray area and dashed lines: 350 – 600 ms), event-related oscillations (C), and power spectral density (D) in Pre and Post-tACS block in both sessions. CHAPTER 4 128 Table 4Descriptive (mean and SD) and inferential statistics in ERP, ERO, and PSD analysis for target-P3 Note. Absolute change is the subtraction of Post – Pre Active tDCS Sham tDCS t / V p-value Pre Post Absolute Change Pre Post Abs. Change Targe tP3 (250 – 450 ms ) ERP (µV) 3.45 (1.63) 3.49 (1.15) 0.04 (1.02) 3.96 (1.17) 2.83 (0.98) -1.14 (0.96) 63 0.005 Delta (dB) 5.31 (4.99) 6.62 (3.82) 1.31 (4.16) 8.29 (9.66) 6.26 (3.99) -2.03 (9.20) 1.55 0.153 Theta (dB) 3.75 (3.75) 4.58 (3.69) 0.83 (3.61) 3.96 (5.73) 4.47 (4.04) 0.50 (5.53) 0.26 0.797 Adjusted Frequency (dB) 4.80 (4.01) 5.76 (3.25) 0.96 (3.01) 6.86 (8.48) 5.86 (3.59) -1.01 (8.14) 0.98 0.349 Target-P3 (Adjusted time: ±150ms around P3 latency) ERP (µV) 2.86 (1.18) 2.78 (1.16) -0.09 (0.86) 3.11 (0.89) 2.08 (0.64) -1.03 (0.95) 2.84 0.017 Delta (dB) 5.08 (4.56) 6.22 (3.28) 1.14 (3.66) 7.71 (8.58) 5.64 (3.73) -2.06 (7.75) 1.59 0.142 Theta (dB) 4.02 (4.01) 5.22 (4.17) 1.20 (3.57) 4.35 (5.76) 4.28 (3.57) -0.07 (5.44) 0.79 0.448 Adjusted Frequency (dB) 4.85 (3.60) 6.01 (3.27) 1.16 (3.17) 6.71 (7.77) 5.64 (3.73) -1.07 (7.33) 1.11 0.294 Spectral Analysis Delta (dB) 7.62 (2.41) 7.08 (2.34) -0.54 (2.21) 7.43 (2.27) 6.81 (2.59) -0.62 (2.27) 37 0.765 Theta (dB) 0.75 (2.17) 0.43 (1.83) -0.31 (1.48) 1.24 (3.02) 0.42 (2.21) -0.82 (2.82) 45 0.32 Adjusted Frequency (dB) 5.09 (3.53) 4.72 (3.84) -0.39 (1.71) 5.54 (4.26) 4.22 (4.08) -1.32 (3.25) 0.73 0.484 CHAPTER 4 129 Table 5 Descriptive (mean and SD) and inferential statistics in ERP, ERO, and PSD analysis for cue-P3 Note. Absolute change is the subtraction of Post – Pre Active tDCS Sham tDCS t / V p-value Pre Post Absolute Change Pre Post Abs. Change Cue - P3 (350 – 600 ms ) ERP (µV) 0.40 (0.58) 0.19 (1.41) -0.20 (1.52) 0.43 (1.05) 0.29 (0.69) -0.14 (1.28) -0.11 0.912 Delta (dB) -0.57 (1.19) -1.19 (1.33) -0.62 (1.65) -0.31 (0.95) 0.02 (0.81) 0.33 (1.36) -2.06 0.067 Theta (dB) -0.29 (0.91) -1.20 (0.63) -0.91 (0.75) 0.18 (0.93) -0.34 (1.18) -0.51 (1.20) -1.54 0.154 Adjusted Frequency (dB) -0.48 (1.13) -1.05 (0.79) -0.57 (1.20) -0.19 (0.82) -0.14 (0.96) 0.06 (1.31) -2.07 0.032 Spectral Analysis Delta (dB) 7.47 (2.76) 7.08 (2.34) -0.39 (2.50) 7.26 (2.14) 6.81 (2.59) -0.44 (2.22) 0.04 0.972 Theta (dB) 0.39 (2.48) 0.43 (1.83) 0.04 (1.79) 0.94 (2.77) 0.42 (2.21) -0.51 (2.53) 0.46 0.658 Adjusted Frequency (dB) 4.94 (3.87) 4.72 (4.84) -0.22 (1.93) 5.26 (4.02) 4.22 (4.08) -1.04 (3.08) 0.61 0.556 CHAPTER 4 130 4.4.4. Behavioral analysis The paired t-test did not reveal statistically significant effects in the number of premature responses between the absolute change in the active and sham session (t(11) = -0.51, p = 0.615). Likewise, nonparametric analysis also did not show significant effects in the absolute change of total money earned/loss (V(11) = 41, p = 0.505) and release time (V(11) = 41, p = 0.91) between both sessions (Table 6). 4.5. Discussion This preliminary study tested the feasibility of individualizing tACS based on the individual P3 (latency and frequency) in order to match tACS to the target-P3 ERO estimated for each subject during a premature response paradigm. Specifically, the tailored delta/theta tACS counteracted the expected decrease of target-P3 amplitude along the session (Polich, 1989). In the sham session the amplitude decreased in the post-tACS block, whilst in the active session this decrease was not observed. Nonetheless, it no tACS effects were detected in the ERO analysis during the target-P3 time-window. This is of particular interest given that a decrease of the ERO activity in the active session during cue-P3 was observed. Furthermore, tACS neither result in a broader oscillatory activity modulation as suggested by PSD analysis, nor impacted significantly any behavioral outcome, as assessed in CPRT. CHAPTER 4 131 Table 6 Descriptive and inferential statistics for CPRT outcomes Note. Absolute change is the subtraction of Post – Pre Active tDCS Sham tDCS t / V p-value Pre During Post Abs. Change Pre During Post Abs. Change Cued Premature Response Task Premature Responses 17 (11.21) 13.5 (8.94) 16 (10.88) -1 (7.64) 16.67 (10.96) 15.42 (10.5) 17 (11.39) 0.33 (5.12) - 0.51 0.615 Monetary Gain/Loss 21.13 (38.02) 13 (32.51) 17.13 (29.17) -4 (15.53) 21.54 (30.61) 20.13 (24.79) 16.92 (25.92) -4.63 (15.72) 41 0.505 Release time (ms) 189.27 (48.67) 203.35 (45.89) 193.51 (61.34) 4.24 (27.69) 192.58 (54.31) 196.3 (49.47) 187.17 (47.91) -5.4 (36.99) 41 0.91 CHAPTER 4 132 Our findings emphasize the need of phase synchronization between the tACS and the endogenous activity. This can be partially explained by the effect known as Arnold tongue which states that the entrainment of neuronal oscillations is achieved with lower intensity stimulation if they share the same frequency and phase (Notbohm et al., 2016). Our results support this notion given that the match of the phase and frequency between tACS and P3 ERO was performed specifically to target-P3, which was successfully increased in the active session. Therefore, this study suggests that tACS effects can be limited to transient activity (i.e., P3), instead of the broad oscillatory activity measured in the PSD. The neuronal oscillations have been recently suggested to be rhythmic bursts instead of sustained oscillations (Jones, 2016; van Ede et al., 2018). Previous studies have already demonstrated that theta tACS is capable of increasing the transient theta activity during cognitive tasks (Hsu et al., 2017; Vosskuhl et al., 2015), whilst no effect was detected in the resting theta activity (Mosbacher et al., 2021; Wischnewski & Schutter, 2017). Thus, higher target-P3 amplitude observed during the active tACS session might be due to the increase of delta/theta bursts after the target, rather than an increase of sustained delta/theta activity (Mendes, Galdo-Álvarez, et al., 2022). Nevertheless, our results failed to detect statistically significant effect in delta activity during target-P3, although descriptive statistics show that target-P3 amplitude and delta activity increased during active session, whilst both decreased in the sham session (see Limitations and Future Directions). On the other hand, the cue-P3 amplitude was not modulated by tACS. Nonetheless, it was revealed a decrease observed in evoked-delta during cue-P3, which might suggest an anti-phasic effect between the stimulation and the evoked oscillation (see Figure 14.A). The decrease in event-related delta activity during a cognitive task was already observed after the application of delta tACS (Wischnewski & Schutter, 2017). However, the authors of the previous study did not synchronize tACS and oscillatory activity, which can explain the unexpected effect (i.e., decrease of evoked-delta) through the mismatch between both signals (Wischnewski & Schutter, 2017). In line with this, the decrease in delta-activity might also be explained with the spike-timing dependent plasticity (STDP) hypothesis, which suggests that tACS is mostly successful in the oscillatory activity above the stimulation frequency (Vogeti et al., 2022; Zaehle et al., 2010). If the dominant oscillation between two neurons is higher than the stimulation frequency, there is a strengthening of the synapse (i.e., Long-term Potentiation; LTP) because pre-synaptic events occur before the post-synaptic. On the other hand, if stimulation frequency is higher than the ongoing oscillation, it will allow pre-synaptic events to occur after post-synaptic, which decreases the synaptic strength (Long-term Depression; LTD) (Vossen et al., 2015). However, the decrease of delta