Presence of Distractor Improves Time Estimation Performance in an Adult ADHD Sample

Abstract

Objective: It is widely accepted that patients with ADHD exhibit greater susceptibility to distractors, especially during tasks with higher working memory load demands. However, no study to date has specifically measured the impact of distractors on timing functions, although these have consistently shown alterations in ADHD. In this investigation, we aimed to elucidate the neural mechanisms mediating distractor effects on timing functions. Method: We employed a time estimation functional magnetic resonance imaging (fMRI) paradigm including a distracting element in half of the trials in a sample of 21 patients with ADHD and 24 healthy controls. Results: As expected, the effect of the distractor was greater in ADHD patients, where it was associated with increased orbitofrontal activity compared with controls. Behaviorally, time estimation performance benefited from the presence of distractors in both groups. In turn, such improvement correlated with medial frontal and insular activity in the brain. Conclusion: These results suggest that distractors could be stimulating recruitment of frontal resources in ADHD, thus contributing to increase focus on the task.

Keywords

ADHD adult ADHD fMRI distractors orbitofrontal time estimation

Introduction

Ignoring distracting stimuli to maintain current task goals depends on inhibitory control abilities. Cognitive, affective, and motor inhibition deficits have been proposed to underlie core symptoms of ADHD, such as inattention and impulsivity, which, in turn, have been consistently associated with timing dysfunctions (Rubia, Halari, Christakou, & Taylor, 2009a). However, most of the research on inhibitory control focuses on working memory tasks, leaving many open questions about the impact of distractors on timing functions. Furthermore, although most of the studies on inhibition control in ADHD target children, symptom persistence in 65% of the cases (Faraone & Biederman, 2005) makes research in adults relevant and necessary.

Current accounts on ADHD establish dopamine dysfunction in mesocortical and mesolimbic circuitry as a potential factor underlying deficient modulation of attentional processes and abnormal reward management, respectively, leading to overall impulsive and disorganized behavior (Sonuga-Barke, Bitsakou, & Thompson, 2010). Deficient top-down cognitive inhibition over non-relevant disruptive stimuli has indeed been related to impaired working memory capacity in ADHD (Cornoldi et al., 2001; Palladino & Ferrari, 2013; Prehn-Kristensen et al., 2011). Such impairment in cognitive inhibitory systems has been related to medial prefrontal dysfunction (Salavert et al., 2018). Accordingly, processes which are highly dependent on working memory, such as time estimation (Fink & Neubauer, 2005), have been observed to be impaired in ADHD (Hart, Radua, Mataix-Cols, & Rubia, 2012; Noreika, Falter, & Rubia, 2013; Smith et al., 2013; Wilson, Heinrichs-Graham, White, Knott, & Wetzel, 2013). As Vloet et al. (2010) point out, crucial neural regions for interference control, such as the basal ganglia and parietal cortex, are seemingly decisive for encoding time intervals. Such findings suggest a strong link between time estimation difficulties and inhibitory control deficiencies in ADHD.

The effect of distractors on task performance is still a matter of debate in ADHD literature. In a number of studies, distractors are found to increase reaction times (Escera, Alho, Winkler, & Näätänen, 1998; Gumenyuk, Korzyukov, Alho, Escera, & Näätänen, 2004; van Mourik, Oosterlaan, Heslenfeld, Konig, & Sergeant, 2007) leading to poorer task performance (Gumenyuk et al., 2005; Mason, Humphreys, & Kent, 2005; Tsujimoto et al., 2013). However, some studies point to possible beneficial effects of distractors on ADHD (Abikoff, Courtney, Szeibel, & Koplewicz, 1996; Leung, Leung, & Tang, 2000; Zentall & Meyer, 1987), leaning on the idea that extra-task stimuli optimize the otherwise low levels of arousal in ADHD to meet task demands (Sergeant, 2005; Sergeant, Oosterlaan, & van der Meere, 1999). Non-relevant stimuli have thus been argued to both disrupt and enhance performance, presumably, in a task-dependent fashion. For instance, while distractors deteriorated performance in tasks with high working memory load (Cornoldi et al., 2001; Palladino & Ferrari, 2013; Prehn-Kristensen et al., 2011), tasks with lower working memory load demands, such as the digit categorization task used by López-Martín, Albert, Fernández-Jaén, and Carretié (2013), benefitted from the presence of distractors.

The aim of this study is to evaluate the impact of distractors on performance and brain activity dynamics during the completion of a time estimation task in an adult ADHD sample. Because time estimation relies on working memory, our predictions are, behaviorally, that distractors will negatively affect performance. Neurally, we expect brain areas associated with inhibition control, such as the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC; Blasi et al., 2006; Chambers, Garavan, & Bellgrove, 2009; Garavan, Ross, Murphy, Roche, & Stein, 2002; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004), to show an abnormal activation in the distractor condition. Finally, given the deficits in inhibitory control and timing functions reported in ADHD, we predict poorer performance in time estimation in this sample compared with controls, as well as a stronger effect of the distractor on behavioral and neurofunctional variables.

Method

Participants

Twenty-one adults with combined ADHD (10 women) and 24 healthy participants (12 women) were recruited for the study (see Table 1). Both groups were matched for age, gender, and IQ. The ADHD patients were carefully selected by a specialized team of psychiatrists and psychologists from Vall d’Hebron Hospital in Barcelona (Spain). All of them met the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria for ADHD combined subtype and were medication naive.

Table 1.

Demographic and Clinical Data of the Adult Sample.

Characteristic	ADHD (n = 21)	Controls (n = 24)	t value (df)	p value
Characteristic	M (SD)	M (SD)	t value (df)	p value
Age (range = 19-50 years)	36.48 (6.90)	34.33 (7.73)	0.975 (43)	0.331
ADHD Rating Scale	36.38 (7.51)	5.78 (4.98)	16.05 (42)	<.000
WURS	54.67 (12.43)	16.32 (10.77)	10.82 (41)	<.000
CAARS
Inattention	51.10 (23.40)	5.46 (5.27)	−8.74 (21.78)	<.001
Hyperactivity	48.52 (20.30)	7.45 (5.60)	−8.96 (22.67)	<.001
Impulsivity	54.10 (24.47)	4.71 (4.74)	−9.01 (21.32)	<.001

Note. Two controls did not complete the ADHD Rating Scale and CAARS (n = 22), and three did not complete the WURS (n = 21). WURS = Wender Utah Rating Scale; CAARS = Conners’ Adult ADHD Rating Scale.

Clinical scales were administered in both groups to assess clinical severity and ADHD prevalent subtype, including the Conners’ Adult ADHD Rating Scale (CAARS; Conners, Sitarenios, Parker, & Epstein, 1998), the Wender Utah Rating Scale (WURS; Ward, Wender, & Reimherr, 1993), and the ADHD Rating Scale (DuPaul, Power, Anastopoulos, & Reid, 1998). All ADHD scores were significantly higher in the ADHD sample.

Exclusion criteria included comorbidity with other psychiatric diseases or personality disorders, assessed by the Structured Clinical Interview for DSM-IV Axis I (SCID-I; First, Spitzer, Gibbon, Williams, & Benjamin, 1994) and Axis II Disorders (First, Spitzer, Gibbon, & Williams, 1997). Participants with substance abuse disorder, including those who consumed tobacco and cannabis within the last 6 months, were also excluded. Participants with an estimated IQ lower than 80 were not included. The study was approved by the Hospital de Vall d’Hebron Ethics Committee, and informed consent was obtained from all participants before taking part in the study.

fMRI Paradigm

A rapid event-related fMRI paradigm with randomized trials and jittered inter-trial intervals (ITIs) was designed using E-Prime Studio [Psychology Software Tools, Inc. (2012), http://www.pstnet.com]. All participants practiced the task thoroughly before starting the experiment.

The paradigm was divided in eight blocks with 10 trials each. Each block was separated by short 15-s intervals. An inter-trial fixation cross was presented at random intervals from 1 to 3 s. During each trial, participants completed a time estimation task. First, they were shown a red light and were instructed to estimate for how long it was on for. Red lights were presented at variable intervals ranging from 1 to 6 s. After a short delay, a green light appeared on screen. Participants had to press the response button at the moment they considered the green light had been on for the same amount of time as the red light. Fifty percent of the trials included a distracting moving element, such as bird or a ladybug, located in the upper left corner of the red light screen (see Figure 1). The total duration of the paradigm was 16 min.

Figure 1.

Time estimation paradigm with half of the trials including a distracting element in upper left corner of the red light screen.

Analysis of Performance Data

A two-way repeated measures ANOVA was conducted with group membership as between-subjects factor and trial type (distractor vs. no distractor) as within-subjects factor. The dependent variable was the participant’s absolute mean time estimation error in each condition.

fMRI Image Acquisition and Analysis

Images were acquired in a Philips Achieva 3T scanner. A T1-weighted pulse sequence with repetition time (TR) = 500 ms, echo time (TE) = 50 ms, fli angle (FA) = 8°, matrix size = 240x200 and slice thickness=1mm was employed for the structural acquisition. A T2-weighted gradient single shot echo sequence (EPI) allowed obtaining the functional volumes, each comprising thirty 3mm thick slices (TR 3000 ms, TE: 35 ms, FA: 90°, gap = 1.0 mm, matrix size: 76x75).

Functional MRI data were analyzed with the software package SPM8 (Welcome Department of Imaging Neuroscience, London, United Kingdom). Functional images were realigned to correct for motion-related artifacts, normalized into Montreal Neurological Institute (MNI) standard space and spatially smoothed with a 8 mm full-width-at-half-maximum Gaussian kernel.

The General Linear Model (GLM) matrix design included two regressors, one for distractor trials and one for non-distractor trials, spanning across the encoding stage of the trial, as the green light (with or without distractor) is presented. The alternating 15-s fixation blocks and the 1- to 3-s inter-trial delay were used as baseline activity. For each of the participants, neural activity during distractor, non-distractor trials, and baseline was contrasted.

After a first-level individual analysis, parameter estimates were submitted to a second-level group analysis. The distractor’s effect was assessed using a series of two-sample t tests intended to capture group differences associated with the presence of a distractive element. Moreover, to evaluate the predictive value of the ADHD Rating Scale, WURS, and difference in performance between conditions over neural activity during distractor trials, a multiple regression analysis was conducted. In each contrast, a whole-brain exploration was performed with a threshold of p < .05 corrected for multiple comparisons using the false discovery rate (FDR; Genovese, Lazar, & Nichols, 2002). In addition, a region-of-interest analysis of the ACC and the DLPFC was performed to assess differences in inhibitory control between groups.

Results

Behavioral Results

Against expectations, time estimation error measures were not significantly different in the ADHD group compared with controls. However, the absolute difference in performance between distractor and non-distractor trials was significantly larger, t(43) = −2.213, p = .032, in the ADHD group (M = 3.29; SD = 2.11) than in controls (M = 2.06; SD = 1.62), that is, the ADHD patients were more susceptible to distractors (see Table 2). In turn, the difference in performance between conditions significantly correlated with scores in the ADHD Rating Scale (r = .365, p = .015) and the WURS (r = .382, p = .011) in the whole sample.

Table 2.

Performance Data for Adult ADHD Patients and Healthy Control Participants Calculated in Percentage of Absolute Error in Time Estimation.

Absolute time estimation error condition	Controls (n = 24)	ADHD (n = 21)
Absolute time estimation error condition	Mean percentage error (SD)	Mean percentage error (SD)
No distractor	20.86 (7.41)	23.96 (7.28)
Distractor	19.76 (7.60)	22.20 (6.44)
Difference between conditions	2.06 (1.62)*	3.29 (2.11)*

Note. No differences within conditions and between groups were detected. However, difference in performance between conditions was larger in patients with ADHD, that is, higher effect of the distractor, t(43) = −2.213, p = .032.

t(43) = −2.213, p = .032.

The repeated measures ANOVA analysis confirmed the within-group effect of the distractor, Wilks’s Lambda = 0.807; F(1, 43) = 10,25; p = .003, although the interaction between the distractor condition and group membership was not significant (see Figure 2). Interestingly, the directionality of the distractor’s effect was the opposite as predicted, with enhanced performance in distractor trials in both groups.

Figure 2.

Repeated measures ANOVA analysis with group membership and trial type as between- and within-subjects factor, respectively, and absolute time estimation error as the dependent variable.

Imaging Results

The evaluation of neural activity associated with the presence of a distracting element during a cognitive task provided further insight into the neural processes involved in inhibitory control in ADHD.

When comparing distractor trials with baseline, ADHD patients exhibited greater superior orbitofrontal activation than controls (Table 3). In turn, ADHD was associated with decreased right pre-central, post-central (18_x / −30_y / 60_z, p = .027 FDR-corrected), and bilateral supplementary motor area (9_x / 21_y / 66_z, p = .005 FDR-corrected) activations with respect to controls during non-distractor trials compared with baseline. Interestingly, difference in performance between conditions was a significant predictor of neural activity in the right medial frontal gyrus, right thalamus and cerebellum (p < .001 FDR-corrected), and the left posterior insula, superior temporal gyrus, and pre-central gyrus (p < .002 FDR-corrected) in the whole sample (Figure 3). The region of interest (ROI) analyses of the ACC and DLPFC were non-significant for each of the contrasts.

Table 3.

Results of a Whole-Brain Two Sample t-Test Analysis on an ADHD and a Control Sample Showing Significant Bilateral Superior Orbitofrontal Activation During Distractor Trials Compared With Baseline.

Differential effect of distractor on groups	Peak MNI coordinates			Number of voxels	p values (FDR-corrected)	Effect sizes (T)
Differential effect of distractor on groups	x	y	z	Number of voxels	p values (FDR-corrected)	Effect sizes (T)
Contrast: Distractor > baseline; ADHD > controls
Bilateral superior orbitofrontal gyrus	6	45	−24	78	.019	4.35

Note. p value is FDR-corrected. FDR = false discovery rate.

Figure 3.

Brain activations resulting from a whole-brain multiple regressions with difference in performance between distractor conditions as the predictor on the whole sample. Images of sagital and transversal slices showing positive activity in different areas indicated with MNI coordinates and FDR-corrected p values.

Discussion

The aim of this study was to evaluate, for the first time to our knowledge, the impact of distractors on time estimation abilities in adult ADHD patients compared with healthy participants. Presence of distractors leads to an improvement in time estimation accuracy in both groups, with enhanced susceptibility to distractors in the ADHD group, as predicted. Against expectations, there were no significant differences in time estimation performance between ADHD patients and controls. Furthermore, areas typically involved in interference control, such as the DLPFC and ACC, did not show differential activity in distractor trials in ADHD patients. Thus, neither the behavioral nor the neurofunctional data supported the hypothesis of a reduced interference control capacity in ADHD. Conversely, the ADHD sample exhibited greater involvement of bilateral orbitofrontal areas during distractor trials compared with controls. Although such higher recruitment could be compensating higher cognitive demands in the ADHD sample, orbitofrontal regions are not typically associated with cognitive working load. Rather, they have been related to motivational aspects of executive function in ADHD (Cubillo, Halari, Smith, Taylor, & Rubia, 2012; Dibbets, Evers, Hurks, Marchetta, & Jolles, 2009; Rubia, Smith, Halari, Matsukura, Mohammad, Taylor, & Brammer, 2009b). These findings suggest that higher salience of the distracting shapes during the encoding phase might have helped ADHD patients focus on the task, improving their performance.

Previous studies on inhibitory control reported increased lateral prefrontal activations in ADHD patients during distractor trials (Schulz et al., 2004; Schweitzer et al., 2000; Vloet et al., 2010; Wang et al., 2013). Such distractor-associated lateral prefrontal activity has been correlated with ADHD symptom severity (Tsujimoto et al., 2013), and is often interpreted as a compensatory mechanism arising from inefficient interference inhibition (Sheridan, Hinshaw, & D’Esposito, 2007; Tsujimoto et al., 2013; Wager et al., 2005). However, in the present study, no activity differences were found in the two cortical networks known to modulate inhibitory control, involving the DLPFC and the ACC (Blasi et al., 2006; Chambers et al., 2009; Garavan et al., 2002; Ridderinkhof et al., 2004). Therefore, the present results do not support the hypothesis that poorer inhibitory control in ADHD accounts for a greater effect of the distractor. On the contrary, the distractor seems to be improving performance by increasing the stimulus salience in ADHD, as pointed out by the higher recruitment of orbitofrontal circuits during distractor trials in this sample. Interestingly, the presence of distractors leads to better time estimation accuracy in the whole sample.

In line with this enhanced-salience hypothesis, improvement in time estimation accuracy associated with distractors was a significant predictor of right fronto-cerebellar, insular, and superior temporal activity during the encoding phase in the whole sample. Cerebellar regions in association with fronto-parietal circuits have been involved in duration estimation and temporal prediction tasks (Christakou, Brammer, & Rubia, 2011; Coull, Cheng, & Meck, 2011; Hart et al., 2012; Noreika et al., 2013; Wiener, Turkeltaub, & Coslett, 2010). Hence, fronto-cerebellar networks might be holding task-related activity, while left superior temporal areas could be sustaining language-related processes such as counting seconds, leading to a more accurate estimation of time intervals. In turn, the higher posterior insular activity associated with distractor-elicited improvement might be related to increased somatosensorial attention. Thus, distractors might be increasing focus on the present moment, ultimately benefitting task performance in the whole sample. This mechanism seemed to be stronger in ADHD patients, who showed significantly higher distractor-elicited improvement in performance, which, in turn, correlated with higher scores in the ADHD Rating Scale and WURS in the whole sample.

In light of our results, it is plausible that distractor presentation increased general salience of the task in ADHD patients, increasing recruitment of frontal networks and, ultimately, mobilizing attentional resources. Impaired detection of salient targets has been reported in ADHD patients before (Tamm, Menon, & Reiss, 2006). Therefore, in our experiment, addition of a distractor might have buffered target detection abilities in ADHD, increasing somatosensorial attention and frontal recruitment directed to the task at stake. These observations fit well into the optimal stimulation theory framework, which states that extra-task stimulation can be beneficial for ADHD patients in some instances, uplifting their arousal level to meet task requirements (Sergeant, 2005; Sergeant et al., 1999). In controls, although distractors were associated with decreased orbitofrontal activity compared with ADHD patients, we cannot rule out that the salience of the task might have increased with distractors, although in a lesser extent than in the ADHD group. Indeed, distractors also had beneficial, although not significant, effects on time estimation accuracy in controls, again in association with higher recruitment of somatosensorial attentional circuits.

In sum, our findings endorse the notion that, to preserve focus on the task, ADHD patients require an optimal level of stimulation dependent on the task, a phenomenon that is also observed in controls at a more modest level. Such seemingly higher stimulation demands in ADHD might be explained by the weaker of frontal recruitment and salience detection abnormalities that, at least in part, underlie attentional deficits in this population (Tamm et al., 2006). Thus, in this case, extra-task stimuli might have benefitted task performance presumably by heightening the stimuli’s salience, thus capturing more attentional resources. In this scenario, an interesting question arises as to whether inhibitory control, a gating mechanism that filters out noise in favor of goal maintenance, is deficient in ADHD as reported in many studies (Cornoldi et al., 2001; Palladino & Ferrari, 2013; Prehn-Kristensen et al., 2011) or whether the stimuli generally presented by researchers are by themselves not salient enough to ADHD patients. New studies will have to elucidate this question by specifically testing whether there is a turning point where extra-task stimuli with increasing perceptual load stop benefitting salience detection and start affecting inhibitory control in ADHD.

Conclusion

The aim of this research was to evaluate the impact of distractors on a time estimation task in an adult ADHD sample. Distractors were associated with higher orbitofrontal activations in the encoding stage that could be related to greater motivational impact of distractors in ADHD patients. Behaviorally, ADHD patients performed as accurately as controls, although they improved to a greater extent when distractors were presented. Thus, distractors had a larger effect on ADHD patients, leading to a greater performance improvement. Such improvement was, in turn, associated with higher recruitment of somatosensorial attentional systems mediated by the posterior insula in the whole sample. Hence, at least in some cases, presence of distractors might be beneficial in ADHD by increasing recruitment of frontal resources and, subsequently, attention to the task at stake.

Footnotes

Authors’ Note

Work was performed at the Universitat Autònoma de Barcelona, Departament de Psiquiatria i Medicina Legal, Cerdanyola del Vallès, España. Clara Pretus and Marisol Picado have contributed equally to the elaboration of this manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Clara Pretus Gómez earned her bachelor’s degree in Biology from the Universitat de Barcelona (Spain) in 2011 and her master’s degree in Neuroscience from King’s College London (UK) in 2012. Currently, she is researching on ADHD as a PhD student under the supervision of Dr. Oscar Vilarroya.

Marisol Picado Rossi obtained her PhD in Psychiatry and Neuroscience in the Department of Medicine, Universitat Autònoma de Barcelona, in 2013. She is currently working in Fundación Clínic per a la Recerca Biomédica, Barcelona, Spain. Her research is centered on clinical, neuropsychological and neuroimaging aspects of affective disorders such as bipolar disorder, depression and anxiety, mostly in youth.

Josep Antoni Ramos-Quiroga, MD, PhD is an associate professor of Department of Psychiatry and Legal Medicine at Universitat Autònoma de Barcelona, Spain, and Head of the Department of Psychiatry at Vall d’Hebron University Hospital. His research interests include the study of ADHD across life span. Viviana Nasillo is psychologist of Department of Psychiatry at Vall d’Hebron University Hospital. Her research interests include the study of ADHD across life span.

Susana Carmona, PhD, is a research scientist with 12 years of research experience in neuroimaging. Her research is focused on elucidating the neural mechanisms underlying neurodevelopmental disorders such as Attention Deficit Hyperactivity Disorder. Dr. Carmona is currently a researcher at the Biomedical Imaging and Instrumentation Group of the Hospital General Gregorio Marañón, in Madrid, Spain.

Vanesa Richarte, MD, is psychiatrist and coordinator of Adult ADHD Program of Department of Psychiatry at Vall d’Hebron University Hospital. Her research interests include the study of ADHD across life span.

Jordi Fauquet, PhD, is professor of Data Analysis at the Universitat Autònoma de Barcelona. His research focuses on the field of statistical modelling and mathematical neuroscience. Currently, its interest lies in the study of the theoretical and mathematical basis of a framework to connect models of physical brain activity with cognitive models, by means of the connections between information theory and thermodynamics.

Oscar Vilarroya, MD, PhD, is associate professor of Psychiatry at the Universitat Autonoma de Barcelona (UAB), where he is the director of the Cognitive Neuroscience Unit (URNC) as well as director of the “Social Brain” Chair. He is also the coordinator of the Neuroimaging Research Group (NRG) at the Fundació IMIM. Dr. Vilarroya has made contributions in theoretical neurobiology, social neuroscience and in the neuroimaging of mental disorders.

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