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International Journal of Neuropsychopharmacology logoLink to International Journal of Neuropsychopharmacology
. 2024 Jan 5;27(1):pyae003. doi: 10.1093/ijnp/pyae003

The Ability to Voluntarily Regulate Theta Band Activity Affects How Pharmacological Manipulation of the Catecholaminergic System Impacts Cognitive Control

Astrid Prochnow 1,2, Moritz Mückschel 3,4, Elena Eggert 5,6, Jessica Senftleben 7, Christian Frings 8, Alexander Münchau 9, Veit Roessner 10, Annet Bluschke 11,12, Christian Beste 13,14,✉
PMCID: PMC10810285  PMID: 38181228

Abstract

Background

The catecholaminergic system influences response inhibition, but the magnitude of the impact of catecholaminergic manipulation is heterogeneous. Theoretical considerations suggest that the voluntary modulability of theta band activity can explain this variance. The study aimed to investigate to what extent interindividual differences in catecholaminergic effects on response inhibition depend on voluntary theta band activity modulation.

Methods

A total of 67 healthy adults were tested in a randomized, double-blind, cross-over study design. At each appointment, they received a single dose of methylphenidate or placebo and performed a Go/Nogo task with stimuli of varying complexity. Before the first appointment, the individual’s ability to modulate theta band activity was measured. Recorded EEG data were analyzed using temporal decomposition and multivariate pattern analysis.

Results

Methylphenidate effects and voluntary modulability of theta band activity showed an interactive effect on the false alarm rates of the different Nogo conditions. The multivariate pattern analysis revealed that methylphenidate effects interacted with voluntary modulability of theta band activity at a stimulus processing level, whereas during response selection methylphenidate effects interacted with the complexity of the Nogo condition.

Conclusions

The findings reveal that the individual’s theta band modulability affects the responsiveness of an individual’s catecholaminergic system to pharmacological modulation. Thus, the impact of pharmacological manipulation of the catecholaminergic system on cognitive control most likely depends on the existing ability to self-modulate relevant brain oscillatory patterns underlying the cognitive processes being targeted by pharmacological modulations.

Keywords: EEG, Inhibitory control, MPH, MVPA, perception-motor integration

Significance Statement.

Methylphenidate (MPH) play a central role in the treatment of disorders such as ADHD. The manipulation of the catecholaminergic system using MPH frequently reveals a wide variability in its effects. In a nonclinical sample, we show that mechanisms of catecholaminergic modulation depend on the individual’s ability to voluntarily modulate theta band activity. The results reveal a new mechanism of constraint in pharmacological treatments using MPH, possibly important to predict whether individuals benefit from MPH treatment.

INTRODUCTION

Goal-directed behavior is closely related to the catecholaminergic (norepinephrine and dopamine) system. Catecholamines are associated among others with impulse control (Hershey et al., 2004; Chambers et al., 2009; Willemssen et al., 2009; Aron, 2011; Badgaiyan and Wack, 2011; Ghahremani et al., 2012; Bari and Robbins, 2013). Catecholaminergic neural transmission can be modulated by methylphenidate (MPH) (Linssen et al., 2014; Bensmann et al., 2019; Mückschel et al., 2020; Eggert et al., 2021). MPH acts as a catecholamine reuptake inhibitor, thereby increasing the concentration of dopamine and norepinephrine in the synaptic cleft (Ramos and Arnsten, 2007; Iversen et al., 2009; Walitza et al., 2016; Xing et al., 2016; Faraone, 2018). Previous studies have demonstrated that a single dose of MPH can modulate goal-directed actions, particularly during high demands on cognitive processes (Linssen et al., 2014; Bensmann et al., 2019; Mückschel et al., 2020), because particularly the increase of task-dependent norepinephrine release likely leads to an optimization of response selection (stimulus-response mappings) performance in task-related decision processes (Aston-Jones and Cohen, 2005). In the context of response inhibition (i.e., the ability to overcome a pre-potent/inappropriate response tendency), the manipulation of demands in terms of increasing decision complexity can be conceptualized by the theory of event coding (TEC) (Hommel et al., 2001). A central mechanistic element in TEC are temporary, internal representations of stimulus-response mappings—the so-called “event files” (Hommel, 2009). Chmielewski and Beste (2019) developed a TEC-inspired Go/Nogo task in which successful inhibition requires retrieval of stimulus-response mappings (i.e., the event file) in 1 condition (“nonoverlapping” condition) and the additional reconfiguration of such an event file in another condition due to overlapping stimulus features between Nogo and Go stimuli (“overlapping” condition; also see Figure 1). Inhibition performance requirements increase with increasing event file processing complexity, that is, with the need for event file reconfiguration compared with a condition in which only the retrieval of an event file is sufficient (Chmielewski and Beste, 2019; Prochnow et al., 2021, 2022a, 2022b). A recent study (Eggert et al., 2022) demonstrated that increasing catecholamine levels by administering methylphenidate (MPH) resulted in improved response inhibition, particularly in the more complex condition involving event file reconfiguration processes. However, studies in which healthy controls are administered MPH consistently show large between-subject variability (van der Schaaf et al., 2013; Linssen et al., 2014; Froböse et al., 2018; Bensmann et al., 2019; Mückschel et al., 2020). Similarly, when MPH is administered in a clinical context, some patients do not respond (Avital et al., 2020; Herrera-Morales et al., 2022). This raises the question whether or how interindividual differences reflected in, for instance, neurophysiological measurements might modulate this pharmacological effect.

Figure 1.

Figure 1.

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Task stimuli and procedure. (A) The stimuli used in the task. As can be seen, the stimuli differ in the features color and letter sequence. These features are completely distinct between the non-overlapping Go and Nogo conditions. On the other hand, the features are shared and thus overlap between the overlapping Go and Nogo conditions. (B) The timing of correctly answered trials for the Go (top) and Nogo (bottom) condition. In Go trials, the trial is ended by the response, whereas in Nogo trials, the trial ends 1700 milliseconds after the stimulus presentation. After the end of a trial, an inter-trial interval is presented, the duration of which varies randomly between 700 and 1100 milliseconds.

Crucially, performing well during increasing demands on cognitive control functions is not only associated with an increase in catecholaminergic levels. Instead, an increase of cognitive control demands, for instance, in the context of response inhibition and conflict is also related to increased activity in the theta frequency band, mostly in the medial frontal cortex (Cavanagh et al., 2012; Cavanagh and Frank, 2014; Cohen, 2014; Chmielewski et al., 2016; Mückschel et al., 2016). Thus, medial-frontal theta frequency band oscillatory activity and catecholaminergic neural transmission are involved in cognitive control and thus in similar processes. Interindividual differences in medial-frontal theta band activity might thus further elucidate interindividual differences in the responsiveness to MPH. Because the effects of MPH are based on the modulation of the catecholaminergic system, a marker of interest for this purpose might be the modulation of theta band activity as well. However, although task-related modulations of theta band activity may reflect interindividual differences (Zamorano et al., 2020; Chidharom et al., 2021a, 2021b), the increase in theta band activity during a task is always a function of task demands and might thus not be an unambiguous individual characteristic of theta band activity (Chmielewski et al., 2016; Dippel et al., 2017). Thus, the interindividual differences in the modulation of theta band activity might be better captured when examined independently of task-related demands, for example, using a neurofeedback-like setup in which participants are asked to upregulate their brain activity above a certain threshold (Enriquez-Geppert et al., 2017; Pscherer et al., 2019, 2020). Given +the association of theta band activity and the catecholaminergic system with similar cognitive processes, a voluntary adaptation of theta band activity might provide a marker for the responsiveness of the catecholaminergic system to MPH. This voluntary adaptation of theta band activity, which we refer to as voluntary theta band modulability in the following, is defined as the ability of an individual to modulate theta band activity only for the sake of modulating theta band activity, that is, without a task “incidentally” causing a modulation of theta band activity. Importantly, this does not refer to a training of theta band activity but only to the assessment of an individual ability. However, because this is an ability that can be explicitly trained, for example, by means of neurofeedback (Corlier et al., 2016; Enriquez-Geppert et al., 2017; Kerick et al., 2023), and a trainable ability should—at least to a certain extent—be stable, we strongly assume that theta band modulability can be considered a stable individual ability. In the current study, we examine the relationship between the catecholaminergic system and voluntary theta band modulability using a neurophysiological data analysis approach.

The catecholaminergic system is related to the stability of mental representations (Durstewitz and Seamans, 2008; Hamilton et al., 2010; Arnsten, 2011; Cools and D’Esposito, 2011), which should be particularly evident at the neurophysiological level. Importantly, the stability of mental representations (their generalization over time), and particularly the alteration of these mental representations by pharmacological manipulations, cannot be captured well by rather less complex analysis methods such as event-related potentials, as demonstrated in a recent study by Eggert et al. (2022). Following this study, we will apply a multivariate approach: multivariate pattern analysis (MVPA) (King and Dehaene, 2014). In particular, temporal generalization MVPA offers the possibility of gaining insight into the temporal dynamics of neurophysiological processes, that is, when and for how long mental representations are maintained (King and Dehaene, 2014). Thus, using MVPA, more nuanced changes in neurophysiological patterns can be captured, and, because MVPA makes use of the whole data of a trial, more data can be integrated and thus more complex relationships can be unveiled. The change in stability of mental representations due to pharmacological manipulation with MPH modeled by MVPA therefore offers deeper insights in the effects of a catecholaminergic manipulation and should differ depending on the modulability of theta band activity. Furthermore, to obtain a better understanding at which cognitive processing levels this relationship is of relevance, a temporal decomposition (residue iteration decomposition [RIDE]) (Ouyang et al., 2015) of the EEG data into so-called clusters is performed before MVPA. Each of these clusters is associated with a specific cognitive processing level—the S-cluster captures stimulus-related processes such as stimulus detection and categorization, and the C-cluster capturing translational processes linking stimulus and response such as response selection (Ouyang et al., 2015). Such a response selection process can include the selection of nonresponse, that is, response inhibition, as well (Mostofsky and Simmonds, 2008). Thus, the clusters obtained after RIDE decomposition reflect the theoretical assumptions of the TEC (Opitz et al., 2020; Takacs et al., 2020a, 2020b) and BRAC framework (Beste et al., 2023). In particular, processes captured in the C-cluster, which reflect processes of stimulus-response linking and thus response selection, can be related to the event file coding processes central to the theory (Kleimaker et al., 2020; Opitz et al., 2020; Takacs et al., 2020b). This cluster also reliably reflected the manipulation of task complexity and of the catecholaminergic system by MPH administration in previous studies (Adelhöfer et al., 2018; Prochnow et al., 2021, 2022a; Eggert et al., 2022). On the other hand, the stimulus-related S-cluster is assumed to correspond to the TEC “object file.” The combination of RIDE, allowing a separate evaluation of object (i.e., perceptual) and event file (i.e., response selection and control) dynamics, and MPVA, capturing the stability of mental representations, therefore makes it possible to investigate the stability or flexible management of object and event files. Due to the relevance of the C-cluster to event file coding processes and the influence of the catecholaminergic system particularly on this cluster, we hypothesize that particularly the C-cluster reflects modulations due to task complexity and MPH administration and that these modulations vary depending on the individual’s ability to voluntarily modulate theta band activity.

METHODS

A detailed description of the methodological procedures can be found in the supplemental Material.

Participants

The final sample for the analysis consisted of n = 67 individuals (24.1 ± 2.7 years, 38 male, IQ: 111 ± 13). Before participation, all individuals were screened individually by using a structured questionnaire assessing general psychiatric symptoms and general information, completing a semi-structure interview assessing a history of and current drug consumption, and conducting an IQ measurement. None of the individuals had any prior experience with the applied neurofeedback protocol. All participants provided written informed consent to take part and received either financial compensation or course credit for their participation. The Ethics Committee of the Medical Faculty of the TU Dresden approved all study procedures.

Design and Procedure

The participants conducted 2 appointments in a double-blind cross-over design. During one appointment they received MPH (0.25 mg/kg body weight), and during the other appointment they received a placebo compound. The order of administration was assigned randomly to each participant and was used as grouping factor for subsequent analyses (placebo first vs MPH first group). EEG measurement began approximately 75 minutes after tablet ingestion (on both appointments) to ensure that task performance occurred during the maximum effect period of MPH (Challman and Lipsky, 2000; Rösler et al., 2009).

The assessment of task-unrelated theta band activity (4–7 Hz) was conducted with the participants during the first one-half hour of the first appointment by using a neurofeedback setup and the SAM (Self‐regulation and Attention Management) software (Gevensleben et al., 2009), deriving the signal of interest from a single electrode at position Cz. This measurement was performed directly after taking the tablet so that an effect of MPH (in the MPH first group) can be excluded, because MPH does not develop any effect in the first 30 minutes after oral ingestion (Kodama et al., 2017). After the measurement of resting theta band activity for 2 minutes, participants were instructed to make a cartoon character on the screen walk in a 1-minute interval, which would happen when the theta band activity increased above the individual resting state average value. According to their modulation performance, the participants were assigned to either the highmodulation or the lowmodulation group using a median split. Moreover, participants performed additional 11 one-minute theta band modulation intervals with the same instruction, which were used to evaluate the internal consistency of the theta modulability measurement.

Task

Participants performed a TEC-inspired Go/Nogo task, in which Go and Nogo stimuli either shared the features color and letter sequence (overlapping condition) or were completely distinct with respect to these features (non-overlapping condition) (Chmielewski and Beste, 2019). They were asked to press the space key in Go trials and to not press any key in Nogo trials. The trials were presented in a pseudorandomized sequence in 7 blocks of 120 trials each, consisting of more Go than Nogo trials (ratio 70:30). More detailed information is displayed in Figure 1. The false alarm rate as outcome measure of Nogo trials was calculated by dividing the number of Nogo trials with an erroneously given response by the total number of trials in each condition.

EEG Recording and Analysis

EEG data were recorded at a sampling rate of 500 Hz from 60 Ag/AgCl electrodes with equidistant arrangement. After the pre-processing of the EEG data and their segmentation locked to the stimulus, we applied RIDE decomposition (Ouyang et al., 2015) to derive the S- and the C-cluster. Afterwards, MVPA was performed on the RIDE-decomposed EEG data by applying the MVPA-light toolbox (Treder, 2020), either comparing the classes placebo and MPH (i.e., session classification) or the classes non-overlapping and overlapping (condition classification) within groups and within conditions or sessions, respectively.

Statistics

Behavioral data were analyzed with a mixed-effects ANOVA containing the factors Overlap (non-overlapping vs overlapping condition), Substance (placebo vs MPH session), Substance Order (placebo-first vs MPH-first group), and Modulation (lowmodulation vs highmodulation group). Depending on the normal distribution of the variables (Supplemental Table 1), either parametric or nonparametric post hoc tests were calculated. Effect sizes were computed for every significant comparison. For descriptive statistics, the mean and SD of the mean are given. The MPVA results were statistically compared using cluster-based permutation tests.

RESULTS

Median Split

Based on the 12 one-minute theta band modulation intervals, Cronbach alpha revealed an excellent internal consistency of the theta band modulability measurement (α = .933). To distinguish between participants with a lower versus a higher spontaneous theta frequency band modulation, the sample was split at the sample median of the spontaneous theta frequency band modulation parameter (x~ = .065 µV/m²). This resulted in a lowmodulation group (n = 33, −.040 ± .057 µV/m²) and a highmodulation group (n = 34, .228 ± .142 µV/m²). Because the chi-square test between the 2 between-groups factors (Substance Order, median split factor Modulation) revealed no significant relationship between both factors (χ²(1) = .367, P = .544, φ = .074), the group factor Modulation could be used for the subsequent analyses (Iacobucci et al., 2015). Moreover, both Modulation groups did not differ from each other with respect to age (P = .153), gender distribution (P = .527), IQ (P = .553), or days between appointments (P = .723).

Behavioral Results

The mixed-effects ANOVA showed main effects of the within-subject factors Overlap (F(1,63) = 303.48, P <.001, η²p = .828) and Substance (F(1,63) = 36.89, P < .001, η²p = .369), with increased false alarm rates in the overlapping (.33 ± .16) compared with the non-overlapping (.01 ± .02) condition and decreased false alarm rates in the MPH session (.15 ± .08) compared with the placebo session (.19 ± .10). Importantly, the interaction of Overlap x Substance x Modulation (F(1,63) = 7.01, P = .010, η²p = .100) was significant, as well as the corresponding lower-order interactions of Overlap x Substance (F(1,63) = 21.24, P < .001, η²p = .252) and of Substance x Modulation (F(1,63) = 5.62, P = .021, η²p = .082).

Resolving the significant interaction of Overlap x Substance x Modulation by Modulation group, the following picture emerges. In the lowmodulation group, the main effects of Overlap (F(1,32) = 114.44, P < .001, η²p = .781) and Substance (F(1,32) = 8.23, P = .007, η²p = .205) were significant, but the interaction of Overlap x Substance was not significant (F(1,32) = 2.21, P = .147; Figure 1 left panel). In the highmodulation group, the main effects of Overlap (F(1,33) = 221.49, P < .001, η²p = .870) and Substance (F(1,33) = 29.79, P < .001, η²p = .474) were also significant. Importantly, in the highmodulation group, the interaction of Overlap x Substance was significant (F(1,33) = 19.15, P < .001, η²p = .367; Figure 2 right panel). The conditions differed significantly in the placebo session (non-overlapping: .02 ± .03; overlapping: .38 ± .16; Z = −5.09, P < .001, r = .872) as well as in the MPH session (non-overlapping: .01 ± .01; overlapping: .30 ± .13; Z = −5.01, P < .001, r = .860), but this binding effect was smaller in the MPH session (.29 ± .13) compared with the placebo session (.36 ± .14; t(33) = 4.38, P < .001, d = .750) due to a larger effect of MPH administration in the overlapping (.08 ± .10) compared with the non-overlapping condition (.01 ± .03; Z = −3.50, P <.001, r = .601). Resolving the interaction of Overlap x Substance x Modulation by the factor Overlap, there was no significant interaction of Substance x Modulation in the non-overlapping condition (F(1,65) = .11, P = .747), but this interaction was significant in the overlapping condition (F(1,65) = 5.92, P = .018, η²p = .083). The false alarm rates differed between sessions in the lowmodulation group (placebo: .34 ± .19; MPH: .31 ± .18; t(32) = −2.32, P = .014, d = −.403) as well as in the highmodulation group (placebo: .38 ± .16; MPH: .30 ± .13; Z = −4.15, P < .001, r = −.711); however, the effect of MPH administration was larger in the highmodulation group (.08 ± .10) compared with the lowmodulation group (.03 ± .08; t(65) = −2.43, P = .018, d = −.595). There were no differences between the groups in the placebo session (Z = −1.19, P = .236) or the MPH session (t(65) = .37, P = .716).

Figure 2.

Figure 2.

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Behavioral results. False alarm rates. The upper panel shows the distribution of the false alarm rates in the 2 conditions in the 2 sessions in the lowmodulation group (left side) and the highmodulation group (right side). Asterisks denote outliers. The lower panel shows the interaction of the factors Overlap and Substance in the lowmodulation group (left side) and the highmodulation group (right side).

Besides the main effects and interactions described above, there were no other significant main effects or interactions on false alarm rates, more specifically no effects including the factor Substance Order (F ≤ 3.02, P ≥ .087).

MVPA Results

Session Classification: C-Cluster

The separate results of the session classification in the C-cluster for the 2 groups and the 2 conditions are given in Table 1 and displayed in Figure 3.

Table 1.

Results of the Session Classification (Placebo vs MPH) in the C-clustera

Group Condition MVPA across time Temporal generalization MVPA
AUC Sign. time Mean AUC Pct. sign. Extent
Mean Min Max
High Non-overlapping .72 .56 .89 246–816 ms .66 85% 453 ms
Overlapping .71 .55 .88 246–1160 ms .67 89% 650 ms
Low Non-overlapping .69 .55 .84 258–828 ms .65 88% 448 ms
Overlapping .71 .54 .86 258–1012 ms .66 91% 557 ms
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a Abbreviations: Extent, average temporal extent of the significant classification around the diagonal; High, highmodulation group; low, lowmodulation group; Pct. Sign., percentage of classifications having a significant AUC; Sign. Time, time period of significant classification relative to stimulus onset. AUC = area under the curve, MVPA = multivariate pattern analysis, MPH = methylphenidate.

Figure 3.

Figure 3.

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Session classification C-cluster. (A) The AUC of the session classification in theMVPA in the RIDE C-cluster. Left, results for the lowmodulation group; right, results for the highmodulation group. Upper panel, results for the non-overlapping condition; lower panel, results for the overlapping condition. Line graphs show the AUC of the MVPA classification across time for each time point; thick lines indicate significant above chance classification. The plots with scaled colors show the results of the temporal generalization MVPA; color indicates AUC; plots are masked so that only significant above chance classification is displayed. (B) Difference plots of the session classification accuracy for the different conducted comparisons. Line graphs show the accuracy of the binary classification as well as their difference for each time point; grey rectangles indicate time intervals with significant differences. The plots with scaled colors show the differences of the temporal generalization MVPA; color indicates the difference in classification accuracy; plots are masked so that only significant clusters are displayed in bright colors, nonsignificant areas are shown greyed out.

The cluster-based permutation tests comparing the session classification accuracy of the classification across time between conditions (non-overlapping vs overlapping) in each group (highmodulation and lowmodulation) revealed a positive cluster in the highmodulation group (Tsum = 333) and 2 positive clusters in the lowmodulation group (Tsum = 338; Tsum = 142). Further, cluster-based permutation tests comparing the session classification accuracy of the temporal generalization analysis between conditions in each group revealed 2 positive clusters (Tsum = 32.509; Tsum = 18, 868) and a negative cluster (Tsum = −5.345) in the highmodulation group as well as 2 positive clusters in the lowmodulation group (Tsum = 31.401; Tsum = 15.191). The cluster-based permutation tests comparing the session classification accuracy between the groups in the conditions revealed no significant differences (P ≥ .239).

Session Classification: S-Cluster

The separate results of the session classification in the S-cluster for the 2 groups and the 2 conditions are given in Table 2 and displayed in Figure 4.

Table 2.

Results of the Session Classification (Placebo vs MPH) in the S-clustera

Group Condition MVPA across time Temporal generalization MVPA
AUC Sign. time Mean AUC Pct. sign. Extent
Mean Min Max
High Non-overlapping .68 .57 .91 0–1500 ms .61 85% 527 ms
Overlapping .70 .61 .88 0–1500 ms .63 85% 516 ms
Low Non-overlapping .66 .55 .87 0–1500 ms .60 90% 576 ms
Overlapping .69 .60 .84 0–1500 ms .62 86% 521 ms
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a Abbreviations: Extent, average temporal extent of the significant classification around the diagonal; High, highmodulation group; low, lowmodulation group; Pct. Sign., percentage of classifications having a significant AUC; Sign Time, time period of significant classification relative to stimulus onset.

Figure 4.

Figure 4.

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Session classification S-cluster. (A) AUC of the session classification in the MVPA in the RIDE S-cluster. Left, results for the lowmodulation group; right, results for the highmodulation group. Upper panel, results for the non-overlapping condition; lower panel, results for the overlapping condition. Line graphs show the AUC of the MVPA classification across time for each time point; thick lines indicate significant above chance classification. The plots with scaled colors show the results of the temporal generalization MVPA; color indicates AUC; plots are masked so that only significant above chance classification is displayed. (B) Difference plots of the session classification accuracy for the different conducted comparisons. Line graphs show the accuracy of the binary classification as well as their difference for each time point; grey rectangles indicate time intervals with significant differences. The plots with scaled colors show the differences of the temporal generalization MVPA; color indicates the difference in classification accuracy; plots are masked so that only significant clusters are displayed in bright colors, nonsignificant areas are shown greyed out.

The cluster-based permutation tests comparing the session classification accuracy of the binary classification between conditions (non-overlapping vs overlapping) in each group (highmodulation and lowmodulation) revealed a negative cluster (Tsum = −28) and a positive cluster (Tsum = 24) in the highmodulation group. Regarding the comparison of the session classification accuracy of the binary classification between conditions in the lowmodulation group, no significant differences could be found (P ≥ .004). Further, cluster-based permutation tests comparing the session classification accuracy of the temporal generalization analysis between conditions in each group revealed 2 positive clusters (Tsum = 579; Tsum = 371) and a negative cluster (Tsum = −329) in the highmodulation group as well as 6 positive clusters in the lowmodulation group (Tsum = 1, 066; Tsum = 690; Tsum = 507; Tsum = 475; Tsum = 459; Tsum = 408). The cluster-based permutation tests comparing the session classification accuracy between the groups in the conditions revealed no significant differences regarding the binary classification (P ≥ .136). However, the cluster-based permutation tests comparing the session classification accuracy of the temporal generalization between the groups revealed a negative cluster (Tsum = −292, 472) and 3 positive clusters (Tsum = 58, 808; Tsum = 40, 073; Tsum = 21, 515) in the overlapping condition as well as a negative cluster (Tsum = −265, 542) and 4 positive clusters (Tsum = 46, 295; Tsum = 29, 207; Tsum = 22, 403; Tsum = 4, 083) in the non-overlapping condition.

Condition Classification: C-Cluster

The separate results of the condition classification in the C-cluster for the 2 groups and the 2 sessions are given in Table 3 and displayed in Figure 5.

Table 3.

Results of the Condition Classification (Non-overlapping vs Overlapping) in the C-clustera

Group Session MVPA across time Temporal generalization MVPA
AUC Sign. time Mean AUC Pct. sign. Extent
Mean Min Max
High Placebo .67 .55 .78 246–660 ms .58 82% 492 ms
MPH .68 .54 .83 246–719 ms .57 81% 476 ms
Low Placebo .65 .55 .74 258–660 ms .58 84% 385 ms
MPH .68 .55 .78 254–668 ms .57 82% 406 ms
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a Abbreviations: Extent, average temporal extent of the significant classification around the diagonal; High, highmodulation group; low, lowmodulation group; Pct. Sign., percentage of classifications having a significant AUC; Sign Time, time period of significant classification relative to stimulus onset.

Figure 5.

Figure 5.

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Condition classification C-cluster. (A) AUC of the condition classification in the MVPA in the RIDE C-cluster. Left, results for the lowmodulation group; right, results for the highmodulation group. Upper panel, results for the placebo session; lower panel, results for the MPH session. Line graphs show the AUC of the MVPA classification across time for each time point; thick lines indicate significant above chance classification. The plots with scaled colors show the results of the temporal generalization MVPA; color indicates AUC; plots are masked so that only significant above chance classification is displayed. (B) Difference plots of the condition classification accuracy for the different conducted comparisons. Line graphs show the accuracy of the binary classification as well as their difference for each time point; grey rectangles indicate time intervals with significant differences. The plots with scaled colors show the differences of the temporal generalization MVPA; color indicates the difference in classification accuracy; plots are masked so that only significant clusters are displayed in bright colors, nonsignificant areas are shown greyed out.

The cluster-based permutation tests comparing the condition classification accuracy of the binary classification as well as the condition classification accuracy of the temporal generalization analysis between sessions (placebo and MPH) in each group (highmodulation and lowmodulation) revealed no significant differences (P ≥ .062). Further, also the cluster-based permutation tests comparing the condition classification accuracy of the binary classification as well as the condition classification accuracy of the temporal generalization analysis between groups in each session revealed no significant differences (P ≥ .514).

Condition Classification: S-Cluster

The separate results of the condition classification in the S-cluster for the 2 groups and the 2 sessions are given in Table 4 and displayed in Figure 6.

Table 4.

Results of the Condition Classification (Non-overlapping vs Overlapping) in the S-clustera

Group Session MVPA across time Temporal generalization MVPA
AUC Sign. time Mean AUC Pct. sign. Extent
Mean Min Max
High Placebo .63 .52 .84 0–1395 ms .56 71% 178 ms
MPH .63 .52 .86 47–1172 ms .55 68% 166 ms
Low Placebo .61 .52 .80 0–1406 ms .55 74% 165 ms
MPH .62 .52 .83 0–1477 ms .55 68% 155 ms
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a Abbreviations: Extent, average temporal extent of the significant classification around the diagonal; High, highmodulation group; low, lowmodulation group; Pct. Sign., percentage of classifications having a significant AUC; Sign Time, time period of significant classification relative to stimulus onset.

Figure 6.

Figure 6.

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Condition classification S-cluster. (A) AUC of the condition classification in the MVPA in the RIDE S-cluster. Left, results for the lowmodulation group; right, results for the highmodulation group. Upper panel, results for the placebo session; lower panel, results for the MPH session. Line graphs show the AUC of the MVPA classification across time for each time point; thick lines indicate significant above chance classification. The plots with scaled colors show the results of the temporal generalization MVPA; color indicates AUC; plots are masked so that only significant above chance classification is displayed. (B) Difference plots of the condition classification accuracy for the different conducted comparisons. Line graphs show the accuracy of the binary classification as well as their difference for each time point; grey rectangles indicate time intervals with significant differences. The plots with scaled colors show the differences of the temporal generalization MVPA; color indicates the difference in classification accuracy; plots are masked so that only significant clusters are displayed in bright colors, nonsignificant areas are shown greyed out.

The cluster-based permutation tests comparing the condition classification accuracy of the binary classification as well as the condition classification accuracy of the temporal generalization analysis between sessions (placebo and MPH) in each group (highmodulation and lowmodulation) revealed no significant differences (P ≥ .213). Further, the cluster-based permutation tests comparing the condition classification accuracy of the binary classification as well as the condition classification accuracy of the temporal generalization analysis between groups in each session also revealed no significant differences (P ≥ .607).

DISCUSSION

The current study examined whether the impact of pharmacological manipulation of the catecholaminergic system on cognitive control (using MPH) depends on the ability to self-modulate relevant brain oscillatory patterns underlying cognitive control (i.e., resting state theta band activity). An interplay between catecholaminergic effects, as induced by MPH, and theta band activity modulability is likely as both seem to be involved in similar cognitive processes. For this purpose, participants performed a Go/Nogo task (Chmielewski and Beste, 2019) during 2 appointments, once under the influence of MPH with increased catecholamine levels and once without a manipulation of the catecholaminergic system. In addition, participants were asked to spontaneously upregulate their theta band activity above their previously individually measured mean resting theta band activity value. Based on the theta band activity upregulation performance, participants were divided into a high modulation and a low modulation group to include voluntary theta band modulability in MVPA, that is, to perform MVPA separately for the groups and to compare the results between the groups.

In the behavioral data, main effects of the task manipulation and the MPH administration could be observed. Regarding the difference between Nogo trials that shared features with Go trials compared with Nogo trials that did not, the false alarm rate was increased in the former case. This is in line with previous findings (Chmielewski and Beste, 2019; Prochnow et al., 2021, 2022a, 2022b) as well as with the theoretical assumptions of the TEC that the reconfiguration of event files due to overlapping features is a time-consuming and error-prone process. Also the beneficial effect of MPH administration on false alarm rates is in line with previous findings (Linssen et al., 2014; Schmidt et al., 2017; Simon and Moghaddam, 2017). Importantly, MPH administration and the voluntary modulability of theta band activity had an interactive effect on behavioral performance. In the low modulation group, only the main effects described above were evident. In the high modulation group, the increased catecholamine levels affected the inhibition performance under increased demands (overlapping condition) even more than under lower demands (non-overlapping condition). Thus, individuals who were good at voluntarily modulating their theta band activity performed even better on higher task demands at elevated catecholamine levels than individuals who were less able to voluntarily regulate their theta band activity. This demonstrates that increased theta band modulability plays a critical role in how well pharmacological enhancement of cognitive control functions can be achieved by means of MPH. This finding adds to the growing neurophysiological literature on manipulating cognitive control and response inhibition in particular (Friehs et al., 2021).

On the neurophysiological level, MVPA was conducted using either the non-overlapping and overlapping conditions or the placebo and MPH sessions as classes, which is referred to as condition classification or session classification, respectively. Overall, there was a successful classification of the different conditions and sessions using MVPA (King and Dehaene, 2014). This means that both the neural codes between conditions (non-overlapping vs overlapping) as well as those between sessions (placebo vs MPH) differ systematically from each other, indicating that the stability of the mental representation of the task differs between conditions and between sessions. These differences were particularly strong in the RIDE C-cluster, indicating that the differences in the stability of task representations are particularly evident in response selection (Kleimaker et al., 2020; Opitz et al., 2020; Takacs et al., 2020a, 2020b). However, also in the S-cluster, which is associated with stimulus representation (Ouyang et al., 2015), differences between conditions or sessions were apparent. Regarding the condition classification, the classification accuracy in both RIDE clusters did not differ between sessions or between groups. Thus, neither catecholamine levels nor theta band modulability influence the magnitude of difference between the conditions. However, regarding the session classification, a different picture emerged. In the S-cluster, there were considerable differences in session classification accuracy between the groups, whereas the conditions did not differ substantially from each other. Here, session classification, tended to be larger in individuals with high theta band modulability compared with those with low theta band modulability, indicating that theta band modulability influences the magnitude of the MPH effect. In contrast, there were no group differences in session classification accuracy in the C-cluster, but there were considerable differences between the conditions. The difference between task processing with unaffected compared with increased catecholamine levels tended to be larger in the more complex condition in which event file reconfiguration was required compared with the condition where it was merely necessary to retrieve an event file. Thus, theta band modulability and task complexity interact with the modulation of catecholamine levels at different processing stages. Changes in the perception of stimuli (or stimulus features) by varying catecholamine levels are modulated by individual theta band modulability, whereas changes in the association of stimuli and responses by varying catecholamine levels are modulated by the complexity of the task. The separate influences on the different stages of processing result in the interaction of all influencing factors involved on the behavioral level, with a performance advantage for individuals with high theta band modulability, especially when facing high demands. So, how do differences of processes in the theta frequency band influence the effect of a pharmacological manipulation of the catecholaminergic system? An underlying common mechanism might refer to “gain control” processes, which improves the overall signal-to-noise ratio when gain is high (Aston-Jones and Cohen, 2005; Ferguson and Cardin, 2020). Both theta oscillations and the catecholaminergic system improve gain control functions (Servan-Schreiber et al., 1990; Aston-Jones and Cohen, 2005; Ponjavic-Conte et al., 2012; Warren et al., 2016; Adelhöfer et al., 2019; Eggert et al., 2021). Given the interaction of differences in the theta frequency band and the manipulation of the catecholaminergic system, it is likely that both factors may be expressions of the same processes (Adelhöfer et al., 2019; Chen et al., 2020). In conjunction with the absence of a group main effect at the behavioral level, this suggests that theta band modulability reflects how receptive to manipulation an individual’s catecholaminergic system is. This also explains why no effects have been found for theta band modulability alone in a previous study (Pscherer et al., 2019). The interaction of theta band modulability and MPH-modulations is particularly evident at the level of sensory processing. Both the theta frequency band and the catecholaminergic system have been associated with gain control in the context of sensory processing (Ponjavic-Conte et al., 2012; Yousif et al., 2016). The interplay of those factors in stimulus-related processing indicates that in individuals with high voluntary theta band modulability, the benefit of a catecholamine level increase on signal-to-noise ratio in the perception of environmental stimuli are further enhanced, leading to a better differentiation of stimuli. At the processing stage linking stimulus and response, the effect of catecholamine level elevation is particularly evident in the more complex task condition (Eggert et al., 2022). This effect is independent of theta band modulability. Individuals who had an advantage in stimulus perception may benefit even more from the MPH effect and consequently show better performance in behavior, especially in the more complex task condition. To further substantiate the proposed relationship between the theta band modulability, MPH effects, and event file coding processes, further studies on this relationship should examine, for instance, the pattern of outcomes at different MPH doses. Furthermore, also the role of other neurotransmitter systems should be subject to future studies, because other neurotransmitter systems such as the glutamatergic system also might influence catecholaminergic transmission (Eskenazi et al., 2021; Cai and Tong, 2022). Moreover, despite the excellent internal consistency found in the current study, future research should address how stable the ability for voluntary theta band modulation can be considered to be. In addition, would be intriguing to examine the relationship between voluntary theta band modulability and task-related theta band activity, because resting state theta band activity has already been shown to be relevant to task-related neurophysiological processes (Pscherer et al., 2019, 2020). Because the median split as grouping approach does not come without issues such as the potential reduction of statistical power (Maxwell and Delaney, 1993), voluntary theta band modulability as a continuous predictor is warranted in future studies, which was not possible in the current study due to the requirements of the MVPA.

Taken together, the findings suggest that inter-individual differences in modulability of the theta frequency band are linked to differences in the responsiveness of the catecholaminergic system. When theta band modulability (intrinsic modulability) is high, pharmacological intervention (extrinsic modulation) is more effective than in individuals with lower theta band modulability. This has important implications for the clinical application of MPH treatments. Potentially, an individual’s voluntary modulability of theta band activity can be used an as indicator of how well this individual may respond to MPH treatment. This might be of particular importance for a more targeted treatment of ADHD in which both the control of theta band activity and the catecholaminergic system are known to be involved. This scenario of personalized treatment should be investigated in future studies.

Supplementary Material

pyae003_suppl_Supplementary_Material
Click here for additional data file. (5.6MB, docx)

Acknowledgments

We thank all participants.

This work was supported by grants from the Deutsche Forschungsgemeinschaft (SFB940, FOR2790, FOR2698).

Contributor Information

Astrid Prochnow, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany.

Moritz Mückschel, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany.

Elena Eggert, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany.

Jessica Senftleben, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.

Christian Frings, Cognitive Psychology, Institute of Psychology, University of Trier, Trier, Germany.

Alexander Münchau, Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.

Veit Roessner, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.

Annet Bluschke, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany.

Christian Beste, Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany; University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany.

Interest Statement

None.

Data Availability

The data underlying this article are available in the Open Science Framework at https://osf.io and can be accessed with https://osf.io/8p3a7/?view_only=101f26e7c4444f7282a0b3a93fc0ad7b.

Author Contributions

Astrid Prochnow (Data curation [Equal], Formal analysis [Equal], Investigation [Equal], Methodology [Equal], Project administration [Equal], Resources [Equal], Visualization [Equal], Writing—original draft [Equal], Writing—review and editing [Equal]), Moritz Mückschel (Methodology [Equal], Resources [Equal], Software [Equal], Writing—review and editing [Equal]), Elena Eggert (Data curation [Equal], Formal analysis [Equal], Investigation [Equal], Methodology [Equal], Visualization [Equal], Writing—original draft [Equal], Writing—review and editing [Equal]), Jessica Senftleben (Formal analysis [Equal], Writing—original draft [Equal]), Christian Frings (Resources [Equal], Writing—review and editing [Equal]), Alexander Münchau (Conceptualization [Equal], Funding acquisition [Equal], Writing—review and editing [Equal]), Veit Roessner (Conceptualization [Equal], Resources [Equal], Supervision [Equal], Writing—review and editing [Equal]), Annet Bluschke (Conceptualization [Equal], Project administration [Equal], Writing—original draft [Equal], Writing—review and editing [Equal]), and Christian Beste (Conceptualization [Lead], Funding acquisition [Lead], Methodology [Equal], Project administration [Equal], Resources [Lead], Validation [Equal], Writing—original draft [Equal], Writing—review and editing [Equal]).

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Data Availability Statement

The data underlying this article are available in the Open Science Framework at https://osf.io and can be accessed with https://osf.io/8p3a7/?view_only=101f26e7c4444f7282a0b3a93fc0ad7b.

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