Transcranial Alternating Current Stimulation (tACS) as a Tool to Modulate P300 Amplitude in Attention Deficit Hyperactivity Disorder (ADHD): Preliminary Findings

P300 (or P3b) generation has been associated with processes in later stimulus processing stages (as compared to the earlier occurring P3a component), when targets need to be discriminated from standards for response selection. These attention-driven processes involve working memory, when stimuli are compared against the remembered mental schemes of the (non-) targets which in turn will be updated if necessary (Polich 2007). This widely accepted model is called the context updating theory of P300 (Donchin and Coles 1988; Linden 2005; Polich 2003). While P300 latency is understood to reflect processing speed, P300 amplitude is related to the intensity of processing (Kok 2001). Because P300 amplitude has been shown to depend on stimulus probability (with amplitude and probability being negatively correlated) and task difficulty, it has been connected to attention-driven resource allocation processes (Polich 2007). In this framework, the ADHD-related reduction of P300 amplitude is attributed to a mal-functioning of resource allocation and can be related to ADHD typical cognitive performance deficits that are observed in visual oddball/go-nogo tasks. These comprise increases in reaction time mean and reaction time variability, a well as higher error rates for false alarms (commission errors), and omission errors (Bezdjian et al. 2009; Derefinko et al. 2008; Hervey et al. 2004; Uebel et al. 2010). Among these findings, reaction time variability is probably the most commonly reported one (Tamm et al. 2012). The assumption of a relationship between P300 alterations and ADHD symptomatology seems likely, given the obvious attention-related nature of its functional correlates. The relationship has been supported by correlational (Woltering et al. 2013) as well as other studies investigating treatment interventions showing effects on P300 amplitude: Schoenberg et al. (2014) showed a normalizing effect of Mindfulness-Based Cognitive Therapy on P300 alterations in ADHD. An increase in P300 amplitude was accompanied by an improvement in symptomatology. Further, an ameliorating effect of ADHD medication on error rates while showing the inverse effect on P300 amplitude was found (Jonkman et al. 2007). From this likely relationship it can be followed that when P300 characteristics are changed, related deficits should change as well. In addition to the relational argument, tACS has been proven to be able to influence cognitive performance and to decrease reaction times (Antal et al. 2008; Antonenko et al. 2016; Fröhlich et al. 2015; Kar and Krekelberg 2014; Polanía et al. 2012).

ERPs components like the P300 are conceptualized as brief and transient brain responses to an occurring event. Brain oscillations, that are typically targeted by tACS interventions are usually associated with longer durations. When conceiving ERPs as part of oscillations like that, they can be viewed as event-related oscillations (EROs; Başar 1998; Herrmann et al. 2014). Another aspect why ERPs and EROs are classically dealt with separately is that ERPs are typically viewed in the time domain only, while EROs are represented in the time–frequency domain. However, ERPs also can be investigated in the time–frequency domain, which has even been demonstrated to offer valuable insight in the context of their functional cognitive correlations (Başar et al. 1999). Figure 1 displays an exemplary ERP wave represented in the time (upper panel) and in time–frequency (lower panel) domain, taken from this study. Later ERP components like the P300 are captured in lower frequency ranges, namely theta (4–8 Hz) and delta (0–4 Hz) range. Based on these considerations, we assume that tACS could potentially influence ERPs in the same way as it can influence brain oscillations in general.

When viewing the P300 as part of an oscillation or looking at it in the time–frequency domain, it is represented by an ERO in the delta/theta frequency range. In line with that view, several studies were able to illustrate the functional link between oscillatory activity in the delta and theta frequency range and the generation of the P300 complex in auditory and visual oddball tasks (see the extensive review of Güntekin and Başar 2016). First, a frequency specific power increase was demonstrated for the delta and theta frequency range in the target compared to the non-target condition (Başar-Eroglu et al. 1992, 2001; Demiralp et al. 2001; Kolev et al. 1997; Schürmann et al. 1995). Second, time–frequency components in the delta and theta frequency range occurring at the typical latency of the P300 component were identified even on a single trial basis and matched the characteristic parietal scalp topography of the P300 (Demiralp et al. 1999, 2001; Kolev et al. 1997; Quian Quiroga et al. 2001). Consequently, delta and theta oscillations represent a possible mechanism for the generation of the P300 component.

tACS has been shown to increase the amplitude of endogenous brain oscillations within the stimulated frequency range during the time of stimulation the so-called on-line effect of tACS (Helfrich et al. 2014; Neuling et al. 2015). In addition, tACS results in elevated EEG amplitudes at the frequency of stimulation also after the end of stimulation. This effect is referred to as off-line effect or after-effect (Neuling et al. 2013; Zaehle et al. 2010). For the alpha frequency range, this off-line effect lasts for at least 30 to 70 min after stimulation (Kasten et al. 2016; Neuling et al. 2013; Zaehle et al. 2010). The on-line effects are often explained by entrainment, i.e., by EEG oscillations synchronizing to a rhythmic external force or spike-timing dependent plasticity (Thut et al. 2011; Vossen et al. 2015). Recent research demonstrated that tACS is capable of influencing brain oscillations. As reviewed above, a functional link of oscillations in the delta and theta frequency range with the P300 component has been shown. Consequently, targeting this ERP component using tACS represents a promising approach of modulating the P300 response.

In summary, the present study evaluates the potential of tACS to modulate the altered P300 amplitude in ADHD. The key points made so far are: a reduction in P300 amplitude in ADHD patients compared to healthy controls has been reported. This amplitude reduction is understood to reflect difficulties in attention resource allocation processes and a link between P300 and ADHD typical symptomatology has been shown. Furthermore, it has been demonstrated that ERP components can be viewed as EROs. Therefore, tACS can be assumed to influence ERPs in a similar way as it influences brain oscillations in general. The P300 component’s oscillatory equivalent is an ERO in the delta/theta frequency spectrum. Hence, it can be targeted with tACS stimulation within respective frequency range. tACS of a certain frequency can increase the amplitude of brain oscillations within the respective frequency band. Therefore, P300 amplitudes should be increased via tACS stimulation. Based on these considerations, the following directed hypotheses were formulated:

The present study is a sham-controlled study on individuals diagnosed with ADHD. The study is designed as a two × two mixed design with factors time (pre/post intervention) and group (stim/sham). Each patient attended one experimental session of approximately two and a half hours duration. The session was split into three blocks (pre, during, post conditions), the first and last of which were EEG-only blocks. In the second block, tACS stimulation or sham stimulation was applied. Measurements took place at the laboratories of the Experimental Psychology Lab in the Department of Psychology at the University of Oldenburg, Germany, in an electrically shielded room. A 32 channel EEG was recorded according to the international 10–10 System plus right Electrooculogram (EOG). Signals were amplified using a BrainAmp amplifier (Brain Products, Gilching, Germany), digitized at a sampling rate of 1000 Hz. The amplitude digitization range of the amplifier was ± 3.2768 mV with a resolution of 0.1 µV. tACS was delivered using a battery-operated stimulator system (DC-stimulator plus, Neuroconn, Ilmenau, Germany). The visual task was implemented using Presentation software (Version 18.01, Neurobehavioral Systems Inc., Albany, CA, USA). Data preprocessing and outcome variable extraction were conducted with Matlab (Version 9.2.0, The Mathworks Inc, Natick, MA, USA) and the interactive Matlab toolbox eeglab (Delorme and Makeig 2004). Statistical analyses were conducted with Matlab and the R software package (Version 3.3.0, R Foundation for Statistical Computing, Vienna Austria).

The study sample comprised 18 patients (7 females, age: M = 31.3, SD = 9.89 years, ranging from 19 to 57 years of age). Nine patients received tACS stimulation, the other nine received sham stimulation The reader is referred to Table S2 (Online Resource) for all sample characteristics specified by group. All patients were diagnosed with ADHD and were currently undergoing treatment at the specialized outpatient clinic for adult ADHD of the University Hospital for Psychiatry and Psychotherapy at the University Oldenburg, Germany. Diagnosis according to DSM-IV was based on psychiatric expert assessment and was validated using observer rating scales and self-rating scales including the Wender Utah Rating Scale (Retz-Junginger et al. 2002), the ADHD diagnostic checklist (Rösler et al. 2004), the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) (Fydrich et al. 1997; Wittchen and Fydrich 1997). Concurrent use of ADHD medication (reported by nine of the participating patients; agent: methylphenidate) was discontinued at least three days prior to the measurement under the supervision of the medicating doctor. Exclusion criteria comprised left-handedness, metal near brain or skull, epilepsy in medical record or direct family, comorbid neurologic conditions, severe affective disorders and schizophrenia, intake of psychopharmacological medication, substance addiction, autism, dermatosis and pregnancy. Participation was voluntary. Eligible patients, as decided by their medicating doctor, would be contacted by phone and invited to take part, if inclusion criteria were met. Patients received monetary compensation. Written informed consent was obtained from all participants. All investigations were in accordance with the Declaration of Helsinki and the GCP (good clinical practice). The study received ethics committee approval from the medical ethics review committee of the University of Oldenburg.

Eligible patients were randomly allocated to groups in a one-to-one ratio using a simple randomization script. The experiment consisted of three blocks (pre, during, post) during which the patients sat in a dark room in front of a computer screen performing a visual oddball task. The task consisted of frequent, irrelevant ‘O’-stimuli (0.75 probability of occurrence), further referred to as standards, and infrequent ‘X’-stimuli (0.25 probability of occurrence), further referred to as targets. Both letters were presented in white on black background (see Fig. 2 for schematic representation). In response to the targets, a button was to be pressed with the right-hand index finger. In between stimuli, the patient was instructed to fixate on a cross symbol displayed in the center of the screen. Stimuli were presented for 1000 ms, inter-stimulus interval (fixation cross displayed) jittered between 1000 and 2000 ms. The pre and post condition included 400 trials (approximately 300 standards and 100 targets) with an average duration of 16.6 min (400 × 2500 ms). The during condition had a fixed duration of 20 min, after which stimulator and visual presentation turned were off automatically. Stimulus size was 7 mm × 7 mm for fixation crosses, and 10 mm × 10 mm for targets and standards. Patients’ distance from the monitor was 1 m.

In the stim condition 20 min of tACS stimulation were delivered at an intensity of 1 mA (peak-to-peak). 20 min stimulation length was chosen as it was the maximum stimulation length approved by the local ethics committee at that time. The current was ramped up and down over the first and last 10 s of stimulation in order to minimize discomfort. In the sham condition, the procedure and stimulation parameters (see below) were set and determined in the same way as for the stim condition to blind the patient from the experimental condition. In contrast to the stim condition, the stimulation in the sham condition faded in for 10 s and after reaching the amplitude of 1 mA, the signal immediately faded out for another 10 s. All stimulation parameters were manually entered to the device. Therefore, the experimenter could no longer be blinded after the pre condition ended. EEG electrodes were used for stimulation. Before running the stimulation, chosen EEG electrodes were connected to the stimulator (further details below). Patients were then familiarized with the stimulation. The waveform of the applied stimulation was sinusoidal without DC offset.

There are two fundamental aspects that we considered requirements to the successful manipulation of P300 via tACS stimulation: for one, the phase of the oscillatory stimulation current and the phase of the theoretical P300 oscillation should match. Otherwise, stimulation peaks could coincide with P300 oscillation troughs, which would result in cancelling out the P300 wave instead of amplifying it. Secondly, the electrode montage should be designed to direct the current towards the areas of the brain involved in P300 generation.

To meet these requirements, stimulation parameters were determined on a single patient basis. After the pre condition ended, data were briefly preprocessed (low-pass filter: 20 Hz, high-pass filter: 0.5, epoch length: − 3 to 4 s around stimulus onset, baseline removal relative to − 50 to 0 ms interval). In the following, mean P300 latency (in ms, relative to stimulus onset) was estimated. This was done by finding the maximum value of the averaged ERP wave at electrode Pz between 0 and 900 ms after stimulus onset. Visual oddball tasks have a typical P300 latency of 400 ms (Polich and Criado 2006). Accordingly, mean amplitude latency in a pilot study including healthy subjects was 440 ms (Popp et al. 2019). Figure 1 illustrates stimulation frequency estimation. A time–frequency decomposition was computed using a wavelet transform (epoched Pz data: − 3 to 4 s normalized to − 3 to 0 s baseline interval, frequency range: 1.5 to 20 Hz, 3 cycles in each analysis wavelet, frequency resolution: 0.5 Hz, time resolution: 0.024 s). Individual stimulation frequency was the frequency at the maximum event-related spectral perturbation (ERSP) within ± 150 ms time window around individual P300 latency (maximum in time of maxima in frequency). The employed rationale was that P300 ERO would be reflected by the strongest contributing frequency component within the P300 time window (see “Introduction” section). The time interval around P300 latency was introduced in order to account for temporal smearing of the employed time–frequency decomposition method. Resulting stimulation frequencies had a mean of 3.00 Hz and a standard deviation of 1.24 Hz (M_stim = 3.06 Hz, SD_Stim = 1.40 Hz).

Right-tailed Mann–Whitney test comparison testing for a higher increase of P300 amplitude, in % relative to pre condition, in the stimulation group (M = 26.49, SD = 33.90) than in the sham group (M = − 6.78, SD = 20.00) was significant at the chosen alpha level, U = 110, p = 0.016. Figure 5 displays the mean ERP at electrode Pz featuring P300 responses of stimulation and sham groups pre and post intervention. While the pre and post ERPs are very similar in the sham condition (left), there is a noticeable difference between the pre and post condition in the stimulation group (right).

As supported by the group comparisons regarding comparability, a group difference in P300 amplitude pre intervention was not significant (see “Group Comparability” section). Individual ERPs and pre-to-post data for statistical comparison can be found in the Online Resource.

Accordingly, topographies at mean P300 latency (Fig. 6) showed P300-related parietal activity to increase in the stimulation group pre-to-post.

The data recorded in this ADHD sample featured a high amount of noise that caused challenges to data analysis. It resulted in imprecision of individual tACS parameter estimation after the pre condition. Increasing its precision is desirable in order to achieve the best possible stimulation outcome. In this study, reliability of P300 parameter determination for offline analysis could be increased by the following preprocessing: LP filtering with an 8 Hz cut-off, visual ICA artefact correction and re-referencing to average reference.

Main sources of noise (as apparent from ICA decomposition and visual inspection of the data) were related to eye blinks and facial muscle activity. Regarding hyperactivity being one of the core symptoms of ADHD, it seems reasonable to assume a relationship between ADHD characteristic behavior and the high amount of muscle related artefacts. This is supported by the notion that the data from a pilot study, which included a healthy sample, did not face the problem of increased noise (Popp et al. 2019). It is further affirmed by examples from the literature: for example, Fried et al. (2014) report increased rates of average microsaccade and blinks in ADHD patients (the authors even advocate for using the altered ocular activity as a marker in the differential diagnosis of ADHD). The larger blink rates and microsaccades in ADHD reported by Fried et al. were especially larger in the time interval around stimulus onset. The same observation was made in this study, even though the patients were instructed not to blink during stimulus presentation. Ochoa and Polich (2000) investigate the effects of instructions to suppress eye blinking on the P300 component. They find the visual oddball P300 amplitude to be smaller and its peak latency to be longer in the “do not blink” condition. ADHD patients often deal with difficulties to inhibit motor responses (Schachar et al. 2004). Therefore, instructing them to refrain from blinking might have caused problems in this study. In addition to noise in the data, low frequency resolution of the time–frequency decomposition in the online analysis (0.5 Hz) might have impeded the precision of stimulation frequency choice in this study. The relationship between better online parameter estimation and omission error improvement found in the stimulation group might indicate further potential of tACS to influence this behavioral outcome.

Despite the abovementioned limitations, the overall study design proved to be feasible to investigate tACS-induced changes in P300 amplitude and behavioral changes relevant for ADHD symptomatology. Furthermore, the fact that P300 amplitude as well as spectral perturbation at individual P300 frequency increased pre-to-post stimulation indicates that tACS could potentially modulate EROs and related ERP components. However, further studies including bigger sample sizes and healthy controls are necessary to support these results. An interesting extension of our study design, which was based on mean P300 responses, would be to investigate tACS effects on single trial P300 activity. In the clinical context, this would allow to individually tailor tACS stimulation better accounting for interindividual differences in P300 latency variability. Additionally, single trial analyses would help to understand tACS effects as well as P300 generating mechanisms better: the increase in P300 amplitude found in our study could have resulted from either increases in single trial P300 amplitudes or decreases in single trial P300 latency via entrainment. This ambiguity could be resolved using single trial analyses. In line with the two different possible types of interpretation of our results, we further reference the reader to a related on-going debate regarding ERP generating mechanisms, namely the evoked power versus phase reset model, and suggest tACS as well as our specific study design extended by a single trial assessment to help elucidating the origin of ERPs. Finally, missing support for the manipulation of RT mean in this study could be explained by the same argument made regarding P300 latency: in this study, stimulation was delivered time-matched to mean P300 latency. Given the relationship between P300 latency and RT, this might also have set a constant time lag between stimulation and reaction time, prohibiting modulation of RT mean. We encourage future studies to further investigate the relationship between P300 amplitude and RT-V modulation during tACS, as it could help reveal possible entrainment effects. Furthermore, relating stimulation, single trial P300 responses and respective behavioral data would allow a more direct investigation of the effects of tACS in ADHD.

In this pilot study, tACS was introduced as a possible candidate to influence the altered P300 component in ADHD. Interestingly, ADHD is not the only disorder related to P300 alterations. For example, schizophrenia, Alzheimer’s disease and major depression have also been linked to decreases in P300 amplitude (Bramon 2004; Kaustio et al. 2002; Lee et al. 2013). Therefore, tACS might offer similar potential for other disorders. We reference the reader to recent studies investigating tACS effects in such disorders (Ahn et al. 2019; Alexander et al. 2019; Mellin et al. 2018). Furthermore, this study presented first support that tACS could be used to increase the amplitude of the P300 using its respective EROs as targets. We further suggest our study design to be extended by single trial assessment to shed light on the mechanisms of tACS-induced changes and the generation of ERP components including P300.