We recruited two groups of participants: clinically stable adults with ADHD and healthy controls. Inclusion criteria were assessed via a two-step procedure. First, all applicants were required to fill in a digital questionnaire regarding age, health, handedness, alcohol/substance use, diagnosis/es and medications. Applicants were excluded at this stage if they were older than 50 years; were dominantly left-handed; reported having medical or psychiatric conditions or disabilities that could affect the quality of the data (e.g., severe acute psychiatric disorders such as but not exclusively: psychotic disorder, bipolar disorder, current severe MDD; ASD or hearing loss) or reported frequent use of alcohol/substances. Participants medicated with medication that could affect attention or wakefulness such as neuroleptics, sedatives, and/or opioids were excluded. Because a vast majority of aADHD (up to 75%) also have comorbid anxiety and/or depressive symptoms (Kessler et al., 2005; Kooij et al., 2012), ADHD-applicants on stable doses for at least 2 months of common antidepressants were not excluded. Stable medication with SSRI or SNRI indicates stability in the comorbid condition, while not unnecessarily excluding a representative group of participants. In addition, applicants with ADHD were included only if medicating with central stimulants (methylphenidate, dexamphetamine, lisdexamfetamine, etc.) and prepared to undergo a 48 h washout period prior to testing or were currently unmedicated for their ADHD.

Second, inclusion criteria were further assessed on the day of participation. Participants were screened for normal hearing thresholds (<20 dB HL) with pure-tone audiometry at six frequencies ranging from 250 to 8000 Hz (as described in: American Speech-Language-Hearing Association, 2005). Clinical assessments of attention, ADHD-symptom severity, and the presence of comorbid disorders and problems with substance abuse were investigated in more detail via the d2-R Test of Attention (Brickenkamp et al., 2010), the 18-item adult ADHD self-report scale (ASRS) v.1.1 (Kessler et al., 2005; Rodriguez et al., 2007) and the Mini-International Neuropsychiatric Interview (MINI) 7.0.2 DSM-5 for ADHD studies (Sheehan et al., 1998) respectively. Individual scores for the ASRS were calculated by summing scores from items associated with self-reported attentional difficulties (Part A: 1–4; Part B: 1–5), hyperactivity/impulsivity problems (Part A: 5–6; Part B: 6–12), as well as the sum of all 18-items as a general index of symptom severity. Standard scoring procedures were used for each index in the d2-R and MINI.

The data for this study was obtained from 34 participants who met the inclusion and exclusion criteria, forming two equal groups. The ADHD-group consisted of eleven females (age: M = 27 years, SD = 7.0) and six males (age: M = 29 years, SD = 7.0). 15 out of the 17 ADHD participants were prescribed stimulant medication (eight lisdexamfetamine, six methylphenidate and one dexamphetamine), and abstained from their medication for 48 h prior to testing. The remaining two participants were unmedicated for ADHD. In the ADHD group five participants had SSRI medication and one had lamotrigine in stable doses during at least 2 months. The control group consisted of 13 females (age: M = 25 years, SD = 4.9) and 4 males (age: M = 26 years, SD = 6.2).

The scanning protocol utilized a block design and was programmed in Presentation Software (21.1, build September 05, 2019).¹ Participants used their right index finger to trigger the response button and response times (RTs), performance accuracy and false alarms (i.e., responding when no target was present) were recorded via a response box (LUMINA, Cedrus Corporation, San Pedro, CA, United States) interfaced with the stimulus presentation. In all experimental blocks, participants were exposed to both auditory and visual stimuli. Auditory stimuli were presented to participants via OptoActive^TM active noise canceling headphones (OptoAcoustics Ltd., Tel Aviv, Israel). The noise canceling headphones both passively and actively attenuated the background echo planar imaging (EPI) gradient noise to ∼ 58 dB SPL. And the headphones were kept in place via inflatable positioning pads (Pearltec MRI/CT Multipad Plus, MagMedix, Fitchburg, MA, United States) which also served to minimize head movements within the head cage. Visual stimuli were presented in the center of the visual field via MRI-goggles (Resonance Technology Company, Inc., Los Angeles, CA, United States).

Prior to the scan, subjects underwent a training session to ensure that they clearly understood all the task requirements. For the visual n-back task, participants were told to perform one of three n-back conditions whilst ignoring a streaming acoustic signal. N-back stimuli consisted of a sequence of 15 letters drawn pseudo-randomly from the set: K, M, Q, R, S, T, and W (white text on black background, font size: 18 points). Letters were individually presented for a duration of 500 ms followed by an interstimulus interval indicated by a fixation cross of 1022 ms, and for a total block duration of 22.8 s (equivalent to 30 repetition times; TR = 761 ms). The 0-back condition required participants to press the response button to the target letter, K. In the 1-back condition, the response button was to be triggered when participants detected two consecutive, identical letters. The 2-back condition required participants to respond when they saw a letter identical to a letter presented two trials prior. Each n-back block contained four target letters in total.

In the ATD task, participants were told to ignore the visual n-back stimuli and instead shift their attention to the streaming auditory stimulus. The task required participants to press the response button every time they heard a deviant tone (1000 Hz) amidst a stream of standard tones (500 Hz). Tones of 150 ms (rise and fall = 22 ms) were presented every 104 ms with a total of four deviant tones within random distances of circa 2–6 s per 22.8 s experimental block. The perceived loudness of the standard and deviant tones was set at −16 LUFS and presented to participants at a sound pressure level of ∼ 75 dB. The presentation order of the experimental conditions was partially counterbalanced over the entire experiment and divided into three runs of eight task blocks, wherein each experimental condition was presented twice per run (see Figure 1). Every experimental block was proceeded by a 15.2 s resting baseline condition (=20 TR) and initiated by a 5.3 s (=7 TR) task instruction.

Outside of the scanner, and in a quiet room, participants completed two WM span tests. The Reading span task (Rönnberg et al., 1989) to measure WM maintenance, and the Size-comparison span test (Sörqvist et al., 2010) as a measure of both WM gating and maintenance; the procedures for which are published in Blomberg et al. (2019).

Whole-head fMRI scans were performed on a Siemens Prisma 3T scanner with a 64-channel head coil at the Centre for Medical Imaging and Visualization (CMIV), Linköping University Hospital, Sweden. A 3D, T1-weighted MPRAGE (magnetization prepared rapid gradient echo) anatomical scan was acquired with the following parameters: repetition time (TR) = 2300 ms; echo time (TE) = 2.36 ms; flip angle (FA) = 8°; field of view (FOV) = 250 × 250 × 225 mm; acquisition matrix = 288 × 288 × 208; slice orientation = sagittal; slice thickness = 0.9 mm; number of slices = 208; voxel size = 0.87 × 0.68 × 0.9 mm. Whole brain, blood oxygen level dependent (BOLD) fMRI was conducted using EPI with the following parameters: TR = 761 ms; TE = 24 ms; FA = 53°; FOV = 204 × 204 mm; acquisition matrix = 68 × 68; number of slices = 45; slice thickness = 3 mm; voxel size = 3 × 3 × 3 mm. Field map imaging was performed with a double-echo spoiled gradient echo sequence, generating one magnitude and two phase images [TR = 520 ms; TE = 4.92/7.38 ms; FA = 60°; total EPI readout time = 16.415 ms; blip direction = 1]. Participants underwent a 12 min resting state scan (this data is reported elsewhere) immediately prior to the current study’s task-based scan. Before commencing the task-based session, participants were removed from the scanner so that the ear plugs they wore during the resting state scan could be removed and so that the active noise-canceling headphones could be refitted and recalibrated to ensure the quality of the signal-to-noise ratio.

The CONN toolbox v.20.b (Whitfield-Gabrieli and Nieto-Castanon, 2012, 2017; RRID:SCR_009550)², which is powered by SPM12 (Statistical Parametric Mapping v. 12, Wellcome Department of Imaging Neuroscience, University College London, United Kingdom), was used to pre-process the data in MATLAB R2020B software. We used the default pre-processing pipeline for volume-based analyses but with indirect normalization to standard stereotactic (MNI) space as we had obtained gradient field maps during image acquisition (Nieto-Castanon, 2020). The pipeline consisted of the following five steps and parameters:

Table 1 presents group descriptives for education, hearing acuity, MINI, ASRS and d2-R measures. The majority of participants (both ADHD and Controls) were studying (or had completed studies) at an undergraduate level of education. There were no differences between groups in age F(1, 33) = 0.35, p = 0.557; or hearing acuity, Welch’s F(1, 25.3) = 1.4, p = 0.252. The majority of individuals in the control group reported having relatively infrequent difficulties with inattention and hyperactivity/impulsivity as measured by the ASRS and MINI. Although one control participant was categorized as inattentive on the MINI, this participant only scored 17 for inattentiveness on the ASRS which is well under the cut-off score (24) for diagnostic evaluation. However, excluding this participant from the analysis did not change the pattern of results. The reason for this discrepancy between scales may be because the MINI requires a forced choice, yes/no answer for each item, whereas the ASRS is more nuanced and allows the participant to reflect and grade in more detail the frequency of symptoms. The majority of ADHD participants (11) had the combined subtype, the remaining six had mainly inattentive subtype according to MINI. In addition, our sample of aADHD had, as expected, significantly higher ASRS scores on both inattentive F(1, 32) = 97.1, p < 0.000, η² = 0.75; and hyperactivity/impulsivity F(1, 31) = 20.6, p < 0.000, η² = 0.40; subscales compared to Controls. The ADHD group did not however demonstrate significantly poorer performance than Controls on the d2-Test measures of concentration Welch’s F(1, 30.4) = 1.3, p = 0.262; processing speed F(1, 32) = 0.31, p = 0.581; or precision F(1, 32) = 0.63, p = 0.432.

Statistical details of group differences in WM capacity and in-scanner performance measures are reported in Table 2. In measures of WM capacity, our sample of aADHD performed on par with Controls. In the n-back task, no differences in mean RTs, or the number of false alarms were observed between groups. Group means in accuracy performance were slightly lower in the ADHD group compared to Controls, but the difference in means was only significant in the highest load (2-back) condition (η² = 0.303). We were surprised to see relatively low accuracy scores on the ATD task from both groups. Closer inspection of the data revealed that the majority of errors were associated with false alarms and occurring milliseconds–seconds prior to the onset of the oddball. Thus, the low accuracy scores in the ATD task was likely an outcome of participants responding impulsively to the anticipation of an oddball (see section “Auditory Target Detection in Noise” in the Discussion).

Two-sample t-tests of whole brain voxel-wise activity did not detect significant differences between groups in any contrasted condition after controlling for multiple comparisons. Within-group analyses however did reveal significant cluster-level activations in all contrast conditions. Cluster-level statistics and slice-by-slice maps of significant effects for within-groups analyses are reported in Supplementary Table 1 and Supplementary Figures 1–4. For the ATD–rest contrast, activity was observed in auditory, superior temporal and supplementary motor cortices within both groups. For the n-back–ATD contrasts, the number of active regions increased with increasing WM load and was observed mainly in higher order visual processing regions, the exterior cerebellum as well as superior partial and middle frontal gyri in both groups.

Figure 2 presents the detailed results of the univariate ROI analysis for the ATD task (see also Supplementary Table 2). Significant increases relative to the resting baseline for the ATD condition were observed in all SN-hubs in Controls, and all except the dACC and left rPFC in ADHD when correcting for multiple comparisons (i.e., all seven hub ROIs) but uncorrected p-values were significant. Between-group comparisons revealed no significant differences for the SN-rest contrast suggesting that all seven SN-hubs were on average, more active relative to baseline in both groups. Initial analysis of the auditory ROIS indicated that only the core, and belt ROIs were significantly more activated than baseline during target detection. Hence, contrary to expectations from previous literature, our posterior insula ROIs were not actively involved in the ATD task; and this was evident in both groups. However, we considered that this may be a result of averaging over relatively large regions of the posterior insula and that a within ROI cluster analysis may prove more informative. To investigate, we used the SPM wfu_pickatlas tool to perform cluster analyses. Results identified significantly active clusters of 26 voxels (p FWE-corr = 0.024) in the left dpI (peak coordinates: −46, −8, 6) and eight voxels (p FWE-corr = 0.035) in the gpI (peak coordinates: −34, −30, 8) in the ADHD group. In the Control group, a significant cluster of 38 voxels (p FWE-corr = 0.011) was identified in the left dpI (peak coordinates: −40, −2, 2). Thus, in line with previous studies implicating the posterior insula in early pitch discrimination tasks, our data does indicate that at least the left posterior insula was involved in our ATD task. Statistical between-group comparisons did not reveal any differences in auditory cortical activation, indicating that all auditory ROIs were similarly activated in both groups throughout the ATD task.

Functional connectivity analysis revealed group differences in ROI–ROI connectivity for the ATD task. ADHD participants demonstrated stronger auditory-SN connectivity than Controls of which the majority of connections involved left lateralized auditory communications with dACC, aI and SMG hubs of the SN (see Figure 3 for details). We additionally investigated if individual differences in behavioral performance on the ATD task would correlate with activity levels in any of the auditory or SN ROIs. Here we observed a negative correlation between RTs associated with accurate trials and increases in BOLD activity the right SMG, r = −0.39, p = 0.027. Hence, faster RTs were associated with heightened activity of the right SMG. No other significant brain–performance correlations were observed.

Results of the mixed repeated-measures ANOVAs are displayed in Figure 4. Both hemispheres presented evidence for a main effect of auditory attenuation as a function of WM load: F_left (2, 64) = 34.6, p = 0.000; F_right (2, 64) = 29.3, p = 0.000); and within-participant contrasts confirmed that the effect was linear: F_left (1, 32) = 30.9, p = 0.000; F_right (1, 32) = 45.6, p = 0.000). The interaction term, WM load × Group, was also significant in both hemispheres: F_left (2, 64) = 8.8, p = 0.000; F_right (2, 64) = 6.2, p = 0.003, and post hoc inspection of the interaction indicated that the Control group’s auditory responses to the task-irrelevant sound source decreased significantly as WM load increased, and was more suppressed compared to ADHD participants in both hemispheres during the high-load condition (see Figure 4A and Supplementary Table 3 for post hoc results). Auditory attenuation increased marginally in the ADHD group across load conditions and was shown to be statistically significant only in the right hemisphere between the 0- and 2-back conditions.

A significant, main effect of ROI was also observed in both hemispheres: F_left (2.5, 78.9) = 13.2, p = 0.000; F_right (2.1, 67.7) = 14.5, p = 0.000).; and the interaction term ROIs × Group was consequently significant: F_left (4.128) = 5.6, p = 0.000; F_right (4.128) = 3.7, p = 0.007. The interactions: WM load × ROI, and WM load × ROI × Group were not significant in either hemisphere, nonetheless post hoc analysis of the WM load × ROI × Group interaction was conducted as we consider it necessary to report the specific ROIs where group differences in attenuation were observed. In both hemispheres, attenuation was significantly less evident in the PP and the dpI for the ADHD group compared to Controls under the high-load condition (see Figure 4B and Supplementary Table 3 for detailed results). Trends in the remaining ROIs were observable but the post hoc test did not reach the conservatively corrected threshold for significance.

We correlated the mean change in auditory attenuation under the high-load condition within each ROI with our WM capacity measures: Reading span and SIC-span. Contrary to expectations, we did not observe a significant relationship with WM capacity in any auditory ROIs. However, the magnitude of auditory attenuation in the high-load condition did correlate significantly with individual differences in self-rated inattentiveness across the majority of auditory ROIs (see Figure 5A for details) which suggests that participants who have difficulties suppressing exogenously triggered responses to task-irrelevant auditory stimulation are also highly susceptible to distraction. In addition, accuracy performance on the 2-back condition also correlated with attenuation levels in the majority of auditory ROIs (see Figure 5B). No other performance related correlations were observed. Taken together, these findings support the hypothesis that heightened cortical responses to task-irrelevant auditory stimulation can both negatively impact task performance and contribute to subjective experiences of auditory distraction.

We were not expecting aADHD to perform on par with Controls in measures of WM capacity. In a previous study performed by our lab group (Blomberg et al., 2019), adolescents (<18 years) with ADHD demonstrated significantly reduced capacity than matched (age and gender) healthy controls on both the Reading span and the Size-comparison span tasks. This finding, in combination with general associations of ADHD with deficient WM capacity (Willcutt et al., 2005; Alderson et al., 2013) was a major premise for using these measures again in our adult sample. Although ADHD is known to persist into adulthood in over half of patients, numerous researchers have noted that many neurocognitive deficits normalize after the developmental transition into adulthood and suggest that ADHD may mainly be attributable to a developmental delay (Shaw et al., 2007; de Zeeuw and Durston, 2017). A related, yet different perspective, associates ADHD with life-time subcortical (basal ganglia and cerebellum) dysfunction, and that age-related reductions in neurocognitive deficits in some cases are attributable to a healthy maturation of frontal lobes and the improved ability to issue cognitive control over subcortical systems (Halperin and Schulz, 2006). Presumably, such developmental differences between groups were exemplified through our WM capacity measures in our previous study with adolescents (Blomberg et al., 2019). We similarly note that concentration scores from the d2-Test of attention were also differential between groups in our adolescent study, but not in the current study. Because our ADHD participants consisted mostly of young adults undergoing higher education and performed on par with Controls in WM and the d2-Test, it is tempting to consider our sample as relatively “high functioning”–a notion that fits well with the aforementioned developmental models of ADHD. However, we must take into consideration that our ADHD group had poorer accuracy performance in the demanding, in-scanner WM condition (2-back). Hence, there is some evidence to suggest that some WM related difficulties reside in our sample of young adults.

Possibly, the neurocognitive profile of ADHD in adults may be more readily observable under challenging experimental contexts. Our sample of aADHD may well have been able to maintain a commensurate number of items in WM to that of TDa in a quiet, isolated room when undergoing the Reading span and Size-comparison span tests; but during the scanning session, the addition of irrelevant sound stimuli and background noise whilst performing the n-back task likely rendered the ADHD group vulnerable to cognitive interference. The latter conclusion is consistent with two other studies in aADHD. Pelletier et al. (2016) reported that visual serial recall performance in their sample of aADHD was more strongly affected by the presence of irrelevant sound; and Dige et al. (2010) observed greater noise interference effects upon verbal memory in a dichotic memory task relative to controls.

We had hypothesized that the additional demands placed by the acoustic environment in the scanner would impact cognitive processing, particularly under the high-load condition, given that control resources needed to be shared between both regulating attention and WM toward one modality whilst suppressing exogenously evoked distractions in another. On this basis, we also expected that participants with high WM capacity would demonstrate more efficient neural attenuation of the acoustic environment and perform overall better on the 2-back condition, a hypothesis supported in previous work by Sörqvist et al. (2012) from our work group (see Sörqvist and Rönnberg, 2014 for a review). This was not supported in our results. We note that in Sörqvist et al. (2012), the authors derived a composite measure of WM capacity from several complex span tasks and the composite score only correlated with auditory brainstem attenuation to task-irrelevant sound in their 3-back condition (i.e., not 2-back). It is thus possible that the WM capacity measures we used in the current study were not sufficiently sensitive indexes of the type of control mechanisms involved in the successful regulation of attention in our 2-back condition.

We did however observe a relatively robust negative correlation between individual differences in auditory attenuation of task-irrelevant sound and 2-back task accuracy. This result provides us with an indication that better cognitive performance on the high-load WM task was intricately related to participants’ capacity to regulate resources between modalities and inhibit distraction. In additional support of this conclusion, participants’ subjective experiences of inattentiveness in daily life (as determined by the ASRS) also robustly correlated with the degree of auditory cortical attenuation under the high-load condition. Our participants with ADHD, that consisted of inattentive or combined (i.e., both inattentive and hyperactive/impulsive) subtypes, were thus more susceptible to auditory distraction and demonstrated poorer performance capacity under the most challenging WM condition.

The ATD task, in which an infrequent “oddball” tone was to be detected amongst a rapid stream of standard tones, places relatively little demand upon cognitive control systems when performed in quiet and should result in high accuracy scores–especially in TDa with normal hearing thresholds. Despite our use of an active noise canceling system which enabled us to present the acoustic stimulus at an audible signal-to-noise ratio of ∼17 dB SPL, behavioral responses from both groups contained a relatively high number of false alarms that occurred just milliseconds-to-seconds prior to the onset of a target. This result suggests that participants were likely responding impulsively to the anticipation of an oddball. The presentation times of oddball-targets were randomly distributed over the 22 s duration of each task-block (four targets per block), so anticipating the exact timing of a target was not possible; however, the audibility of background scanner noise may have been interfering with expectations of an oddball occurring, resulting in impulsive false-alarm responding.

We expected the SN to be heavily involved in the ATD task due to its involvement in vigilant anticipation, detection, and response-mediation of behaviorally salient stimuli; and we explored this network on the grounds that a number of studies have reported aberrant SN functional connectivity in ADHD (see Castellanos and Aoki, 2016 for a review). Significant hyper auditory–SN connectivity was observed in aADHD. Although our test protocol makes it difficult to discern the exact reason for these stronger SN–auditory interactions in the ADHD group, the explanation we consider aligns well with our more general thesis that noise places increased demands on control systems. Given their reported difficulties with inattentiveness, the suboptimal listening situation may have impacted ADHD participants such that the informational exchange between auditory ROIs and SN-hubs was enhanced to facilitate successful oddball detection. As we observed in the 2-back task, heightened auditory responses to the acoustic environment were associated with poorer accuracy performance and symptomatic inattentiveness. And we argued that the challenging 2-back condition taxed cognitive resources and impeded on ADHD participants’ ability to suppress exogenous responses to the acoustic environment. Even though we observed differences in functional connectivity between groups, there were no differences in performance outcomes on the ATD task. Because we do not consider the ATD task cognitively challenging, it is likely our sample of aADHD had the resources available to enhance oddball detection and reduce erroneous responding under suboptimal listening conditions; hence compensating for their presumed symptomatic susceptibility to noise interference.

Increased activity in the right SMG of the SN was also negatively correlated with individual differences in RTs on the ATD task, of which the RTs corresponded only to accurate trials. Hence, faster, accurate RTs were associated with increased activity in the right SMG. The SMG is a core hub of the SN, and the right lateralized region in particular has been implicated in the mediation of exogenous attention to behaviorally salient events (Corbetta and Shulman, 2002; Corbetta et al., 2008; Cabeza et al., 2012; Vossel et al., 2014). Given this proposed functional role of the right SMG in sensory target detection, our combined results suggests that rapid and successful detection of the oddball was associated with effective assignment of saliency to the oddball tone mediated by the right SMG.

With respect to our results, it appears that “high-functioning” aADHD perform relatively well during cognitive tasks but at a greater cost. This notion is in line with earlier literature indicating that a college student with ADHD may need to work twice as hard as their non-ADHD counterparts in order to achieve satisfactory grades (Faraone et al., 2015). Hence, in order to perform in the complex reality of a college or work environment, aADHD may need to deploy more cognitive effort in order to inhibit distraction. The implication of this increased effort is potentially a contributing factor to the reportedly higher levels of perceived stress in college students with ADHD symptoms and the prevalence of anxiety and stress-related pathologies in aADHD (Salla et al., 2019; Gbessemehlan et al., 2020; Öster et al., 2020). Given that some participants were unmedicated during this study, we suggest that more research is needed to understand how medication can compensate for these deficiencies. To prevent stress related problems later in life, clinical interventions should also address adapting work/study environments in order to minimize disturbance and utilize psychoeducation to better improve stress and time-management.

This study has several limitations. First, comparisons of ROIs and networks are difficult across literature due to differences in nomenclature and methods of definition. Hence, our decision for using predefined ROIs and networks through freely available analysis applications, together with as many default analysis settings as possible, was in the hope of facilitating future researchers’ ability to confer/replicate our results. Second, scanner noise was audible throughout the entire experiment. Even during the resting baseline condition. Although we were able to present the auditory stimulus at a SNR of ∼ 17 dB SPL, the active noise canceling system was not 100% stable in that the dampened background could fluctuate; and we speculate that the effect of background noise interfered with performance in both participant groups on the ATD task. Nonetheless, the addition of background noise was not a problematic influence with respect to our most important finding–that our sample of aADHD were more susceptible to auditory distraction when task demands taxed cognitive resources. Third, care must be taken when generalizing results. The manifestation of ADHD in adults is highly heterogenous and often confounded by comorbidities. Around half of our sample were on stable SSRI medication which is indicative of earlier problems with anxiety and depression however, both anxiety and depression reside at the lower spectrum of expected of psychiatric comorbidities in adults with ADHD (Katzman et al., 2017). Our sample was also imbalanced in male:female ratio, of which there were more females than males with ADHD. Although childhood ADHD is more common in boys, differences in prevalence between sexes diminish almost completely in adulthood (Faraone et al., 2015; Matte et al., 2015) and symptomatic differences in hyperactivity and inattention between sexes also wane with older age (Ramtekkar et al., 2010); so we should not expect the gender imbalance in our sample to dramatically affect more general conclusions of our results. That notwithstanding, our sample was small and relatively high-functioning and may not be entirely representative for the more severe spectrum of the disorder. If, however subjects with ADHD in general are expected to have more severe symptoms and functional impairment, group differences detected between our sample and controls are likely to underestimate the overall differences rendering our results conservative. With respect to these limitations, we have cautiously opted to confine discussion to our small sample rather than boldly extrapolate our findings to the population as a whole. That being said, our general findings contribute important evidence to extant theories of cognitive control, auditory distraction, and the pathophysiology of ADHD in adults.