All subjects underwent standardized instructions for mental strategies to obtain voluntary control of localized brain activation (based on a written instruction set, see Supplementary Material). The instructions suggested to either recall positive emotional autobiographic memories, to imagine performing their hobby (like engaging in sportive or musical exercise), or to concentrate on a specific perception (like the temperature in one of their feet) in order to increase the activity in the ROI. The NF procedure

was explained in detail, including the delay of the NF signal, and they were instructed to try each regulation strategy for at least 10 s. These instructions were delivered by the experimenter in a personal contact before the first measurement, and on the other days, participants received reminder of the task. Additionally, after each session the participants were asked which strategies they used in order to control their brain activity.

# Design Specification

All participants were trained to control their ACC activity by means of rt-fMRI NF on three separate days within 1 week. On each day, they performed three NF training sessions and two test sessions. We investigated neural correlates of three different conditions: (A) NF, i.e., up-regulation of the localized ACC activation with online feedback from the ROI signal (calculated over all nine NF sessions). The feedback signal here was either the bar or the avatar display; (B) transfer, i.e., up-regulation of the ACC ROI without feedback; and (C) generalization to a cognitive interference task, i.e., activity during the Simon task after NF.

#### Neurofeedback

In the social feedback group, 12 participants received social feedback in which a male avatar (created with Poser Pro, Smith Micro Inc.), with either dark or fair hair (alternating and counterbalanced among subjects) provided a rewarding smile when subjects succeeded to increase ACC activity. The avatar became neutral when ACC activity decreased. The facial expression changed gradually within 100 frames. The second avatar was presented motionless and created a baseline (**Figure 1A**; see also Mathiak et al., 2010).

In the standard feedback group (control group), twelve further participants underwent the same ACC NF training. For this particular group, the change of ACC activity was indicated by either increase or decrease of a green moving bar. A blue motionless bar indicated the baseline condition in this group (**Figure 1B**; see also Gröne et al., 2014). Each NF session consisted of eight NF blocks and nine baseline blocks (30 s each; see exemplary session in **Figure 1**). The feedback was updated every repetition time (TR; 1 s). During baseline blocks, participants' instructions were to count backwards from 100.

#### Transfer

In order to test for the transfer of the NF training, subjects were instructed to regulate their localized brain activity without receiving feedback directly before (pre-test) and after (post-test)

baseline blocks (backwards counting) of 30 s duration each. (A) In the social feedback condition, a dynamic avatar (here the one with dark hair) rewarded successful up-regulation with a smile while the other avatar (blond) indicated the baseline. From the completed datasets, 5

participants the blond one provided the feedback. (B) During the standard feedback condition, green bars moved toward the red one to indicate increase of activity while blue bars provided a cue to count backwards (baseline).

the NF sessions. The static stimuli from the NF training—the avatar or the green bar respectively—were presented in four blocks indicating to use a mental strategy to regulate ACC activity. As in the NF sessions, the baseline stimuli indicated to count-backwards (five blocks).

# Generalization

In order to test for behavioral effects of the ACC NF and for a generalization effect of the training, a cognitive visuospatial interference task, an adapted version of a Simon task, was conducted in the pre- and post-test. A fixation cross was presented in the middle of the screen and accompanied by arrows pointing up or down on either the left or the right side of the cross (**Figure 2**). The participants responded with the button press (right or left) to the direction of the arrow (up or down). Thus, when the subject had to press the button on the opposite side of the arrow, a conflict occurred (incongruent trials). The Simon task was presented in eight blocks of 42 s each. The response buttons were counterbalanced between subjects and the events were presented in a pseudo-randomized order. Reaction time and accuracy of each trial were collected as behavioral measures during the Simon task. The stimulation for the transfer and generalization task was programmed with Presentation software (Version 16.3, www.neurobs.com).

# Data Acquisition and Analyses

fMRI scanning was conducted using a three Tesla whole body scanner (Magnetom TIM TRIO, Siemens, Erlangen, Germany). Echo planar imaging (EPI) covered 16 transverse slices parallel to the AC-PC line at a repetition time TR of 1 s (echo time TE = 28 ms; 64 × 64 matrix with 3 × 3 mm2 resolution; 3 mm slice thickness plus 0.75 mm gap). We obtained 520 volumes for each NF training run (about 8.5 min) and 760 volumes for each preand post-test (12.5 min). A custom made anatomical template of the ACC defined the ROI (Mathiak et al., 2010).

Online spatial preprocessing of the acquired brain volumes was conducted using a custom toolbox based on standard SPM procedures (Koush et al., 2012). In short, motion correction used spline interpolation with co-registration to the preselected template. The NF signal was extracted from each voxel in the ROI during the NF conditions, averaged for each volume and calculated as percentage of signal change relative to the preceding

baseline block. Low frequency drifts were removed with an exponential moving average algorithm to improve the signal-tonoise ratio. A modified Kalman filter reduced outliers and highfrequency fluctuations. For feedback, the signal was rescaled in a fixed ratio such that about 1% signal change represented the full scale from neutral to maximally smiling face or from lowest bar position to the high target. Real-time analysis was performed on a separate PC using a custom Matlab toolbox for online fMRI preprocessing, analysis, and online feedback (for details on the online processing, see Koush et al., 2012).

Offline analysis of the imaging data comprised standard preprocessing and first level analysis in a block design. For the main effect, all runs and days were averaged since no specific time course of learning could be predicted. Group analysis was implemented as second-level two-sample t-test using the rather conservative family-wise error (FWE) correction for whole brain analysis and confirmative ROI analyses. In detail, the mapping analysis consisted of standard preprocessing steps with realignment, normalization, resampling with 2 mm isometric voxels, and smoothing (8-mm full-width at half-maximum Gaussian kernel) with SPM8 (FIL, http://www.fil.ion.ucl.ac.uk/ spm/). The first 10 volumes of each run were excluded from the analyses to account for T1-saturation effects. For the NF runs, the regulation was modeled in a block design applying a generic hemodynamic response function. Transfer and generalization conditions were modeled in a block design as well. T-maps for contrasts of interest in the second-level group analyses were corrected for multiple comparisons across the volume using FWE correction and are shown at corrected threshold (p < 0.05). For data exploration, interaction of transfer and learning in the social reward condition are presented for a voxel-wise uncorrected threshold (p < 0.001). Threshold for cluster extend was always 15 voxels. Anatomical labeling was conducted in accordance with the Anatomy toolbox for SPM8 (Eickhoff et al., 2005).

In addition to the whole brain analyses, we conducted ROI analyses using small volume correction focusing on the ACC and on the reward system, respectively. Thereby we could specifically address the hypotheses 1–4 and ensure that signal changes encompassed the ACC or reward system ROI. The definition of the ACC was based on three-dimensional probability cytoarchitectonical maps, which offer a precise tool for the localization of brain functions as obtained from functional imaging studies (Amunts et al., 2007; Zilles and Amunts, 2010). The mask for the reward system comprised putamen and caudate nucleus as well as globus pallidus and was created using WFU PickAtlas toolbox for SPM8 Maldjian et al., 2003). Activation clusters were displayed at a threshold according to p < 0.05 FWE-corrected for the small volumes with cluster size bigger than 15 voxels.

For data exploration, we extracted average hemodynamic responses from ROIs for ACC and the reward system and as baseline control—from bilateral parieto-occipital clusters (MarsBaR toolbox; Brett et al., 2002). Correlation between ACC regulation and reward responses were calculated. To study learning effects over the runs and sessions, the baseline-corrected ACC ROI signal entered into a repeated-measures ANOVA using linear predictors for run and day and the inter-subject variable group. All calculations were performed using Matlab 2010b (The Math Works, Natick, MA).

# Results

# Behavioral Data

Social and the standard feedback group did not differ with respect to the demographic variables age [t(22) = −0.891, p > 0.383] or education [t(22) = −0.266, p > 0.792]. For the positive affect subscale of the PANAS, repeated-measures ANOVA revealed significant main effects of days [F(2, 42) = 11.829, p < 0.0001; day 1: 27.2 ± 0.9, d2: 24.0 ± 1.3, d3: 23.7 ± 1.3] and session [before vs. after fMRI measurement; F(1, 21) = 5.801, p < 0.025; before: 26.0 ± 1.0, after: 23.9 ± 1.3]. Neither group [F(2, 42) = 1.709, p > 0.521] nor the interactions between group and days [F(2, 42) = 1.709, p > 0.193] and session [F(1, 21) = 0.329, p > 0.572] yielded a significant effect. The negative affect exhibited the same pattern [days: F(2, 42) = 11.829, p < 0.0001, d1: 11.9.3, d2: 11.0 ± 0.2, d3: 10.7 ± 0.2; session: F(1, 21) = 16.774, p < 0.025; before: 11.7 ± 0.3, after: 10.7 ± 0.2; group or interaction with group: all p > 0.09]. In summary, the random allocation yielded comparable groups and general blunting over time but no effect of the feedback strategy on the reported mood emerged.

Reaction times and accuracies of responses collected during the Simon task were assessed with ANOVAs for repeated measures. One participant was excluded from this analysis due to missing data (from the standard feedback group). Since the sphericity assumption was violated for days [Mauchley's test χ 2 (2) = 15.88, p < 0.0001] and for the interaction of days with congruency [χ 2 (2) = 6.2, p < 0.045], the Greenhouse-Geisser correction was applied. Days [F(1.29, 27.13) = 15.065, p < 0.0001], session [pre- vs. post-test, F(1, 21) = 11.731, p < 0.003] and the Simon effect [F(1, 21) = 43.301, p < 0.0001] yielded significant effects on the reaction time. Subjects responded faster over the 3 days (day 1: 535.8 ± 12.4; day 2: 501.4 ± 11.2: day 3: 491.8 ± 14.0 ms) faster during post- than pre-tests (pre: 520.1 ± 13.2; post: 499.2 ± 10.7 ms), and faster during congruent than incongruent trials (congruent: 490.7 ± 10.8, incongruent: 528.6 ± 13.1 ms). Accuracy was only affected by congruency [F(1, 21) = 26.318, p < 0.0001; congruent: 97.4 ± 0.8%, incongruent: 94.6 ± 0.8%]. In summary, a clear effect of stimulus congruency on performance in the Simon task was replicated and training speeded the responses, but no effect of the specific NF training on behavior emerged.

#### Neurofeedback

In the feedback runs, a distributed network was more active during NF as compared to the counting backward baseline (**Figure 3A**). In addition to the ACC, this network comprised bilateral lateral occipital complex, striatum, and dorsolateral prefrontal cortex. In contrast, activation decreased in bilateral posterior insula, postcentral gyrus, and the posterior cingulum (**Table 1A**). Masking with the anatomically defined ACC and reward system confirmed the localization of this activation pattern to encompass the ACC (MNI = [−4, 28, 36], tpeak = 10.02, pFWE < 0.0001) and the reward system with peaks in bilateral caudate nucleus (left: [−12, 6, 14], tpeak = 11.94, pFWE < 0.0001; right: [14, 2, 18], tpeak = 13.13, pFWE < 0.0001).

The group comparison revealed a higher effectiveness of the social NF over the standard feedback, as demonstrated by a significantly higher bilateral ACC activity (tpeak = 10.67, pFWE < 0.0001; **Figure 3B**). Furthermore, an extended activation cluster emerged encompassing bilateral inferior frontal gyrus, the left occipital gyrus, and the left middle temporal gyrus (**Table 1B**). Anatomical ACC and reward system masks confirmed the localization of higher activation during social feedback in the ACC ([−10, 34, 10], tpeak = 9.00, pFWE < 0.0001) and the reward system bilaterally with peaks in bilateral putamen (left:

FIGURE 3 | NF training. (A) Both modes of neurofeedback led to increased activity in ACC and in reward-related brain areas. (B) In the social feedback group, activity was higher in bilateral ACC and in the reward system as compared to the standard feedback

group. Moreover, clusters in prefrontal, occipital, and temporal lobe emerged in this group comparison as well (see Table 1 for details). All maps are displayed at a threshold according to p < 0.05, FWE-corrected.

#### TABLE 1 | Activation clusters during NF training.

\*The cluster sizes for the ACC were calculated in a mask based on three-dimensional probabilistic cytoarchitectonic maps.

[−32, −10, 2], tpeak = 9.51, pFWE < 0.0001; right: [36, 0, −4], tpeak = 9.06, pFWE < 0.0001). Thus, hypotheses 1 and 2 were confirmed with higher ACC and reward system activity during social feedback. Notably, the average responses in the ACC ROI correlated with the one from the reward system [r(24) = 0.535, p = 0.0071], suggesting a direct relationship of reward processing and learning success.

Learning of NF related regulatory control may be associated with increase of signal change over time. After baseline correction for the bilateral parieto-occipital junction clusters, average signal change in the ACC ROIs revealed a complex learning pattern influenced by the repetition over three runs on 3 days each (see **Figure 4**). Learning curves in NF may be complex and highly non-linear (Sarkheil et al., 2015), but frequently are approximated by linear curves. Therefore, repeated-measures ANOVA included runs and days as separate linear predictors and revealed a clear days × group interaction [F(1, 23) = 8.239, p < 0.0089] but no main effect or other interaction [all F(1, 23) < 1.8, p > 0.19, except a trend for days, F(1, 23) = 3.022, p = 0.0961]. Further, the probability that individuals achieved control over the signal was estimated on their run-wise success rate and varied across subjects but not between the groups (mean ± SD: 69.4 ± 32.3%). In summary, the differential signal increase observed in the ACC seemed stronger in the social feedback group across runs as well as days, which was statistically confirmed for a stronger linear increase across days only.

# Transfer

Transfer conditions revealed significantly higher ACC activity during the post-test regulation blocks without feedback compared to baseline blocks; in addition to ACC activity,

distributed activation clusters emerged in bilateral inferior frontal gyrus and occipital gyrus, in the right middle occipital and middle temporal gyrus, left posterior cingulate cortex as well as thalamus (**Figure 5A**, **Table 2A**). ROI masks confirmed localization of activity in the ACC ([−10, 32, 24], Tpeak = 5.23, pFWE < 0.0001).

To test the prediction that transfer may differ between the two learning conditions, the interaction of transfer and learning groups was calculated. Indeed, during regulation blocks higher

transfer condition i.e., regulation without feedback (p < 0.05, FWE-corr.). (B) Social feedback led to higher transfer than standard feedback, although this

social learning group yielded higher activity in the left inferior frontal gyrus and the left inferior parietal cortex (see Table 2).

ACC activity was found in the social feedback group as compared to standard feedback (**Figure 5B**, **Table 2B**) but this interaction survived only an uncorrected threshold (p < 0.005) with an cluster-extend threshold of 15 voxels. Only peaks at the left inferior frontal gyrus and inferior parietal cortex survived the FWE-correction (**Table 2B**). The ROI analysis indicated higher regulation increase of the ACC in the social feedback group, but the peak did not survive the FWE-correction (MNI = [6, 10, 34], Tpeak = 2.58, puncorr < 0.005). Lacking a higher activation in the social transfer condition after FWE-correction, we could not confirm Hypothesis 3.

# Generalization

Generalization was tested as the effect of the transfer (regulation without feedback) on a subsequent block with the cognitive interference task, i.e., the group-by-task interaction during the Simon task. We found that ACC activation during cognitive interference processing was reduced after social reward compared to standard feedback. Higher ACC activity emerged in the non-social feedback group compared to social feedback group (Tpeak = 5.34, pFWE < 0.001; **Table 3**; **Figure 6**). ROI analysis confirmed the localization in the ACC (MNI = [−8, 34,−6], Tpeak = 4.63, pFWE < 0.001). Hypothesis 4 stated stronger effects on ACC activity during the generalization task after social NF training and this was corroborated by the data.

# Discussion

The present study investigated the effectiveness of social reward in rt-fMRI NF training of the ACC and compared it to a standardtype feedback in form of a moving bar. As predicted, social reward led to stronger ACC activity during NF training. After the training, both groups were able to regulate ACC activity without receiving feedback, with a trend for better performance in the social feedback group. Furthermore, during a cognitive interference task a significant difference for ACC activation emerged suggesting stronger generalization of the social feedback training on cognitive processing.

We extended previous studies using monetary reward (Bray et al., 2007) and created an innovative NF training based on a real-time social reward. In operant conditioning, a desired response is repeatedly paired with reward, resulting in increasing probability that the response occurs again. A conscious process is not necessary for the learning to take place. Although NF is believed to be based on principles of operant conditioning, no reward is delivered for a correct response in typical fMRI NF paradigms. The learning requires instead the explicit knowledge of the task in order to perform it correctly. Although changing the size of the color bar according to instruction can be satisfying as it signals success (the own satisfaction serves as a reward in this case), in a different context, e.g., during watching a movie with a color bar changing, it would not represent a rewarding value. Bray et al. (2007) made a first step in implementing an implicit feedback in a behavioral shaping paradigm; subject's responses were gradually changed by reinforcing small changes leading to a desired target behavior (Dinsmoor, 2004). The subjects did not need to have explicit knowledge of the task, but learned it gradually via receiving or missing a financial reward, depending on their performance. Although monetary reward constitutes a strong reinforcer, it is difficult to deliver in a real-time feedback in order to gradually shape the behavior.

#### TABLE 2 | Activation clusters during transfer.

uc.: uncorrected p-value

TABLE 3 | Group comparison of generalization (Simon task).

Emotional expressions aim to communicate our experiences and to influence the behavior of others (Horstmann, 2003). Social reward offers therefore a more ecologically valid paradigm to shape the behavior of subjects in real-time as compared to monetary reward. This common social learning mechanism can directly influence the level of localized brain activity using a BCI. Indeed, the social reward led to stronger localized brain activity than the standard feedback. Subjects learned to differentially regulate brain activity depending on the avatar faces. The use of differential stimuli to shape behavior opens new perspectives for developing social feedback paradigms with implicit learning, circumventing explicit cognitive control.

The presence of social reward led to bilateral activation of an anatomically-defined ROI in the corpus striatum (putamen, caudate nucleus, and globus pallidus). These structures belong to a network activated by pleasant and rewarding events (Haber and Knutson, 2010). They are involved in driving incentivebased learning and choosing appropriate responses to stimuli, thereby helping to achieve rewards and avoid punishments, and consequently allow the development of goal-directed behavior (Robbins and Everitt, 1996; Delgado, 2007; Liljeholm and O'Doherty, 2012). Social reward was demonstrated to share comparable neural pathways with monetary reward (Izuma et al., 2008). A number of fMRI and neurophysiology studies confirmed that neural activity in the striatum is modulated by social rewards and by learning in a social context (for a review see Báez-Mendoza and Schultz, 2013; Ruff and Fehr, 2014). Our results are compatible with these studies; moreover we demonstrated that the learning of control over the brain activation improves due to the direct reward.

During the generalization condition, activation in the ACC decreased more in the social feedback group. Although cerebral activation typically increases with higher task load, it is well established that in the course of skill training one can observe the decrease of brain activation (Chein and Schneider, 2005). The effects of training on brain plasticity have been studied in the

sensorimotor system, demonstrating a systematic decrease in the motor and somatosensory cortex (Ikegami and Taga, 2008; Kwon et al., 2013; Walz et al., 2014). In trained musicians, gray matter density decreased with expertise in bilateral perirolandic and striatal areas that are related to sensorimotor function, possibly reflecting high automation of motor skills (James et al., 2013). In a similar vein, in a working memory task, the activation in the right inferior frontal gyrus and the right intraparietal sulcus initially increased with improved performance, but decreased when performance consolidated after the prolonged training (Hempel et al., 2004). Moreover, low-performance led to large and loaddependent activation increases in distributed cortical areas when exposed to excessive task requirements, suggesting a recruitment of additional attentional and strategy-related resources by low- as compared to high-performing participants (Jaeggi et al., 2007). In general, the recruitment of a large-scale neural network decreases in the automatic phase, as stimulus-response associations become better and task performance progresses from a consciously controlled manner in the early learning phase to an unconscious form in the late automatic phase (Toni et al., 1998; Müller et al., 2002; Dobbins et al., 2004). Kozasa et al. (2012) compared the performance of trained meditators with non-meditators in a Word-Color-Stroop task, i.e., a cognitive interference task based on a similar principle as the Simon task. Although there were no group differences for the behavioral interference effect, non-meditators activated attention and motor control higher than meditators. The authors suggested that the meditation training improved efficiency via enhanced sustained attention and impulse control. Similarly, in our study, after up to 2 weeks of NF-training, subjects who received social reward could maintain the similar behavioral results in Simon task while engaging less ACC activity than subjects who received standard feedback. The behavioral effects in our study demonstrated an increase of the performance in the Simon task over the training time, reflecting the accompanying decrease in ACC activation due to learning and the corresponding shift from a large network to more specialized regions. In combination with the lack of effects on the behavioral level, we conclude that the social reward led to a reduced neural recruitment to achieve a similar behavioral performance in the Simon task.

Rapid technological advance in fMRI and BCI extends the range of NF applications leading to its increasing popularity.  The first applications of NF in patient groups suggest its potential in the treatment of several disorders, including chronic pain (deCharms et al., 2005), chronic tinnitus (Haller et al., 2010), Parkinson's disease (Subramanian et al., 2011), depression (Linden et al., 2012; Young et al., 2014), obesity (Frank et al., 2012), nicotine addiction (Canterberry et al., 2013; Li et al., 2013), or schizophrenia (Ruiz et al., 2013).

A well-designed feedback system is crucial in order to achieve a successful training of regional brain activation (Sitaram et al., 2008; Sokunbi et al., 2014) and allow its further development into an effective and accurate clinical intervention. Social feedback, offering direct reward for successful regulation, increased the effectiveness of the NF training. We applied a social smile of a changing intensity, which is a very simplified form of social reward. Indeed, more complex social stimulation (including social gestures, prosody, and complex emotional expression) could serve as an even stronger reinforcer and further improve performance. Sokunbi et al. (2014) propose to choose the visual stimuli that relate to the function of the target brain area. In accordance with this view, social feedback could be particularly well fitted to train impaired social interactions in psychiatric patients in implicit learning tasks.

#### Limitations

Although we studied a relatively large group of participants for such a complex paradigm, the group size is a limitation. Possibly due to the small group size, we failed to demonstrate stronger ACC regulation during the transfer sessions (regulation without feedback) and behavioral effects on the Simon task in the social feedback group. Despite the high variability of learning success, subgroup analyses with the focus on learners and non-learners are not feasible at this stage. It would be of particular importance to determine the variability between subjects in learning and reward sensitivity during NF and determine predictors for this (Scheinost et al., 2014). In particular, we did not consider the individual learning processes over the three session in 3 days each. Moreover, the test for difference in transfer effects between the social and standard NF might not be optimally selected. Although the test was identical with the learning procedure, it could have a different meaning for both groups. While in the standard feedback the bar in itself presented no rewarding value, it was not the case with the smiling faces. During social NF, subjects received social reward. In the transfer task, they were presented with slightly smiling facial expressions that might have had negative emotional value relative to smiling faces they viewed while regulating successfully. Showing subjects a neutral stimulus while trying to regulate their ACC activation without feedback might improve those results.

The reward system is typically associated with the basal ganglia, but many other brain regions respond to reward as well, including the ACC, the orbital prefrontal cortex, the midbrain dopamine neurons, the dorsal prefrontal cortex, amygdala, hippocampus, thalamus, lateral habenular nucleus, and specific brainstem structures such as the pedunculopontine nucleus and the raphe nucleus (Haber and Knutson, 2010). The exact role of ACC in reward processing is however not fully understood. It has been hypothesized to play a role in sustaining effective choice behavior based on the previous experience (Chudasama et al., 2013) and particularly in anticipation of loss by risky decisions (for a review, see Liu et al., 2011). A recent metaanalysis of brain imaging studies on social decision making in the ultimatum game suggested that the ACC controls and monitors conflicts between emotional and cognitive motivation, in line with its postulated role in general conflict monitoring (Gabay et al., 2014). In this respect, replacing the moving bar with an explicit social reward should not lead to additional ACC involvement, among others, because both tasks require similar involvement to obtain the desired outcome and only the rewarding value of this outcome differs. Although introducing social reward in the NF paradigm improved learning, we cannot rule out a direct impact on reward on the ACC activation, e.g., via increasing the net value of the expected reward (Apps and Ramnani, 2014). Future research may focus on other brain regions to examine if the effect of social reward is universal for all brain structures, or if it specifically facilitates learning in reward-sensitive regions.

Finally, the sequential group allocation based on the order of inclusion does not preclude observer biases. This should be addressed by using random allocation. This in turn, however, may introduce time effects depending on the block size for random allocation (see Tamm and Hilgers, 2014). Another problem in this study design, like in many other feedback trials, is the limited possibility to blind the conditions to the participant as well as the experimenter. In particular for therapeutic trails, this remains a challenge to blind control conditions in fMRI neurofeedback.

# Conclusions

We suggest that social reinforcers can lead to improved learning of self-regulation and improve effects of fMRI-based NF on underlying neural processes such as cognitive interference processing. The advantage of social feedback over standard visual feedback or over monetary rewards is the online provision of a direct external reward that we can experience every day in social interactions. Further research is needed to evaluate if social feedback training has the potential to make the learning process more implicit (deCharms et al., 2005; Sulzer et al., 2013).

# Author Contributions

KAM: Development of study paradigm and data analysis. Supervision over data analysis and interpretation. Manuscript revision. EA: Data acquisition, data analysis and interpretation. Manuscript writing. YK: Implementation of toolbox for realtime fMRI and technical support by data acquisition. Revising the manuscript. MD: Contribution to design and data analysis. Revising the manuscript. JC: Contributions to data collection and statistical analysis. Revising the manuscript. TG: Contribution to design and data collection. Revising the manuscript. FZ: Contribution to design. Revising the manuscript. NP: Contribution to development of ACC masks and data analysis. Revising the manuscript. PS: Contribution to data acquisition. Revising the manuscript. SB: Contribution to analysis of behavioral data. Revising the manuscript. MZ: MRI support and technical support by data acquisition. Revising the manuscript. KM: Supervision of and conceptual contributions to study. Data analysis and interpretation. Manuscript revision. All the authors read and approved the final version of the manuscript. All the authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

# Acknowledgments

Krystyna A. Mathiak was supported by a habilitation grant (Habilitationsstipendium) of the Faculty of Medicine, RWTH Aachen. This research project was supported by the German Research Foundation (MA 2631/6-1, BE 5328/2-1, IRTG 1328), the Federal Ministry of Education and Research (APIC: 01EE1405B), and the START-Program of the Faculty of Medicine, RWTH Aachen. Support was provided by the Brain Imaging Facility of the Interdisciplinary Center for Clinical Research.

# Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnbeh. 2015.00136/abstract

# References

Zotev, V., Krueger, F., Phillips, R., Alvarez, R. P., Simmons, W. K., Bellgowan, P., et al. (2011). Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS ONE 6:e24522. doi: 10.1371/journal.pone.0024522

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Mathiak, Alawi, Koush, Dyck, Cordes, Gaber, Zepf, Palomero-Gallagher, Sarkheil, Bergert, Zvyagintsev and Mathiak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

# **David J. White1\*, Marco Congedo2 and Joseph Ciorciari 3**

1 Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia

2 Grenoble Images Parole Signal Automatique (Gipsa-lab), CNRS and Grenoble University, Grenoble, France

3 Brain and Psychological Sciences Research Centre, School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia

#### **Edited by:**

Niels Birbaumer, University of Tuebingen, Germany

#### **Reviewed by:**

David E. Linden, Cardiff University, UK Martijn Arns, Research Institute

Brainclinics, Netherlands

#### **\*Correspondence:**

David J. White, Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Mail H24, PO Box 218, Hawthorn, VIC 3122, Australia e-mail<EMAIL_ADDRESS>
A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback.

**Keywords: neurofeedback, EEG, blind source separation, BSS, theta**

# **INTRODUCTION**

The activity-dependent nature of neuroplasticity in the brain has highlighted the potential for manipulations of brain activity in enhancing our understanding of brain processes, but also treating clinical conditions (Cramer et al., 2011). A number of methods exist which apply external stimulation or manipulations to alter brain activity, these include pharmacological interventions, electrical stimulation methods (e.g., deep brain stimulation, transcranial direct current and alternating current stimulation) and Transcranial Magnetic Stimulation (TMS). Unlike these external stimulus driven methods, neurofeedback offers a noninvasive technique capable of manipulating endogenous brain activity. A developing literature supports the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD (Arns et al., 2013, 2014) and epilepsy (Sterman and Egner, 2006; Tan et al., 2009). In addition, experimental neurofeedback enables the study of brain activity as the independent variable, providing a powerful method for studying the functional significance of endogenous brain activity (Weiskopf, 2012).

Traditional EEG neurofeedback methods typically utilize a small number of active electrodes on the scalp. Scalp EEG at a given electrode site reflects a linear mixture of activity of multiple brain sources and artifacts, with skull and other tissue having a spatial smearing effect (Congedo et al., 2008). With this in mind, sources optimally aligned and in closer proximity to the scalp electrode represent a greater proportion of the observed activity, but far from the entirety of the observed signal. Thus, traditional neurofeedback methods training single electrode sites are likely influencing multiple brain regions and processes. It is therefore not surprising that training methods using a single scalp site influence large scale EEG dynamics beyond the training frequency and site (for example, Egner et al., 2004). Additionally, as the observed signal reflects multiple brain processes, it has also been suggested that this may impede the ability to acquire control over the feedback signal. This point was highlighted by Philippens and Vanwersch (2010), who demonstrated learned sensory-motor rhythm (SMR) enhancement in four sessions of neurofeedback in non-human primates using intracranial recordings. These authors stressed the ability to acquire control in such a short training period may have partially been a result of the increased spatial resolution, and reduced influence of EMG artifact. Given these limitations of traditional EEG neurofeedback methods, a number of more spatially and functionally specific neurofeedback techniques have been explored. The major development in this area is fMRI-based neurofeedback (Yoo and Jolesz, 2002; Weiskopf et al., 2003, 2004), but also includes spatially specific MEG-based neurofeedback (Florin et al., 2014).

In light of these emerging neurofeedback methods, efforts to develop methods which maximize the functional and spatial specificity of EEG-based neurofeedback techniques remain pertinent given the comparative availability and affordability of such technology, and the capacity to directly target endogenous electrophysiological activity (cf. fMRI methods based on the BOLD response). While source-based EEG neurofeedback using source localization methods has been demonstrated (Congedo et al., 2004), offering potential for an improved spatial precision of a training region, these methods remain limited by the susceptibility of source localization methods to artifacts, the inability to isolate neighboring but functionally separate sources, and the spatial precision offered. Perhaps for these reasons, the capacity for learned regulation using these methods has been inconsistently shown (Maurizio et al., 2014). Blind Source Separation (BSS) is a group of processing techniques which seek to identify source activity from a mixed signal. These methods have been employed in a variety of fields including speech processing (Jang et al., 2002), face recognition (Yuen and Lai, 2002), wireless communication (Van Der Veen et al., 1997), radar applications (Fiori, 2003), and with a range of biomedical signals (James and Hesse, 2005). The blind nature of these methods has facilitated such widespread applicability, where no knowledge of the source activity or mixing process is required. Given the properties of scalp EEG, viewed as an instantaneous linear mixture of multiple brain sources and artifacts as a result of volume conduction, BSS was identified as a method suited to EEG signal processing (Makeig et al., 1996), and has subsequently seen widespread use in EEG research both in the identification and removal of artifacts (Vigário, 1997; Delorme et al., 2007; Romero et al., 2008), and the exploration of functionally and spatially distinct brain sources (Makeig et al., 2004; Onton et al., 2005, 2006; Congedo et al., 2008; Kopˇrivová et al., 2011). It has recently been proposed that BSS methods may have important advantages in multi-channel neurofeedback beyond those methods based on source localization. Specifically, BSS-based neurofeedback may address the limitations of previous source-based neurofeedback methods by offering enhanced spatial and functional specificity of the training substrate, while being less susceptible to artifacts and noise, and

being computationally inexpensive (Congedo and Joffe, 2007; Grandchamp and Delorme, 2009).

Neurofeedback based on sources derived from signal processing methods such as BSS may be ideally suited to isolating a spatially and functionally distinct source, which may be less susceptible to common artifacts, representing significant advantages over traditional methods. Further, given the prominence of BSS-based signal processing methods in the field of cognitive neuroscience, particularly with EEG, demonstrating the "trainability" of functional sources derived from these methods with neurofeedback may open future investigations to study the functional significance of identified sources via trained perturbation of this activity. To this end, the present investigation explores the capacity to learn enhanced activity on a BSS-derived source derived from functional brain activity during completion of a complex cognitive task.

# **MATERIALS AND METHODS**

### **PARTICIPANTS AND EXPERIMENTAL DESIGN**

Four healthy right-handed adult volunteers aged 24–38 years old participated in the study (1 female). Written informed consent was obtained from all participants, with all procedures carried out in accordance with the Swinburne University Human Research Ethics Committee. Participants underwent 20 sessions of neurofeedback over the course of 7 weeks. In addition, three assessment sessions were completed across the course of the neurofeedback period, one at baseline, one after 10 sessions, and a final assessment after the 20 sessions. Beyond the data from the baseline assessment session used to isolate individual sources for neurofeedback, these assessment sessions will not be further discussed in the present report.

#### **BSS-BASED NEUROFEEDBACK**

The linear BSS problem can be defined as:

$$\mathbf{x}(t) = A\mathbf{s}(t)$$

where *x*(*t*) is the observed data and *s*(*t*) the underlying source signals, *A* is a time-invariant mixing matrix. Following matrix algebra, estimated source activity is thus given by:

$$
\hat{s}(t) = B\mathbf{x}(t)
$$

where *B*, known as the separating or demixing matrix, is the pseudo-inverse of *A*. In this way, reconstructed source activity is given by multiplying the separating matrix by the observed data. This BSS model assumes that observed signals are an instantaneous linear mixture of underlying sources (Cardoso, 1998). These methods typically seek the separating and mixing matrices through cancellation of second order or higher order statistics, seeking maximally independent sources.

When applied to EEG data, *x*(*t*) above is an *n* (electrodes) by *t* (time points) matrix of observed scalp EEG, the mixing matrix (*A*) describes the relative weights with which each source projects to the scalp, and the separating matrix (*B*) obtains the estimated brain source activity, in the form of arbitrarily scaled reconstructed source time-series, when multiplied by the observed EEG. It follows that the estimated activity, or time-series, of a single source of interest (ˆ*si*(*t*)) is obtained by:

$$
\hat{s}\_i(t) = B\_i \mathbf{x}(t)
$$

that is, by multiplying the observed scalp data by the vector of weights (*Bi*) from the separating matrix which corresponds to the source of interest. The separating matrix can thus be conceived as a spatial filter, used to estimate source activity. In the context of real-time BSS-neurofeedback, online multiplication of scalp EEG by the vector of the separating matrix corresponding to the target training source will obtain the source time-series. The major issue with such an approach is identifying and obtaining a stable estimate of the spatial filter for the training source. The method adopted in order to achieve this in the present experiment was to base the training on a robust task-related source identified in a group BSS analysis, from which the most closely related individual source was sought.

# **Determining individual neurofeedback sources**

The functional brain source selected for neurofeedback training was based on a previously reported BSS-derived source including medial-temporal lobe (MTL) and parietal lobe regions in which spatial memory performance was associated with source theta oscillatory activity (White et al., 2012). As part of this study, EEG data during completion of a spatial navigation task was analyzed using a BSS method known as Approximate Joint Diagonalization of Cospectral matrices (AJDC; Congedo et al., 2008). Using this method, a source was identified which demonstrated significantly increased theta oscillatory activity during navigation. Within a sample of 25 healthy adults, greater theta power within this source, localized to MTL and parietal regions using sLORETA (Pascual-Marqui, 2002), was associated with better task performance.

As part of the neurofeedback protocol, each participant required individually determined weights corresponding to the BSS component showing the strongest correlation with the group MTL—parietal theta source identified in White et al. (2012) during completion of this same task. Using identical EEG acquisition and pre-processing routines as that used in White et al. (2012), individual participants' EEG data during spatial navigation was decomposed using the identical BSS method (AJDC using the same parameters previously reported, using ICoN software, Version 3.11 ). Source time-series derived from the individual BSS decomposition were then correlated with the group MTL/parietal theta source time-series described in White et al. (2012), with the individual source showing the strongest correlation selected as the feedback source (for all four participants, *r* ≥ ± 0.550). The weights corresponding to this component in the separating matrix were extracted for use as a spatial filter for neurofeedback, using a subset of electrodes which did not compromise the source signal (39–42 electrodes were retained for neurofeedback sessions, from the original 62). Peak theta for the feedback training band was determined as peak power within

# **Neurofeedback protocol**

All participants underwent 20 neurofeedback sessions across 7 weeks. Each neurofeedback session involved a resting eyes open baseline, then five blocks of training each lasting 4 min. As this study represented a preliminary investigation exploring the feasibility of BSS-based training, no control neurofeedback group was included. Instead, a series of trials were conducted at a follow-up session upon completion of the 20 sessions in which participants were asked to increase or decrease the feedback signal. As the focus of this experiment was the feasibility of learned regulation of BSS-derived source activity, these trials were included to probe for evidence of learned volitional regulation of the target signal. SynAmps2 amplifiers and Acquire 4.3 software were used to acquire the EEG data as part of the neurofeedback sessions (Neuroscan Inc., Abbotsford, VIC, Australia). Data acquisition for neurofeedback sessions employed a band-pass filter from 1–50 Hz, with a linked mastoid reference. This was done to ensure consistency with the reference used in the off-line analysis of the previous experiment. A second computer running the Open-ViBE software platform (Renard et al., 2010) provided the on-line processing and feedback required by the neurofeedback paradigm. The set-up made use of the built-in client/server operations available in Scan Acquire 4.3 software; whereby the acquisition system acted as the server which sent acquired data on to the client system (Open-ViBE) via a Local Area Network. An acquisition driver written in C++ facilitated this process within the Open-ViBE platform.

A "Scenario" was developed for each participant within the Open-ViBE software which applied a processing chain to generate the feedback signal, before providing visual feedback to the participant with minimal delay. The Scenario for each participant applied the individually defined spatial filter to the incoming EEG data, generated a ratio of peak theta (*p*θ = peak ± 0.5 Hz) to total theta (*tot*θ = 4–8 Hz) for the source time-series, then provided visual feedback. In order to obtain on-line band-power estimates for *p*θ and *tot*θ the time-series was first band-pass filtered in the designated frequency range (Butterworth filter, 0.5 dB band-pass ripple), then segmented into 1 s epochs with a 250 ms moving window, the data were then squared and an average calculated for each 1 s epoch. Finally, each estimate was log transformed (ln(*x +* 1)) to minimize deviations from normality (Kiebel et al., 2005). The feedback signal was simply the ratio of these log transformed band-power estimates for peak and total theta (*p*θ*/tot*θ). Electro-oculogram (EOG) artifact remains an important consideration when dealing with thetaband activity. This motivated the use of a ratio, as opposed to absolute power. Eye blink and movement artifact is generally maximal in the delta range, decreasing in a steep and monotonous manner with increasing frequency (Gasser et al., 1985; Hagemann and Naumann, 2001). We reasoned that in using the ratio of peak theta activity relative to total theta, the potential confounding influence EOG artifact would be minimized. For example, it is highly unlikely that EOG artifact would manifest itself as

the 4–8 Hz band via Fast-Fourier Transform of individual source activity during completion of the navigation task.

1http://sites.google.com/site/marcocongedo/software/icon

a frequency-specific power increase coinciding with peak theta activity, and much more likely that the presence of EOG artifact would result in broadband theta power increases, largest at the low end of the bandwidth, resulting in little change or a drop in the *p*θ*/tot*θ ratio.

The feedback received by the participant contained both continuous and discrete elements (see **Figure 1**). A scrolling line graph showing the exact ratio level formed the continuous feedback, whilst a reward box flashed blue and registered a point each time the ratio exceeded a predefined threshold. The continuous feedback has the advantage of being easy to interpret for the participant (Weiskopf et al., 2004), whilst the threshold score and blue flash provided a discrete reward, which utilizes the common conception of neurofeedback learning by means of operant conditioning. The importance of discrete feedback in neurofeedback protocol design has recently been emphasized (Sherlin et al., 2011). This threshold was determined by calculating a percentile during the baseline recording of the first neurofeedback session, where 6–12 discrete rewards would be received per minute at baseline levels.

# **Assessment of neurofeedback learning**

The capacity to regulate the feedback signal, in the form of neurofeedback learning, forms the primary outcome for this feasibility study. A number of methods have been adopted for operationalizing and quantifying relative success at neurofeedback learning, yet there is little agreement on the most appropriate method (Dempster and Vernon, 2009). In order to demonstrate learned control over the feedback signal, evidence that changes occur beyond baseline levels appears a minimum requirement. In order to first assess this, non-learners were identified with an initial paired samples *t*-test, contrasting mean source *p*θ*/tot*θ ratio scores at all 20 feedback sessions for each participant with the corresponding baseline. Only those to demonstrate significantly elevated source *p*θ*/tot*θ ratio during feedback when contrasted with corresponding baseline were analyzed for evidence of neurofeedback learning. Learning within and across sessions involve desired changes emerging over the course of training, and thus can be plotted as a learning curve. In each of these cases, the presence of learning is demonstrated by the desired increase or decrease in the signal of interest across sessions or over blocks of time within sessions. Within and across sessions learning was assessed by ordinary least squares regression, with the time within training as the predictor (session number for across sessions, and minute from beginning of feedback for within sessions; both 1–20). For both learning analyses, data was normalized with respect to the mean and standard deviation of source *p*θ*/tot*θ ratio during a baseline period. For within sessions learning, mean data was extracted for each minute of neurofeedback at each session, and normalized to the corresponding baseline data for that session. Across sessions learning analysis used the mean source *p*θ*/tot*θ ratio for each session normalized to the baseline period at the first neurofeedback session.

Clearest demonstrations of volitional self-regulation use a series of trials in which the participant is instructed to produce the desired change in signal, contrasted with trials where the opposite change or no change are desired. Neurofeedback for Slow Cortical Potentials (SCP; for a review see Birbaumer, 1999) lends itself to this type of analysis, and self-regulation of positive and negative shifts have been demonstrated in this way (e.g., Schneider et al., 1992). Volitional self-regulation has also been demonstrated with the use of specifically conceived experimental design following LORETA neurofeedback training for enhanced low beta activity in the anterior cingulate gyrus (Congedo et al., 2004). As part of the present exploration, participants completed a follow-up session upon completion of the 20 neurofeedback sessions in which they were asked to increase or decrease the feedback signal in eight randomized blocks of 3-min each (total of four "up" and four "down" trials). A randomization *t*-test (Edgington, 1987) comparing the mean *p*θ*/tot*θ ratio feedback signal obtained in each up trial vs. the mean of each down trial exploits the design of the trials, providing an appropriate assessment of volitional self-regulation of the feedback signal post-training.

A designated exploration of the relationship between ocular artifact and the feedback signal was undertaken offline, using data from the first neurofeedback session for each participant. Power in the delta range (1–3 Hz), averaged across frontal electrode sites, formed a surrogate measure of EOG artifact. For each participant, power estimates were calculated in the same way as those used to derive the source peak theta to total theta ratio, with an additional parallel processing stream calculating frontal delta power. The relationship between the two signals was then assessed by correlating the power estimates for the two signals for the first session in each neurofeedback participant.

## **Assessing the impact of neurofeedback training on navigation performance**

Owing to the exploratory nature of the current experiment, the sample size limited the scope for a full exploration of the impact of the neurofeedback intervention, instead focussing on the feasibility of such a protocol. While this prevented the use of traditional statistical analysis of group differences in outcome measures across the intervention period, we briefly describe the trends in behavioral performance on the navigation task from which the neurofeedback source was derived across the neurofeedback training period contrasted with an age and gender matched no-treatment control group. Three assessment points were completed across 7 weeks for both neurofeedback and control participants (pre-treatment baseline, week 4, week 7 (post-treatment)). The difference in these trends, in the form of the gradient of the slope estimated by Ordinary Least Squares from all navigation performance observations for each group, was then contrasted between neurofeedback and control group using a small sample *t*-test for parallelism (Kleinbaum and Kupper, 1978).

# **RESULTS**

### **EXCEEDING RESTING LEVELS**

Paired samples *t*-tests revealed two of the four participants demonstrated significantly elevated source *p*θ*/tot*θ during neurofeedback sessions, when contrasted with baseline at each session (see **Figure 2** below). Analysis of learning trends was pursued for these two participants only. Surprisingly, one participant showed a significant reduction in the feedback ratio during feedback sessions compared to baseline, while one showed slight nonsignificant increases from baseline to feedback.

#### **NEUROFEEDBACK LEARNING WITHIN AND ACROSS SESSIONS**

Both participants to show elevated source *p*θ*/tot*θ during neurofeedback sessions demonstrated evidence of neurofeedback learning. **Figure 3** summarizes the results of linear regression analyses probing neurofeedback learning within and across sessions for these two participants. This learning emerged across sessions for Participant B, but within sessions for Participant C. Participant B appeared to demonstrate a sharp within sessions learning trend in the first half of each session, before significantly dropping away.

#### **VOLITIONAL CONTROL OF SOURCE p**θ**/tot** θ **RATIO**

Results of the follow-up session exploring volitional control of the feedback signal corroborated findings of the neurofeedback learning analyses. **Figure 4** shows Participants B and C again demonstrated significantly greater *p*θ*/tot*θ source activity during

the "up" trials than the "down" trials (Participant B: *t* = 4.19, *p* = 0.0143; Participant C: *t* = 2.75, *p* = 0.0143). In addition, the findings from these trials suggested Participant D obtained some level of volitional control over the signal that did not translate into neurofeedback learning during the training period (Participant D: *t* = 2.15, *p* = 0.0429).

#### **OCULAR ARTIFACT**

The selection of a ratio feedback signal (*p*θ*/tot*θ) was motivated by a desire to minimize the influence of artifacts in neurofeedback sessions. However, the potential influence of ocular artifact was explored offline, correlating delta power estimates at frontal electrode sites with the feedback signal during neurofeedback. Results of this analysis suggested minimal relationship between the source feedback ratio and frontal delta power for each participant (Participant A: *r* = 0.004; Participant B: *r* = −0.002; Participant C: *r* = 0.115; Participant D: *r* = 0.043). As the maximum value, observed in Participant C corresponds to approximately 1% of variance explained, it appears unlikely that any participant experienced the capacity to achieve the desired signal increases through increasing the presence of ocular artifact.

#### **NAVIGATION PERFORMANCE**

Trends in behavioral performance on the navigation task assessed across the training period showed evidence of improvement in both neurofeedback and control groups. The two groups showed large baseline differences in performance, and evidence of practice effects across repeated assessments, but the trend across the training period did not significantly differ between

the groups (*t*(20) = −0.27, *p* > 0.05). These trends, in the form of the gradient of the slope estimated by Ordinary Least Squares (β), are plotted for performance on the navigation task in **Figure 5**.

**and (B) and Participant C, see panels (C) and (D)**. Participant B did not

# **DISCUSSION**

The current study described the implementation of a neurofeedback paradigm using spatial filtering of scalp EEG to obtain ongoing activity of a BSS component derived from functional brain activity. The study aimed to explore the feasibility of training enhanced theta using this BSS-based neurofeedback. Results showed evidence of learned augmentation of source peak theta activity in 50% of the neurofeedback sample, providing preliminary evidence in support of the feasibility of BSS-based neurofeedback. Beyond this evidence of neurofeedback learning trends, the study also demonstrated volitional control of the feedback signal upon completion of the neurofeedback training period in three of the neurofeedback participants. No differences in behavioral performance were observed for the neurofeedback group on the navigation task from which the training source was derived, when compared to an age and gender matched no-treatment control group. Thus, the findings of this study suggest learned regulation of oscillatory activity derived from a BSS component represents a plausible line of inquiry for future research.

within sessions learning **(C)**, but no linear increases across sessions.

#### **NON-LEARNERS AND STUDY LIMITATIONS**

In the present study, two participants failed to show evidence of learned control over peak theta activity beyond baseline levels. An important consideration in analyzing neurofeedback learning is that not all those exposed to training will gain significant control over the feedback signal. Previous explorations of neurofeedback learners and non-learners suggest that as much as half of those participating in neurofeedback training may not demonstrate significant learning (Weber et al., 2011). In line with this, 50% of the present sample belongs to this non-learner group, while 25%

**neurofeedback training**. For each participant, the mean source pθ/totθ ratio for each trial is shown. A significantly elevated ratio was observed in up trials, contrasted with down trials using a randomization t-test, for three of the four participants, where \* = p < 0.05.

showed no evidence of volitional control of the feedback signal upon completion of the training. This aspect of neurofeedback training is relatively unexplored, and the characteristics and predictors of learners and non-learners is an area requiring further empirical exploration.

One possible explanation for the difficulty in training source peak theta activity in these non-learners is that the source contained a spectral profile which included a low alpha component. The low alpha band shows basic attentional correlates and desynchronizes in response to task demands (for a review, see Klimesch, 1999). Individually determined peak theta for the two non-learners was slightly higher than in the other two participants. As the training frequency for these participants was adjacent to the low alpha band, spectral power associated with alpha activity may have leaked into peak theta estimates. Indeed, the functional source on which the feedback was based showed a clear alpha peak in the original group data around 10– 11 Hz during a resting baseline (see White et al., 2012). Thus, a reduction in low alpha activity during training could have confounded efforts to increase peak theta activity for these nonlearners during neurofeedback trials, as the increased attention associated with the training periods would be reasonably expected to reduce low alpha activity. Whilst the BSS-derived training source is argued to be functionally and spatially specific, this does not preclude spectral activity across multiple frequencies. Thus, the lack of learning in these participants may have been a result of contamination of the desired peak theta signal from the adjacent low alpha band. This may be particularly relevant for Participant A, who showed significant reductions in the feedback signal during feedback when contrasted with baseline. The findings of the present study further emphasize the need to account for fluctuations in a number of frequency bands beyond the training band in neurofeedback research.

As this study represents a preliminary investigation into BSSbased neurofeedback, interpretation of findings remain limited by the design implemented and small sample size. Neurofeedback research is increasingly utilizing experimental designs which incorporate control conditions to allow for stronger evidence of efficacy and specificity, but also the feasibility of learning. In examining efficacy and specificity of clinical and experimental neurofeedback protocols, the use of non-contingent feedback, variable feedback contingencies (e.g., Hoedlmoser et al., 2008), or alternate target bands for neurofeedback control groups have been used to minimize concerns to do with comparable therapist contact, placebo effects, and other non-specific effects of training. Having provided this preliminary evidence of the feasibility of training a BSS-based source, future work may further explore the validity and potential applications of neurofeedback training using controlled designs which can target sources identified from functional or resting state brain processes.

#### **APPLICATIONS OF BSS-BASED NEUROFEEDBACK**

Developing neurofeedback methods are increasingly refining the spatial and functional specificity with which the training neural substrate can be targeted. Using a BSS-derived source as the feedback source offers advantages in this respect, and the findings of the present study support the feasibility of such methods. Thus, future applications of BSS-based neurofeedback are only limited by the extent to which a stable estimate of the target source can be obtained. As BSS-derived sources can be identified from functional or resting activity, future research can extend the present work by applying neurofeedback protocols based on BSS components based on manipulating functional activity such as that described herein, or training problematic BSS components identified in clinical applications. The potential for clinical applications of BSS-based neurofeedback has recently been explored by Kop ˇrivová et al. (2013), who tested neurofeedback training of a medial frontal EEG source identified as showing abnormally elevated low-frequency activity in Obsessive-Compulsive Disorder patients compared to healthy controls. This BSS-based neurofeedback training was associated with greater clinical improvement than a sham feedback control group and non-significant trends towards a shift in the trained frequency band, however, clinical improvement was not associated with EEG changes. In demonstrating the capacity for regulation of a task-derived BSS source, this study supports the use of BSSderived sources in neurofeedback applications, as task-related decompositions such as that used to derive the feedback source herein can be considered stable enough to target outside the context of the specific task from which they were based. As such, experimental BSS-based neurofeedback may train functionally and spatially isolated brain sources, facilitating the study of these sources as the independent variable, in turn providing a powerful method for studying the functional significance of functionally and spatially isolated endogenous brain processes. Using BSS-based neurofeedback may enhance the success of neurofeedback protocols, reducing the influence of artifacts, and providing optimal conditions for training of the target activity.

#### **CONCLUSIONS**

The research described herein builds upon the increasing use of BSS methods in the study of brain function. Adopting BSS methods across a range of methods for recording brain activity, including fMRI and EEG, has offered novel insights into brain function (eg. Greicius et al., 2004; Onton et al., 2005). These findings provide preliminary evidence of the feasibility of sourcebased neurofeedback training derived from BSS, future work may explore further validation and potential applications of BSSbased neurofeedback training, targeting sources identified from functional or resting state brain processes.

#### **ACKNOWLEDGMENTS**

This work was partially supported by a research grant from the Barbara Dicker Brain Sciences Foundation.

#### **REFERENCES**

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 17 April 2014; accepted: 09 October 2014; published online: 22 October 2014*. *Citation: White DJ, Congedo M and Ciorciari J (2014) Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation. Front. Behav. Neurosci. 8:373. doi: 10.3389/fnbeh.2014.00373*

*This article was submitted to the journal Frontiers in Behavioral Neuroscience*.

*Copyright © 2014 White, Congedo and Ciorciari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms*.

# Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting

#### **Stefanie Enriquez-Geppert 1,2\*, René J. Huster 1,3 , Christian Figge2 and Christoph S. Herrmann1,3,4**

1 Experimental Psychology Laboratory, Department of Psychology, European Medical School, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

#### **Edited by:**

Sergio Ruiz, Pontificia Universidad Catolica de Chile, Chile

#### **Reviewed by:**

Andrew A. Fingelkurts, BM-Science-Brain and Mind Technologies Research Centre, Finland Silvia Erika Kober, University of Graz, Austria

#### **\*Correspondence:**

Stefanie Enriquez-Geppert, Experimental Psychology Laboratory, Department of Psychology, European Medical School, Carl von Ossietzky University of Oldenburg, A7, Ammerländer Heerstr. 114–118, 26129 Oldenburg, Germany e-mail<EMAIL_ADDRESS>Frontal-midline (fm) theta oscillations as measured via the electroencephalogram (EEG) have been suggested as neural "working language" of executive functioning. Their power has been shown to increase when cognitive processing or task performance is enhanced. Thus, the question arises whether learning to increase fm-theta amplitudes would functionally impact the behavioral performance in tasks probing executive functions (EFs). Here, the effects of neurofeedback (NF), a learning method to self-up-regulate fmtheta over fm electrodes, on the four most representative EFs, memory updating, set shifting, conflict monitoring, and motor inhibition are presented. Before beginning and after completing an individualized, eight-session gap-spaced NF intervention, the three-back, letter/number task-switching, Stroop, and stop-signal tasks were tested while measuring the EEG. Self-determined up-regulation of fm-theta and its putative role for executive functioning were compared to an active control group, the so-called pseudo-neurofeedback group. Task-related fm-theta activity after training differed significantly between groups. More importantly, though, after NF significantly enhanced behavioral performance was observed. The training group showed higher accuracy scores in the three-back task and reduced mixing and shifting costs in letter/number task-switching. However, this specific protocol type did not affect performance in tasks probing conflict monitoring and motor inhibition. Thus, our results suggest a modulation of proactive but not reactive mechanisms of cognitive control. Furthermore, task-related EEG changes show a distinct pattern for fmtheta after training between the NF and the pseudo-neurofeedback group, which indicates that NF training indeed tackles EFs-networks. In sum, the modulation of fm-theta via NF may serve as potent treatment approach for executive dysfunctions.

**Keywords: frontal-midline theta, neurofeedback, cognitive enhancement, executive functions, EEG, proactive and reactive control**

# **INTRODUCTION**

Time-frequency analyses of electroencephalographic (EEG) recordings reveal synchronous processes of neural networks known as neuronal oscillations. Nowadays, these neural oscillations are considered to provide a linkage of neural activity with behavior and thought. As such, they are supposed to coordinate neuronal spiking between and within brain circuits (e.g., Buzsáki, 2006; Buzsáki et al., 2013), whereby different neural oscillations may appear at the same time and interact with each other in a hierarchical way in order to implement perception and cognition (Herrmann et al., 2004; Canolty et al., 2006; Basar and Güntekin, 2008; Fingelkurts and Fingelkurts, 2014).

Regarding higher cognitive functions, frontal-midline (fm) theta oscillations are of particular interest. Fm-theta oscillations are recorded over fronto-medial brain regions at frequencies between 4–8 Hz and are suggested to be generated in the midcingulate cortex (MCC; Mitchell et al., 2008; Cavanagh and Frank, 2014), a highly interconnected brain structure (Beckmann et al., 2009; Vogt, 2009) that is part of the superordinate cognitive control network (Niendam et al., 2012). The MCC is known to be crucially involved in executive functioning (Cavanagh et al., 2012), which enables goal directed behavior (e.g., Lezak, 2012). Increases of fm-theta power have been associated with enhanced coupling between neuronal spikes and the phase of the population theta cycle, and thus are suggested to organize neural processes during decision points where executive functioning is needed and information is integrated to inform action selection (Cavanagh and Frank, 2014). Enhanced cognitive processing is accompanied with increases of fm-theta (Mitchell et al., 2008), specifically in tasks involving working memory (WM; Mitchell et al., 2008) and executive functions (EFs; Nigbur et al., 2011). In addition, fm-theta activity has been related to efficient WM maintenance (Tóth et al., 2014), and increases of fm-theta activity during task processing have been shown to predict successful behavioral performance

2 Karl-Jaspers Clinic, European Medical School Oldenburg-Groningen, Oldenburg, Germany

3 Research Center Neurosensory Science, Carl von Ossietzky University, Oldenburg, Germany

4 Center for Excellence 'Hearing4all', Carl von Ossietzky University of Oldenburg, Oldenburg, Germany

(Sederberg et al., 2003) and RTs in a Simon task involving conflict monitoring (Cohen and Donner, 2013). Correspondingly, the absence of fm-theta up-regulation, when executive functioning is required, has been reported to be associated with reduced performance (e.g., Donkers et al., 2011). In short, fm-theta has been proposed as a universal mechanism for EFs with the MCC acting as hub for the integration of relevant information (Cavanagh et al., 2012).

The possibility to modulate neural oscillations marks an important step beyond simply focusing on correlations between oscillations and cognitive performance. The feasibility to selfregulate endogenous neural activity has been nicely demonstrated in extracellular recordings for activity of single neurons (e.g., Olds and Olds, 1961), local field potentials (LFPs) in the animal model (e.g., Sterman et al., 1970), as well as for EEG activity measured on the scalp of humans (Kamiya, 1968). Nowadays the technique by which the modulation of neuronal activity is achieved in a reward-based fashion by giving feedback on the real-time status of the participants' brain activity who thereby learn to voluntarily control it, is termed neurofeedback (NF; Sherlin et al., 2011; Huster et al., 2014).  The modulation of neural oscillations by EEG-NF has been shown for diverse frequencies in association to different cognitive (sub) processes (see review of Gruzelier, 2014), for instance regarding enhanced upper alpha band and improved mental rotation (e.g., Hanslmayr et al., 2005; Zoefel et al., 2011), increased gamma-band activity and enhanced episodic retrieval (Keizer et al., 2010), or enhanced sensorimotor rhythm (SMR) and improved declarative learning (Hoedlmoser et al., 2008).

With respect to fm-theta oscillations, its basic modulability by NF has recently been demonstrated (e.g., Enriquez-Geppert et al., 2014). van Schie et al. (2014) designed a study to investigate effects of controlled fm-theta down-and up-regulation on WM performance. They demonstrated that the up-regulation led to increased, the down-regulation to attenuated performance. Concerning fm-theta NF training, positive effects on cognition have been shown for WM and attention (Wang and Hsieh, 2013). Notwithstanding this, effects of fm-theta modulation on EFs by NF remain to be elucidated.

The modulation of fm-theta by NF can be categorized within a broader framework of theta protocols. Generally, theta protocols can be performed at diverse electrode positions thereby focusing different neural networks (see review Gruzelier, 2014). A subdivision of protocols pertains to NF for theta up- or theta-downregulation. In case of the clinical application, down regulation of theta is most often combined with the modulation of other frequencies (e.g., theta-beta training, e.g., Gani et al., 2008; Arns et al., 2013), as for example seen with attention deficit hyperactivity disorder (ADHD). Here, EEG deviations have been observed during resting state compared to healthy controls, namely increased theta activity in context of beta activity (e.g., Chabot and Serfontein, 1996; Clarke et al., 2001). However, this deviation of theta measured at rest reflects a tonic condition that has to be dissociated from phasic theta responses, which in turn have been linked to specific cognitive functions. An important fact concerning the dissociation of tonic and phasic amplitude is the notion that successful behavior seems to be related to both of these EEG phenomena as good performance is related to a decrease in tonic and increased phasic theta activity (see review, Klimesch, 1999).

Taken together, NF seems perfectly suited to induce positive effects on EFs. This issue becomes imperative since EFs are crucial for success in daily life as they mediate learning processes (St Clair-Thompson et al., 2010), the control of emotions (e.g., Fikke et al., 2011), and predict academic achievement and social functioning (e.g., Miller et al., 2012b). Furthermore, age-related declines in EFs have been reported to lead to reduced success in everyday activities (Vaughan and Giovanello, 2010). Moreover, disturbances of EFs are associated with neurocognitive, and psychiatric impairments (e.g., Elliott, 2003).

The aim of this study is therefore to investigate the behavioral and neuronal effects of fm-theta NF on EFs. We set up an eight session personalized NF training for up-regulation of fm-theta and compared the achieved training effects to those of a pseudo NF training constituting an active control condition. A pre/post-test training design was implemented measuring the four most independent and representative EFs (Miyake et al., 2000; Miyake and Friedman, 2012) namely memory updating, set shifting, conflict monitoring, and motor inhibition, experimentally operationalized by a three-back, a number-letter task-switching, a Stroop-, and a stop-signal task. Concurrently, the participants' EEG was recorded. We hypothesized that NF for self-up-regulation of fm-theta would lead to enhanced performance in EFs at the behavioral level, and would also translate to enhanced fm-theta during executive functioning in the respective tasks compared to the pseudo NF group.

# **MATERIAL AND METHODS**

#### **PARTICIPANTS**

40 students (19 men; mean age: 24.8, standard deviation: 3.3) participated in this study. Participants were pseudo-randomly assigned to the experimental (NF; *n* = 19, 8 men; mean age: 23.8, standard deviation: 2.7) or the active control group (pseudo-NF, *n* = 21, 11 men; mean age: 25.8, standard deviation: 3.8) in order to balance the groups according to age, education level and gender. All subjects were right handed, as indicated by the Edinburgh Handedness Inventory (Oldfield, 1971), and had normal or corrected to normal vision. None of them reported a history of psychiatric or neurological disorders. Before the experiment, all participants gave written consent to the protocol approved by the ethics committee of the University of Oldenburg. For study participation, participants were rewarded with 8 e per hour. The study was conducted in accordance with the Declaration of Helsinki.

#### **PRE-POST TRAINING DESIGN**

To measure the effects of up-regulation of fm-theta on EFs, an individualized and adaptive eight session NF training was performed on consecutive working days within 2 weeks, comparing the effects to an active control group, the so-called pseudo-NF. One day before and one day after finishing the NF training, an EFs test-battery, assessing the four most important and independent EFs. The study was carried out at the Carl von Ossietzky University of Oldenburg.

#### **Executive function test-battery, EEG recordings and preprocessing**

In the first task of the EFs test-battery (see **Figure 1**), the visual three-back task, participants were presented with two kinds of letter sequences, the three-back and the zero-back sequence. In the three-back sequence, participants were instructed to respond via button press (using the right index finger), whenever a letter had already been presented three trials before the current one (three-back target condition). In the zero-back condition subjects were instructed to simply respond whenever a letter matched a target letter presented at the beginning of the letter sequence. In all other cases, participants were asked not to react. There were ten three-back and nine zero-back sequences that were presented in an alternating fashion with 24 white letters presented on a black background for 1000 ms, and followed by a fixation cross with a duration of 1000 ms (total trial length: 2000 ms; total trial number per sequence: 24; target numbers per sequence: 8). It is assumed that the three-back condition requires memory updating processes whereas the zero-back condition does not.

The number-letter task-switching consisted of number-letter pairs that were presented on a colored background. Participants were instructed to either classify the numbers (in even or odd numbers) or the letters (in vowels or consonants) by a right or left index button press. The classification depended of the specific background color (red, orange, pink vs. green, blue, and turquoise). Two versions with different number-color and letter-color assignments were utilized. Letter-number pairs were presented for 2000 ms followed by a white fixation cross presented for 1000 ms (trial length: 3000 ms). The task consisted of two parts. In the first part that included unmix-blocks, only the letters (unmix-block 1; 60 trials) had to be classified, followed by a second block during which only numbers (unmixblock 2; 60 trials) had to be processed. In the second part that included mixing blocks, participants had to switch between both classification types (trial number: 234; switches: 70). In contrast to the unmix-blocks, the mixing blocks required the switching between the two categorization tasks, thus it involves set-shifting.

The Stroop task contained the presentation of the colorwords blue, green, yellow, and red, which were either presented congruently in colors matching the word meaning (e.g., the color-word "blue" presented in blue) or incongruently in an unmatching fashion (e.g., the color-word "blue" presented in red). Participants were instructed to indicate via a right or left index button press the presentation color, but not the word-color. A trial started with a fixation cross presented for a randomized duration of 1200–1400 ms, and followed by the color word for 500 ms. Afterwards a second fixation cross was presented for a randomized duration of 100–500 ms. Trials were separated by an inter-trial interval (ITI) of a randomized duration of 400–800 ms (total trial number: 128, number of incongruent trials: 64). Regarding the involved EFs, conflict-monitoring is supposed to detect the conflict associated with incongruent trials relative to the congruent condition.

In the visual stop-signal task, left- or rightward pointing arrows were presented that changed their color during presentation time from purple to green, blue, or orange. Participants were instructed to press the button of a two-button box according to the arrow direction either with the right or the left index in a fast and accurate way directly after beginning of the arrow presentation (go-trials). However, a specific color change (for instance from purple to orange) was indicating that participants had to abort their initiated response (stop-trials). Two versions with different color-response assignments were used. The timing of the color-change was adjusted dynamically via a certain stimulus onset asynchrony (SOA; Logan et al., 1997) such that participants could stop their response in 75% of the stop-trials. Every trial with a length of 2 s started with a fixation cross that was randomly set with a duration of 300–600 ms. Then, the arrow was presented with an initial duration of 250 ms (this SOA was adjusted by adding 50 ms after every second correct or subtracting 50 ms after every failed stop trial to reach the intended error rate) before the color changed. The color change remained on screen for another 200 ms. The trial was ended with the presentation of a fixation cross (total number of trials: 300; number of stop trials: 100). The stop-trials of this task require motor inhibition as EFs, in contrast to motor execution during go-trials.

Before each task, a short exercise period was implemented to familiarize participants with the task requirements and the presentation. The four tasks each had a duration of roughly eight to 18 min. All four EFs tasks were implemented using the Presentation software1 .

The measurements of the EFs test-battery, as well as the NF training, were conducted in an electrically shielded and sound attenuated room. EEG recordings were done using the Brain Vision Recorder software and a Brain Amp EEG amplifier (Brain Products GmbH, Filching, Germany). Electrode impendences were kept below 5 k for the continuous EEG recording, data was sampled at 500 Hz, and a low-pass online filter of 250 Hz was used. During the measurement of all four EFs, EEG activity was recorded from 32 Ag/AgCl electrodes (Easycap, Falk Minov Services, Munich, Germany), placed in accordance with the extended version of the international 10–20 system, with a nose electrode as online reference, and an electrode attached beneath the right eye for recording the electrooculogram (EOG) and quantification of ocular artifacts.

The offline-preprocessing was performed using the EEGLAB software2 and included the following steps. First, data were lowpass (80 Hz) and high-pass filtered (0.5 Hz), and then downsampled to 250 Hz. To correct for ocular artifact, the Infomax ICA algorithm (Bell and Sejnowski, 1995; Makeig et al., 2004) was subsequently used on the continuous data. Blink-related independent components (ICs) were identified by comparing the IC activity to the eye blink artifacts in the EOG. The corresponding topographical maps of putative ICs had to show a frontal distribution. Eye-blink related ICs were then excluded from back-projection to the EEG channels. Afterwards, stimuluslocked epochs were computed comprising an interval of −1250 to +1250 ms corresponding to the stimulus presentation. Trials were then baseline-corrected. To correct for residual artifacts, a semiautomatic correction procedure was used based on the epoched data. Thereby single trials that crossed a self-set threshold (set to 60 µV) were marked automatically for visual inspection and rejection. Thereby 5.3 epochs were rejected on average per condition and task. Incorrect responses were discarded from further analyses.

Afterwards, so-called event-related spectral perturbations (ERSPs) were calculated for each task that represent logtransformed changes of power in dB relative to the baseline (Delorme and Makeig, 2004). For this time-frequency decomposition, a sinusoidal wavelet transform, using an increasing number of cycles with increasing frequency was used (range: 1–50 Hz; starting with 1 cycle at 1 Hz and increasing by 0.5 Hz per frequency; using 300 frequency steps). To visualize power changes relative to the pre-stimulus activity, the average power across the trials was divided by the frequency specific baseline values separately for each frequency. Mean ERSP values were calculated for the fm ROI over electrodes Fz, FC1, FC2, Cz.

#### **Individualized and adaptive neurofeedback training**

For up-regulating fm-theta by NF, eight 30-minutes training sessions were conducted. Because it was shown that fm-theta exhibits large inter-individual variability, but high intra-individual stability (e.g., Näpflin et al., 2008), a procedure for the detection of the individual fm-theta peak was used. Thus, the dominant individual fm-theta frequency peak was estimated based on the average of four peaks (+/−1 Hz), detected by the ERSPs computed from the four EF tasks. Fm-theta was shown to fall

1Version 14.8, www.neurobs.com

2 freely available from http://www.sccn.ucsd.edu/eeglab/

into the time range of the N200/P300 complex (Huster et al., 2013), as well as fm-negativities (Huster et al., 2013), and furthermore to have a maximum at Fz (Ishihara et al., 1981; Cavanagh et al., 2012). Thus, the individual fm-theta frequency was determined from electrode Fz, between 4–8 Hz in the corresponding ERP time range of conditions requiring EFs. Each of the eight NF sessions furthermore consisted of six five-minute blocks of NF with self-paced breaks in-between. Before and after these blocks, a five minute start/end-baseline was measured to assess resting state activity. Frequency spectra were computed using a fast Fourier transformation and a hamming window for data segments of 2 s, shifted along the data in steps of 200 ms. For NF, the software NF Suite 1.0 (Huster et al., 2014) was used.

In the following, all five steps of the basic set-up for NF (Huster et al., 2014), forming the real-time processing pipeline, are described. First, the data acquisition was based on five electrodes placed at positions Fz, FC1, FC2, FCz, and Cz, thus covering the fm-brain region, with the nose as reference, and Fp1 and Fp2 for monitoring ocular activity. Before the start-baseline measurement, an EOG calibration method (3 min) was implemented that calculates the subject-specific, artifact-associated frequency band. This was used for all following measurements for eye blink detection and rejection during further measurements (for details see Huster et al., 2014). The second step refers to the data online-preprocessing. A crucial feature of this step is the monitoring of artifacts, specifically that of eye activity. This technique is important to avoid falsely modulating eye- rather than actual brain activity, since the power of eye activity unfolds in several frequency bands. Thus, the subject-specific artifactassociated frequency band that was calculated in the EOG calibration measure was monitored. Whenever the mean amplitudes of a 2 s segment was higher than the subject-specific artifactassociated frequency band (minus one standard deviation), the segment was rejected and not used for feedback. Third, for feature generation and selection, the raw power value of each 2 s segment for the individualized fm-theta frequency was compared to the baseline power of the same frequency. Fourth, to feed back the extracted feature to the participant, a visual procedure was used in form of a visual display of a colored square (range: highly saturated red over gray to highly saturated blue, each with 40 color steps). The color saturation depended on the fm-theta activity changes, and was updated every 200 ms. The color red indicated an fm-theta power increase, blue a decrease, and gray either an eye blink or no power change relative to the start-baseline of the specific NF session. The feedback saturation scale enclosed 95% of the amplitude range, whereby values above 97.5% or below 2.5% were indicated by a maximum red or blue saturation. Fifth, participants were instructed to color the square as red and as often as possible. To do so, they received a list of strategies based on the NF literature and were encouraged to find own self-invented strategies.

Participants of the pseudo-NF were matched to participants of the actual NF group according to age and gender. During a NF block, they received a playback of the same session and block of their matched participant. Thereby, both groups received a similar visual stimulation. To enhance the credibility of the pseudo manipulation, participants of the pseudo-NF received real feedback of their own eyeblink activity, by haltering the feedback replay and introducing the gray color feedback as it is done in the actual NF group.

# **DATA ANALYSIS**

# **NF training effects**

To analyze the self-upregulation of the individual fm-theta (indfm-th) by NF, the relative change of the fm-theta amplitude was quantified for all training sessions during training blocks as change in percent relative to the values of the first training session. This procedure was chosen because it minimizes some of the inter-subject variability caused by unspecific effects such as amplitude variability due to the visual stimulation during feedback. Thus, this specific calculation has the advantage to compare fm-theta increases relative to a condition in which subjects are also up-regulating their brain activity, but with lower success, thereby making sure that the same system or process is engaged. However, as a drawback, it attenuates some of the relevant inter-group variance (by essentially removing training effects of the first session), therefore reducing the likelihood to find a group by session interaction. Correspondingly, a main effect is expected, as the dependent variable represents a difference measure, indicating that proper NF induces increased fm-theta not seen with the pseudo-NF training. Consequently, NF training effects were investigated by a repeated-measures ANOVA with the factors SESSION (2–8) and GROUP (NF vs. pseudo-NF group). These calculations and analyzes were also performed for the alpha (ind-fm-th + 2 Hz to ind-fm-th + 7 Hz) and beta (ind-fm-th + 7 Hz to ind-fm-th + 15 Hz) frequency bands. In case of violations of sphericity, Greenhouse-Geisser corrections were performed, and corrected *p*-values and -values are reported.

# **Data analysis: behavioral performance of executive function tasks**

To investigate transfer effects of NF training on cognitive performance, mean RT and mean accuracy of correct responses were calculated for all conditions of the four EFs tasks, namely for the three-back vs. zero-back conditions of the three-back task; the unmix vs. stay vs. switch conditions of the letter number task-switching task, the congruent vs. incongruent conditions of the Stroop-task, and the go vs. stop conditions of the stopsignal task. Extreme values exceeding the mean by more than 2.5 standard deviations were excluded for statistical analyses. To investigate performance gains after NF training, performance differences between pre- and post measurements were calculated for all the conditions of the four EF tasks. A stronger performance gain was expected in conditions requiring EFs. In other words, concerning the three-back task, performance gains are expected after NF in the three-back condition, because for this condition a higher load of memory-updating is involved compared to the zero-back condition (larger effects in NF compared to pseudo-NF training). Similarly, larger effects were expected in the stay and switch conditions compared to the unmix condition after NF-training relative to the pseudo-NF training. Likewise, stronger performance gains regarding conflict monitoring were expected after NF in the incongruent compared to the congruent condition of the Stroop task. Finally, behavioral performance gains were expected in the stop condition as reflected in reduced SSRT after proper NF. Thus, pre-post measurement differences were tested for by means of independent-samples *t*-tests comparing the training and the control group. To examine the range of significant results, Cohen's d (Cohen, 1988) was calculated for the pretest-posttest differences or the standardized mean difference in performance between pre- and post measurements (the difference was divided by the pooled standard deviation for the measurements occasions) for each group and corrected for small sample bias using the Hedges and Olkin (Hedges and Olkin, 1985) correction factor (d') only for significant results. As result, a pre-post measurement effect size (ES) of *d*' = 1 reflects a mean difference between pre-and post-measurements of one standard deviation.

## **Data analysis: effects of NF training on fm-theta in executive functions tasks**

For the evaluation of NF effects on fm-theta during the four EFs tasks, ERSP values were extracted for the time range used for the detection of the individual fm-theta peaks (three-back task: 100–300 ms, number/letter task-switching: 100–300 ms, Stroop: 220–500 ms, stop-signal task: 300–500 ms) and the theta frequency range of 4–8 Hz. These values were then averaged for each condition, task and subject. Then, difference scores were calculated by subtracting the values before from values after training: (pre-post difference of letter number task-switching, the congruent and incongruent pre-post-differences of the Strooptask, and the go and stop pre-post-differences of the stop-signal task. To investigate if these difference scores differed significantly between the NF and the pseudo-NF group, a MANOVA with the above-mentioned pre-post-difference scores as dependent variables and GROUP (NF vs. pseudo-NF group) as independent variable was run.

# **RESULTS**

#### **NF TRAINING EFFECTS**

NF training effects on the fm-theta amplitude are depicted in **Figure 2**. The repeated-measures ANOVA resulted in a main effect of SESSION (*F*(4.346,39) = 6.487, = 0.724, *p* < 0.001) showing that every training session let to a change in fm-theta selfupregulation. The further expected main effect was the GROUP (*F*(6,39) = 6.225, *p* < 0.05) effect, demonstrating the validity of the experimental manipulation resulting in stronger enhancements of activity in the NF group regarding fm-theta amplitude, starting already at the second training session when compared to the pseudo-NF group.

Regarding the alpha range, a trend for a main effect of SES-SION (*F*(6,37) = 8.173, *p* < 0.1) was observed, no further effects were detected. Similarly, within the beta frequency range also only a trend for the main effect of SESSION (*F*(6,37) = 3.157, = 0.308, *p* < 0.1) was observed.

#### **BEHAVIORAL PERFORMANCE IN EXECUTIVE FUNCTIONS TASKS**

The results of the pre- and post measurements (RTs and accuracy) are shown in **Figures 3**–**6** computed from correct responses of all conditions for the three-back, task-switch, Stroop-, and

**FIGURE 2 | shows frequency effects during NF. (A)** Depicts the means of fm-theta increases of the NF (blue lines) and the pseudo-NF group (green) within the eight training sessions (error bars represent standard error of mean). In **(B)** the frequency spectra are shown for the start baseline measure of the first session (in gray) and during the last training session (S8, black) for each group (left side: NF, right side: pseudo-NF group). Unspecific alpha effects are observed in both groups, whereas fm-theta is specifically increased in the experimental group.

**group (right) for each condition (three-back vs. zero-back) before and after the training intervention**. Significant differences (marked with a star) between accuracy enhancements (pre- post differences as depicted with curly brackets) were detected in the three-back condition between both groups showing stronger improvements after proper NF training.

stop-signal tasks for both training types (NF and pseudo-NF). Descriptively, these figures hint to behavioral changes, particularly in the three-back and task-switching tasks. Although these enhancements are more pronounced in the NF group, behavioral

**FIGURE 4 | Shows the behavioral results of the task-switching (mean RT and standard error of mean) for the NF group (left) and the pseudo-NF group (right) for all three conditions (unmix, stay, switch) before and after the training intervention**. Significant differences (pre-post differences as depicted with curly brackets) are marked with stars and are depicted between both groups as training results. Stronger enhanced RTs can be observed for the NF group in the stay and switch condition.

changes appear also in the pseudo-NF group. However, any study that is using a pre-post design has to expect repetitionrelated effects that may simply arise from repeatedly testing a task. Such unspecific repetition-related effects are particularly well known in behavioral training studies and are found in all subjects, for instance in task-switching (e.g., Karbach and Kray, 2009) or three-back tasks (e.g., Schneiders et al., 2011). Therefore, the utilization of a control group is urgently needed

to dissociate such repetition-related or non-specific effects from true enhancements of cognitive functions, which in turn are reflected in stronger improvements in the intervention group than in the control group (Campbell and Stanley, 1963; Oken et al., 2008).

Concerning the three-back task, the expected stronger increases in accuracy was statistically confirmed in the three-back condition (*t*(38) = 1.786; *p* < 0.05) after proper NF training when compared to the pseudo-NF training; such effects were absent in the zero-back condition (see **Figure 3**). These results were supported by the pretest-posttest ES. ES were larger after NF training (three-back: *d*' = 1.059) than after pseudo-NF training (threeback: *d*' = 0.346). For the zero-back condition, the following ES are reported (NF training: *d*' = −0.076; pseudo-NF training: *d*' = −0.494).

I. As expected, stronger RT decreases were observed in the switch condition (*t*(38) = 1.966; *p* < 0.05), and in the stay condition (*t*(38) = 1.695; *p* < 0.05) after proper NF training as compared to the pseudo intervention (see **Figure 4**). No differences were found for neither the unmix condition (*t*(38) = 1.036; *p* = n.s.) between both training types nor for accuracy scores (switch: *t*(38) = 1.633; *p* = n.s.; stay *t*(38) = −1.651; *p* = n.s. unmix: *t*(38) = −0.388; *p* = n.s.). These results were supported by the pretest-posttest ES. For both, switch and stay conditions, EF were larger after NF training (switch: *d*' = 1.67; stay: d' = 1.09) than after pseudo-NF training (switch: *d*' = 0.582; stay: *d*' = 0.649). However, no differences in performance gains were shown in accuracy (switch: *t*(38) = 1.633, *p* = n.s.; stay: *t*(38) = −1.651, *p* = n.s.). ES for the unmix condition were the following, NF training: *d*' = −0.408 and pseudo-NF training: *d*' = −0.063, (the ES for differences in accuracy scores are also given subsequently, NF training: switch *d*' = 0.607, stay *d*' = 0.718, unmix *d*' = 0.091; pseudo-NF training: switch *d*' = 0.879, stay *d*' = 0.929 unmix *d*' = 0.142 ).

II. Different from what was expected, no differences were observed when comparing NF to pseudo-NF training regarding the Stroop task, neither concerning RTs (incongruent: *t*(34) = 0.247, *p* = n.s.; congruent: *t*(38) = 0.142, *p* = n.s.) nor accuracy (incongruent: *t*(38) = 0.989, *p* = n.s.; incongruent: *t*(38) = 0.81, *p* = n.s.), see **Figure 5**. Similarly, low ES values were recorded for the NF and the pseudo-NF training (RTs differences in the incongruent condition: NF training: *d*' = 0.337 and pseudo-NF training: *d*' = 0.749; RTs differences in the congruent condition: NF training: *d*' = −0.469 and pseudo-NF training: *d*' = −0.143; accuracy differences in the incongruent condition: NF training *d*' = 0.846 and pseudo-NF training *d*' = 0.55; accuracy differences in the congruent condition: NF training: *d*' = 0.42 and pseudo-NF training: *d*' = 0.244).

III. Similarly, in the stop-signal (see **Figure 6**) task no differences were observed comparing the pre-post difference of the NF- and the pseudo-NF-training groups neither concerning SSRTs of the stop condition (*t*(38) = −1.558, *p* = n.s.) nor RTs of the go-condition (*t*(36) = 1.65, *p* = n.s.). Accordingly, low ES scores were registered: differences in the stop condition: NF training *d*' = 0.159, pseudo-NF training *d*' = 1.147; differences in the go condition: NF training *d*' = 0.159, pseudo-NF training *d*' = 0.147.

# **EFFECTS OF NF ON FM-THETA IN EXECUTIVE FUNCTION TASKS**

In the following, a descriptive overview will be given for each task and group before and after the training; thereafter the fmtheta effects will be reported, as computed within the multivariate analysis framework.

I. Irrespective of group, power enhancements can especially be observed in the delta and theta frequency range around 300 ms post stimulus onset, accompanied by power reductions in the beta and slight power enhancements in the gamma range in the threeback condition of the three-back task (see **Figure 7**), which are in agreement with those obtained by e.g., Pesonen et al. (2007). Slight frequency changes can be observed in both groups after training.

II. The ERSPs in the task-switching task before and after the training intervention are characterized by a distinct thetaalpha power enhancement at around 200 ms, accompanied by weaker effects with a similar time pattern in the beta and gamma frequency range with a duration of approximately 100 ms, in accordance with previous studies as Prada et al. (2014). In the subsequent time frame, the power enhancement of theta and delta remains stable and the beta power reduction (20 Hz) becomes apparent.

III. To turn to the ERSPs in the Stroop task, here theta and beta power enhancement effects are observed corresponding with prior finding in literature (e.g., Cavanagh et al., 2012) beginning at 180 ms, whereby the theta power enhancement becomes maximal at 300 ms and remains stable until the end of the analyses time window. Minor frequency changes can visually be detected after the training intervention for both groups.

IV. With respect to the stop-signal task, maximal theta power enhancements are detected starting at about 220 ms till 600 ms post stimulus, consistent with those reported in the review of Huster et al. (2013), these power enhancements are very similar before and after training.

V. To inspect training induced changes in fm-theta more closely, pre-post fm-theta difference scores, representing changes in fm-theta power after training compared to the initial power before training, are depicted in **Figure 7** (lower panels of A–D) for each EF condition of the four EFs task and groups (NF and pseudo-NF group). As can be seen in **Figure 7**, fm-theta is decreasing in the pseudo-NF group after the control intervention (an exception is the congruent condition in the Stroop task), whereas the NF group shows amplitude increases. An exception is the stop-signal task, in which the fm-theta activity was decreasing for both groups. Indeed, the training intervention led to significantly different fm-theta changes between the NF and the pseudo-NF group as indexed by the multivariate assessment that revealed a multivariate main effect of GROUP (Wilks' λ = 0.468, *F*(9,25) = 2.774, *p* < 0.05), meaning an increase of 0.0036 dB in the NF group and a decrease of −0.2122 dB in the pseudo-NF group over all conditions and tasks. Univariate analyses for the effects of the group type significantly predicted fm-theta changes in the three-back task, such as significant fm-theta reductions were observed in the zero-back condition (*p* < 0.05) in the pseudo-NF group. A trend (*p* < 0.1) was furthermore observed within the univariate analyses for the prediction of group type concerning fm-theta power changes in the incongruent condition.

# **DISCUSSION**

Since fm-theta has been proposed as neural "working language" of brain communication for EFs, the current study investigated the effects of an individualized and adaptive eight session NF training to up-regulate fm-theta compared to a pseudo-NF intervention by utilizing a pre/post-test training design, analyzing memory updating, set-shifting, conflict monitoring, and motor inhibition. As a result, it was shown that proper NF training led to facilitated memory updating and mental set shifting, two EFs relying on proactive control, but not to enhanced conflict monitoring and motor inhibition, two EFs that built on reactive control. Furthermore, analyses of neural effects after NF training demonstrated that learning to self-upregulate fm-theta during NF translated to fm-theta changes in tasks engaging EFs after proper training compared to the pseudo-NF intervention. In the following, aspects concerning the subtypes of EFs, the fm-theta NF protocols, and the neural transfer effects are discussed.

With regard to the dual nature of the findings, the differential NF training effects of this fm-theta NF protocol on EFs reflect the current qualitative distinction of cognitive control mechanisms into proactive and reactive control. Indeed, a dual mechanisms framework (DMS; Braver, 2012) has been suggested that works by means of these two distinct operating modes that probably differ concerning their temporal dynamics and relevant neural networks. As such, within the DMS, proactive control is conceptualized as an anticipatory mechanism, actively maintaining task goals that serve as a source of top-down bias, thus supporting facilitated processing of expected events with a high cognitive demand before they actually occur. For instance, in the threeback task, subjects can process upcoming targets by subvocally repeating the sequence they have to keep in WM, and comparing it with their mental representation of the target stimulus. In contrast, reactive control is conceptualized as a reactive bottomup mechanism that is recruited only when it is required, for instance when interference is detected by a conflict monitoring

system or when a stop-signal requires the inhibition of an initiated response as is the case with the Stroop and the stop-signal tasks.

Interestingly, although all EFs, regardless of whether they require proactive or reactive control mechanisms, recruit the superordinate cognitive control network (Niendam et al., 2012), the subtypes seem to rely on different sub-networks. Menon (2011) distinguishes the central executive network, a frontoparietal system, anchored in the dorsolateral prefrontal cortex (DLPFC) and lateral posterior parietal cortex (PPC), crucially involved in actively maintaining task goals, from the salience network, a cingulate-frontal-opercular system, reacting to detected task events. Similarly, Dosenbach et al. (2008) proposes a dualnetwork architecture for EFs based on graph analytic methods, differentiating the fronto-parietal from a cingulo-opercular network, implementing proactive and reactive cognitive control, respectively.

From the lack of behavioral effects in the Stroop- and stopsignal tasks, one might be tempted to conclude that fm-theta is not necessarily crucial for the implementation of reactive control. Yet, the current fm-theta protocol of this study might have primarily targeted fm-theta used in the proactive control network and might less effectively address the network implementing reactive control, as fm-theta might primarily affect the detection signals generated in the MCC and less the resolution activity or the compensatory mechanisms processed in other brain regions of the EFs network (Botvinick et al., 2004). Indeed, fm-theta is thought to enable the transmission of information over different cortical brain areas by entraining activity in disparate neural systems (Cavanagh and Frank, 2014). More precisely, the properties of theta oscillations showing high-amplitude-low-frequency modulations denote an ideal neural parameter for neural organization over distal brain regions (Buzsáki and Draguhn, 2004). Theta phase synchrony between the MCC and distal brain regions has been observed in different studies on EFs, such as the study of Cohen and Cavanagh (2011), who reported of single-trial phase synchrony between the MCC and lateral prefrontal brain areas that was modulated by RTs during conflict monitoring. Nigbur et al. (2011) presented enhanced synchrony in the theta range between frontomedial and lateral frontal electrode sites, interpreted as cooperated work to allocate control during conflict monitoring, as well as between fronto-medial electrode sites with those over the contra-lateral motor area during conflict monitoring, possibly reflecting the renewed need of response selection during conflicts. Just recently, Oehrn et al. demonstrated by means of intra-cranial recordings that fm-theta originated from MCC as conflict detection signal, causally leading to entrained theta in the DLPFC, and finally accomplishing a coupling between DLPFC-gamma power and MCC oscillations for conflict resolution (Oehrn et al., 2014). Ultimately, accounting for the dual nature of EFs provides potentially new avenues for more elaborative feature extraction procedures concerning fm-theta NF protocols.

Apart from the beneficial behavioral effects of proactive control, the current study revealed an overall alteration of fmtheta in EF-tasks after proper NF training as compared to the active control intervention. This finding generally demonstrates that the targeted EF network has been affected by fm-theta NF. However, one would have expected EF-specific fm-theta effects in the proactive tasks to be paralleled by the above described behavioral effects. When looking more specifically at the neural effects, this does not seem to be the case, since results demonstrate that fm-theta effects between the proper NF group and the active control group are detected in the non EF-condition of memory updating, whereas no differences in fm-theta changes were found between both groups with task-switching. Although both the behavioral and the neural findings are interesting in itself, one should keep in mind that only one of the possible neural parameters, namely the fm-theta amplitude, has been assessed. It is conceivable that the fm-theta NF training could have influenced other neural parameters, such as coherence, frequencycoupling, or even myelination, paralleling the behavioral effects in proactive control in a more consistent way. The behavioral effects observed in proactive control could also correspond to structural changes within axonal pathways of relevant networks (e.g., changes in the integrity of white matter or the velocity of conductivity). In fact, glance at the EEG-NF literature indicates that the neural mechanisms underlying NF and putative transfer effects to neurocognition are still not well understood. Little is known about which specific network elements are changed and how the elements are altered due to NF. Behavioral performance increases aren't necessarily associated with increases in neural activity; rather, reduced neural activity may also result from stimulus and task repetition (see review Grill-Spector et al., 2006). Furthermore, associations between behavioral task proficiency and neural activity after training do not necessarily have to follow a linear function. For example, domain-proficient relative to domain-naive participants may show lower neural activity with simple tasks, but stronger neural processing with higher task difficulty (e.g., Prat and Just, 2011; Dunst et al., 2014). Hence, complex interactions of repetition-related performance changes, training-induced changes in neural responding and behavior, and task difficulty may exist.

Those functional and structural studies, on neural mechanism mediating behavioral improvements, undertaken so far either focus: (a) on immediate and long-lasting functional EEG effects, (b) on immediate fMRI effects and network activity; and finally (c) on long-lasting structural effects found in white matter pathways, usually examined via diffusion tensor imaging (DTI). For instance, Egner and Gruzelier (2001, 2004) were among the first to study neural changes induced by NF by using a protocol to increase activity in the 12–18 Hz frequency range. In their study EEG training effects were accompanied by improved attention. More importantly, after NF an increase of the oddball P300 was detected and interpreted as indicator for improved neural integration of relevant environmental stimuli. In order to investigate immediate neural effects after NF, Ros et al. (2010) examined possible changes of motor evoked potentials (MEP) by means of transcranial magnetic stimulation (TMS) to assess variations in the strength of neurotransmission from the motor cortex to the muscle. By using two types of TMS protocols (single vs. paired pulses), they could dissociate increases in cortico-spinal excitability (CSE), short-interval intra-cortical inhibition (SICI), and intra-cortical facilitation (ICF). This way, Ros et al. (2010) demonstrated that down-regulation of alpha oscillations, considered as a marker of cortical activation, indeed led to increased CSE and decreases of SICI. Similar increases of SICI were recently reported after completion of a 20 session theta/beta training (Studer et al., 2014). Here as well, a decreased P300 observed in the Attention Network Test (ANT) was considered to indicate increased efficiency of stimulus processing. Later on, Ros et al. (2013) conducted an independent component analysis (ICA) of brain activity recorded via fMRI during an oddball task to identify functionally coupled brain regions and to assess putative connectivity changes directly after a single session NF intervention. Immediately after alpha down-regulation, increased activation of brain regions belonging to the salience network were detected, namely within the MCC, the bilateral insular, thalamic, basal ganglia, cerebellar and ponto-mesencephalic regions. Structural changes after NF were assessed by DTI by Ghaziri et al. (2013) after a 3 months beta up-regulation training that resulted in enhanced sustained attention as evidenced in behavioral measures. Interestingly, microstructural changes were observed in three white matter pathways that are part of the sustained attention network, namely the cingulum bundle (a tract connecting the MCC with the DLPFC and the PPC), the anterior corona radiata (connecting the frontal cortex with the brainstem), and the splenium of the corpus callosum (a tract supporting inter-hemispheric processing), which seem not only to speak for enhanced neural transmission, but for increased myelination and faster conduction velocity (CV) underlying improved performance triggered by NF.

Regarding the relation between NF and myelination, participants learn to change oscillatory activity by recruiting and synchronizing the activity of neurons in a specific brain region, which again may affect myelination that preferable takes place at electrically active axons (e.g., Demerens et al., 1996; Ishibashi et al., 2006; Fields, 2010; Wake et al., 2011). Myelin plasticity has been furthermore suggested as an experience and learning dependent mechanisms (Wang and Young, 2014), which is thought to be relevant throughout adulthood (Miller et al., 2012a). Overall, myelin plasticity seems to crucially reflect an important lifelong process of relevance for oscillatory activity and cross-regional coupling (e.g., Pajevic et al., 2014; O'Rourke et al., 2014), and thus may also represent important mechanisms underlying NF-induced neural modulations.

Altogether, self-regulation of endogenous fm-theta increases capacities of EFs and represent a feasible neuroscientific training approach. Theta protocols may also be adapted according to the specific needs of the participants, with a focus on enhancing general EFs or proactive and reactive subtypes, in neurological and psychiatric disorders. Given that age-related changes of fmtheta seem to be related to EFs declines in age (Kardos et al., 2014), fm-theta NF protocols may also serve as an ideal tool to decelerate aging effects.

### **REFERENCES**

Buzsáki, G. (2006). *Rhythms of the Brain.* Oxford, NY: Oxford University Press.

Buzsáki, G., and Draguhn, A. (2004). Neuronal oscillations in cortical networks. *Science* 304, 1926–1929. doi: 10.1126/science.1099745

motor imagery related cortical activation. *PloS One* 7:e32234. doi: 10. 1371/journal.pone.0032234

network connectivity in schizophrenia. *Hum. Brain Mapp.* 34, 200–212. doi: 10. 1002/hbm.21427

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 27 August 2014; accepted: 17 November 2014; published online: 05 December 2014*.

*Citation: Enriquez-Geppert S, Huster RJ, Figge C and Herrmann CS (2014) Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting. Front. Behav. Neurosci. 8:420. doi: 10.3389/fnbeh.2014.00420*

*This article was submitted to the journal Frontiers in Behavioral Neuroscience*.

*Copyright © 2014 Enriquez-Geppert, Huster, Figge and Herrmann. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms*.

# Detaching from the negative by reappraisal: the role of right superior frontal gyrus (BA9/32)

#### *Rosalux Falquez 1 \*, Blas Couto2,3, Agustin Ibanez 2,3,4, Martin T. Freitag5, Moritz Berger 5, Elisabeth A. Arens 1, Simone Lang1 and Sven Barnow1*

*1 Department of Clinical Psychology and Psychotherapy, Institute of Psychology, University of Heidelberg, Heidelberg, Germany*

*2 Laboratory of Experimental Psychology and Neuroscience, Institute of Cognitive Neurology, Favaloro University, Buenos Aires, Argentina*

*3 UDP-INECO Foundation Core on Neuroscience, Diego Portales University, Santiago, Chile*

*4 Departamento de Psicología, Universidad Autónoma del Caribe, Barranquilla, Colombia*

*5 Department of Radiology, German Cancer Research Center, Heidelberg, Germany*

#### *Edited by:*

*Sergio Ruiz, Pontificia Universidad Catolica de Chile, Chile*

#### *Reviewed by:*

*Christian E. Salas Riquelme, Head Forward Rehabilitation Centre, UK Bryan Thomas Denny, Mount Sinai School of Medicine, USA*

#### *\*Correspondence:*

*Rosalux Falquez, Department of Clinical Psychology and Psychotherapy, Institute of Psychology, University of Heidelberg, Hauptstrasse 47-51, 69117 Heidelberg, Germany e-mail: rosalux.falquez@ psychologie.uni-heidelberg.de*

The ability to reappraise the emotional impact of events is related to long-term mental health. Self-focused reappraisal (REAPPself), i.e., reducing the personal relevance of the negative events, has been previously associated with neural activity in regions near right medial prefrontal cortex, but rarely investigated among brain-damaged individuals. Thus, we aimed to examine the REAPPself ability of brain-damaged patients and healthy controls considering structural atrophies and gray matter intensities, respectively. Twenty patients with well-defined cortex lesions due to an acquired circumscribed tumor or cyst and 23 healthy controls performed a REAPPself task, in which they had to either observe negative stimuli or decrease emotional responding by REAPPself. Next, they rated the impact of negative arousal and valence. REAPPself ability scores were calculated by subtracting the negative picture ratings after applying REAPPself from the ratings of the observing condition. The scores of the patients were included in a voxel-based lesion-symptom mapping (VLSM) analysis to identify deficit related areas (ROI). Then, a ROI group-wise comparison was performed. Additionally, a whole-brain voxel-based-morphometry (VBM) analysis was run, in which healthy participant's REAPPself ability scores were correlated with gray matter intensities. Results showed that (1) regions in the right superior frontal gyrus (SFG), comprising the right dorsolateral prefrontal cortex (BA9) and the right dorsal anterior cingulate cortex (BA32), were associated with patient's impaired down-regulation of arousal, (2) a lesion in the depicted ROI occasioned significant REAPPself impairments, (3) REAPPself ability of controls was linked with increased gray matter intensities in the ROI regions. Our findings show for the first time that the neural integrity and the structural volume of right SFG regions (BA9/32) might be indispensable for REAPPself. Implications for neurofeedback research are discussed.

#### **Keywords: emotion regulation, self-focused reappraisal, VLSM, VBM, right SFG**

# **INTRODUCTION**