2.1.

Participants

Sample sizes for the main analytic approaches were calculated using G*Power version 3.1, based on the recent study suggesting that if the goal of a NF study is to show that individuals can improve their behavior compared to baseline, effect sizes may be large.⁴² We calculated the sample size required for a repeated measures analysis of variance (ANOVA) with two groups and two behavioral task time-points to detect effects with large to very large effect sizes. Thus, assuming an ES of 0.4 and a predefined $\alpha$ of 0.05, a power of at least 0.80 results in a total sample size of 52, or 26 per group. Therefore, a total of $n=60$ healthy young students were enrolled in the present study. To control for the unspecific effects of the training procedures on the primary outcomes, the NF training was embedded in a randomized, sham-controlled between-subject experimental design. Participants were randomly assigned (30 participants in each group) to receive either real-time feedback (the experimental group) or sham feedback (the control group) during the training. Participants were randomized without stratifying for further variables. All participants provided written informed consent. The study had full ethical approval from the local ethics committee of East China Normal University.

2.2.

Experimental Protocols and Procedures

Participants were scheduled for six experimental sessions (as shown in Fig. 1), including one baseline session (referred to as Baseline), and one fNIRS-NF training session (referred to as fNIRS-NF), as well as four follow-up sessions (referred to as post, day2, day3, and week1). Baseline, fNIRS-NF, and post-training sessions were conducted within 1 day, and day2 and day3-training sessions were conducted 1 and 2 days after the fNIRS-NF training, respectively. The final assessment was scheduled 1 week after the fNIRS-NF training (week1). During all sessions, participants were administered an SWM task,⁴³ including forward and backward versions of the task. Specifically, participants were administered the forward task in all sessions while the backward task was administered at baseline, post, and day3-training sessions. Previous studies demonstrated that the backward visuospatial span task was more difficult and involved greater WM demands than the forward task.^44,45 Thus, we initially considered that a single training session might be more likely to improve the forward task, and also to avoid repetition-associated practice effects for the behavioral task, only the forward task was included in day2 and week1-training sessions. During baseline and post-training sessions, participants were also administered the digit cancellation test (D-CAT).⁴⁶ The D-CAT aims to evaluate focused attention and selective attention, which are associated with SWM.

Fig. 1

Experimental procedures for the six experimental sessions.

The State Anxiety Inventory⁴⁷ and the Beck depression inventory-II (BDI II)⁴⁸ were administered during the baseline training session to control for potential confounding effects of baseline between-group differences in psychopathological symptom load, while the positive and negative affect schedule (PANAS)⁴⁹ was administered on each experimental day to control for nonspecific effects of training on mood. To further control potential effects between the real and the sham training group, all participants were required to rate their training success (scale ranging from $-4$ to 4) and self-report the experimental condition (real feedback or sham feedback).

2.3.

Neurofeedback Training Protocols, NIRS Data Acquisition, and FEEDBACK

The fNIRS-NF training session included six runs of alternating rest and regulation blocks (four blocks per run, block duration being 25 s). The experimental group received real-time feedback from fNIRS channel 9 located over the right DLPFC, whereas the control group received feedback from one random participant who had previously undergone the experimental training (yoke feedback). The HR signals were assessed using an fNIRS System (ETG-7100, Hitachi Medical Corporation, Tokyo, Japan) at a sampling rate of 10 Hz. Locations of the optodes and feedback channel are displayed in Fig. 2. To accustom the participants to the equipment and to reduce variance related to trial-and-error attempts during the initial training run, all participants received real-time feedback in run1 and were required to explore suitable regulation strategies during the subsequent four regulate blocks. Participants were told that they could employ the strategies they discovered during the run1 or continue to find new strategies to increase brain activity during the subsequent training.

Fig. 2

One 3*5 probe patch (3-cm distance between the emitter and detector) was placed over the prefrontal regions of each participant. The middle optode of the lowest row of the patch was placed on the frontal pole midline point (FPz in the International 10 to 20 system, as the reference site). The middle column of the probe was aligned along the sagittal reference plane. The correspondence between the fNIRS channels and the measurement points was referred to in the positioning template provided by Jichi University⁵⁰ (as shown in Table S1 in the Supplementary Material). The other 4*4 probe patch was placed over the rTPJ of each participant, with the optode placed on P6, according to the International 10 to 20 system. The row of the probe was aligned along the sagittal reference plane. The correspondence between the fNIRS channels and the measurement points on the cerebral cortex was displayed based on the results of the virtual registration method, which had been confirmed by a multi-subject study of anatomical craniocerebral correlation,⁵¹ as shown in Table S2 in the Supplementary Material.

In line with previous fNIRS-NF studies, the feedback was based on the oxy-Hb signal.^26,52,53 Oxy-Hb signals were acquired by the ETG-7100 system, and online receiving, preprocessing, and real-time feedback of the oxy-Hb signal were computed using a novel real-time fNIRS-NF platform developed by the authors (MindGym, MG®, Patent No. CN202110959972.3). Specifically, to reduce the physiological noise caused by heartbeat, respiration, and other physiological processes, the raw oxy-Hb signal was smoothed using a 2 s moving average window. A baseline was calculated by taking the average of signals 10 s before the first block and was subsequently subtracted from the smoothed signal. The feedback signal was computed in real-time from the signal of the a priori target channel 9.

The online feedback was visually displayed to the participants in a similar way to a time series (see Fig. 1). The height of the line visualized the neural activity level in the chosen feedback channel 9 (the higher the height of the line, the higher the activity). Participants were asked to raise the line height as high as possible by regulating their brain activity by using mental strategies. We decided to embed the feedback in this way to help participants compare the training effects of different regulate blocks and increase training success. The conditions (rest/regulate) were visually presented to the participant via two background colors (see Fig. 1). The rest condition was indicated by a rainy background, and the regulate condition was indicated by a sunny background.

2.4.

Instructions for the Participants

Participants were informed that the purpose of the fNIRS-NF training was to examine whether they could learn to upregulate their brain activity. Given that explicit strategy instruction is not necessary for successful NF-assisted acquisition of neural regulation,⁵⁴ no explicit strategies for regulation were provided to the participants. Participants were instructed not to control the height of the line by physical means, such as breathing or head/body motion but rather to discover efficient mental control strategies.

2.5.

Behavioral Assessments

To assess whether fNIRS-NF of the right DLPFC leads to generalized improvements in cognitive control, participants were administered the SWM task and the D-CAT.

During the SWM task, participants were presented with sequences of visuospatial stimuli of varying lengths and repeated sequences of stimuli in the same (forward) or reversed (backward) order. The stimuli consisted of squares appearing at 25 different locations and were pseudo-randomized into 24 sequences. The sequence length varied between 3 and 10 stimuli and 3 sequences of each length in both the forward and backward tasks. Thus, the forward or backward task has 24 trials each in a single run, with each run lasting $\sim 5\mathrm{mins}$. Each cross was visible in the matrix for 2000 ms. The inter-stimulus interval was 800 ms, during which the matrix was empty. Each time a sequence ended; the participants repeated the squares by clicking on the correct locations with the mouse. In both tasks, the participants did not know the sequence length beforehand. The dependent variables were the total number of correctly recalled location sequences.⁴³ The order of stimulus length was randomized between the times of tasks.

The D-CAT sheet consisted of 12 rows of 50 digits, each containing 5 sets of the numbers 0 to 9 arranged in random order. Participants were instructed to search for target number(s) [the first involving a single target number (6); a second with two target numbers (9 and 4); and a third with three (8, 3, 7)] with a slash mark as quickly and as accurately as possible. During the second and third trials, it was stressed that all of the target numbers should be canceled without omission.⁴⁶ Each test sheet had a time limit of 60 s, and the sheets were presented one right after the other. The order of digits was randomized between the times of tasks.

2.6.

Offline Preprocessing and Analyses

The fNIRS raw data were preprocessed and analyzed using the NIRS toolbox in statistical parametric mapping (SPM)^55,56 and in-house scripts in MATLAB (The MathWorks, Inc.). During preprocessing, an NIRS analysis package was applied to correct head motion during training, and a second-order detrending was applied to remove the baseline drifts and low-pass filtering [Gaussian smoothing with full width at half maximum (4s)] was employed to remove high-frequency noise. A generalized linear model (GLM) approach was employed to model the task-related HR on the individual level, including the regulation periods modeled by a boxcar function while the rest periods were included as an implicit baseline. Serial autocorrelations in the GLM analysis were accounted for by employing pre-whitening as incorporated in NIRS SPM [AR (1) model, in line with recommendations by Huppert⁵⁷].

For the group-level analyses, beta estimates were obtained for each participant and channel. The primary outcome to determine the training success on the neural level was oxy-Hb change over the training runs in the target channel (right DLPFC; channel 9). To further control the unspecific effects of training or effects of mental effort on DLPFC activity, individual-level beta values from the target channel were subjected to group-level activation analyses comparing the experimental and the control group. Differences were considered significant using a channel-level threshold of $p<0.05$ (Bonferroni-corrected).

2.7.

Evaluation of Training Success and Primary Outcomes

Training-induced oxy-Hb changes in the DLPFC target channel served as the primary outcome to evaluate the training success on the neural level. First, channel-specific activity beta estimates were employed as dependent variables, and the effects of training were initially determined by employing mixed ANOVA models, including the between-subject factor Group (real feedback versus sham feedback) and the within-subject factor training Time (run2/run3/run4/run5/run6). Significant effects were explored by employing appropriate Bonferroni-corrected post hoc tests. Then channel-specific activity beta estimates were employed as dependent variables again, and the effects of training were further determined by employing one-way ANOVA models in each group including the within-subject factor training Time (run1/run2/run3/run4/run5/run6). Significant effects were also explored by employing appropriate Bonferroni-corrected post hoc tests.

The total number of correctly recalled location sequences in the SWM task was employed as dependent variables, and the effects of training on the behavioral level were determined by employing mixed ANOVA models, including the between-subject factor Group (real feedback versus sham feedback) and the within-subject factor time (baseline/post/day2/day3/week1). Significant effects were further explored by employing appropriate Bonferroni-corrected post hoc tests. The total performance in the D-CAT task was used as the dependent variable in mixed ANOVA models which included a between-subject factor of group (real feedback versus sham feedback) and a within-subject factor of time (baseline versus post). These mixed ANOVAs were performed to test the effects of training on the attention performance. Associations between neural and behavioral training success were explored by employing analyzing correlations between training-induced DLPFC oxy-Hb changes and behavioral indices in the experimental group. Statistical analyses were carried out using SPSS version 22.0 (IBM, Inc.).

2.8.

Up-regulation Strategies

After the training, all participants were asked to report the strategies they employed during the training. The reported strategies were qualitatively assessed by five independent raters (two males). To control for different strategies between the training groups, the frequencies of the reported regulation strategies were compared between the experimental group and the control group using the Pearson ${\mathrm{X}}^2$ test.⁵⁸

3.1.

Data Quality Control

While 55 out of the 60 participants completed the whole experiment procedure, initial examination of the primary neural outcome (oxy-Hb) and behavioral outcome (total number of correctly recalled locations in SWM task) data quality identified 3 participants as outliers (outliers were defined as subjects that deviated larger than 2 standard deviations from the mean, outliers were additionally confirmed using the SPSS outlier detection function). Consequently, data from these participants were excluded from all analyses resulting in a total of $n=52$ participants for the primary analysis ($n=27$, experimental group; $n=25$, control group).

3.2.

Assessment of Confounders

The demographic information and psychopathological load of all participants were reported in Table 1. A mixed two-way ANOVA with the factors time (baseline/post/day2/day3/week1) and group (real feedback versus sham feedback) and the dependent variable self-rated mood levels revealed a main effect of time ($F_{\mathrm{PANAS}\text{-}P(4200)}=2.97$, $p_{\mathrm{PANAS}\text{-}P}=0.021$, ${{\eta }^2}_{\mathrm{PANAS}\text{-}N}=0.023$; $F_{\mathrm{PANAS}\text{-}N(4200)}=11.63$, ${\mathrm{p}}_{\mathrm{PANAS}\text{-}N}<0.001$, ${{\eta }^2}_{\mathrm{PANAS}\text{-}N}=0.079$), but no main effect of group and no other interaction effects. These findings suggested that there were no unspecific effects of the training on mood that may interfere with cognitive improvement. Moreover, the training groups reported a comparable evaluation of their perceived training success ($t=1.399$, $p=0.168$, Cohen’s $d=0.388$) and experimental conditions ($t=1.059$, $p=0.295$, Cohen’s $d=0.294$).

Table 1

Demographic information and pretraining psychopathological symptom load in the two training groups, mean, and SDs (in brackets) are reported.

3.3.

Primary Behavioral Outcomes

Examining effects on the total number of correctly recalled locations in the forward tasks using mixed two-way ANOVA with the factors group (real feedback versus sham feedback) and time (baseline/post/day2/day3/week1) revealed a main effect of time ($F_{(4200)}=25.40$, $p<0.001$, ${\eta }^2=0.108$) and group ($F_{(150)}=6.16$, $p=0.016$, ${\eta }^2=0.073$), as well as a significant interaction effect between group and time ($F_{(4200)}=2.49$, $p=0.045$, ${\eta }^2=0.011$, see Fig. 3). Post-hoc comparisons demonstrated that the SWM performance significantly increased after the real feedback training (baseline < post, $p<0.001$; baseline < day2, $p<0.001$; baseline < day3, $p<0.001$; baseline < week1, $p<0.001$, two-tailed, Bonferroni-corrected, see Fig. 3). Concordant analysis of the sham group data also found the significant changes (baseline < day2, $p<0.001$; baseline < day3, $p=0.003$; baseline < week1, $p<0.001$, two-tailed, Bonferroni-corrected, see Fig. 3), which may be explained by the day-to-day practice effect. Importantly, directly comparing the training groups further revealed that the experimental group exhibited significantly better SWM performance during post/day3/week1-training sessions (ps < 0.031, two-tailed, Bonferroni-corrected, see Fig. 3) as compared to the control group; however, the group did not exhibit differences during baseline and day2-training sessions (ps > 0.108, two-tailed, Bonferroni-corrected), thus further confirming training success on the behavior level. However, the indices of the backward task revealed a main effect of time ($F_{(2100)}=36.16$, $p<0.001$, ${\eta }^2=0.087$) in the absence of the main effect of Group and the interaction effect (both $p\text{values}>0.5$).

Fig. 3

Changes in SWM forward task performance across all sessions. ∗$p<0.05$; ∗∗∗$p<0.001$. Error bars indicate the standard error of the mean (SEM).

Examining effects on the total performance in the D-CAT using mixed two-way ANOVA analyses with the factors group (real feedback versus sham feedback) and Time (baseline versus post) revealed the main effect of time ($F_{(150)}=22.61$, $p<0.001$, ${\eta }^2=0.023$) and the significant interaction effect between group and time ($F_{(150)}=4.29$, $p=0.043$, ${\eta }^2=0.004$, see Fig. 4) in the absence of the main effect of Group. Post-hoc comparisons demonstrated that performance significantly increased after the real feedback training (baseline < post, $p<0.001$, two-tailed). Concordant analysis of the sham training data did not yield significant results. However, a direct comparison between the groups did not reveal better performance in the experimental group as compared to the control group at baseline and post-training sessions (ps > 0.071, two-tailed).

Fig. 4

NF-induced behavioral changes in D-CAT task (Post versus baseline-training sessions).∗∗∗$p<0.001$. Error bars indicate the SEM.

3.4.

Evaluation of Neural Training Success

A mixed two-way ANOVA with the factors time (run2/run3/run4/run5/run6) and group (real feedback versus sham feedback) and the dependent variable DLPFC activity as measured by the beta values (oxy-Hb) from the target channel revealed a main effect of group ($F_{(150)}=10.44$, $p=0.002$, ${\eta }^2=0.103$, see Fig. 5), but no main effect of Time and no other interaction effects. A one-way ANOVA with the factors Time (run1/run2/run3/run4/run5/run6) and the dependent variable DLPFC activity as measured by the beta values (oxy-Hb) from the target channel in the experimental group revealed a main effect of time ($F_{(5130)}=3.96$, $p=0.002$, ${\eta }^2=0.132$), post-hoc comparisons demonstrated that activation in the target channel significantly increased throughout the real feedback training (run1 < run3, $p=0.045$; run1 < run5, $p=0.004$, two-tailed, Bonferroni-corrected, see Fig. 5). Concordant analysis of the sham training data did not yield significant changes in the target channel ($F_{(5120)}=0.488$, $p=0.785$, ${\eta }^2=0.02$), thus further confirming training success on the neural level.

Fig. 5

The oxy-Hb signal in the target channel significantly increased throughout the real feedback training runs but not during the sham NF training, *$p<0.05$. Error bars indicate the SEM.

3.5.

Association Between Behavioral Outcomes and Neural Training Success

Given that previous studies have reported significant associations between fNIRS-assessed DLPFC activity and SWM,^33,35–38 a subsequent correlation analysis examined the relationship between behavioral outcomes and neural training success. Results revealed that NF-induced activation changes in the DLPFC (changes in run5 > run1 activation in oxy-Hb in the target channel) were significantly and positively correlated with improved SWM performance (the difference in the total number of correctly recalled locations in the forward task between baseline and post-training sessions) in the experimental group ($r=0.406$, $p=0.018$; after excluding two outliers based on the in oxy-Hb, which showed higher neural learning success, $r=0.232$, $p=0.132$ one-tailed, see Fig. 6). Concordant analysis of the sham training data did not yield significant results: NF-induced activation changes in DLPFC were not correlated with changes in SWM performance ($r=0.243$, $p=0.121$, one-tailed, see Fig. 6).

Fig. 6

In the experimental group, stronger training-induced DLPFC activity changes (run5 > run1) were positively associated with better performance in the SWM forward task, and in the sham NF group, no association is observed. Outliers were marked by stars.

To further confirm the association between training effects, a mediation analysis using bootstrap was conducted to explore the relationship between fNIRS-NF treatment (real feedback versus sham feedback) and the SWM performance changes.⁵⁹ The simple mediation model can be formalized by the following equations:

This model reflects a causal sequence in which $X$ (a predictor) is postulated to affect $M$ (the mediator), and this effect then propagates causally to $Y$ (the dependent variable). The previous study argued that there need not be a significant $rXY$ in a proper mediation analysis and the only requirement for mediation is that the indirect effect $X\text{-}M*M\text{-}Y$ be significant.⁶⁰ The bootstrapping procedure a computer-intensive resampling technique first introduced by Efron (1979)⁶¹ has been proposed to test the significance of the indirect effect. The number of bootstrap samples for bias corrected bootstrap confidence intervals is 5000 and level of confidence for all confidence intervals is set to 95. Results revealed that the indirect effect (${\beta }_{\text{Treatment}\text{-}\mathrm{DLPFC}}*{\beta }_{\mathrm{DLPFC}\text{-}\mathrm{SWM}}$) was significant (Effect = 0.355, BootSE = 0.167, BootCI = [0.098, 0.765]), suggesting fNIRS-NF treatment significantly increased the DLPFC activity and that increased DLPFC activity led to improved SWM performance, see Fig. 7 and Table 2.

Fig. 7

The mediation effect of the DLPFC activity on the relationship between the treatment and the SWM. The effect of fNIRS-NF treatment on SWM was fully mediated by the DLPFC activation in the experimental group.

Table 2

Mediation analysis of the relationship between the fNIRS-NF treatment and the SWM during the forward task among participants in the experimental group.

In summary, these findings were consistent with other reports of training-driven modulations in prefrontal cortical activity, supporting training-induced neuroplasticity as the mechanistic basis of these behavioral training effects.

3.6.

Exploratory Analysis – Regional Specificity of the Training Effects

These improved SWM and attention performance results suggested that a common, underlying mechanism of cognitive control was enhanced by fNIRS-NF. To assess this further, we examined the regional specificity of the fNIRS-NF training by quantifying the effect of training on all PFC channels. A mixed ANOVA with the factors Time (run2/run3/run4/run5/run6), group (real feedback versus sham feedback), channel (channels 1 to 22), and the dependent variable brain activity as measured by the oxy-Hb beta values revealed a main effect of group ($F_{(150) }=5.86$, $p=0.019$, ${\eta }^2=0.04$), a main effect of channel ($F_{(8390) }=4.05$, $p<0.001$, ${\eta }^2=0.016$), and a channel × group interaction effect ($F_{(8390) }=2.03$, $p=0.043$, ${\eta }^2=0.008$), but no main effect of time and no other interaction effects. To control for multiple comparisons, a Bonferroni correction was used to account for all channels tested. Post-hoc comparisons demonstrated that significant differences between the experimental group and the control group were observed in the target channel 9 ($p=0.002$, two-tailed) and the adjacent channels 3-8, 13, and 17-18 (all ps < 0.04, two-tailed). For all other channels, the interaction effect was not significant (all ps > 0.055, two-tailed).

A one-way ANOVA with the factors time (run1/run2/run3/run4/run5/run6) and the dependent variable DLPFC activity as measured by the beta values (oxy-Hb) in the experimental group revealed a main effect of time in channels 9 and 13 (channel 9: $F_{(5130)}=3.96$, $p=0.002$, ${\eta }^2=0.132$; channel 13: $F_{(5130)}=2.33$, $p=0.046$, ${\eta }^2=0.082$), post-hoc comparisons demonstrated that activation in the channels 9 and 13 significantly increased throughout the real feedback training (channel 9: run1< run3, $p=0.045$, run1 < run5, $p=0.004$; channel 13: run1 < run5, $p=0.055$, two-tailed, Bonferroni-correction). Concordant analysis of other adjacent channels did not yield significant changes (all $\mathrm{ps}>0.093$), indicating that training specifically modulated activity in the right DLPFC (channels 9 and 13).

The effect of training on all TPJ channels was additionally explored. A mixed ANOVA with the factors time (run2/run3/run4/run5/run6), group (real feedback versus sham feedback), and channel (channels 1 to 24) and the dependent variable brain activity as measured by the oxy-Hb beta values revealed a main effect of channel ($F_{(8400)}=3.24$, $p=0.001$, ${\eta }^2=0.015$), but no other main effects and other interaction effects.

3.7.

Exploratory Analysis – Training Effect on the Frontoparietal Connectivity

In addition, we analyzed the correlation between frontal and posterior brain regions, a functional connectivity measure also associated with cognitive control.^7,62–66 Region-averaged oxy-Hb signals corresponding to the prefrontal cortex ($X_A$, averaged oxy-Hb signal from channels 9 and 13 in BA46, as shown in Table S1 in the Supplementary Material) and parietal cortex ($X_B$, averaged oxy-Hb signal from channels 11-12, 15-16, 18-24 in BA7, BA39, and BA40, as shown in Table S2 in the Supplementary Material) were calculated, and the Pearson’s correlation ($r$) between $X_A$ and $X_B$ was then calculated, and finally, a Fisher r-z transform was performed on the calculated Pearson’s correlation.³⁰

The group difference of NF-induced frontoparietal connectivity was not significant in run1 ($t_{(50)}=0.73$, $p=0.469$, Cohen’s $d=0.203$, two-tailed). Then a mixed ANOVA with the factors time (run2/run3/run4/run5/run6), group (real feedback versus sham feedback), and the dependent variable frontoparietal connectivity as measured by the oxy-Hb $z$ values revealed a main effect of group ($F_{(150)}=4.54$, $p=0.038$, ${\eta }^2=0.04$) in the absence of the main effect of time and the interaction effect, indicating that fNIRS-NF training of DLPFC modulated the frontoparietal connectivity.

3.8.

Regulation Strategies Reported by the Participants

In line with a previous study that evaluated regulation strategies during NF training with the present platform,⁵⁸ the content analysis identified five main clusters of upregulation strategies: (1) imagination, (2) experience recall, (3) concentration, (4) calculating, and (5) singing without a voice. Importantly, the groups did not differ in the regulation strategies employed during the training (Pearson $\chi 2$ test, $p=0.856$, two-tailed, Table 3), arguing against the confounding effects of different regulation strategies on the observed neural and behavioral between-group differences.

Table 3

Regulation strategies reported by the participants.