Modulation of resting-state networks following repetitive transcranial alternating current stimulation of the dorsolateral prefrontal cortex

doi:10.21203/rs.3.rs-2705540/v1

Transcranial alternating current stimulation (tACS) offers a unique method to temporarily manipulate the activity of the stimulated brain region in a frequency-dependent manner. However, it is not clear if repetitive modulation of ongoing oscillatory activity with tACS over multiple days can induce changes in resting-state functional connectivity. The current study addresses this question by applying multiple-session theta band stimulation on the left dorsolateral prefrontal cortex (L-DLPFC) during arithmetic training. Fifty healthy participants (25 males and 25 females) were randomly assigned to the experimental and sham groups, half of the participants received individually adjusted theta band tACS, and half received sham stimulation. Resting-state functional magnetic resonance (rs-fMRI) data were collected before and after three days of tACS-supported procedural learning training. Resting-state network analysis showed a significant increase in connectivity for the frontoparietal network (FPN) with the precuneus cortex. Seed-based analysis with a seed defined at the primary stimulation site showed an increase in connectivity with the precuneus cortex, posterior cingulate cortex (PCC), and lateral occipital cortex. We conclude that multi-session task-associated tACS can induce significant changes in resting state functional connectivity.

Transcranial Alternating Current Stimulation (tACS)

Dorsolateral Prefrontal Cortex (DLPFC)

Functional Magnetic Resonance Imaging (fMRI)

Functional connectivity

Frontoparietal Network

Neuroimaging studies have provided convincing evidence that frequencies generated in the brain are not just a cumulative sum of underlying neural activity but rather represent fundamental mechanisms that drive various functions in the brain (Sejnowski and Paulsen, 2006). Non-invasive brain stimulation methods provide an interesting method to study these brain oscillations and associated behaviors by externally manipulating the target brain region with electric currents(Clark and Parasuraman, 2014). Transcranial Alternating Current Stimulation (tACS) is of particular interest because it influences cortical excitability in a frequency-dependent manner by aligning the phase of endogenous brain oscillations with externally applied electrical currents. The approach opens an avenue for understanding the causal functioning of the stimulated region and provides a method for possibly enhancing cognitive skills. In numerical cognition, alpha and theta band activity, especially in the left hemisphere, has been associated with different aspects of arithmetic processing(Grabner and De Smedt, 2011), and single-session theta band stimulation has been reported to enhance performance in different aspects of arithmetic learning(Hauser et al., 2013; Simonsmeier et al., 2018; Mosbacher et al., 2021). In addition, single-session theta band stimulation over the Left Dorsolateral Prefrontal Cortex (L-DLPFC) has also been reported to induce connectivity changes in resting-state brain networks (Abellaneda-Perez et al., 2019; Mondino et al., 2019). The current study investigated whether multiple-session stimulation of L-DLPFC with theta band stimulation during arithmetic training can induce changes in functional connectivity of resting state grey and white matter networks.

DLPFC is one of the most crucial brain regions involved in cognitive functions and is often the target in stimulation studies (Dedoncker et al., 2016). From the behavioral point of view, stimulation of the DLPFC has been reported to influence a wide variety of cognitive functions, including attention (Gladwin et al., 2012; Parris et al., 2021), memory (Fregni et al., 2005), arithmetic learning (Hauser et al., 2013; Mosbacher et al., 2021), and various other cognitive domains (Dedoncker et al., 2016). In arithmetic processing, prefrontal regions are an important part of the frontoparietal network involved in successful task conduction (Grabner et al., 2009; Menon, 2015). A recent study systematically comparing single session theta band stimulation effects on arithmetic learning demonstrated that theta tACS improves learning of novel arithmetic facts and enhances performance in fact-learning problems (Mosbacher et al., 2021). In addition, a few recent neuroimaging studies have investigated neural changes in relation to one-session theta band stimulation over the DLPFC. For example, Abellaneda-Perez et al. (Abellaneda-Perez et al., 2019) utilized functional magnetic resonance imaging (fMRI) to investigate the impact of single-session theta band stimulation over the L-DLPFC on resting-state functional networks. The authors of this work reported a significant increase in grey matter functional connectivity in regions of the default mode network (DMN), including the precuneus cortex (PCU), posterior cingulate cortex (PCC), and left inferior parietal lobule (L-IPL). Another tACS study that targeted the L-DLPFC reported a significant increase in functional connectivity between the L-DLPFC and inferior parietal lobule after single-session theta band stimulation over the L-DLPFC (Mondino et al., 2019). These studies demonstrate that a single session of tACS stimulation can manipulate brain activity in brain regions and networks critical for cognitive processing. Our study builds on these behavioral and neuroimaging findings to investigate whether the effect of multiple-session stimulation along with arithmetic training can induce a significant impact on resting-state brain networks.

The Default mode network (DMN), the Frontoparietal network (FPN), and the Salience network are crucial brain networks involved in cognitive processing (Sridharan et al., 2008). The activity of the DMN decreases whenever a person is focused on anything in the outside world (Greicius et al., 2003). The FPN is a hub of cognitive control, it gets engaged whenever cognitive demand is increased, and mental arithmetic is strongly associated with frontoparietal activity and connectivity (Greicius et al., 2003; Sridharan et al., 2008; Menon, 2015; Marek and Dosenbach, 2018), and the Salience network has been reported to be involved in the switch between DMN and FPN networks (Goulden et al., 2014). We analyzed these three cognitive networks to investigate if brain stimulation can induce changes in their connectivity patterns. Conventionally, fMRI studies have only focused on grey matter functional connectivity measures of the brain, while white matter is regressed out from the analysis as a nuisance variable - mainly due to weaker BOLD response compared to grey matter (Fraser et al., 2012). However, recent studies have demonstrated that white matter also carries functional information that is detectable with existing imaging protocols (Peer et al., 2017; Li et al., 2019). Furthermore, it has been reported that the temporal and spatial properties of the BOLD signal in white and grey matter are similar (Ding et al., 2013), and white matter works in concert with grey matter, giving rise to complex human cognitive abilities (Filley and Fields, 2016). For example, it has been reported that there is a significant difference in connectivity patterns in white matter clusters in the occipital lobe when participants watch movies compared to resting state (Marussich et al., 2017). Owing to these reasons, our study investigated functional changes in grey and white matter to develop a broader understanding of the stimulation-induced changes in the brain.

In addition, brain structure and function varies greatly between individuals (Forkel et al., 2022), and it plays a critical role in determining the impact of stimulation on the targeted region (Filmer et al., 2020). Using a priori-defined stimulation can result in non-linearities between individuals in their physiological response to the stimulation (Filmer et al., 2020). For instance, the initial brain state has been demonstrated to play a critical role in determining the impact of tACS (Bullard et al., 2011). Some reports indicate that the after-effects of the alignment with the individual dominant frequency determine the efficacy of tACS (Neuling et al., 2013). Despite these findings, most of the existing stimulation studies have not considered individual differences. In our study, we utilize electrophysiological markers to individualize the stimulation protocol for each participant to maximize the impact of stimulation on the target region.

In summary, the objective of the study was to understand if multiple sessions of individually adjusted theta band stimulation over the L-DLPFC along with arithmetic training can induce changes in resting-state functional connectivity. Participants underwent a 5-day experiment in which on three days (Day 2 to Day 4) an intensive calculation training with individualized theta tACS over the L-DLPFC was provided. Baseline assessments and post-stimulation assessments were carried out on Day 1 and Day 5 respectively. We hypothesized that electrical stimulation would influence resting-state grey matter networks including frontoparietal, default mode, and salience networks. Furthermore, an exploratory analysis was performed to investigate if stimulation can influence the functional connectivity of white matter networks.

2.1 Grey Matter Analysis

2.1.1 Resting state networks

ANOVA revealed a significant interaction between the frontoparietal network (FPN) and the precuneus cortex (F (4, 192) = 4.82, p- FDR corrected < 0.001) as shown in Figure 2A. Further analysis revealed a significant increase in functional connectivity in the stimulation group (t (24) = 3.104, p = 0.005, d = 0.623) and a decrease in the sham group (t (24) = −2.925, p = 0.007, d = 0.585) depicted in Figure 2B. Results are further detailed in Table 1. No other main or interaction effects were observed.

2.1.2 Seed-based based analysis

A significant Group x Time interaction was observed, and three clusters were identified: the precuneus (p-corrected < 0.001), the PCC (p-corrected < 0.001), and the left occipital cortex (p-corrected < 0.001) as shown in Fig. 3A and Table 1. Further analysis revealed an increase in connectivity for the stimulation group for precuneus (t(24) = 3.952, p = 0.001, d = 0.790), PCC (t(24) = 3.719, p = 0.001, d = 0.744), and left occipital cortex (t(24) = 3.410, p = 0.002, d = 0.682) and a decrease for the sham group for each of the three clusters i.e., the precuneus (t(24) = 3.698, p = 0.001, d = 0.740), PCC (t(24) = 4.857, p < 0.001, d = 0.834), and the left occipital cortex (t(24) = 4.904, p < 0.001, d = 0.981) shown in Fig. 3B.

Table 1. The significant clusters identified after RSN analysis and seed-based analysis are shown. For network analysis, a significant cluster was identified in the precuneus cortex with FPN. With L-LPFC selected as a seed, three clusters were identified, including precuneus cortex, cingulate gyrus, and lateral occipital cortex. P-FDR represents the p-value after false discovery rate correction.

Clusters	Cluster (x, y, z)	Cluster size	p - FDR	Regions
FPN Cluster 1	[+ 02–66 + 28]	52 voxels	0.000033	Precuneus Cortex
L-LPFC Cluster 1	[-04 -64 + 32]	114 voxels	0.000011	Precuneus Cortex
Cluster 2	[-02 -46 + 10]	98 voxels	0.000021	Cingulate Gyrus, posterior division
Cluster 3	[-40 -72 + 36]	65 voxels	0.001761	Lateral Occipital Cortex, superior division Left

2.1.3 White Matter Analysis

Stable white matter clusters were identified at baseline, as shown in Fig. 4. However, no significant Group x Time interaction was observed for any identified clusters. The results are displayed in Table 2.

Table 2. The table shows ANOVA results for white matter functional networks with group (experimental vs. sham) as a between-subject variable and time (pre vs. post) as a within-subject variable for each of the identified clusters

Clusters	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
F-value	2.041	0.005	0.150	0.234	0.047	0.399	0.203	0.033	1.508	2.110
P-value	0.160	0.944	0.701	0.631	0.849	0.530	0.654	0.858	0.225	0.153

The entrainment of ongoing oscillatory activity using brain stimulation opens an exciting possibility to drive neural changes in the brain circuitry and eventually modulate behavioral performance. For example, theta band stimulation of the L-DLPFC region has been reported to enhance arithmetic learning (Mosbacher et al., 2021), and it has also been reported to induce changes in the resting state functional brain activity (Abellaneda-Perez et al., 2019). These stimulation related effects have mostly been observed in single-session studies; however, it is not clear if the effect occurs for repetitive stimulation during cognitive training over multiple days. The current study investigated if repeated stimulation over the DLPFC region during arithmetic learning could induce changes in resting state functional connectivity. Participants underwent three days (Day-2 to Day-4) of arithmetic training along with stimulation. Resting-state fMRI and behavioral data were collected on Day-1 and Day-5 of the experiment. The results demonstrated that stimulation of the L-DLPFC induces significant changes in the connectivity of the FPN network and L-DLPFC with regions in the parietal cortex. However, no significant differences were observed in white matter networks after stimulation.

Analysis of the grey matter networks showed a significant increase in connectivity of the FPN with the precuneus in the stimulation group and a decrease in the sham group. A seed-based analysis further showed that connectivity changes of the precuneus were associated with the stimulated brain region. The precuneus, situated in the posterior part of the medial parietal cortex, is a crucial brain region involved in various complex cognitive tasks with arithmetic being among them (Cavanna and Trimble, 2006; Fehr et al., 2007). A remarkable study by Utevsky and colleagues (Utevsky et al., 2014) compared functional changes in the brain when participants were engaged in reward-based decision tasks requiring externally focused attention as compared to rest. They observed state-dependent functional connectivity in the precuneus in a way that the functional connectivity between FPN and precuneus increased during the task, while the connectivity between DMN and precuneus increased during the resting state. The authors concluded that the precuneus interacts with DMN and FPN to distinguish cognitive states. In our study, stimulation selectively increased the functional connectivity between precuneus and FPN after training. The modulation of FPN is of clinical relevance. For example, a meta-analysis of resting-state functional connectivity in Major depressive disorder (MDD) reported hypoconnectivity within FPN (Kaiser et al., 2015). Disruption of the FPN has also been observed in schizophrenia (Chahine et al., 2017), spatial neglect (He et al., 2007), and bipolar disorder (Rai et al., 2021). Our findings provide evidence for enhanced connectivity with the FPN after cognitive training, and future studies could possibly explore the therapeutic potential in neuropsychiatric patients.

Along with the precuneus, we observed significant connectivity changes between L-LPFC and PCC. PCC is a critical node in the default mode network and a crucial region actively engaged in various cognitive tasks (Leech and Sharp, 2014). There is no consensus on a specific role of the PCC, however, neuroimaging studies have consistently shown its abnormal function in various neuropsychiatric disorders, including Alzheimer’s disease (Li et al., 2002), schizophrenia (Whitfield-Gabrieli et al., 2009), autism (Cherkassky et al., 2006), attention deficit hyperactivity disorder (Castellanos et al., 2008), and various other diseases (Zhang and Raichle, 2010). Further studies should investigate how the modulation of these two crucial brain regions can benefit the neuropsychiatric population.

Two recent studies stimulating the L-DLPFC with tACS reported modulation of parietal brain regions after one stimulation session (Abellaneda-Perez et al., 2019; Mondino et al., 2019). The first study compared the effect of stimulation over the L-DLPFC with tDCS and tACS. During rest, tDCS increased functional connectivity within the DMN, while tACS decreased it. An opposite effect was observed during a working memory task in which tDCS decreased the functional connectivity within the DMN while tACS increased it. The study by Modino et al. (Mondino et al., 2019) also compared the effect of tDCS and tACS over the L-DLPFC. They reported that tDCS and tACS increased functional connectivity between L-DLPFC and parietal brain regions. Manipulation of connectivity patterns between frontal and parietal brain regions has also been reported in studies utilizing tDCS (Keeser et al., 2011; Pena-Gomez et al., 2012). Although there is some inconsistency regarding the direction of change induced by stimulation, the available evidence suggests that stimulating the L-DLPFC affects the connectivity between the fronto-parietal brain regions. Moreover, our findings indicate that this modulation can persist for atleast one day following multiple sessions of stimulation training.

We further analyzed resting-state white matter networks to study if stimulation can influence the connectivity patterns of these networks. Traditionally, signals from the white matter, which comprises half of the brain, have been regressed out from analysis as noise, and there has been a controversy on whether BOLD responses can be observed in the white matter (Gawryluk et al., 2014). However, in recent years, there has been an upsurge in studies on resting-state white matter networks, and functional networks have been consistently identified in a number of studies (Peer et al., 2017; Li et al., 2019). Using pre-stimulation data, we identified 10 stable clusters in the white matter. These clusters were used as masks to study changes in their connectivity patterns due to the stimulation. Though we did not observe significant differences in any identified cluster, we provide a pathway for future studies not to exclude white matter from the analysis as it also carries functional information. One of the possible explanations for not observing a significant difference is that the BOLD signal in the white matter is much weaker than the grey matter (Gore et al., 2019), and a larger sample size may be required to observe changes in white matter networks.

4.1 STUDY DESIGN

The current study was part of a more extensive investigation on the effect of different stimulation training paradigms on arithmetic learning. The current study only investigates the functional changes induced by stimulation, while the behavioral part of the study is the focus of a different study. The study adopted a between-subject, double-blinded, placebo-controlled, randomized parallel group design, was in line with the Helsinki declaration on ethical standards and approved by the local ethics committee at the University of Graz. Before any assessments started, the participants were informed about the study goals, and they read the study description. Then they were screened for inclusion and exclusion criteria, and if they were eligible, they were asked to sign a consent to enroll in the study. They were pseudo-randomly assigned to one of the two groups, i.e., experimental and sham. The experiment had a total duration of five days. On day one, we collected baseline behavioral and fMRI data. At the start, participants filled out a demographic questionnaire, a questionnaire ascertaining their handedness (Nicholls et al., 2013), and a short mood questionnaire (Steyer et al., 1997). In addition, participants performed two computerized tests assessing working memory (WM; N-Back task) and proactive interference (recent probes task, RPT). Inside the MRI-Scanner, functional data were recorded while participants performed the first blocks of the arithmetic learning task. Additionally, we collected anatomical, diffusion tensor imaging (DTI), and rs-fMRI data in this session. On days 2 to 4, participants came to the electroencephalography (EEG)-Lab, and after a short instruction, the EEG and the tACS electrodes were mounted. At each day, we determined the individual stimulation intensity and the individual stimulation frequency using resting state EEG recordings. Participants worked through the arithmetic learning blocks using these individual settings while being stimulated by tACS. On day 5, participants first repeated the WM and proactive interference task outside the scanner. We first collected a second set of anatomical, DTI, and rs-fMRI scans inside the scanner. After these data were recorded, participants worked through the last set of arithmetic learning tasks while we recorded their associated functional brain activity. Finally, they were asked to answer the MDBF, a short questionnaire regarding adverse side effects of tACS, and whether they thought they were truly stimulated or part of the sham group. The design of the study is shown in Fig. 5.

4.2 PARTICIPANTS

Fifty healthy, right-handed subjects (25 females) with normal or corrected-to-normal vision were recruited for this study with a mean age of 23.48 ± 4.13 years. These individuals were randomly divided into a stimulation and sham group. Statistical tests showed no significant group difference in age (t(48) = 0.407, p = 0.686) and sex (χ²(1) = 0.080; p = .777). We excluded individuals with neurological or psychiatric diseases, neuropsychological or sensory disorders, cardiorespiratory or orthopedic diseases, pregnancy, claustrophobia, MRI contraindications (including implanted metallic or electronic devices), or current regular intake of medication (including drugs) which can affect normal brain function, from participation.. All participants gave informed consent and were compensated with either 150 euros or a study-participation certificate for course credits (9 hours).

4.3 FUNCTIONAL MAGNETIC RESONANCE IMAGING

4.3.1 Data Acquisition

MRI scanning was performed using a 3T Siemens Magnetom Vida MR scanner. Blood Oxygen Level Dependent (BOLD) fMRI images were acquired using a gradient echo-planar-imaging (EPI) sequence (time of repletion (TE) = 1200 ms, time of echo (TE) = 30 ms, flip angle = 72^◦, slices/volume = 56, slice thickness = 2.5mm, multiband acceleration factor = 3). The anatomical, T1-weighted, images were collected with an ultrafast spoiled gradient echo pulse sequence (T1-TFE; TR = 1.60 ms, TE = 2.38 ms, flip angle = 9^◦, slices = 224, slice thickness = 1mm).

4.3.2 Grey Matter Analysis

Preprocessing was performed using Statistical parametric mapping (SPM) 12 (https://www.fil.ion.ucl.ac.uk/spm/), Data Processing & Analysis of Brain Imaging (DPABI) (Yan et al., 2016), and the CONN toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012). Anatomical images were segmented into white matter, gray matter, and Cerebrospinal fluid (CSF). Standard preprocessing steps were performed, including slice time correction, motion correction, co-registration with the anatomical image, and outlier identification based on the global signal and framewise displacement. Functional images were normalized to standard Montreal Neurological Institute (MNI) space, and spatial smoothing was performed with a 6mm isotropic Fixed Width Half Maximum (FWHM) Gaussian Kernel. The data were filtered with a 0.01–0.10 Hz temporal band-pass filter and regressed on motion parameters, white matter, and CSF. For multiple error correction, false discovery rate (FDR) corrections were used (Nichols and Hayasaka, 2003) with a voxel threshold of p < 0.001 and a cluster threshold of p < 0.05.

Resting State Network Analysis

Resting-state network (RSN) analyses were restricted to the RSN templates provided by the CONN toolbox. Three networks were included: the frontoparietal (FPN), the default mode network (DMN), and the salience network (Keeser et al., 2011; Chand and Dhamala, 2016). More detailed information about the included brain regions and networks is provided in Table 2. Statistical analyses were conducted using the CONN 2nd level analysis module and the SPSS 27 statistical software package (IBM SPSS Statistics, NY, US). In order to study stimulation-induced changes, we performed analyses of variance (ANOVA) for the three networks with group (stimulation vs. sham) as a between-subject variable and time (pre- vs. post-stimulation) as a within-subject variable. To correct for multiple error, we used false discovery rate (FDR) corrections. For the significant interactions, we extracted mean values of the BOLD signal from each significant cluster, and performed paired samples t-tests over time in each group. All comparisons were subjected to Bonferroni correction.

Seed Based Analysis

In addition to the network approach, we defined the Left-Lateral Prefrontal Cortex (L-LPFC) as a region of interest (ROI) to investigate stimulation effects at the stimulation site. Functional connectivity was estimated between this ROI and the whole brain. For statistical analyses, Pearson correlations were used to estimate the strength of functional associations, and z-transformed (Fisher’s z).

Table 3

The table shows the seed regions for each of the networks, including the Frontoparietal Network (FPN), Default Mode Network (DMN), and Salience Network
RSNs	Region	Coordinates (x, y, z)	Abbreviation
FPN	Left-Lateral Prefrontal Cortex	[-43 + 33 + 28]	L-LPFC
	Left-Posterior Parietal Cortex	[-46 -58 + 49]	L-PPC
	Right-Lateral Prefrontal Cortex	[+ 41 + 38 + 30]	R-LPFC
	Right-Posterior Prefrontal Cortex	[52–52 + 45]	R-PPC
DMN	Medial Prefrontal Cortex	[1 + 55 − 3]	MPFC
	Left-Lateral Parietal Cortex	[-39 -77 + 33]	L-LP
	Right-Lateral Parietal Cortex	[+ 47–67 + 29]	R-LP
	Posterior Cingulate Cortex	[+ 1–61 + 38]	PPC
Salience Network	Anterior Cingulate Cortex	[0 + 22 + 35]	ACC
	Left-Anterior Insula	[-44 + 13 + 1]	L-AI
	Right-Anterior Insula	[+ 47 + 14 0]	R-AI
	Left-Rostral Prefrontal Cortex	[-32 + 45 + 27]	L-RPFC
	Right-Rostral Prefrontal Cortex	[+ 32 + 46 + 27]	R-RPFC
	Left-Supramarginal gyrus	[-60 -39 + 31]	L-SMG
	Right-Supramarginal gyrus	[+ 62 − 35 + 32]	R-SMG

4.3.3 White Matter Analysis

White matter analysis was based on a recent study demonstrating the existence of white matter functional networks (Peer et al., 2017). SPM, DPABI, and custom MATLAB (The MathWorks Inc., Natick, MA, USA) codes were used to analyze our data. First, we created masks based on the segmentation results in which each voxel was identified as part of gray matter, white matter, or CSF. These masks were then averaged across subjects to obtain the percentage of subjects in which any given voxel was identified as white matter. Based on overlapping voxels in more than 60% of the subjects, a group white matter mask was created and compared to the functional data to remove any wrongly identified white matter voxels. Finally, each white matter voxel was correlated to all other white matter voxels using Pearson correlation to obtain whole-brain connectivity patterns.

These individual correlation matrices were then averaged across subjects to obtain a group-level correlation matrix. K-means clustering was performed on the averaged group-level correlation matrix to identify networks, and the number of clusters varied from 2 to 22. The connectivity matrix was randomly divided into four equal-sized sub-matrices to identify stable clusters, and clustering was individually performed on each of the four sub-matrices. An adjacency matrix was constructed for each submatrix where each cell represented whether two voxels belonged to the same cluster. These adjacency matrices were compared to each other using Dice’s coefficient. After identifying stable clusters, the clusters were used as masks to extract the average time series for each cluster before and after training to study stimulation-induced changes.

4.4 STIMULATION PARAMETERS

Theta tACS stimulation during the arithmetic training was delivered for 25 minutes using a battery-driven NeuroConn (NeuroConn, Germany) DC-stimulator Plus. The target electrode was a 3x3 cm rubber electrode placed at the F3 location (based on the 10–20 system) targeting the L-DLPFC. The return electrode was a 7x5 cm rubber electrode placed on the left shoulder. We also adjusted the stimulation intensity and the simulation frequency for each subject. Stimulation intensity was adjusted with a stepwise increase of 0.25mA starting at 1mA until either the participants reported skin sensations or phosphenes or the current reached 1.5mA. If skin sensations or phosphenes were reported at or below 1mA, the current intensity was reduced by 0.1 mA until no sensations were reported. We used a similar procedure for the sham group, but the stimulation lasted only 30 seconds. Theta frequency was determined based on resting-state IAF assessment (IAF minus 5Hz) before the arithmetic learning session. Participants were asked to close their eyes for 2 Minutes while we recorded EEG data on a limited set of electrodes (P5, P3, P1, Pz, P2, P4, and P6). Using the MNE Toolbox (Gramfort et al., 2013) and a custom-built python code, we determined the individual stimulation frequency.

5.1 Ethical Approval

The study was approved by the ethics committee of the University of Graz, all participants gave written informed consent and were compensated with either 20 € or a study-participation certificate for course credits of 2.5 hours.

5.2 Authors’ contributions

AK and JAM analyzed the data and wrote the main manuscript text. JAM, MB, MW, and AM collected the data. The study was designed by JAM, SEV, and RHB and supervised by RHB, MAN, SH, KP, and RKY. All authors reviewed the manuscript.

5.3 Funding

This research was funded in whole, or in part, by the Austrian Science Fund (FWF) [Grant Number: P30050-GBL]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

5.4 Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

5.5 Conflict of Interest Statement

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. MAN is in the Scientific Advisory Board of Neuroelectrics.

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No competing interests reported.

Modulation of resting-state networks following repetitive transcranial alternating current stimulation of the dorsolateral prefrontal cortex

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Journal Publication

Version 1

Abstract

Figures

1 Introduction

2 Results