Attention-related EEG markers in adult ADHD

Author’s Accepted Manuscript Attention-related EEG markers in adult ADHD Roland Hasler, Nader Perroud, Hadj Boumediene Meziane, François Herrmann, Paco Prada, Panteleimon Giannakopoulos, Marie-Pierre Deiber www.elsevier.com/locate/neuropsychologia

PII: DOI: Reference:

S0028-3932(16)30159-2 http://dx.doi.org/10.1016/j.neuropsychologia.2016.05.008 NSY5989

To appear in: Neuropsychologia Received date: 17 July 2015 Revised date: 4 May 2016 Accepted date: 8 May 2016 Cite this article as: Roland Hasler, Nader Perroud, Hadj Boumediene Meziane, François Herrmann, Paco Prada, Panteleimon Giannakopoulos and Marie-Pierre Deiber, Attention-related EEG markers in adult ADHD, Neuropsychologia, http://dx.doi.org/10.1016/j.neuropsychologia.2016.05.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Attention-related EEG markers in adult ADHD Roland Haslera,b, Nader Perroudb, Hadj Boumediene Mezianea, François Herrmannc, Paco Pradab, Panteleimon Giannakopoulosd, Marie-Pierre Deibera,e* a

Biomarkers of Vulnerability Unit, Division of General Psychiatry, Department of Mental

Health and Psychiatry, University Hospitals of Geneva, Belle Idée, Chemin du Petit-Bel-Air 2, 1225 Chêne-Bourg, Switzerland b

Division of Psychiatric Specialties, Department of Mental Health and Psychiatry, University

Hospitals of Geneva, 20bis rue de Lausanne, 1201 Geneva, Switzerland c

Division of Geriatrics, Department of Internal Medicine, Rehabilitation and Geriatrics,

University Hospitals of Geneva, Chemin du Pont Bochet 3, 1226 Thônex, Switzerland d

Division of General Psychiatry, Department of Mental Health and Psychiatry, University

Hospitals of Geneva, Belle Idée, Chemin du Petit-Bel-Air 2, 1225 Chêne-Bourg, Switzerland e

INSERM U1039, Faculty of Medicine, Bâtiment Jean Roger, 38700 La Tronche, France

*Corresponding

author: Biomarkers of Vulnerability Unit, Division of General Psychiatry,

Department of Mental Health and Psychiatry, Belle Idée, Chemin du Petit-Bel-Air 2, 1225 Chêne-Bourg, Geneva, Switzerland. [email protected]

Keywords: ADHD, attention networks, event-related potentials, oscillatory activities Abstract ADHD status affects both bottom-up sensory processing and top-down attentional selection, impairing professional and social functioning. The objective of the study was to investigate the functional mechanisms of attention deficits in adult ADHD by examining the electrophysiological activities associated with bottom-up attentional cueing (temporal and spatial orienting of attention) and top-down control (conflict resolution). Continuous EEG was recorded in 21 adult ADHD patients (40.05 ± 9.5 years) and 20 healthy adults (25.5 ± 4

2 years) during performance of the Attention Network Test (ANT). We examined the cue and target-related P1, N1 and P3 components as well as the contingent negative variation (CNV) developing between cue and target. Oscillatory responses were analyzed in the alpha (8-13 Hz) and beta (14-19 Hz) frequency bands. ADHD patients performed similarly to controls but showed reduced P3 amplitude, larger early CNV decrementing over time, reduced preparatory activation in both alpha and beta bands, as well as flattened target-related posterior alpha and beta responses. As compared to controls, the inverted CNV pattern suggested peculiar preparatory processing in ADHD patients. The singular pattern of target-related beta response indicated increased inhibitory processes in the case of easier task resolution and more generally, the lack of association between conflict resolution speed and beta activity supported alternative executive processing in ADHD patients. Overall, the reduced activation of the functional networks devoted to bottom-up and top-down attention suggests that adult ADHD patients engage reduced cortical resources in this composite task, compatible with the cortical hypoarousal model.

1. Introduction Attention

deficit

hyperactivity

disorder

(ADHD),

one

of

the

most

common

neurodevelopmental disorders in children, often persists in adulthood with an established prevalence between 2.5 to 4.9% (Faraone, Biederman, & Mick, 2006; Simon, Czobor, Bálint, Mészáros, & Bitter, 2009). In adults, the disorder is characterized by increased distractibility and difficulties sustaining attention, impulsiveness and hyperactivity with a subjective feeling of inner restlessness (Biederman & Faraone, 2005; Bush, 2010). As described in childhood (Biederman, Petty, Clarke, Lomedico, & Faraone, 2011; Tamm & Nakonezny, 2015), adult

3 ADHD cases also display marked difficulties in directing and maintaining selective attention to relevant informations, as well as increased distractibility towards irrelevant stimuli (Biederman & Faraone, 2005; Bush, 2010; Lubow, Kaplan, & Manor, 2014). In clinical settings, ADHD patients often report being drowned by too many sensory stimuli, preventing them to focus on relevant ones (Barkley, 1990; Barkley, 1997; Bush, 2010). ADHD status affects both bottom-up sensory processing and top-down attentional selection (Janssen, Gelade, van Mourik, Maras, & Oosterlaan, 2016; Nesterovsky et al., 2015; Sergeant, 2005), leading to poorer outcomes and impairments in professional and social functioning (Barkley, Murphy, & Fischer, 2008). Event-related potentials (ERPs) are issued from transient post-synaptic neural responses to a stimulus. They include positive and negative electrophysiological components that reflect various aspects of the stimulus cerebral processing (Luck & Kappenman, 2011). The early ERP components correspond to basic sensory stimulus processing (e.g., P1 and N1 peaking at 80-120 ms and 130-170 ms, respectively), whereas the later components reflect perceptual and cognitive processes, including encoding, classification, task control, selection and preparation of response (Banaschewski & Brandeis, 2007; Portella et al., 2012). ERPs have been widely used to address the sensory and cognitive processing in ADHD (for review see Johnstone, Barry, & Clarke, 2013; Tye, McLoughlin, Kuntsi, & Asherson, 2011). Conflicting data were obtained regarding P1 and N1 amplitude and latency in both children and adults with ADHD (Barry et al., 2009; Perchet, Revol, Fourneret, Mauguière, & Garcia-Larrea, 2001), but the intact attention-related modulation of N1 amplitude supported the idea that early cortical attention processes are preserved in ADHD (López et al., 2006). In contrast, the P3 component, which occurs approximately 300 ms after a behaviorally relevant stimulus and denotes the attentional updating of its representation (Polich, 2007), is reported of reduced amplitude in children (Jonkman et al., 1997; Kemner et al., 1996; Kratz et al., 2011;

4 Lawrence et al., 2005; Steger, Imhof, Steinhausen, & Brandeis, 2000) and adults with ADHD (Ibanez et al., 2012; McLoughlin et al., 2010; Valko et al., 2009; Woltering, Jung, Liu, & Tannock, 2012). This P3 reduction is thought to reflect impaired attentional resource allocation leading to decreased evaluative and processing abilities. The contingent negative variation (CNV) is a slow negative component that develops during the interval between a warning and an imperative stimulus and relates to forthcoming stimulus anticipation and motor response preparation (McCallum, 1988). The CNV is reduced in amplitude in children (Banaschewski et al., 2003; Banaschewski et al., 2008; Perchet et al., 2001) and adults with ADHD (Mayer, Wyckoff, & Strehl, 2015; McLoughlin et al., 2010; McLoughlin et al., 2011), indicating deficits in anticipatory and preparatory processes.

Event-related spectral perturbations (ERSPs), derived from oscillatory neural activity, refer to the rhythmic electrical activity generated by neuronal populations in response to a stimulus. ERSP amplitude varies with the number of synchronously active neurons and the precision of synchrony, reflecting neural processes activated upon specific cognitive demands (Luck & Kappenman, 2011). The main approach to obtain ERSPs is time-frequency analysis, which involves decomposition of the EEG signal in the frequency domain and characterization of the spectral changes over time with respect to the external stimulus. ERSPs within 8 to 30 Hz are known to be modulated by visual attention, with a suppression of alpha (8-13 Hz) and beta (14-30 Hz) spectral power in posterior brain regions actively engaged in the attentional processing of visual information (Engel & Fries, 2010; Klimesch, 2012; Pomper, Keil, Foxe, & Senkowski, 2015). In a recent study investigating adult ADHD patients performing working memory n-back tasks (in which the target is a stimulus matching the one presented n trials back), our group evidenced a dysfunction of neural activities sub-serving directed visual attention (Missonnier et al., 2013). ADHD patients performed similarly to controls which a

5 slight tendency for worse scores (longer reaction time and reduced accuracy), reducing the probability of a ceiling effect in performance. The time course of their alpha event-related desynchronization/synchronization (ERD/ERS) cycle was modified, with lower initial alpha ERD and higher subsequent alpha ERS, suggesting that ADHD patients used late compensatory mechanisms in order to preserve working memory task performance.

While traditional views consider attention as a uniform concept, Posner and Petersen suggested a model describing attention as a set of independent control networks (Posner & Petersen, 1990). Hence, the attention system can be divided into three subsystems performing distinct but interrelated functions, namely alerting, spatial orienting, and executive control, designed as attentional networks. Alerting corresponds to the achievement and maintenance of an alertness state (increased readiness to react to an external warning stimulus), orienting to the selection of relevant information from external inputs, and executive control to the resolution of conflict among potential responses. Based on these attentional networks, Fan and collaborators designed the attention network test (ANT) to evaluate alerting, orienting and executive attention within a single paradigm combining the cued reaction time and flanker tasks (Fan, McCandliss, Sommer, Raz, & Posner, 2002). Unlike the traditional n-back design in which attentional, encoding and retrieval processes are overlapping, the ANT test makes it possible to separate attention and memory-related processes over time. In this experimental setting, ERP and ERSP measures allow for close examination of the brain responses to cue and target related to each of the three attentional networks (Deiber, Ibanez, Missonnier, Rodriguez, & Giannakopoulos, 2013; Fan et al., 2007). To date, only two studies have used the ANT paradigm coupled with electrophysiological recordings in childhood ADHD (Kratz et al., 2012; Kratz et al., 2011), with no exploration made in adulthood ADHD. In order to better circumscribe the functional mechanisms of attention deficits in adult ADHD

6 patients, the present work focuses on electrophysiological responses (ERPs and ERSPs) associated with ANT-related bottom-up attentional cueing and top-down control. We used a modified version of the ANT with substantial increase of the duration of cue presentation and cue-target interval that favors temporal orienting (involuntary attentional orientation) over alerting (bottom-up arousal) (Deiber et al., 2013). Our analysis encompassed the early (P1/N1) and late (P3) ERPs associated with cues and targets, as well as the CNV. Regarding ERSPs, we focused on alpha and beta as the most sensitive frequency bands to both anticipatory attention and motor-preparation processes (Engel & Fries, 2010; Tan, Leuthold, & Gross, 2013; Thut, Nietzel, Brandt, & Pascual-Leone, 2006).

2. Material and Methods 2.1. Participants Twenty one adult ADHD patients (40.05 ± 9.5 years, 7 females) and 20 young adults (25.5 ± 4 years, 13 females) participated in the study. Although matched in terms of gender repartition (Fischer’s test, p=0.06) and education (Mann-Whitney test, p=0.094), the two populations differed in terms of age (ADHD older than control cases, t= 6.48, p<0.001). All of the subsequent analyses were thus adjusted for age. The ADHD patients were recruited in a specialized center for the treatment of adult ADHD. Diagnosis was established according to DSM-IV-TR criteria by two trained psychiatrists (NP & PP) and was also based on the French version of the Diagnostic Interview for Genetic Studies (DIGS), a semi-structured interview including a detailed investigation of childhood ADHD and its persistence into adulthood (Preisig, Fenton, Matthey, Berney, & Ferrero, 1999). The diagnoses were confirmed by a best estimate procedure. All of the subjects, including controls, also filled out the Adult Self-Report Scale (ASRS-1.1) (Adler et al., 2006)

7 exploring current ADHD symptoms, and the Wender Utah rating scale (Bayle, Krebs, Martin, Bouvard, & Wender, 2003) exploring childhood ADHD. All participants had normal or corrected-to-normal visual acuity, and none reported a history of major medical disorders (cancer, cardiac illness), sustained head injury, neurological disorders, and alcohol or drug abuse. Other current psychiatric disorders were exclusion criteria for ADHD patients. Controls were free of any lifetime psychiatric disorders. All participants were free of any psychotropic medication with the exception of psychostimulants for ADHD cases, interrupted 48h before the trial. Informed written consent was obtained from all participants included in the study. The study was approved by the Ethical Committee of the University Hospitals of Geneva, and was in line with the Helsinki Declaration.

2.2. Experimental design The experimental design, data acquisition and processing are fully described elsewhere (Deiber et al., 2013). The participants, comfortably seated in a sound- and light-attenuated room, watched a computer-controlled displayed screen at a distance of 60 cm, and were instructed to maintain their gaze on the central fixation cross throughout the experimental session. In each trial, either a cue was presented for 400 ms or the fixation cross remaining unchanged (the no-cue condition) (Figure 1). After a fixed delay of 1600 ms, the target was presented until the participant responded or 1700 ms elapsed. With a variable fixation period after response, the duration of each trial summed up to 5200 ms. There were three warning conditions: W1: no-cue (baseline), W2: center cue (temporally informative), and W3: spatial cue (temporally and spatially informative). Spatial cues always indicated the valid upcoming target’s location. Target stimuli consisted of five horizontally arranged arrows presented above or below the center of the screen. By button press with the index or middle finger of

8 their right hand, participants had to indicate the direction of the central arrow irrespective of flanking conditions. Flankers were arrows either pointing to the same (T1, congruent flanker condition) or opposite direction (T2, incongruent flanker condition). A single arrow subtended 1.05° of visual angle, and the contours of adjacent arrows were separated by 0.1° of visual angle. The stimuli (one central and 4 flanker arrows) subtended a total of 5.63° of visual angle, and were presented at 0.95° of visual angle above or below the central point of the screen. The cues were asterisks subtending a visual angle of 0.67° x 0.67°. The 6 trial combinations of 3 (no cue, center cue, spatial cue) x 2 (congruent, incongruent flankers) were pseudorandomized across 2 blocks of 96 trials to ensure an equal representation of each trial type and finger response. Overall, there were 64 trials per cue condition (targets combined) and 96 trials per target condition (cues combined). The total duration of the experimental session was around 17 min. In agreement with the original ANT design, the session was preceded by a 16 trial-training block including feedback on performance, to ensure appropriate task skill acquisition. During testing, participants were instructed to respond as fast and accurately as possible, and no feedback on performance was provided. Neuropsychological and electrophysiological assessments were performed one week apart in the morning. [Figure 1]

2.3. Data acquisition Continuous EEG was recorded using 30 surface electrodes mounted on a cap (NeuroScan Quick Cap) and referenced to the linked earlobes. Electrode impedances were kept below 5 kΩ. Electrophysiological signals were sampled at 1000 Hz with lower cutoff at 0.05 Hz and upper cutoff at 200 Hz (DC amplifiers and software by NeuroScan, Texas, USA). The electrooculogram was recorded using two pairs of bipolar electrodes in both vertical and horizontal

9 directions. Visual stimuli and button presses were automatically documented with markers in the continuous EEG file, which were used off-line to segment the continuous EEG data into epochs time-locked to cue onset.

2.4. Data processing Reaction time (RT) was measured as the time interval between target onset and response button press. Accuracy was defined as the percentage of correct responses. The effect of attentional cueing on RT was then assessed using simple subtractions as follows: temporal orienting (TO) score = RTW1- RTW2, spatial orienting (SO) score = RTW2- RTW3, and conflict resolution (CR) score = RTT2- RTT1. While larger TO and SO scores indicate faster-cue related performance, larger CR scores indicate worse performance (longer time to resolve conflict). EEG analysis was conducted with Brain Vision Analyzer 2 software (Brain Products GmbH). After signal downsampling at 512 Hz, independent component analysis (ICA) was used to identify vertical and horizontal ocular artifacts and to remove them from the signal (Jung et al., 2000). The EEG was segmented per cue type into epochs of 5200 ms, starting 1200 ms before cue onset. Epochs with voltage step above 50 µV or peak-to-peak signal deflection exceeding 200 µV within 300 ms intervals were automatically rejected. A minimum of 47 over 64 trials was analyzed per cue condition, and 70 over 96 trials per target condition.

2.5. Data analysis 2.5.1. ERPs ERPs were obtained in response to cue (W2, W3) and target (T1, T2) by stimulus-locked averaging of the signal with a 200 ms pre-stimulus baseline correction and a 30 Hz low-pass filter (-48 dB/octave). P1 and N1 visual ERP components were measured at their peak

10 amplitude on electrodes identified in the grand-average waveforms (O1 and O2). Since the P3 component is formed by subcomponents with distinct antero-posterior distribution, and in order to avoid any a priori topographic hypothesis, P3 was measured within 5 ROIs each composed of 3 electrodes: Parietal (average of P3, Pz, P4), Centro-parietal (average of CP3, CPz, CP4), Central (average of C3, Cz, C4), Fronto-central (average of FC3, FCz, FC4), Frontal (average of F3, Fz, F4). The contingent negative variation (CNV), arising after the center and spatial cues, displayed a widespread topographic distribution similar to P3 and was measured within the same ROIs, within 2 sequential 600-ms-time windows (t1= 600 to 1250 ms; t2= 1250-1900ms).

2.5.2. ERSPs To detect and characterize the event-related EEG oscillations, a time-frequency (TF) analysis based on a continuous wavelet transform of the signal was applied (complex Morlet’s wavelets) (Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998). The TF analysis was performed from 4 to 30 Hz in 1-Hz steps. The resulting dataset consisted in a TF representation of the energy of the signal, from which frequency specific ERSPs can be extracted. A baseline level of TF energy was calculated as the mean energy between 800 and 150 ms before cue onset and subtracted from the TF energy of the entire 5200 ms epoch.

2.5.2.1. Cue-locked ERSPs TF energy was obtained for the 3 cue conditions separately as well as for the temporal and spatial orienting components of attentional control. In agreement with the recent study of Fan et al. (Fan et al., 2007), the effect of each type of attentional cueing on ERSPs was obtained by flipping the contrasts defined for RT (i.e., temporal orienting effect = W2-W1, orienting effect = W3-W2). Selection of the frequency bands for analysis was based on the largest

11 modulation of activity induced by the attentional manipulations, and concerned the alpha (813 Hz) and low beta (14-19 Hz) frequency bands. Analyses were separately performed within these two frequency bands, within 9 regions of interest (ROIs) in ten 200 ms-time-windows (0 to 2000 ms). To account for the topographical distribution of alpha and beta energy, the ROI were defined along the sagittal and frontal axes as follows: LPC (Left Premotor: FC3), RPC (Right Premotor: FC4), MF (Midline frontal: FCz, Fz), LC (Left Central: C3, CP3), RC (Right Central: C4, CP4), LP (Left Parietal: P3), RP (Right Parietal (P4), MP (Midline Parietal: Pz), OCC (Occipital: O1, O2). The ERSPs were measured locally within each ROI and no analysis of synchrony between ROIs was undertaken. Whereas regional measures using ROIs are not exempt from volume conduction effects, this is a widely used approach providing reliable results within a gross spatial resolution perspective (Dickter & Kieffaber, 2013). To determine whether the temporal orienting energy was related to the TO score, we examined the difference in this parameter between individuals with low and high TO score (median split of the TO score values). A similar method was used to determine whether the spatial orienting energy was related to the SO score.

2.5.2.2. Target-locked ERSPs TF energy was obtained for the 2 target conditions, as well as for the conflict resolution component of attentional control (conflict resolution effect = T2-T1). The same baseline as for cue-related TF energy was used. For each ROI, analysis was performed in the 8-13 Hz alpha and 14-19 Hz low beta frequency bands in eight 200 ms-time-windows (0 to 1600 ms). For each group, a comparison of the target-locked energy between individuals with low and high CR scores (median split of the CR score values) was also performed.

2.6. Statistical analysis

12 2.6.1. Behavioral data Linear regression models were built with reaction time and accuracy as dependent variables, and Group (ADHD, CTL), Cue (W1, W2, W3), Target (T1, T2), and age as independent variables. The network scores (TO, SO and CR) were separately entered as dependent variables in linear regression models with Group and age as independent variables.

2.6.2. ERPs The effect of cue and target on P1 and N1 amplitude was separately tested using a repeatedmeasures ANCOVA with 2-level Stimulus (for cue: W2, W3; for target: T1, T2) as withinsubject and 2-level Group (ADHD, CTL) as between-subject factors, with age as covariate. The effect of cue and target on P3 amplitude was separately tested using the same test with 2level Stimulus (for cue: W2, W3; for target: T1, T2) and 5-level ROI (Parietal, Centroparietal, Central, Fronto-central, Frontal) as within-subject and 2-level Group (ADHD, CTL) as between-subject factors, with age as covariate. The same procedure was applied for CNV amplitude with 2-level Cue (W2, W3), 5-level ROI (Parietal, Centro-parietal, Central, Frontocentral, Frontal) and 2-level Time (t1, t2) as within-subject and 2-level Group (ADHD, CTL) as between-subject factors, with age as covariate.

2.6.3. ERSPs For each cue (W1, W2, W3) and target (T1, T2) condition, as well as for each temporal orienting (W2-W1), spatial orienting (W3-W2) and conflict resolution (T2-T1) components, linear regression models were built with alpha and beta ERSPs in each ROI as dependent variables. Group, age and time were considered as independent variables, time being the repeated within-subject factor (10 and 8 time windows for cue-locked and target-locked values, respectively).

13 For each attention network, the association between RT score and network-related energy was tested separately in the ADHD and control groups. Linear regression models were used for each network component (W2-W1, W3-W2, T2-T1) in each ROI, with alpha and beta ERSPs as dependent variables. RT-score defined subgroup (Low and High performers), age and time were considered as independent variables, time being the repeated within-subject factor (10 and 8 time windows for cue-locked and target-locked values, respectively).

3. Results 3.1. Behavioral results RTs were similar in both groups. The cue and target types significantly affected the RT, with the no cue eliciting longer RT than both center (t=5.3, p<0.001) and spatial cues (t=13.1, p<0.001). The center cue elicited longer RT than the spatial cue (t=3.4, p<0.005), and the incongruent target longer RT than the congruent target (t=13.2, p<0.001) (Figure 2). Accuracy was high (98.9% correct responses) and similar in both groups. The cue and target types significantly affected accuracy, worse performance being observed with the no cue compared to the spatial cue (98.8% versus 99.3% correct, t=-2.6, p<0.05), and with the incongruent compared to the congruent target (98.2% versus 99.6% correct, t=-4.1, p<0.001). There was no difference in alerting and orienting scores between the two groups (Figure 2). Although the conflict score tended to be larger in the ADHD patients, the difference did not persist after adjustment for age. [Figure 2]

3.2. ERPs The grand-average ERPs are illustrated in Figure 3 for each group, cue and target conditions. Absent in the no-cue condition, the P1, N1 and P3 components were observed after the center

14 and spatial cues as well as after both targets. A fronto-parietal CNV arose after the center and spatial cues, with a regular increase prior to target occurrence in control subjects contrasting with a sharp increase followed by a slow amplitude reduction in ADHD patients.

3.2.1. Cue-related ERPs 3.2.1.1. P1 and N1 components No significant effect of Cue or Group was observed on P1 and N1 amplitudes, supporting similar early visual processing after center and spatial cues in all cases.

3.2.1.2. P3 component There was a significant effect of Group on P3 amplitude (F(1,39)= 87.4, p<0.001), with smaller values in ADHD compared to control cases. A main effect of ROI was also observed (F(4,156)= 9.7, p<0.005), with larger P3 amplitude in posterior than anterior ROIs. The interaction between Group and ROI was significant (F(4,156)= 5.3, p<0.05), yielded by a strong posteroanterior P3 amplitude gradient present in controls but absent in ADHD cases. No significant effect of Cue on P3 amplitude was observed.

3.2.1.3. CNV There was a significant effect of Cue on CNV (F(1,39)= 45.8, p<0.001), with larger CNV amplitude after the spatial than the center cue. The interactions Group x Cue (F(1,39)= 10.6, p<0.005), Group x Time (F(1,39)= 63.3, p<0.001) and Group x Cue x Time (F(1,39)= 9.28, p<0.005) were all significant. The CNV showed inverted amplitude gradients across the two groups mainly after the spatial cue, i.e., while the early CNV was smaller in controls than ADHD cases, the late CNV showed an inverse pattern.

15 3.2.2. Target-related ERPs 3.2.2.1. P1 and N1 components No significant effect of Target or Group was observed on P1 and N1 amplitudes, supporting similar early visual processing after congruent and incongruent targets in all cases.

3.2.2.2. P3 component There was a significant effect of Group on P3 amplitude (F(1,39)= 27.7, p<0.001), with smaller values in ADHD compared to control cases. Main effects of Target (F(1,39)= 7.6, p<0.01) and ROI (F(4,156)= 10.1, p<0.001) were also observed, with larger P3 amplitude after congruent than incongruent targets and in posterior than anterior ROIs. The interaction between Group and ROI was significant (F(4,156)= 4.2, p<0.05), with a similar pattern as for the cue-related P3. [Figure 3]

3.3. Cue-to-target interval TF analysis The TF plots corresponding to the no cue, center and spatial cue conditions are displayed for each group in Figures 4, 5 and 6 respectively. In addition, ERSP time plots are shown for the 9 ROIs in each alpha and beta frequency band, illustrating the results of the regression analyses. Observation of the TF plots at cue and target presentation revealed a transient increase of visually-related theta ERSP (i.e., theta ERS) that was not further analyzed within the scope of this work that focuses on the decrease of alpha and beta ERSPs (i.e., alpha and beta ERD). During the cue-target interval, an alpha and beta ERD was observed depending on the amount of information carried by the cue.

3.3.1. Cue-related alpha and beta ERSPs 3.3.1.1. No cue (Figure 4)

16 In the absence of cue, there was a marked alpha and beta ERD starting in the middle of the cue-target interval on posterior electrodes in controls but less consistently in ADHD cases. The assessment of regression coefficients showed significant group differences in both alpha and beta bands in the posterior ROIs, with smaller ERD in ADHD compared to control subjects (Alpha: RP, 1200 to 2000 ms; OCC, 1000 to 1600 ms and 1800 to 2000 ms; MP, 1800 to 2000 ms; Beta: LP, 1200 to 2000 ms; RP, 1200 to 1600 ms; OCC, 1000 to 1400 ms and 1600 to 2000 ms; p<0.05).

3.3.1.2. Center cue (Figure 5) The ERD was reduced in ADHD compared to control subjects, with significant group differences in LC (1400 to 2000 ms, p<0.05) and OCC (1400 to 1600 ms, p<0.05) for the alpha band, and in left hemisphere (LC, 1600 to 2000 ms; LP, 1800 to 2000 ms; LPC, 1600 to 2000 ms; p<0.05) for the beta band.

3.3.1.3. Spatial cue (Figure 6) The ERD was reduced in ADHD compared to control subjects, with significant group differences in LC (1000 to 1200 ms and 1400 to 2000 ms, p<0.05) and posterior regions (LP, 1000 to 1200 ms and 1400 to 2000 ms; OCC, 1400 to 2000 ms; RP, 1800 to 2000 ms; p<0.05) for the alpha band, and in left hemisphere (LC, 400 to 800 ms and 1800 to 2000 ms; LP, 400 to 600 ms and 1800 to 2000 ms; p<0.05) for the beta band. [Figures 4, 5, 6]

3.3.2. Alpha and beta energy related to temporal and spatial orienting Temporal orienting was associated with a latero-posterior decrease of W2-W1 alpha and beta energy followed by a local increase in the midparietal region (Figure 9A). The amplitude of

17 this temporal orienting energy pattern was significantly reduced immediately after cue onset in ADHD compared to controls in the beta band in all ROIs except LC. With respect to the spatial orienting effect, the two groups differed in the intensity of posterior W3-W2 energy decrease (Figure 9B), with reduced intensity in ADHD compared to controls in both alpha (from 800 ms) and beta (from 400 ms) bands, mostly in left and mesial centro-posterior regions.

3.3.3. Relationship between RT scores and energy patterns in temporal and spatial orienting conditions No significant difference was observed in the temporal orienting (W2-W1) energy according to the TO score amplitude in the two diagnosis groups. In contrast, higher SO scores (better spatial orienting) were associated with larger W3-W2 alpha and beta energy decrease in both groups (Figure 10). In controls, this association was significant in alpha band in 5 ROIs (LP, RP, MP, LC, LPC) between 200 and 800 ms. In ADHD patients, it was significant in alpha band in all ROIs (except RP and MF) at later latencies (1400 to 1600 ms), and in beta band in 3 ROIs (OCC, LC, LPC) between 400 and 800 ms.

3.4. Target-related TF analysis Congruent and incongruent targets elicited similar modulations of alpha and beta energy that were more pronounced for incongruent targets. The TF plots corresponding to congruent and incongruent targets are illustrated in Figures 7 and 8 respectively. In addition, power time plots are displayed for the 9 ROIs in each alpha and beta frequency band, summarizing the regression analysis results. Target presentation triggered a transient theta ERS associated with alpha and beta ERD followed by a rebound (ERS).

18 3.4.1. Target-related alpha and beta ERSPs In alpha band, smaller ERD/ERS values were observed in ADHD compared to controls in restricted ROIs (T1 and T2, LC, 400 to 600 ms; T2: OCC, 1800 to 2000 ms; p<0.05). In beta band, group differences mainly arose after incongruent target (T2) in all regions but the RPC, ADHD showing smaller beta ERD (0 to 200 ms and/or 400 to 600 ms, p<0.05) and ERS (from 800 ms, p<0.05 to p<0.01) than control cases. [Figures 7, 8]

3.4.2. Alpha and beta energy related to conflict resolution The effect of conflict resolution included an initial posterior T2-T1 energy decrease followed by an increase in both alpha and beta frequency bands (Figure 9C). The amplitude of the beta increase was significantly smaller in ADHD compared to control cases at the end of the analysis window (1400 to 1600 ms). [Figure 9]

3.4.3. Relationship between RT score and energy patterns in conflict resolution In controls, higher CR scores (worse conflict resolution) were associated with larger alpha and beta T2-T1 energy decrease, whereas there was no significant difference between CR score subgroups on T2-T1 energy for any frequency band in ADHD patients (Figure 10). In controls, low performers showed larger alpha T2-T1 energy decrease in four posterior ROIs (LP, RP, MP, OCC) between 600 and 1000 ms, and larger beta T2-T1 energy decrease in all ROIs between 200 and 800 ms. [Figure 10]

4. Discussion

19 To the best of our knowledge, this is the first study in adult ADHD that investigates the electrophysiological indices of attention using the ANT. We adopted a modified version of the ANT coupled with EEG recording to examine the cortical responses associated to bottomup driven (temporal and spatial orienting) and top-down directed attention (spatial orienting), as well as executive functions related to conflict resolution. In presence of comparable behavioral performance and attentional network scores, our results revealed several differences in attention-related cortical processing between adult ADHD subjects and healthy controls. ADHD patients showed reduced P3 amplitude, large early CNV decrementing over time, reduced preparatory alpha and beta ERD as well as flattened target-related posterior alpha and beta ERD/ERS response. Overall, these observations suggest that adult ADHD patients engaged reduced cortical resources in this complex task, compatible with a reduced activation of the brain networks devoted to cognitive attention (Bush, 2011).

4.1. Behavioral performance Compared to controls, ADHD patients displayed similar RT and accuracy levels across all warning cue conditions and target types. Attentional scores were also comparable to the controls, despite a tendency for longer conflict scores that did not persist after adjustment for age. Overall, neuropsychological performances in adult ADHD cases acknowledged a main deficit in focused and/or sustained attention as well as in verbal memory compared to control subjects (Schoechlin & Engel, 2005). However, behavioral reports from EEG attention studies in adult ADHD have been mixed, with some findings deficits (McLoughlin et al., 2010; Valko et al., 2009) while other did not (Couperus, Alperin, Furlong, & Mott, 2014; Kim, Liu, Glizer, Tannock, & Woltering, 2014; Prox, Dietrich, Zhang, Emrich, & Ohlmeier, 2007). Inconsistent results may partly come from variations in experimental design, such as trial duration and/or iteration. In presence of normal performances, one could consider the

20 possibility that ADHD patients reached a ceiling effect consecutive to insufficient task difficulty. However, this is an unlikely scenario in the present dataset since RT to congruent targets were comparable in ADHD and controls independently of the warning condition (i.e., there was no tendency for faster RT in ADHD). Moreover, RT to incongruent targets tended to be longer in ADHD than controls, resulting in longer conflict resolution scores (non significant after adjustment for age), suggesting that conflict resolution indeed represented a greater challenge to ADHD cases.

4.2. ERPs Not surprisingly, while the early visual P1 and N1 components were similar in both groups, the P3 amplitude was strongly reduced in ADHD patients compared to controls both after cue and target stimuli. Earlier findings also indicated that the N1 and P1 components are preserved in adult ADHD (Barry et al., 2009; Missonnier et al., 2013; Prox et al., 2007). In contrast, the P3 component showed an amplitude reduction across various experimental procedures (Szuromi, Czobor, Komlosi, & Bitter, 2011 for review), that could reflect either a dysfunction of the ventral attention network involved in the later phase of event processing (Helenius, Laasonen, Hokkanen, Paetau, & Niemivirta, 2011; Janssen et al., 2015), or the increasing effort involved to override attentional dysfunction (Benikos, Johnstone, & Roodenrys, 2013; Palmer, Nasman, & Wilson, 1994). In a recent study using a delayedmatched to-sample task, Kim and colleagues (Kim et al., 2014) found a reduced P3 following the to-be-encoded stimulus in ADHD patients, suggesting an ineffective allocation of attentional resources in the encoding phase of working memory. Reduction of the P3 component is not specific to adult ADHD since it concerns several psychiatric disorders characterized by dysfunctional target-related attentional processes (Donkers et al., 2013). Target detection-related P3 is also referred to as P3b and culminates in the parietal region,

21 with neuroelectric generators in the inferior parietal, temporal and right prefrontal regions (Polich, 2007). Absence of delimited P3 culmination in ADHD patients may support a reorganization of underlying generators, as a possible consequence of dysfunctional activation of attentional resources. In the interval between cue and target, our data revealed ADHD-related changes in CNV amplitude that were dependent on time from cue onset as well as cue type. The CNV preceding an imperative stimulus is considered as an electrical counterpart of expectancy and motor preparatory processes. Two distinct components can be differentiated: the early CNV is associated with an orienting response to the warning stimulus, whereas the late CNV reflects anticipation of the imperative stimulus in the form of motor preparation and effortful performance control (Brunia, 2004; McCarthy & Donchin, 1978). The amplitude of the CNV is also known to be modulated by the information content of the warning stimulus, more information triggering larger CNV amplitude (Bonnet & MacKay, 1989). Whereas a classical increment of the CNV with time and cue information content was observed in our control group, ADHD patients displayed a large early CNV decrementing over time, the late CNV being markedly reduced compared to control cases. In line with this later observation, an amplitude reduction of the CNV has been previously reported in ADHD patients, although no formal distinction was made between its two subcomponents (Banaschewski et al., 2008; Doehnert, Brandeis, Schneider, Drechsler, & Steinhausen, 2013; Mayer et al., 2015; Ortega, López, Carrasco, Anllo-Vento, & Aboitiz, 2013; Perchet et al., 2001; Tsai, Pan, Cherng, Hsu, & Chiu, 2009). The enhanced early CNV and decreased late CNV in our adult ADHD may reflect an exaggerated orientation response followed by deficient anticipatory processes. Alternatively, one could speculate a modification in the sequence of processing, with the concentration of orientation and expectation processes in early time interval after the cue stimulus. Although our findings did not allow for drawing definite conclusions, the fact that

22 task performance was preserved in the ADHD group suggests that anticipatory processing, including motor preparation and frontal executive control, occurred either with reduced temporal allocation or using alternative functional circuits. Whether the motor preparationrelated neural activation is impaired in ADHD is still a matter of debate. A study of combined inattentive and hyperactive ADHD subtypes reported altered Lateralized Readiness Potentials in Go-NoGo choice RT tasks, supporting weaker central response preparation to both motor activation and inhibition (Gorman Bozorgpour, Klorman, & Gift, 2013). Impaired preparation during sustained attention task was also revealed as a developmentally persistent feature in ADHD cases (Doehnert et al., 2013). However, recent results pointed to the sparing of motor production processes in adult ADHD, contrasting with the alteration of the perceptual and response selection stages (Cross-Villasana et al., 2015). Additional evidence, mainly from childhood ADHD, suggests that decision to respond with a motor act may take place before complete stimulus processing, leading to motor impulsivity (Perchet et al., 2001; Tsai et al., 2009).

4.3. Cue-related ERSPs For all cue types, we observed a smaller alpha and beta ERD in ADHD patients compared to controls mostly during the second period of the stimulus-target interval, i.e., from 1000 ms after cue onset. In the absence of cue, these ERD differences extended largely over the parieto-occipital region, indicating reduced expectation of the forthcoming target (Bastiaansen & Brunia, 2001; Deiber et al., 2013; Thut et al., 2006). In the presence of center and spatial cues, the ERD differences were centered on the left central and parietal regions, possibly reflecting the reduced activation of motor-related areas in preparing the response to the forthcoming target (Deiber et al., 2013; Doyle, Yarrow, & Brown, 2005; Klostermann et al., 2007). The present TF results are consistent with the CNV data suggesting the reduction of

23 late expectation processes related to anticipatory attention (as evidenced in the no-cue situation) and to motor preparation (in the center and spatial cue situations). They are also compatible with previous reports of reduced alpha reactivity in childhood ADHD, either following a visual cue in expectation of a target (Mazaheri et al., 2010), or during stimulus encoding in a Sternberg memory task (Lenartowicz et al., 2014). Contrasting with the prevalent hypothesis of brain hypoarousal (Zentall & Zentall, 1983), increased cortical arousal was suggested in resting state and during sustained attention in adult ADHD cases with high intelligence functioning, possibly due to singular frontal neural organization (Loo et al., 2009). Moreover, increased rightward alpha and beta spectral power asymmetry at rest as well as in sustained attention and visuospatial cued tasks has been described in adult ADHD (Hale et al., 2009; Hale et al., 2010; ter Huurne et al., 2013), reinforcing the hypothesis of an abnormal brain laterality in this disorder (Stefanatos & Wasserstein, 2001). In contrast with these adult ADHD studies, the present work focused on short lasting attention task and analyzed the dynamics of event-locked oscillatory activity rather than time-independent gross spectral power. Importantly, ADHD-related differences in EEG activation were also found in respect to temporal and spatial orienting networks in the present series. The temporal orienting pattern was significantly decreased in the beta band across most brain regions, reflecting the reduced difference between cortical responses to temporal and no cue. The same observation was made for the spatial orienting pattern in both alpha and beta bands. Hence, the increased information content provided by the cue led to a smaller cortical reactivity in ADHD patients compared to controls, in line with the idea of early alterations of the cognitive-attention brain network in this disorder (Bush, 2011). Interestingly, the magnitude of cortical reactivity correlated with spatial orienting performance in both groups, with larger reactivity (larger difference between spatial and center cue) associated with better spatial orienting score.

24

4.4. Target-related ERSPs The target-related posterior alpha and beta ERD/ERS response was of smaller amplitude in ADHD patients than control cases, mainly for incongruent targets. This flattening of the ERD/ERS cycle may traduce a reduction of the cortical excitability/inhibitory processes in this disorder (Klimesch, Sauseng, & Hanslmayr, 2007; Pfurtscheller, 2001). A reduced frontal alpha ERD was similarly described during n-back memory tasks in adult ADHD compared to controls (Missonnier et al., 2013). However, this was followed by an enhanced alpha ERS, probably related with further working memory demands engaging compensatory processes. Together with the amplitude reduction of the P3 and CNV components, the flattened alpha and beta ERD/ERS gives strong support to the cortical hypoarousal model, initially based on enhanced delta-theta activity in resting EEG recordings (Barry, Clarke, & Johnstone, 2003; Geissler, Romanos, Hegerl, & Hensch, 2014; Mayer et al., 2015; Rowe et al., 2005). According to this theory, the association of ADHD with brain state hypoarousal is itself responsible for the hyperactivity and sensation seeking behavior as attempts to stabilize vigilance (Geissler et al., 2014; Mayer et al., 2015; van Dongen-Boomsma et al., 2010; Zentall & Zentall, 1983). In agreement with the cortical hypoarousal theory, the Food and Drug Administration has recently approved the EEG theta/beta ratio to assist the diagnosis of childhood ADHD (Sangal & Sangal, 2014). Interrelated theories include the dysregulation of arousal and/or alterness through the dysfunctional interplay of top-down and bottom-up processes (Sergeant, 2000, 2005). Last but not least, examination of cortical activity related to the conflict resolution network revealed inverted beta ERS patterns in ADHD compared to control cases. Intriguingly, the beta ERS was larger for congruent than incongruent targets in ADHD patients, suggesting increased inhibitory processes under easier task resolution circumstances. In addition, there

25 was no relationship between cortical reactivity and conflict resolution performance in ADHD patients, contrasting with the association between large beta reactivity and conflict resolution slowing in control cases. In presence of normal behavioral performance, these observations suggest differential cortical activity patterns in adult ADHD, who may use alternative functional circuits and/or reactive frequency bands for executive control. EEG and fMRI signatures of executive deficits were previously reported in ADHD cases, with impaired indices of conflict monitoring and error processing (Karch et al., 2014; McLoughlin et al., 2009). Voluntary response selection was shown to induce increased frontal and fronto-central gamma activity in ADHD patients compared to controls, reflecting the recruitment of frontal circuits at alternate high frequencies (Karch et al., 2012). In the same line, an alteration of executive functioning in the form of heightened threshold for triggering top-down control mechanisms has been recently discussed in childhood ADHD. The normal drive of top-down control would only occur in case of high task demands, while easier situations would fail to recruit efficient top-down control, leading to worse performance (Friedman-Hill et al., 2010). Further studies should determine whether this observation is valid in adult ADHD, and to which extent the cortical reactivity depends on the phasic versus sustained characteristics of attention.

4.5. Conclusion The present EEG data obtained during an attention task of relatively short duration suggest that adult ADHD patients engage reduced cortical resources than their normal counterparts, with smaller amplitude of stimulus-locked as well as anticipatory responses. Moreover, despite similar task performance, the CNV dynamics during the cue-to-target interval showed an inverted pattern compared to controls, that could either indicate shorter lasting preparatory processes or the engagement of alternative functional processing, possibly based on high

26 frequency oscillatory activities (i.e., gamma). The lack of association between target-related ERSPs and conflict resolution in our ADHD cases may further support the later hypothesis.

Figure legends Figure 1. Illustration of the modified ANT. In each trial, a cue may appear for 400 ms depending on the condition (no cue, center cue, or spatial cue), a fixation cross being present at all times. After a constant 1600 ms fixation period, the target (center arrow) as well as two left and two right arrows (congruent or incongruent flankers) are presented. The participant indicates the target direction within a time window of 1700 ms. Pre-cue and post-target intervals vary interdependently, ensuring a total trial duration of 5200 ms.

Figure 2. Reaction times (A) and attentional network scores (B), expressed in ms. The network scores do not differ significantly between controls and ADHD, age difference being responsible for the apparent difference in conflict resolution.

27

Figure 3. Grand average ERPs stratified by cue (A) and target (B) condition in controls and ADHD patients on representative electrodes FCz, Pz and Oz. W1: no cue, W2: center cue, W3: spatial cue, T1: congruent target, T2: incongruent target. P1 and N1 components peak at Oz, P3 at Pz. A. In controls, a CNV is developing after W2 and W3 on FCz and Pz until target occurrence at 2000 ms. In ADHD, the CNV displays an early culmination followed by a progressive decrease. B. The ERP amplitude is smaller in ADHD than controls, and the P3 component is larger for congruent than incongruent targets.

28

Figure 4. Time-frequency (TF) plots and relative alpha and beta power time course in the 9 ROIs in the no cue condition (W1). Two left columns: TF plots of the relative EEG power between 4 and 30 Hz in controls and ADHD patients. Vertical lines at no cue (W1) and target (T) onset. Two right columns: time plots of relative alpha (8-13 Hz) and beta (14-19 Hz) power in the ROIs for which a significant difference is obtained between controls and ADHD patients. LPC: Left Premotor (FC3); RPC: Right Premotor (FC4); MF: Midfrontal (FCz, Fz); LC: Left Central (C3, CP3); RC: Right Central (C4, CP4); LP: Left Parietal (P3); RP: Right Parietal (P4); MP: Midparietal (Pz); OCC: Occipital (O1, O2). X-axis displays the upper limits of each 200 ms-time interval from 0 to 2000 ms after cue onset (x = 0 corresponds to the baseline time window, -800 to -150 ms before cue onset). Colored circles: post-hoc independent sample t-tests (p < 0.05); orange = ERD ADHD < ERD controls, purple = ERS

29 ADHD < ERS controls. The early anterior beta ERS and posterior alpha and beta ERD is reduced in ADHD compared to controls.

Figure 5. Time-frequency (TF) plots and relative alpha and beta power time course in the 9 ROIs in the center cue condition (W2). Same conventions as in Fig. 4. The left hemispheric beta ERD is mainly reduced in ADHD compared to controls.

30

Figure 6. Time-frequency (TF) plots and relative alpha and beta power time course in the 9 ROIs in the spatial cue condition (W3). Same conventions as in Fig. 4. The central and parietal alpha and beta ERD are mainly reduced in ADHD compared to controls.

31

Figure 7. Time-frequency (TF) plots and relative alpha and beta power time course in the 9 ROIs in the congruent flanker condition (T1). Two right columns: x-axis displays the upper limits of each 200-ms time interval from 0 to 1600 ms after target onset (x = 0 corresponds to the baseline time window, -800 to -150 ms before cue onset). There are limited differences between ADHD and controls.

32

Figure 8. Time-frequency (TF) plots and relative alpha and beta power time course in the 9 ROIs in the incongruent flanker condition (T2). Same conventions as in Fig. 8. The beta ERD/ERS cycle is mainly reduced in ADHD compared to controls.

33

Figure 9. Topographic distribution of relative alpha or beta TF energy corresponding to the 3 components of attentional cueing (A, B, C) in controls and ADHD patients, and time course of ERSPs at electrode with largest energy difference between the 2 groups. For each group, the time window is subdivided into two equal time segments, each illustrated by two maps (top and back scalp views). X-axis displays the upper limits of each 200 ms-time interval from 0 to 2000 ms after cue onset (A and B), 0 to 1600 ms after target onset (C). X=0 corresponds to the baseline time-window (-800 to -150 ms before cue onset). A. Temporal orienting (W2-W1). The midparietal (MP) W2-W1 beta energy increase is smaller in ADHD than controls, due to smaller differences between W1 and W2 alpha power in ADHD. B. Spatial orienting (W3-W2). The left parietal (LP) W3-W2 alpha energy decrease is smaller in ADHD than controls. C. Conflict resolution (T2-T1). The right parietal (RP) T2-T1 beta energy decrease/increase pattern is smaller in ADHD than controls.

34

Figure 10. Time plots of (A) W3-W2 alpha energy (spatial orienting) and (B) T2-T1 beta energy (conflict resolution) in subgroups of high and low performers in each control and ADHD population. X-axis displays the upper limits of each 200 ms-time interval from 0 to 1600 ms after cue (A) or target (B) onset (x = 0 corresponds to the baseline time window, 800 to -150 ms before cue onset). A. In both controls and ADHD, higher performers (higher SO scores = better spatial orienting) displayed larger W3-W2 alpha energy decrease, with more prominence in controls. B. In controls, higher performers (lower CR scores = faster conflict resolution) displayed reduced T2-T1 beta energy decrease, whereas there was no significant difference between high and low performers ADHD.

35

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40 Lawrence, C., Barry, R., Clarke, A., Johnstone, S., McCarthy, R., Selikowitz, M., & Broyd, S. (2005).

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Highlights

44    

We examined the electrophysiological substrates of adult ADHD attentional networks Inverted CNV pattern in ADHD suggested peculiar preparatory processes ADHD beta activity in conflict resolution indicated alternative executive control Reduced alpha and beta activation in ADHD supported the cortical hypoarousal model