Accepted Manuscript Adult ADHD and Working Memory: Neural Evidence of Impaired Encoding Soyeon Kim, Zhongxu Liu, Daniel Glizer, Rosemary Tannock, Steven Woltering PII: DOI: Reference:

S1388-2457(13)01348-5 http://dx.doi.org/10.1016/j.clinph.2013.12.094 CLINPH 2006941

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Clinical Neurophysiology

Accepted Date:

4 December 2013

Please cite this article as: Kim, S., Liu, Z., Glizer, D., Tannock, R., Woltering, S., Adult ADHD and Working Memory: Neural Evidence of Impaired Encoding, Clinical Neurophysiology (2013), doi: http://dx.doi.org/10.1016/ j.clinph.2013.12.094

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WM encoding in ADHD

Adult ADHD and Working Memory: Neural Evidence of Impaired Encoding

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Soyeon Kim, Zhongxu Liu, Daniel Glizer, Rosemary Tannock*, Steven Woltering*

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Department of Applied Psychology & Human Development, OISE, University of Toronto,

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252 Bloor Street West, Toronto, Ont., Canada, M5S 1V6

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E-mails: Soyeon Kim ([email protected]), Zhong-Xu Liu ([email protected]),

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Daniel Glizer ([email protected]), Rosemary Tannock ([email protected]),

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StevenWoltering ([email protected]).

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* Corresponding authors:

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Steven Woltering

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Department of Applied Psychology & Human Development, OISE, University of Toronto, 252

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Bloor Street West, Toronto, Ont. Canada, M5S 1V6

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Tel: +1-416-978-0923

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[email protected]

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or

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Rosemary Tannock

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Department of Applied Psychology & Human Development, OISE, University of Toronto, 252

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Bloor Street West, Toronto, Ont. Canada, M5S 1V6

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E-mail: [email protected]

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WM encoding in ADHD

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Highlights

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 ADHD college students are compared to their typically developing peers on neural and behavioural indices of working memory (WM).

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 Neural indices of working memory (P3) are examined during working memory encoding using EEG

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 ADHD group showed lower P3 during working memory encoding, suggesting an inefficient encoding process in WM.

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WM encoding in ADHD

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Abstract

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Objectives: To investigate neural and behavioural correlates of visual encoding during a working

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memory (WM) task in young adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD).

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Methods: A sample of 30 college students currently meeting a diagnosis of ADHD and 25 typically

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developing students, matched on age and gender, performed a delayed match-to-sample task with low

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and high memory load conditions. Dense-array electroencephalography was recorded. Specifically, the

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P3, an event related potential (ERP) associated with WM, was examined because of its relation with

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attentional allocation during WM. Task performance (accuracy, reaction time) as well as performance

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on other neuropsychological tasks of WM was analyzed.

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Results: Neural differences were found between the groups. Specifically, the P3 amplitude was smaller

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in the ADHD group compared to the comparison group for both load conditions at parietal-occipital

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sites. Lower scores on behavioral working memory tasks were suggestive of impaired behavioural WM

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performance in the ADHD group.

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Conclusions: Findings from this study provide the first evidence of neural differences in the encoding

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stage of WM in young adults with ADHD, suggesting ineffective allocation of attentional resources

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involved in encoding of information in WM.

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Significance: These findings, reflecting alternate neural functioning of WM, may explain some of the

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difficulties related to WM functioning that college students with ADHD report in their every day

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cognitive functioning.

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Keywords: Working memory, P3, adults with ADHD, encoding.

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WM encoding in ADHD

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INTRODUCTION

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Attention-deficit/Hyperactivity Disorder (ADHD) is the most frequently diagnosed psychiatric

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disorder in childhood with worldwide prevalence rates estimated at 5.3% (Polanczyk et al., 2007).

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Longitudinal studies show that approximately 65% of children with ADHD continue to show symptoms

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in adulthood (Barkley et al. 2002; Faraone et al., 2006; Polanczyk and Rhode, 2007). A newly-emergent

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subset of young adults with ADHD – those who gain entrance into post-secondary education – is of

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interest because of their ongoing impairments despite educational success relative to others with ADHD

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(DuPaul et al., 2009). These students present various impairments in social and academic domains. For

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instance, they exhibit difficulties with college adjustment, social skills and self-esteem (Shaw-Zirt et al.,

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2005), lower Grade Point Averages, poorer academic success, and they are more likely to be placed on

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academic probation (Heiligenstein, et al., 1999; Frazier, et al., 2007). Working memory (WM), seen as

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one of the core deficits of ADHD (Martinussen, et al., 2005; Hervey et al., 2004), could be a key factor

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explaining some of the functional impairments mentioned above. WM allows us to temporarily hold and

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manipulate information “on-line” for a few seconds in order to respond effectively based on that

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information (Baddeley, 2003). WM involves several stages of information processing: encoding,

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maintenance (rehearsal) and retrieval. WM has been linked with many real world activities (e.g., reading

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comprehension, following a conversation, problem-solving; Baddeley and Hitch, 1994) and therefore

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WM impairments may have pernicious effects on everyday life, especially considering the social and

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academic demands students face in college.

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WM processing can be effectively investigated using ERP methodology due to its fine temporal

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resolution. Among ERP components, changes in P3 amplitudes, occurring about 300 ms after the

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stimulus, have been reliably associated with WM functioning. Several theories have been proposed to

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interpret the role of the P3 in working memory tasks (for overview, see Kok, 2001; Polich, 2007),

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however, there is no consensus as to what the P3 represents in the context of a WM task, especially

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during the encoding phase. For example, Donchin and Coles (1988) interpreted the P3 within the

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WM encoding in ADHD

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framework of a "context updating theory" which proposes that P3 is increased when there is a need to

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update, change, or refresh the current contents of WM. Researchers also focus on the intricate role of

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attentional processes in working memory functioning. For instance, some suggest that WM capacity is

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determined by an executive attentional process (Kane et al., 2007; Vogel, 2005), or the „capacity of the

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focus of attention‟ (i.e. the scope of attention; Cowan, 2001). Ford, White, Lim, and Pfefferbaum (1994)

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suggested that P3 amplitude reflects the effort to allocate attention (see also, Polich, 2007). Similarly,

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Kok (2001) proposed that the P3 reflects the „attentional capacity invested in the categorization of task

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relevant events‟. In sum, these studies suggest a possible link between P3 amplitude and allocation of

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attentional resources that subserve working memory functioning. Thus, in the current study, we interpret

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P3 activation as reflecting the direction of attentional resources towards encoding information into WM,

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such as categorizing events or updating mental representations. However, attention acts at many levels

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of processing during working memory, including early perceptual processes (Rutman et al., 2010). For

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instance, ERP components such as the P1 are believed to represent sensory responses elicited by visual

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stimuli as early as 100 ms (Luck, 2005). Thus, we also measure the P1 component to ascertain whether

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perceptual/sensory processes are intact or altered in college students with ADHD.

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A recent meta-analysis of six studies investigating the P3 component in adults with ADHD found

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significantly reduced P3 amplitude compared to controls (Szuromi et al., 2011). However, all of the

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studies used a GoNoGo paradigm: none used a WM task. Moreover, a recently published EEG study of

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WM functioning in adult ADHD utilized an n-back task and mainly investigated time-frequency

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measures (Missonier et al., 2013). Among other findings in that study, Missonier and colleagues (2013)

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reported reduced low frequency oscillations (e.g., theta) in the ADHD group during the period in which

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the P3 would have occurred (e.g., between 200-500 ms), suggesting that both time-and frequency

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domain measures reflect similar underlying neural processes.

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Only one study to date has investigated the P3 component in the context of a „delayed match-to-

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sample‟ WM paradigm and this involved a comparison of participants with and without Schizophrenia

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WM encoding in ADHD

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(Haenschel et al., 2007). In this paradigm, the to-be-remembered target stimuli were presented in a

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sequence that varied in length (e.g., 2 versus 3 stimuli) to measure effects of WM load. Longer

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sequences place greater demands on WM in terms of storage and updating information compared to

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short sequences. Then, after a delay period, the participant is asked whether a test-stimulus matched one

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of the targets. The task uses irregular shapes as stimuli to avoid verbal/semantic processing.

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Significantly reduced mean P1 (an early visual component) and P3 amplitudes were found in individuals

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with Schizophrenia compared to controls. The load effect was present only for the P1 component. The

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authors interpreted the reduced P1 activity as an early visual processing deficit whereas the decreased

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P3 amplitudes were interpreted as evidence of reduced ability in categorizing or evaluating stimuli.

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To investigate WM, the present study adapted Haenschel‟s WM paradigm (Haenschel et al.,

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2007) and used it with a sample of college students with ADHD. This paradigm allowed us to

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investigate the neural effects of encoding stimuli presented in sequence under different load conditions

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(low versus high load). Specifically, the current task required participants to allocate attention to the

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stimulus, register it, and then quickly shift attention to the next stimulus while keeping the previous one

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in mind. This process requires both earlier attention allocation to sensory/perception of the stimuli and

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later allocation of attention to evaluate the stimulus and update the mental representation. To the best of

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our knowledge, no previous study has investigated the encoding phase of WM using a delayed-match-

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to-sample task in participants with ADHD. A complete understanding of ADHD necessitates the study

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of different subpopulations of this clinical condition, which represent different levels of functioning.

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Our sample is unique in that it consists of college students with ADHD who are relatively high

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functioning and successful academically, but who continue to show impairment (DuPaul et al., 2009). A

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better understanding of mechanisms underlying their difficulties may inform interventions designed to

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increase academic achievement and educational attainment in this population.

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To determine whether participants with ADHD are impaired during WM encoding, we measured P1 and P3 amplitudes, which allowed us to ascertain at which stage (early vs. later visual processing)

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WM encoding in ADHD

145

any impairment occurred. Furthermore, to test whether any impairment might vary according to the

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level of WM demand, we utilized two load conditions (low, high) in the delayed match-to-sample WM

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task. We predicted that individuals with ADHD would show impaired WM performance and reduced P3

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amplitude and that these group differences would be most pronounced under the high-load WM

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condition.

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METHOD

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1) Participants:

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A total of 32 unmedicated individuals with ADHD (47% male; aged 19-35) and 25 control

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participants (44% male; aged 19-32) participated in the study. Participants with ADHD were recruited

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from University Student Disability Services in a major urban area, via email lists and flyers. Inclusion

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criteria were; 1) current enrolment in a post-secondary program, 2) a previous diagnosis of ADHD, 3)

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registration with respective university or college Student Disability Services, which requires

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documented evidence of a previously confirmed diagnosis of ADHD (typically, but not invariably in

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elementary school), and 4) aged 19-35. Exclusion criteria were; 1) uncorrected sensory impairment, 2)

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major neurological dysfunction and psychosis, and 3) current use of sedating or mood altering

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medication including stimulant medication for ADHD. Amongst the clinical sample, 4 reported

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comorbid learning disability. Participants in the comparison group were recruited through campus

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advertisements: they were required to have no history or current presentation of mental health disorders.

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All participants completed the Adult ADHD Self-Report Scale (ASRS v1.1), the Symptom

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Assessment-45 (SA-45; Maruish, 1999) and the Cognitive Failures Questionnaire (CFQ; Broadbent,

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Cooper, FitzGerald & Parkes, 1982; Wallace et al., 2002) to quantify current symptoms of ADHD,

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additional psychopathology, and associated levels of cognitive impairment, respectively. The ASRS is a

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reliable and valid scale for evaluating current symptoms of ADHD in adults (Adler et al., 2006). The

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ASRS v1.1 consists of eighteen questions based on the criteria used for diagnosing ADHD in the DSM-

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WM encoding in ADHD

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IV-TR. The SA–45 is a brief assessment of psychiatric symptom. It is based on the well-validated

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longer version (SCL–90–R) to create a brief, yet thorough, measure of psychiatric symptoms. The CFQ

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measures self-reported failures in perception, memory, and motor function in everyday life. This 25-

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item questionnaire uses a 5-point Likert scale and has a good external validity (Broadbent, Cooper,

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FitzGerald & Parkes, 1982; Wallace et al., 2002). Questions require participants to rank how often these

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failures in cognition occur. Both ASRS and CFQ instruments yield a total score whereby a higher score

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represents more ADHD symptoms and more cognitive failures, respectively (Table 1).

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The study was approved by the Research Ethics Boards of the participating universities. All participants provided informed written consent before starting the study.

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2) Tasks

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Neuropsychological tasks measuring WM performance

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Three neuropsychological tasks were used to measure WM performance. The Digit Span subtest from

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the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) was used to assess verbal working

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memory ability (Wechsler, 2008). The Spatial Span (SSP) and Spatial Working Memory (SWM) tasks

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from the Cambridge Neuropsychological Testing Automated Battery (CANTAB) were used to assess

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visual spatial WM. The SSP required participants to remember the spatial sequence of squares flashed

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briefly one at a time on the screen. The scores represent the highest level at which the participant

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reproduces at least one correct sequence. The SWM task requires participants to find a blue token in a

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series of displayed boxes and use these to fill up an empty column, taking care not to return to boxes

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where a blue token had been previously found. The standardized z-score for total number of errors for

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the SWM was used.

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WM encoding in ADHD

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The ERP delayed match-to-sample task The delayed match-to-sample task was adapted from Haenschel et al (2007). Thirty-six different

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abstract figures were used as stimuli. The task consisted of 2 WM load conditions (144 trials total): a

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high load condition in which a sequence of 3 stimuli was presented (72 trials) and a low load condition

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with a sequence of 2 stimuli (72 trials). The low load (2-stimulus) condition is shown in Figure 1). Our

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task differed from that of Haenschel et al (2007) in that we made the task self-paced to better optimize

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participants‟ readiness on computer-paced tasks which are known to be difficult in the ADHD

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population (e.g., interference effects of „set shifting‟ between trials) and thereby obtain a purer measure

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of working memory. Furthermore, we were also hoping to reduce the loss of trials due to artifacts.

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Participants initiated each trial by pressing the space bar on a keyboard, which served to optimize their

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readiness for the start of each trial. Trials were presented in blocks of 12 trials each of either a low or

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high load. The blocks of high (3 Stimuli) and low (2 Stimuli) loads were randomly distributed. Before

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each block, participants were shown which condition to expect (e.g., low or high load) and after each

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block, feedback on accuracy was provided (as the percentage of trials that were correct). After the space

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bar was pressed, a clear time of 600 ms was introduced before the fixation stimulus was presented for

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400 ms. Each target stimulus appeared in the center of the screen for 600 ms (visual angle, 1.34) with an

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inter-stimulus interval of 400 ms. Two seconds after the last target stimulus appeared (delay;

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maintenance), a probe stimulus was shown for two seconds (target; retrieval: see figure 1). Participants

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were asked to indicate whether the target stimulus was one of the previously presented stimuli by

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pressing the keys labelled as „Same‟ or “Different” on the keypad using their dominant hand. Response

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accuracy was emphasized, but to maintain a reasonable pace of processing, participants were required to

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respond within a 2 second time frame, after which the trial would be terminated and scored as incorrect.

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However, response time was recorded. E-prime 1.2 software (Psychology Software Tools, Inc.) was

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used to control stimulus presentation and timing as well as to record the accuracy and latency of

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responses.

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WM encoding in ADHD

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Trial time varied between 4-6 seconds for low load and 5-7 seconds for the high load. Since

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between-trial inter-stimulus intervals would vary because of the self-pacing (participant pressed space bar

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to start the next trial), total time for task completion was calculated and compared between groups. The

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analysis indicated that there were no significant differences between the two groups in terms of total time

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to complete the task (see table 2).

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3) Neural data acquisition, processing: ERP data

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The EEG was recorded with a 128-channel Geodesic Hydrocel Sensor Net at a 500 Hz sampling rate,

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using Netstation stand-alone software (Electrical Geodesics Inc, Eugene, Oregon). Netstation was used

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to filter (FIR, 0.5-30 Hz, 60 Hz notch) and segment the data (400 ms before stimulus onset and 2000

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ms for low load, or 3000 ms for high load, after stimulus onset).

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Segments containing artifacts were removed using automatic algorithms to detect eye blinks and

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eye movements, as well as large drifts or spikes in the data. Eye blinks were detected when the vertical

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eye channels exceeded a threshold of 150 µv (max-min) within a 160 ms (moving) time window

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within each trial after running a 20 ms moving-average smoothing algorithm across the entire trial

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period. Eye movements were detected when horizontal eye channels exceeded a threshold of 100 µv

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(max-min) over a 200 ms time window. Channels were automatically marked bad when they exceeded

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a transition threshold of 200 µv over the entire segment (max-min). Segments containing more than

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20% bad channels were automatically removed. In addition, all epochs were visually inspected by

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trained research assistants blind to the hypotheses. Bad channels were replaced by values interpolated

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from neighbouring channel data using spherical splines. Results were verified manually by a research

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assistant, who was unaware of the study objectives or hypotheses. Because we used a relatively low

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WM encoding in ADHD

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high-pass filter, a linear detrending algorithm was applied to individual epochs to eliminate any left-

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over linear drift if it existed.

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The data were then averaged for each subject and stimulus, average referenced, and coded

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within the segment. The P1 components were scored as the maximum positivity between 80-160 ms

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after stimulus-onset for each stimulus (e.g., 3 stimuli for high load) for electrode sites 75 (Oz), 70 (O1)

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and 83 (O2). Similarly, P3 components were scored as the mean positivity between 250-500 ms after

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stimulus onset for electrode sites 65, 66, 84, and 90 (See Supplementary Figure S1 for electrode map)

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compared to the baseline. The mean activity was chosen to represent P3 because this is a more

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elongated component with peaks (often several) that are less clearly defined compared to „perceptual‟

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components (see also, Luck, 2005; Woltering et al., 2011). Electrodes were selected based on the

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literature (e.g., see Polich, 2007, for rationale) as well as the maximal positivities in the grand average

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waveforms (relative to the 200 ms baseline). Latency ranges were determined based on inspection of

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the individual ERPs as well as Haenschel‟s et al., 2007 study. We acknowledge that, considering the

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paucity of ERP studies using delayed-match-to-sample tasks, our component labeling may vary from

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convention used in other tasks. Before exportation, data were baseline corrected for 200 ms prior to

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stimulus onset, separately, for each individual stimulus. Only correct trials were used in the analysis.

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Averages across electrode sites were used for the P1 and P3 analysis.

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4) Data analysis:

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Independent one-way ANOVAs were used to test for group differences in performance on the

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neuropsychological tasks. Separate repeated measures ANOVAs were conducted to test for group

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differences in accuracy and reaction time on the delayed match-to-sample task, with Load as the

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repeated measure (High load vs. Low) and Group as a between participant factor (ADHD vs. Control).

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By contrast, the incomplete factorial design of the Delayed-Match-To-Sample task (2 stimuli in Low

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Load; 3 stimuli in High Load) precluded a Group (2) x Load (2) x (Stimulus; 2 or 3) repeated measures

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WM encoding in ADHD

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ANOVA to investigate effects on the P1 and P3 ERP components. Thus we used a multi-step approach

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to disentangle the effects of Load, Stimulus, and Group on our ERP components (P1, P3). In Step-1, to

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investigate the effects of Group and WM Load on the P3 and P1, we collapsed across Stimulus, and ran

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a Group by Load Repeated Measures ANOVA. For Step-2 we ran a Group by Load Repeated Measures

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ANOVA for the first and last Stimuli separately. The last stimuli of each load would represent the

275

situation in which a participant would hold the maximum amount of information in mind for each

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condition when encoding new information. This analysis involved a comparison of the second (last)

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stimulus of the low load with the third (last) stimulus of the high load: the second stimulus of the high

278

load was not compared to either the first or the second stimulus of the low load as it was not

279

conceptually equivalent to those stimuli. In Step-3, we ran separate Group by Stimulus repeated

280

measures ANOVAs for each load. For the high load, contrasts were run to test for differences between

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the three stimuli. This step allowed us to investigate sequence effects of stimulus presentation, which

282

may provide insights in the effects of neural resources being allocated during encoding with increasing

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amount of stimuli to be held in mind.

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Two participants, whose ERP data yielded less than 10 trials, were excluded from this analysis.

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Data points (behavioural and ERP) with SD's >3 were regarded as outliers and adjusted using the

286

winsorizing technique (Tabachnick and Fidell, 2001). Three data points in ADHD group and 2 data

287

points in control group were winsorized. No significant differences were found in trial-counts [Low

288

load: F (1, 49) = .258, p=.553, High load: F (1, 49) = .006, p=.938] between the groups.

289

Partial eta-squared values (η2) were computed to ascertain effect size (ES). According to Vacha-

290

Haase and Thompson (2004), ES based on η2 = .01 corresponds to a small effect, η2 = .10 corresponds to a

291

medium effect, and η2 = .25 represents a large effect.

292 293 294

RESULTS

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WM encoding in ADHD

295 296

1. Behavioral Data Analysis of demographic and clinical variables confirmed that the ADHD and control groups

297

were comparable in terms of age [F (53) = 1.358, p=.25] and sex [χ² (1, 21) = .039, p = 1.00]. As

298

expected, ADHD participants reported significantly more ADHD symptoms [F (54) =1, p< .001] and

299

cognitive failures in everyday life than controls [F (42) = 59.91, p< .001]. The ADHD group also

300

reported more obsessive-compulsive symptoms [F (43) = 25.22, p< .001] on the SA-45, compared to the

301

Control group. Means and standard deviations are presented in Table 1.

302

Analysis of neuropsychological tests of WM performance revealed, as expected, that the ADHD

303

group obtained significantly lower scores on the CANTAB-SWM [F (1, 44) = 4.54, p = .039], compared

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to the control group. ADHD group also showed poorer scores in WAIS Digit Span, albeit a non-

305

significant difference [F (1, 48) = 3.29, p = .076]. Groups did not differ on CANTAB-SSP. The mean

306

scores for the ADHD group on these two tests of WM fell in the low average range: their mean SWM z-

307

score was -.10, which corresponds to a scaled score of just below 7 (16th percentile); and their mean

308

Digit Span scaled score was 8.31 (25th percentile). By contrast, scores for the comparison group fell

309

well within the average range: mean Digit Span scaled score of 9.95 corresponds to the 50th percentile

310

and the SWM z-score was .51, which corresponds to a scaled score of 10 or the 50th percentile.

311

As predicted, analysis of the Delayed-Match-To-Sample Task, revealed a main effect of Load

312

on performance accuracy [F (1, 53) = 49.88, p = .000, ES = .49]. Specifically, all participants were less

313

accurate during the high WM load compared to the low WM load condition, indicating the success of

314

the experimental manipulation. The groups did not differ significantly in terms of either their accuracy

315

or response times (to the target stimulus). There was no significant Group-by-Load interaction for

316

either response accuracy or response times. Moreover, there were no group differences in terms of intra-

317

individual variability of response times and neither the main effect of Load nor the Group-by-Load

318

interaction was significant. Means and standard deviations of the neuropsychological tasks and the

319

delayed match to sample task measures are shown in Table 2.

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WM encoding in ADHD

320 321

>

322 323 324

2) ERP data The waveforms for each stimulus are presented in Figure 2, in Panel A for the low load and Panel B

325

for the high load. Table 3 shows the means and standard deviations for the ERP components for each

326

load and for each group collapsed across stimulus. The waveforms and (difference) topoplots for the P3,

327

collapsed across stimulus, are presented in Figure 3, Panel A for the low load and Panel B for the high

328

load. As can be seen in the topoplots, the difference in P3 amplitude between the ADHD and control

329

group was observed primarily in the parietal/occipital regions (see Supplementary Figure S2 for a t-plot).

330 331 332

P1 Analyses For P1 amplitude, Step-1 analysis, which examined the effects of Group and Load collapsed

333

across stimuli, showed a significant main effect of Load [F (1, 53) = 8.26, p = .006, ES = .14] with

334

higher amplitude for high load. There was no main effect of Group or Group-by-Load interaction. Step-

335

2 analyzed Group and Load effects separately for the first and last stimuli. For the first stimulus there

336

was a significant Group-by-Load interaction [F (1, 53 = 6.27, p = .015, ES = .106] in the absence of

337

main effects for either Group or Load. Post-hoc tests revealed that participants with ADHD had

338

increased P1 amplitudes for the High compared to the Low load, whereas the comparison group did not

339

show this effect (p < .05). For the last stimulus, a main effect of Load was present [F (1, 52) = 5.76, p

340

= .02, ES = .10] but neither the main effect of Group nor the Group-by-Load interaction was significant.

341

Step-3 analysis of P1 during the low load condition revealed a main effect of Stimulus, [F (1, 53) = 9.31,

342

p = .004, ES = .15] with lower P1 amplitudes for the first stimulus compared to the last. No Group, or

343

Group-by-Stimulus interaction effect was found. The high load also revealed a main effect of Stimulus,

344

[F (1, 51) = 13.61, p = .000, ES = .35], with lower P1 amplitudes for the first Stimulus compared to the

14

WM encoding in ADHD

345

second and third. No Group or Group-by-Stimulus interaction was found. Last, the P1 did not correlate

346

with any clinical questionnaire or behavioral measures.

347

For P1 latency, Step-1 analysis revealed no main effect of Load, Group, or Load-by-Group

348

interaction for either the high or low load conditions. On the Step-2 analysis, no main effects of Group,

349

Load, or a Stimulus-by-Group interaction were found, nor were there any significant effects for the first

350

stimulus. On the Step-3 analysis, neither the main effects of Stimulus or Group, nor the Stimulus-by-

351

Group interaction was significant.

352 353 354

P3 Analyses For P3 amplitude, our Step-1 analysis showed a significant main effect of Group, [F (1, 53) =

355

6.30, p = .015, ES = .11], with the ADHD group showing smaller P3 amplitudes than the control group.

356

There was also a main effect of Load, [F (1, 53) = 4.04, p = .049, ES = .07], with larger P3 amplitudes

357

for the high load confirming that our P3 index was sensitive to effects of load. No Group-by-Load

358

interaction was found.

359

The Step-2 analysis tested the Group by Load effects separately for the first and last Stimuli.

360

There was a main effect of Group for the first, [F (1, 53) = 5.31, p = .025, ES = .09], and last [F (1, 52)

361

= 5.76, p = .020, ES = .10], stimuli suggesting the group difference was present across stimuli. There

362

was a main effect of Load for the first [F (1, 53) = 6.55, p = .013, ES = .11] but not for the last stimulus,

363

suggesting our general load effect was driven mostly by the first stimulus. Also, no significant Group-

364

by-Load interaction was found for both first and last stimuli.

365

Our Step-3 analysis, testing for sequence effects of stimuli, revealed a main effect of Stimulus,

366

[F (1, 53) = 11.76, p = .001, ES = .18], and a main effect of Group, [F (1, 53) = 4.89, p = .031, ES = .08],

367

during the low load. No Group-by-Stimulus interaction effect was found. The high load also revealed a

368

main effect of Stimulus, [F (1, 53) = 3.17, p = .05, ES = .11], and a main effect of Group, [F (1, 52) =

369

7.63, p = .008, ES = .13], with no Group-by-Stimulus interaction. In general, for both WM loads,

15

WM encoding in ADHD

370

responses to the first stimulus showed lower amplitudes than to the other stimuli (p‟s < .05). For the

371

high load, pairwise comparisons showed that the second and third stimulus did not differ. The ADHD

372

group showed lower P3 amplitudes for all three stimuli compared to the control group.

373

For the P3 latency, the Step-1 latency analysis indicated no main effect of Group, a marginally

374

significant effect for Load, [F (1, 53) = 3.23, p = .078, ES = .058], with shorter latencies for the high

375

load, and no Load-by-Group interaction. Step-2 analysis revealed a main effect of Load for the first [F

376

(1, 53) = 8.16, p = .006, ES = .133], but not for the last stimulus. No main effect of Group or Group-by-

377

Load interaction was found for either the first or last stimulus. The Step-3 analysis showed a main effect

378

of Stimulus for the low load condition [F (1, 53) = 5.47, p = .023, ES = .093]. Post-hoc tests revealed

379

that the ADHD group showed increased P3 amplitude for the second stimulus compared to the first,

380

whereas the comparison group did not show this difference (p = .03). No main effect of Group or

381

Group-by-Stimulus interaction was found for both first and last stimuli.

382 383

>

384 385

>

386 387

>

388 389 390

DISCUSSION The present study is the first to investigate neural changes in P3 amplitudes and latencies during

391

the encoding phase of WM in ADHD, using a delayed match-to-sample task. The main finding was a

392

reduced P3 amplitude for the ADHD group, regardless of WM load or stimulus sequence (i.e., first or

393

last), compared to the control group. Behavioral results showed that all participants were less accurate

16

WM encoding in ADHD

394

during the high WM load condition compared to the low load condition, which attests to the success of

395

our experimental manipulation of WM load.

396

Changes in P3 amplitudes during WM tasks have been associated with differences in the

397

allocation of attention necessary for WM functioning (Donchin and Coles, 1988; Polich, 2007; Kok,

398

2001). Our findings of lower P3 amplitude in the ADHD group compared to the control group,

399

independent of load or stimulus order, suggests a more general problem of attentional allocation in the

400

encoding phase of WM rather than a more specific problem associated with an increasing demand on

401

WM. The reduced P3 amplitude in college students with ADHD found in this study using a WM task is

402

consistent with and extend findings from a recent meta-analysis on P3 in adults with ADHD (to WM

403

encoding during a WM task), which revealed a decreased P3 amplitude recorded during Go-Nogo tasks

404

in ADHD (Szuromi et al., 2011; and also see, Woltering et al., 2013, using a similar sample to that in

405

the present study). Go-Nogo tasks, which traditionally are used to index response inhibition, do involve

406

WM, albeit low-level demands, and some require constant updating of working memory.

407

We believe that the decreased P3 amplitude observed in individuals with ADHD can be

408

interpreted as deficient attentional allocation, which leads to inefficient encoding of information in

409

memory. Theoretically, the notion of P3 reflecting attentional allocation is not that different to Kok‟s

410

notion of capacity since recent insights in the field of working memory have linked attentional processes

411

and working memory capacity closely together (Awh and Vogel, 2008). A functional relationship

412

between P3 amplitude and attentional resource allocation has also been reported in the literature (Ford,

413

White, Lim, and Pfefferbaum, 1994; Kok, 2001). Also, many studies found the P3 to be related with

414

brain regions thought to be involved in attention, such as the parietal lobe, the temporoparietal junction,

415

lateral prefrontal areas and the cingulate gyrus (Linden, 2005; Verleger et al., 1994). Similarly, a study

416

that explored the cortical basis of ERP components revealed an impaired ventral attentional pathway in

417

ADHD participants during the time of the P3 (Helenius et al., 2011). Furthermore, in their meta-analysis,

418

Szuromi et al (2011) interpreted the overall reduction of P3 amplitude in adults with ADHD as a

17

WM encoding in ADHD

419

dysfunction of the ventral attention network since the P3 related to target detection is thought to be

420

generated from the temporoparietal junction. In sum, previous research suggests a possible link between

421

P3 amplitude and attentional resource allocation. Hence, based on previous findings, we speculate that

422

weak activation in attention pathways might play a role in the defective allocation of attentional

423

resources during activities involving WM, accounting for the observed decreased P3 amplitudes in

424

college students with ADHD.

425

Our analyses of P1 amplitude revealed no significant group effects except for one: namely that

426

the ADHD group seems to manifest an increase in P1 amplitude from the low to the high WM load

427

condition in response to the first stimulus only. Compared to Haenschel et al. (2007), who did find

428

robust P1 group differences in Schizophrenia patients, our findings suggest that differences in WM

429

encoding between the ADHD and non-ADHD peer group were due primarily to differences in

430

attentional allocation during the WM encoding phase as opposed to early visual processing.

431

Only one other study to date has investigated neural differences in WM between ADHD adults

432

and their peers using EEG (Missonnier et al., 2013). Despite the use of different methodologies (n-back

433

versus delayed-match-to-sample task, respectively; smaller sample size, and the focus on different

434

neurophysiological measures, such as time-frequency domain), conclusions are similar. That is, both

435

conclude that individuals with ADHD manifest differences in neural correlates linked to the attentional

436

network involved in the allocation of attention subserving WM.

437

Behavioral differences between groups were found during the WM task: namely the ADHD

438

group had slower response times to the retrieval cue, albeit marginally significant, but did not differ in

439

response accuracy. It is possible that the slower response times in the ADHD group indicate their need

440

for more time to make accurate decisions compared to their peers. In this sense, the longer response

441

times may be another manifestation of slow processing speed or a compensatory mechanism. Although

442

accuracy in our ADHD group was comparable to that of their typically developing peers, cognitive

443

mechanisms of processing, such as the allocation of attention, could still function less optimally. It is

18

WM encoding in ADHD

444

possible that this deficit may show in contexts other than within the experimental confines of the current

445

task. This suggestion is strengthened by other findings from this study: namely that for the ADHD

446

group, their mean scores for WAIS Digit Span and CANTAB SWM were in low average range, whereas

447

those of the control group were in the average range; the T-scores for the ADHD group (mean = 41.5)

448

were in the mildly impaired range whereas those for the control group (mean = 50) were within the

449

well-functioning range. Note that the two neuropsychological tasks not only require the maintenance but

450

also the updating of the spatial representations, which renders their WM demands similar to those of the

451

delayed-match-to-sample task. Furthermore, these behavioral and neuropsychological differences were

452

found despite the fact that the participants with ADHD in this study are high functioning (mostly college

453

students) and the task was self-paced to exclude potential effects of poor sustained attention

454

confounding the WM result.

455

In light of our neural results, the lack of differences in behavioral accuracy in the delayed-

456

match-to-sample task could suggest that our group differences found in neural processing indicate

457

inefficient means of attentional allocation; a detriment that may not always be observed in behavioural

458

accuracy. The P3 could be a neural indicator of this inefficient mechanism.

459

From a clinical perspective, the WM impairment (albeit modest) in these college students with

460

ADHD compared to their college-peers may well help explain why this relatively successful subgroup

461

continues to manifest functional impairments in academic, social functioning during college years.

462

Furthermore, these results support findings from a recent meta-analysis suggesting long-term memory

463

difficulties in adult ADHD may be attributable to difficulties in the encoding phase (Skodzik et al.,

464

2013). Our findings highlight the importance of studying WM encoding and in this newly emergent

465

subgroup of young adults with ADHD.

466

This study has several limitations, which should be taken into consideration when interpreting

467

the findings. First of all, we did not have a direct measure of IQ, or scholastic ability, which limits the

468

characterization of our sample. However, these college students must be of at least average intelligence

19

WM encoding in ADHD

469

(as suggested by the WAIS Digit Span) to be able to successfully complete high school and gain college

470

entrance. Furthermore, we did not measure alcohol and recreational drug use in this sample of college

471

students, which could influence allocation of attention or WM or both. Also, the current analyses were

472

restricted to the encoding stage and further research is warranted to characterize the maintenance and

473

retrieval stages of WM.

474

In conclusion, keeping in mind the aforementioned limitations, our results suggest differences in

475

neural processing in WM with adult ADHD. Our results may suggest, based on what is known about

476

changes in P3 amplitudes in the ERP literature, that weak activation in attentional pathways might play

477

a role in defective attention resource allocation to updating representations in working memory.

478

Moreover, our results concerning the P1 versus P3 suggest a temporal as well as syndrome specific

479

neural index of adult ADHD. These findings could have important implications for understanding the

480

cognitive difficulties ADHD adults face, as well as intervention.

481 482 483

20

WM encoding in ADHD

484

Acknowledgements

485

We thank Dr. Corinna Haenschel and Niko Kriegeskorte for letting us use the task stimuli. Also, we

486

appreciate Alex Lamey for his programming assistance with the E-prime task and Peter Fettes for

487

helping with parts of the data analysis. We would like to thank Dr. Marc Lewis for the use of his

488

Canadian Foundation for Innovation (CFI #482246) funded EEG lab. This research was supported

489

financially in part by a CIHR Operating grant (# 245899, Tannock & Lewis) and by the Canada

490

Research Chair program (RT).

491 492

21

WM encoding in ADHD

493

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ADHD Self- Report Scale (ASRS) to Rate Adult ADHD symptoms. Ann Clin Psychiatry

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Baddeley A. Working memory: Looking back and looking forward. Nat Rev Neurosci 2003; 4:829-839.

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Baddeley AD, Hitch GJ. Developments in the concept of working memory. Neuropsychology

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Cowan N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity.

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Behav Brain Sci 2001; 24:87-114. Donchin E, Coles MG. Is the P300 component a manifestation of context updating? Behav Brain Sci1988:11:357-374. DuPaul G, Weyandt L, O‟Dell S, Varejao M. College students with ADHD: Current Status and future directions. J Atten Disord 2009; 13:234. Fabiani M, Karis D, Donchin E. P300 and recall in an incidental memory paradigm. Psychophysiology 1986; 23:298-308.

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Disabil 2007; 40:49-65. Haenschel C, Bittner RA, Haertling F, Rotarska-Jagiela A, Maurer K, Singer W, et al. Contribution of

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impaired early-stage visual processing to working memory dysfunction in adolescents with

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Heiligenstein E, Guenther G, Levy A, Sevino F, Fulwiler J. Psychological and academic

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Helenius P, Laasonen M, Hokkanen L, Paetau R, Niemivirta M. Impaired engagement of the ventral attentional pathway in ADHD. Neuropsychologia 2011; 49:1889-1896. Hervey AS, Epstein JN, Curry JF. Neuropsychology of adults with attention-Deficit/hyperactivity disorder: a meta-analytic review. Neuropsychology 2004; 18:485. Karis D, Fabiani M, Donchin E. “P300” and memory: Individual differences in the von Restorff effect. Cognitive Psychol 1984; 16:177-216. Kane MJ, Conway AR, Hambrick DZ, Engle RW. Variation in working memory capacity as variation in executive attention and control. Oxford: Oxford University Press, 2007. Kok A. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology 2001; 38:557-577.

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Kok A, Ramautar JR, De Ruiter MB, Band GP, Ridderinkhof KR. ERP components associated with

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successful and unsuccessful stopping in a stop‐signal task. Psychophysiology 2004; 41:9-20.

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Linden DEJ. The P300: where in the brain is it produced and what does it tell us? Neuroscientist 2005; 11:563–76. Luck SJ. An introduction to the event-related potential technique (cognitive neuroscience). Cambridge, MA: MIT Press, 2005.

Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of

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working memory impairments in children with attention-deficit/hyperactivity disorder. J

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Am Acad Child Adolesc Psychiatry 2005;44:377-384.

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Missonnier P, Hasler R, Perroud N, Herrmann FR, Millet P, Richiardi J, et al. EEG anomalies in adult

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ADHD participants performing a working memory task. Neuroscience 2013; 241:135-146.

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Polanczyk G, Jensen P. Epidemiologic considerations in attention deficit hyperactivity disorder: a

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review and update. Child Adol Psych Cl 2008; 17:245-260. Polanczyk G, Rohde LA. Epidemiology of attention-deficit/hyperactivity disorder across the lifespan. Curr Opin Psychiatry 2007;20:386-389.

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Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 2007; 118:2128-2148.

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Woltering S, Granic I, Lamm C, Lewis MD. Neural changes associated with treatment outcome in children with externalizing problems. Biol Psychiat 2011; 70:873-879. Woltering S, Liu Z, Rokeach A, Tannock R. Neurophysiological differences in inhibitory control between adults with ADHD and their peers. Neuropsychologia 2013; 51: 1888-1895.

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575

Figure Legends

576 577

Figure 1.Schematic outline of the delayed match-to-sample task for low load condition.

578

Working memory paradigm adopted and modified from Haenschel et al (2007). Two stimuli were

579

presented in low load condition, and 3 stimuli were shown in high load condition for encoding for 600

580

ms each with an inter-stimulus interval of 400 ms. After a 2000 ms delay, a probe stimulus was

581

presented for the participants to judge whether they saw the target among the initial set of the stimuli or

582

not. Participants initiated the next trial by pressing the space bar. 200 ms of clear time was allowed

583

before each trial.

584 585

Figure 2. ERP waveforms at site P3 (averaged across all P3 electrodes) for (A) Low and (B) High

586

load conditions for each stimulus [(A) left: first stimulus, right: second stimuli (B) first, second

587

and last stimuli].

588 589

Figure 3. ERP waveforms at site P3 (averaged across all P3 electrodes) and topoplots (250-500 ms)

590

for (A) Low and (B) high load condition across the stimuli.

591

26

Figure 1

Encoding (600ms per stimuli) 600ms

600ms

Maintenance (delay: 2s)

Retrieval (target:2s) 2000ms

Figure 2

Figure 3

Table 1: Mean and Standard deviations of the ASRS (ADHD Self Report Scale), CFQ (Cognitive Failures Questionnaire), and SA-45 subscales (Symptom Assessment-45) for the ADHD and the Comparison group.

ADHD

Comparison

P-values

ASRS ***

44.97(9.12)

18.71(7.98)

.00

CFQ ***

53.88(12.74)

27.56(8.09)

.00

Anxiety*

8.41(2.37)

6.75(1.83)

.01

Depression

9.37(3.63)

8.05(3.58)

.21

14.72(3.77)

9.25(3.45)

.00

Somatization

7.65(2.59)

6.55(1.73)

.11

Phobic Anxiety

5.52(0.80)

5.29(0.72)

.30

Hostility

6.48(1.71)

5.95(1.50)

.28

Interpersonal Sensitivity

8.67(3.16)

7.76(3.85)

.38

Paranoid Ideation

7.93(2.56)

6.62(2.20)

.07

Psychoticism

5.54(1.07)

5.62(1.02)

.80

SA-45

Obsessive Compulsive ***

* p < .05, ** p < .01, *** p < .001. p-values shown reflect one-way ANOVA’s.

Table 2. Mean and Standard deviations for behavioural measures of the delayed match to sample task [accuracy, as well as the reaction time (in milliseconds), the intra-individual variability (IIV)], and the WM [Digit span, CANTAB: SWM and SSP] neuropsychological tests for the ADHD and Comparison group. ADHD Mean (SD)

Comparison Mean (SD)

P-values

Delayed Match to Sample Task Accuracy Low load

0.73(0.14)

0.80(0.14)

.10

High load

0.62(0.18)

0.64(0.15)

.64

Low load

1260.60(232.85)

1176.17(166.47)

.14

High Load

1309.96(268.49)

1241.70(208.08)

.30

Low load

494.32(241.75)

446.06(216.30)

.44

High load

420.31(242.63)

452.05(228.98)

.62

23.32(3.26)

21.81(2.57)

.07

WAIS Digit Span

8.31(3.31)

9.95(2.94)

.08

CANTAB SWM*

-0.10(1.13)

0.51(0.62)

.04

0.09(0.86)

0.60(1.14)

.10

Reaction Time

Intra-individual variability (RT)

Total duration of the task Neuropsychological tests

SSP

* p < .05, p-values shown reflect one-way ANOVA’s, RT (Reaction Time), SWM (Spatial Working Memory), SSP (Spatial Span)

Table 3: Means and standard deviations for ERP amplitudes and latency. ADHD Amplitude

P1 Low load 1st stimulus 2nd stimulus High load 1st stimulus 2nd stimulus 3rd stimulus P3 Low load 1st stimulus 2nd stimulus High load 1st stimulus 2nd stimulus 3rd stimulus

Latency

Control

P1 Low load 1st stimulus 2nd stimulus High load 1st stimulus 2nd stimulus 3rd stimulus P3 Low load 1st stimulus 2nd stimulus High load 1st stimulus 2nd stimulus 3rd stimulus

3.27(1.30) 2.83(1.51) 3.70(1.54) 3.62(1.22) 3.20(1.52) 3.87(1.70) 3.83(1.13)

4.02(2.09) 3.83(2.35) 4.21(2.05) 4.30(2.08) 3.58(2.42) 4.33(1.90) 5.05(2.36)

1.96(1.52) 1.59(1.70) 2.32(1.68) 2.02(1.45) 1.80(1.42) 2.18(1.60) 2.04(1.61)

3.02(2.06) 2.69(2.61) 3.39(1.85) 3.32(2.07) 3.12(2.20) 3.47(1.99) 3.29(2.18)

127.27(16.98) 127.27(16.98) 124.47(16.19) 125.58(11.99) 124.73(15.21) 124.53(13.47) 126.55(14.19)

127.92(17.05 127.92(17.05) 124.80(16.30) 125.65(11.66) 127.76(19.41) 123.44(16.16) 125.76(10.30)

360.13(26.42) 350.27(42.41) 370.00(38.08) 468.04(29.31) 372.60(48.80) 366.27(35.64) 365.27(42.75)

357.40(22.59) 350.24(37.86) 364.56(23.31) 362.21(18.94) 363.52(31.73) 360.72(24.66) 362.40(25.23)