Clinical Neurophysiology Volume Issue 2013 [Doi 10.1016_j.clinph.2013.12.094] Kim, Soyeon; Liu, Zhongxu; Glizer, Daniel; Tannock, Rosemary; Wo -- Adult ADHD and Working Memory- Neural

8/13/2019 Clinical Neurophysiology Volume Issue 2013 [Doi 10.1016_j.clinph.2013.12.094] Kim, Soyeon; Liu, Zhongxu; Glizer

1/33


2/33

WM encoding in ADHD

1

Adult ADHD and Working Memory: Neural Evidence of1

Impaired Encoding2

3

Soyeon Kim, Zhongxu Liu, Daniel Glizer, Rosemary Tannock*, Steven Woltering*4

5

Department of Applied Psychology & Human Development, OISE, University of Toronto,6

252 Bloor Street West, Toronto, Ont., Canada, M5S 1V67

8

E-mails: Soyeon Kim ([email protected]), Zhong-Xu Liu ([email protected]),9

Daniel Glizer ([email protected]), Rosemary Tannock ([email protected]),10

StevenWoltering ([email protected]).11

12

* Corresponding authors:13

Steven Woltering14

Department of Applied Psychology & Human Development, OISE, University of Toronto, 25215

Bloor Street West, Toronto, Ont. Canada, M5S 1V616

Tel: +1-416-978-092317

[email protected]

19

or20

21

Rosemary Tannock22

Department of Applied Psychology & Human Development, OISE, University of Toronto, 25223

Bloor Street West, Toronto, Ont. Canada, M5S 1V624

E-mail:[email protected]

26

27

28
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://ees.elsevier.com/clinph/viewRCResults.aspx?pdf=1&docID=8582&rev=3&fileID=398911&msid={E12BE538-7556-4FFF-93A2-9DF8EE7A69C9}mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


3/33

WM encoding in ADHD

2

Highlights29

30

ADHD college students are compared to their typically developing peers on neural and31behavioural indices of working memory (WM).32

Neural indices of working memory (P3) are examined during working memory encoding33

using EEG34

ADHD group showed lower P3 during working memory encoding, suggesting an35

inefficient encoding process in WM.36

37

38

39

40

4142

43

44


4/33

WM encoding in ADHD

3

Abstract45

46

Objectives: To investigate neural and behavioural correlates of visual encoding during a working47

memory (WM) task in young adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD).48

Methods: A sample of 30 college students currently meeting a diagnosis of ADHD and 25 typically49

developing students, matched on age and gender, performed a delayed match-to-sample task with low50

and high memory load conditions. Dense-array electroencephalography was recorded. Specifically, the51

P3, an event related potential (ERP) associated with WM, was examined because of its relation with52

attentional allocation during WM. Task performance (accuracy, reaction time) as well as performance53

on other neuropsychological tasks of WM was analyzed.54

Results: Neural differences were found between the groups. Specifically, the P3 amplitude was smaller55

in the ADHD group compared to the comparison group for both load conditions at parietal-occipital56

sites. Lower scores on behavioral working memory tasks were suggestive of impaired behavioural WM57

performance in the ADHD group.58

Conclusions: Findings from this study provide the first evidence of neural differences in the encoding59

stage of WM in young adults with ADHD, suggesting ineffective allocation of attentional resources60

involved in encoding of information in WM.61

Significance: These findings, reflecting alternate neural functioning of WM, may explain some of the62

difficulties related to WM functioning that college students with ADHD report in their every day63

cognitive functioning.64

65

66

Keywords:Working memory, P3, adults with ADHD, encoding.67

68

69


5/33


6/33

WM encoding in ADHD

5

framework of a "context updating theory" which proposes that P3 is increased when there is a need to95

update, change, or refresh the current contents of WM. Researchers also focus on the intricate role of96

attentional processes in working memory functioning. For instance, some suggest that WM capacity is97

determined by an executive attentional process (Kane et al., 2007; Vogel, 2005), or the capacity of the98

focus of attention (i.e. the scope of attention; Cowan, 2001). Ford, White, Lim, and Pfefferbaum (1994)99

suggested that P3 amplitude reflects the effort to allocate attention (see also, Polich, 2007). Similarly,100

Kok (2001) proposed that the P3 reflects the attentional capacity invested in the categorization of task101

relevant events. In sum, these studies suggest a possible link between P3 amplitude and allocation of102

attentional resources that subserve working memory functioning. Thus, in the current study, we interpret103

P3 activation as reflecting the direction of attentional resources towards encoding information into WM,104

such as categorizing events or updating mental representations. However, attention acts at many levels105

of processing during working memory, including early perceptual processes (Rutman et al., 2010). For106

instance, ERP components such as the P1 are believed to represent sensory responses elicited by visual107

stimuli as early as 100 ms (Luck, 2005). Thus, we also measure the P1 component to ascertain whether108

perceptual/sensory processes are intact or altered in college students with ADHD.109

A recent meta-analysis of six studies investigating the P3 component in adults with ADHD found110

significantly reduced P3 amplitude compared to controls (Szuromi et al., 2011). However, all of the111

studies used a GoNoGo paradigm: none used a WM task. Moreover, a recently published EEG study of112

WM functioning in adult ADHD utilized an n-back task and mainly investigated time-frequency113

measures (Missonier et al., 2013). Among other findings in that study, Missonier and colleagues (2013)114

reported reduced low frequency oscillations (e.g., theta) in the ADHD group during the period in which115

the P3 would have occurred (e.g., between 200-500 ms), suggesting that both time-and frequency116

domain measures reflect similar underlying neural processes.117

Only one study to date has investigated the P3 component in the context of a delayed ma tch-to-118

sample WM paradigm and this involved a comparison of participants with and without Schizophrenia119


7/33

WM encoding in ADHD

6

(Haenschel et al., 2007). In this paradigm, the to-be-remembered target stimuli were presented in a120

sequence that varied in length (e.g., 2 versus 3 stimuli) to measure effects of WM load. Longer121

sequences place greater demands on WM in terms of storage and updating information compared to122

short sequences. Then, after a delay period, the participant is asked whether a test-stimulus matched one123

of the targets. The task uses irregular shapes as stimuli to avoid verbal/semantic processing.124

Significantly reduced mean P1 (an early visual component) and P3 amplitudes were found in individuals125

with Schizophrenia compared to controls. The load effect was present only for the P1 component. The126

authors interpreted the reduced P1 activity as an early visual processing deficit whereas the decreased127

P3 amplitudes were interpreted as evidence of reduced ability in categorizing or evaluating stimuli.128

To investigate WM, the present study adapted Haenschels WM paradigm (Haenschel et al.,129

2007) and used it with a sample of college students with ADHD. This paradigm allowed us to130

investigate the neural effects of encoding stimuli presented in sequence under different load conditions131

(low versus high load). Specifically, the current task required participants to allocate attention to the132

stimulus, register it, and then quickly shift attention to the next stimulus while keeping the previous one133

in mind. This process requires both earlier attention allocation to sensory/perception of the stimuli and134

later allocation of attention to evaluate the stimulus and update the mental representation. To the best of135

our knowledge, no previous study has investigated the encoding phase of WM using a delayed-match-136

to-sample task in participants with ADHD. A complete understanding of ADHD necessitates the study137

of different subpopulations of this clinical condition, which represent different levels of functioning.138

Our sample is unique in that it consists of college students with ADHD who are relatively high139

functioning and successful academically, but who continue to show impairment (DuPaul et al., 2009). A140

better understanding of mechanisms underlying their difficulties may inform interventions designed to141

increase academic achievement and educational attainment in this population.142

To determine whether participants with ADHD are impaired during WM encoding, we measured143

P1 and P3 amplitudes, which allowed us to ascertain at which stage (early vs. later visual processing)144


8/33

WM encoding in ADHD

7

any impairment occurred. Furthermore, to test whether any impairment might vary according to the145

level of WM demand, we utilized two load conditions (low, high) in the delayed match-to-sample WM146

task. We predicted that individuals with ADHD would show impaired WM performance and reduced P3147

amplitude and that these group differences would be most pronounced under the high-load WM148

condition.149

150

METHOD151

1) Participants:152A total of 32 unmedicated individuals with ADHD (47% male; aged 19-35) and 25 control153

participants (44% male; aged 19-32) participated in the study. Participants with ADHD were recruited154

from University Student Disability Services in a major urban area, via email lists and flyers. Inclusion155

criteria were; 1) current enrolment in a post-secondary program, 2) a previous diagnosis of ADHD, 3)156

registration with respective university or college Student Disability Services, which requires157

documented evidence of a previously confirmed diagnosis of ADHD (typically, but not invariably in158

elementary school), and 4) aged 19-35. Exclusion criteria were; 1) uncorrected sensory impairment, 2)159

major neurological dysfunction and psychosis, and 3) current use of sedating or mood altering160

medication including stimulant medication for ADHD. Amongst the clinical sample, 4 reported161

comorbid learning disability.Participants in the comparison group were recruited through campus162

advertisements: they were required to have no history or current presentation of mental health disorders.163

All participants completed the Adult ADHD Self-Report Scale (ASRS v1.1), the Symptom164

Assessment-45 (SA-45; Maruish, 1999) and the Cognitive Failures Questionnaire (CFQ; Broadbent,165

Cooper, FitzGerald & Parkes, 1982; Wallace et al., 2002) to quantify current symptoms of ADHD,166

additional psychopathology, and associated levels of cognitive impairment, respectively. The ASRS is a167

reliable and valid scale for evaluating current symptoms of ADHD in adults (Adler et al., 2006). The168

ASRS v1.1 consists of eighteen questions based on the criteria used for diagnosing ADHD in the DSM-169


9/33

WM encoding in ADHD

8

IV-TR. The SA45 is a brief assessment of psychiatric symptom.It is based on the well-validated170

longer version (SCL90R) to create a brief, yet thorough, measure of psychiatric symptoms.The CFQ171

measures self-reported failures in perception, memory, and motor function in everyday life. This 25-172

item questionnaire uses a 5-point Likert scale and has a good external validity (Broadbent, Cooper,173

FitzGerald & Parkes, 1982; Wallace et al., 2002). Questions require participants to rank how often these174

failures in cognition occur. Both ASRS and CFQ instruments yield a total score whereby a higher score175

represents more ADHD symptoms and more cognitive failures, respectively (Table 1).176

The study was approved by the Research Ethics Boards of the participating universities. All177

participants provided informed written consent before starting the study.178

179

>180

181

2) Tasks182Neuropsychological tasks measuring WM performance183

Three neuropsychological tasks were used to measure WM performance. The Digit Span subtest from184

the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) was used to assess verbal working185

memory ability (Wechsler, 2008). The Spatial Span (SSP) and Spatial Working Memory (SWM) tasks186

from the Cambridge Neuropsychological Testing Automated Battery (CANTAB) were used to assess187

visual spatial WM. The SSP required participants to remember the spatial sequence of squares flashed188

briefly one at a time on the screen. The scores represent the highest level at which the participant189

reproduces at least one correct sequence. The SWM task requires participants to find a blue token in a190

series of displayed boxes and use these to fill up an empty column, taking care not to return to boxes191

where a blue token had been previously found. The standardized z-score for total number of errors for192

the SWM was used.193

194


10/33

WM encoding in ADHD

9

The ERP delayed match-to-sample task195

The delayed match-to-sample task was adapted from Haenschel et al (2007). Thirty-six different196

abstract figures were used as stimuli. The task consisted of 2 WM load conditions (144 trials total): a197

high load condition in which a sequence of 3 stimuli was presented (72 trials) and a low load condition198

with a sequence of 2 stimuli (72 trials). The low load (2-stimulus) condition is shown in Figure 1). Our199

task differed from that of Haenschel et al (2007) in that we made the task self-paced to better optimize200

participants readiness on computer-paced tasks which are known to be difficult in the ADHD201

population (e.g., interference effects of set shifting between trials)and thereby obtain a purer measure202

of working memory. Furthermore, we were also hoping to reduce the loss of trials due to artifacts.203

Participants initiated each trial by pressing the space bar on a keyboard, which served to optimize their204

readiness for the start of each trial. Trials were presented in blocks of 12 trials each of either a low or205

high load. The blocks of high (3 Stimuli) and low (2 Stimuli) loads were randomly distributed. Before206

each block, participants were shown which condition to expect (e.g., low or high load) and after each207

block, feedback on accuracy was provided (as the percentage of trials that were correct). After the space208

bar was pressed, a clear time of 600 ms was introduced before the fixation stimulus was presented for209

400 ms. Each target stimulus appeared in the center of the screen for 600 ms (visual angle, 1.34) with an210

inter-stimulus interval of 400 ms. Two seconds after the last target stimulus appeared (delay;211

maintenance), a probe stimulus was shown for two seconds (target; retrieval: see figure 1). Participants212

were asked to indicate whether the target stimulus was one of the previously presented stimuli by213

pressing the keys labelled as Same or Different on the keypad using their dominant hand. Response214

accuracy was emphasized, but to maintain a reasonable pace of processing, participants were required to215

respond within a 2 second time frame, after which the trial would be terminated and scored as incorrect.216

However, response time was recorded. E-prime 1.2 software (Psychology Software Tools, Inc.) was217

used to control stimulus presentation and timing as well as to record the accuracy and latency of218

responses.219


11/33

WM encoding in ADHD

10

Trial time varied between 4-6 seconds for low load and 5-7 seconds for the high load. Since220

between-trial inter-stimulus intervals would vary because of the self-pacing (participant pressed space bar221

to start the next trial), total time for task completion was calculated and compared between groups. The222

analysis indicated that there were no significant differences between the two groups in terms of total time223

to complete the task (see table 2).224

225

>226

227

3) Neural data acquisition, processing:228ERP data229

The EEG was recorded with a 128-channel Geodesic Hydrocel Sensor Net at a 500 Hz sampling rate,230

using Netstation stand-alone software (Electrical Geodesics Inc, Eugene, Oregon). Netstation was used231

to filter (FIR, 0.5-30 Hz, 60 Hz notch) and segment the data (400 ms before stimulus onset and 2000232

ms for low load, or 3000 ms for high load, after stimulus onset).233

Segments containing artifacts were removed using automatic algorithms to detect eye blinks and234

eye movements, as well as large drifts or spikes in the data. Eye blinks were detected when the vertical235

eye channels exceeded a threshold of 150 v (max-min) within a 160 ms (moving) time window236

within each trial after running a 20 ms moving-average smoothing algorithm across the entire trial237

period. Eye movements were detected when horizontal eye channels exceeded a threshold of 100 v238

(max-min) over a 200 ms time window. Channels were automatically marked bad when they exceeded239

a transition threshold of 200 v over the entire segment (max-min). Segments containing more than240

20% bad channels were automatically removed. In addition, all epochs were visually inspected by241

trained research assistants blind to the hypotheses. Bad channels were replaced by values interpolated242

from neighbouring channel data using spherical splines. Results were verified manually by a research243

assistant, who was unaware of the study objectives or hypotheses. Because we used a relatively low244


12/33

WM encoding in ADHD

11

high-pass filter, a linear detrending algorithm was applied to individual epochs to eliminate any left-245

over linear drift if it existed.246

The data were then averaged for each subject and stimulus, average referenced, and coded247

within the segment. The P1 components were scored as the maximum positivity between 80-160 ms248

after stimulus-onset for each stimulus (e.g., 3 stimuli for high load) for electrode sites 75 (O z), 70 (O1)249

and 83 (O2). Similarly, P3 components were scored as the mean positivity between 250-500 ms after250

stimulus onset for electrode sites 65, 66, 84, and 90 (See Supplementary Figure S1 for electrode map)251

compared to the baseline. The mean activity was chosen to represent P3 because this is a more252

elongated component with peaks (often several) that are less clearly defined compared to perceptual253

components (see also, Luck, 2005; Woltering et al., 2011). Electrodes were selected based on the254

literature (e.g., see Polich, 2007, for rationale) as well as the maximal positivities in the grand average255

waveforms (relative to the 200 ms baseline). Latency ranges were determined based on inspection of256

the individual ERPs as well as Haenschels et al., 2007 study. We acknowledge that, considering the257

paucity of ERP studies using delayed-match-to-sample tasks, our component labeling may vary from258

convention used in other tasks. Before exportation, data were baseline corrected for 200 ms prior to259

stimulus onset, separately, for each individual stimulus. Only correct trials were used in the analysis.260

Averages across electrode sites were used for the P1 and P3 analysis.261

262

4) Data analysis:263

Independent one-way ANOVAs were used to test for group differences in performance on the264

neuropsychological tasks. Separate repeated measures ANOVAs were conducted to test for group265

differences in accuracy and reaction time on the delayed match-to-sample task, with Load as the266

repeated measure (High load vs. Low) and Group as a between participant factor (ADHD vs. Control).267

By contrast, the incomplete factorial design of the Delayed-Match-To-Sample task (2 stimuli in Low268

Load; 3 stimuli in High Load) precluded a Group (2) x Load (2) x (Stimulus; 2 or 3) repeated measures269


13/33

WM encoding in ADHD

12

ANOVA to investigate effects on the P1 and P3 ERP components. Thus we used a multi-step approach270

to disentangle the effects of Load, Stimulus, and Group on our ERP components (P1, P3). In Step-1, to271

investigate the effects of Group and WM Load on the P3 and P1, we collapsed across Stimulus, and ran272

a Group by Load Repeated Measures ANOVA. For Step-2 we ran a Group by Load Repeated Measures273

ANOVA for the first and last Stimuli separately. The last stimuli of each load would represent the274

situation in which a participant would hold the maximum amount of information in mind for each275

condition when encoding new information. This analysis involved a comparison of the second (last)276

stimulus of the low load with the third (last) stimulus of the high load: thesecondstimulus of the high277

load was not compared to either the first or the second stimulus of the low load as it was not278

conceptually equivalent to those stimuli. In Step-3, we ran separate Group by Stimulus repeated279

measures ANOVAs for each load. For the high load, contrasts were run to test for differences between280

the three stimuli. This step allowed us to investigate sequence effects of stimulus presentation, which281

may provide insights in the effects of neural resources being allocated during encoding with increasing282

amount of stimuli to be held in mind.283

Two participants, whose ERP data yielded less than 10 trials, were excluded from this analysis.284

Data points (behavioural and ERP) with SD's >3 were regarded as outliers and adjusted using the285

winsorizing technique (Tabachnick and Fidell, 2001). Three data points in ADHD group and 2 data286

points in control group were winsorized. No significant differences were found in trial-counts [Low287

load:F(1, 49) = .258,p=.553, High load:F(1, 49) = .006,p=.938] between the groups.288

Partial eta-squared values (2) were computed to ascertain effect size (ES). According to Vacha-289

Haase and Thompson (2004),ESbased on2= .01 corresponds to a small effect, 2= .10 corresponds to a290

medium effect, and 2= .25 represents a large effect.291

292

293

RESULTS294


14/33

WM encoding in ADHD

13

1. Behavioral Data295

Analysis of demographic and clinical variables confirmed that the ADHD and control groups296

were comparable in terms of age[F(53) = 1.358,p=.25] and sex [ (1, 21) = .039,p= 1.00]. As297

expected, ADHD participants reported significantly more ADHD symptoms [F (54) =1,p< .001] and298

cognitive failures in everyday life than controls [F(42) = 59.91,p< .001]. The ADHD group also299

reported more obsessive-compulsive symptoms [F(43) = 25.22,p< .001] on the SA-45, compared to the300

Control group. Means and standard deviations are presented in Table 1.301

Analysis of neuropsychological tests of WM performance revealed, as expected, that the ADHD302

group obtained significantly lower scores on the CANTAB-SWM [F(1, 44) = 4.54,p= .039], compared303

to the control group. ADHD group also showed poorer scores in WAIS Digit Span, albeit a non-304

significant difference [F(1, 48) = 3.29,p = .076]. Groups did not differ on CANTAB-SSP. The mean305

scores for the ADHD group on these two tests of WM fell in the low average range: their mean SWM z-306

score was -.10, which corresponds to a scaled score of just below 7 (16thpercentile); and their mean307

Digit Span scaled score was 8.31 (25thpercentile). By contrast, scores for the comparison group fell308

well within the average range: mean Digit Span scaled score of 9.95 corresponds to the 50 thpercentile309

and the SWM z-score was .51, which corresponds to a scaled score of 10 or the 50thpercentile.310

As predicted, analysis of the Delayed-Match-To-Sample Task, revealed a main effect of Load311

on performance accuracy [F(1, 53) = 49.88,p= .000, ES= .49]. Specifically, all participants were less312

accurate during the high WM load compared to the low WM load condition, indicating the success of313

the experimental manipulation. The groups did not differ significantly in terms of either their accuracy314

or response times (to the target stimulus). There was no significant Group-by-Load interaction for315

either response accuracy or response times. Moreover, there were no group differences in terms of intra-316

individual variability of response times and neither the main effect of Load nor the Group-by-Load317

interaction was significant. Means and standard deviations of the neuropsychological tasks and the318

delayed match to sample task measures are shown in Table 2.319


15/33

WM encoding in ADHD

14

320

>321

322

2) ERP data323

The waveforms for each stimulus are presented in Figure 2, in Panel A for the low load and Panel B324

for the high load. Table 3 shows the means and standard deviations for the ERP components for each325

load and for each group collapsed across stimulus. The waveforms and (difference) topoplots for the P3,326

collapsed across stimulus, are presented in Figure 3, Panel A for the low load and Panel B for the high327

load. As can be seen in the topoplots, the difference in P3 amplitude between the ADHD and control328

group was observed primarily in the parietal/occipital regions (see Supplementary Figure S2 for a t-plot).329

330

P1 Analyses331

For P1 amplitude, Step-1 analysis, which examined the effects of Group and Load collapsed332

across stimuli, showed a significant main effect of Load [F (1, 53) = 8.26,p= .006,ES= .14] with333

higher amplitude for high load. There was no main effect of Group or Group-by-Load interaction. Step-334

2 analyzed Group and Load effects separately for the first and last stimuli. For the first stimulus there335

was a significant Group-by-Load interaction [F (1, 53 = 6.27,p= .015,ES= .106] in the absence of336

main effects for either Group or Load. Post-hoc tests revealed that participants with ADHD had337

increased P1 amplitudes for the High compared to the Low load, whereas the comparison group did not338

show this effect (p< .05). For the last stimulus, a main effect of Load was present [F (1, 52) = 5.76,p339

= .02,ES= .10] but neither the main effect of Group nor the Group-by-Load interaction was significant.340

Step-3 analysis of P1 during the low load condition revealed a main effect of Stimulus, [F (1, 53) = 9.31,341

p= .004,ES= .15] with lower P1 amplitudes for the first stimulus compared to the last. No Group, or342

Group-by-Stimulus interaction effect was found. The high load also revealed a main effect of Stimulus,343

[F (1, 51) = 13.61,p= .000,ES= .35], with lower P1 amplitudes for the first Stimulus compared to the344


16/33

WM encoding in ADHD

15

second and third. No Group or Group-by-Stimulus interaction was found. Last, the P1 did not correlate345

with any clinical questionnaire or behavioral measures.346

For P1 latency, Step-1 analysis revealed no main effect of Load, Group, or Load-by-Group347

interaction for either the high or low load conditions. On the Step-2 analysis, no main effects of Group,348

Load, or a Stimulus-by-Group interaction were found, nor were there any significant effects for the first349

stimulus. On the Step-3 analysis, neither the main effects of Stimulus or Group, nor the Stimulus-by-350

Group interaction was significant.351

352

P3 Analyses353

For P3 amplitude, our Step-1 analysis showed a significant main effect of Group, [F (1, 53) =354

6.30,p= .015,ES= .11], with the ADHD group showing smaller P3 amplitudes than the control group.355

There was also a main effect of Load, [F (1, 53) = 4.04,p= .049,ES= .07], with larger P3 amplitudes356

for the high load confirming that our P3 index was sensitive to effects of load. No Group-by-Load357

interaction was found.358

The Step-2 analysis tested the Group by Load effects separately for the first and last Stimuli.359

There was a main effect of Group for the first, [F (1, 53) = 5.31,p= .025,ES= .09], and last [F(1, 52)360

= 5.76,p= .020,ES= .10], stimuli suggesting the group difference was present across stimuli. There361

was a main effect of Load for the first [F (1, 53) = 6.55,p= .013,ES= .11] but not for the last stimulus,362

suggesting our general load effect was driven mostly by the first stimulus. Also, no significant Group-363

by-Load interaction was found for both first and last stimuli.364

Our Step-3 analysis, testing for sequence effects of stimuli, revealed a main effect of Stimulus,365

[F(1, 53) = 11.76,p= .001,ES= .18], and a main effect of Group, [F (1, 53) = 4.89,p= .031,ES= .08],366

during the low load. No Group-by-Stimulus interaction effect was found. The high load also revealed a367

main effect of Stimulus, [F (1, 53) = 3.17,p= .05,ES= .11], and a main effect of Group, [F (1, 52) =368

7.63,p= .008,ES= .13], with no Group-by-Stimulus interaction. In general, for both WM loads,369


17/33

WM encoding in ADHD

16

responses to the first stimulus showed lower amplitudes than to the other stimuli (ps < .05). For the370

high load, pairwise comparisons showed that the second and third stimulus did not differ. The ADHD371

group showed lower P3 amplitudes for all three stimuli compared to the control group.372

For the P3 latency, the Step-1 latency analysis indicated no main effect of Group, a marginally373

significant effect for Load, [F (1, 53) = 3.23,p= .078,ES= .058], with shorter latencies for the high374

load, and no Load-by-Group interaction. Step-2 analysis revealed a main effect of Load for the first [F375

(1, 53) = 8.16,p= .006,ES= .133], but not for the last stimulus. No main effect of Group or Group-by-376

Load interaction was found for either the first or last stimulus. The Step-3 analysis showed a main effect377

of Stimulus for the low load condition [F (1, 53) = 5.47,p= .023,ES= .093]. Post-hoc tests revealed378

that the ADHD group showed increased P3 amplitude for the second stimulus compared to the first,379

whereas the comparison group did not show this difference (p= .03). No main effect of Group or380

Group-by-Stimulus interaction was found for both first and last stimuli.381

382

>383

384

>385

386

>387

388

DISCUSSION389

The present study is the first to investigate neural changes in P3 amplitudes and latencies during390

the encoding phase of WM in ADHD, using a delayed match-to-sample task. The main finding was a391

reduced P3 amplitude for the ADHD group, regardless of WM load or stimulus sequence (i.e., first or392

last), compared to the control group. Behavioral results showed that all participants were less accurate393


18/33

WM encoding in ADHD

17

during the high WM load condition compared to the low load condition, which attests to the success of394

our experimental manipulation of WM load.395

Changes in P3 amplitudes during WM tasks have been associated with differences in the396

allocation of attention necessary for WM functioning (Donchin and Coles, 1988; Polich, 2007; Kok,397

2001). Our findings of lower P3 amplitude in the ADHD group compared to the control group,398

independent of load or stimulus order, suggests a more general problem of attentional allocation in the399

encoding phase of WM rather than a more specific problem associated with an increasing demand on400

WM. The reduced P3 amplitude in college students with ADHD found in this study using a WM task is401

consistent with and extend findings from a recent meta-analysis on P3 in adults with ADHD (to WM402

encoding during a WM task), which revealed a decreased P3 amplitude recorded during Go-Nogo tasks403

in ADHD (Szuromi et al., 2011; and also see, Woltering et al., 2013, using a similar sample to that in404

the present study). Go-Nogo tasks, which traditionally are used to index response inhibition, do involve405

WM, albeit low-level demands, and some require constant updating of working memory.406

We believe that the decreased P3 amplitude observed in individuals with ADHD can be407

interpreted as deficient attentional allocation, which leads to inefficient encoding of information in408

memory. Theoretically, the notion of P3 reflecting attentional allocation is not that different to Koks409

notion of capacity since recent insights in the field of working memory have linked attentional processes410

and working memory capacity closely together (Awh and Vogel, 2008). A functional relationship411

between P3 amplitude and attentional resource allocation has also been reported in the literature (Ford,412

White, Lim, and Pfefferbaum, 1994; Kok, 2001).Also, many studies found the P3 to be related with413

brain regions thought to be involved in attention, such as the parietal lobe, the temporoparietal junction,414

lateral prefrontal areas and the cingulate gyrus (Linden, 2005; Verleger et al., 1994). Similarly, a study415

that explored the cortical basis of ERP components revealed an impaired ventral attentional pathway in416

ADHD participants during the time of the P3 (Helenius et al., 2011). Furthermore, in their meta-analysis,417

Szuromi et al (2011) interpreted the overall reduction of P3 amplitude in adults with ADHD as a418


19/33

WM encoding in ADHD

18

dysfunction of the ventral attention network since the P3 related to target detection is thought to be419

generated from the temporoparietal junction. In sum, previous research suggests a possible link between420

P3 amplitude and attentional resource allocation. Hence, based on previous findings, we speculate that421

weak activation in attention pathways might play a role in the defective allocation of attentional422

resources during activities involving WM, accounting for the observed decreased P3 amplitudes in423

college students with ADHD.424

Our analyses of P1 amplitude revealed no significant group effects except for one: namely that425

the ADHD group seems to manifest an increase in P1 amplitude from the low to the high WM load426

condition in response to the first stimulus only. Compared to Haenschel et al. (2007), who did find427

robust P1 group differences in Schizophrenia patients, our findings suggest that differences in WM428

encoding between the ADHD and non-ADHD peer group were due primarily to differences in429

attentional allocation during the WM encoding phase as opposed to early visual processing.430

Only one other study to date has investigated neural differences in WM between ADHD adults431

and their peers using EEG (Missonnier et al., 2013). Despite the use of different methodologies (n-back432

versus delayed-match-to-sample task, respectively; smaller sample size, and the focus on different433

neurophysiological measures, such as time-frequency domain), conclusions are similar. That is, both434

conclude that individuals with ADHD manifest differences in neural correlates linked to the attentional435

network involved in the allocation of attention subserving WM.436

Behavioral differences between groups were found during the WM task: namely the ADHD437

group had slower response times to the retrieval cue, albeit marginally significant, but did not differ in438

response accuracy. It is possible that the slower response times in the ADHD group indicate their need439

for more time to make accurate decisions compared to their peers. In this sense, the longer response440

times may be another manifestation of slow processing speed or a compensatory mechanism. Although441

accuracy in our ADHD group was comparable to that of their typically developing peers, cognitive442

mechanisms of processing, such as the allocation of attention, could still function less optimally. It is443


20/33

WM encoding in ADHD

19

possible that this deficit may show in contexts other than within the experimental confines of the current444

task. This suggestion is strengthened by other findings from this study: namely that for the ADHD445

group, their mean scores for WAIS Digit Span and CANTAB SWM were in low average range, whereas446

those of the control group were in the average range; the T-scores for the ADHD group (mean = 41.5)447

were in the mildly impaired range whereas those for the control group (mean = 50) were within the448

well-functioning range. Note that the two neuropsychological tasks not only require the maintenance but449

also the updating of the spatial representations, which renders their WM demands similar to those of the450

delayed-match-to-sample task. Furthermore, these behavioral and neuropsychological differences were451

found despite the fact that the participants with ADHD in this study are high functioning (mostly college452

students) and the task was self-paced to exclude potential effects of poor sustained attention453

confounding the WM result.454

In light of our neural results, the lack of differences in behavioral accuracy in the delayed-455

match-to-sample task could suggest that our group differences found in neural processing indicate456

inefficient means of attentional allocation; a detriment that may not always be observed in behavioural457

accuracy. The P3 could be a neural indicator of this inefficient mechanism.458

From a clinical perspective, the WM impairment (albeit modest) in these college students with459

ADHD compared to their college-peers may well help explain why this relatively successful subgroup460

continues to manifest functional impairments in academic, social functioning during college years.461

Furthermore, these results support findings from a recent meta-analysis suggesting long-term memory462

difficulties in adult ADHD may be attributable to difficulties in the encoding phase (Skodzik et al.,463

2013). Our findings highlight the importance of studying WM encoding and in this newly emergent464

subgroup of young adults with ADHD.465

This study has several limitations, which should be taken into consideration when interpreting466

the findings. First of all, we did not have a direct measure of IQ, or scholastic ability, which limits the467

characterization of our sample. However, these college students must be of at least average intelligence468


21/33

WM encoding in ADHD

20

(as suggested by the WAIS Digit Span) to be able to successfully complete high school and gain college469

entrance. Furthermore, we did not measure alcohol and recreational drug use in this sample of college470

students, which could influence allocation of attention or WM or both. Also, the current analyses were471

restricted to the encoding stage and further research is warranted to characterize the maintenance and472

retrieval stages of WM.473

In conclusion, keeping in mind the aforementioned limitations, our results suggest differences in474

neural processing in WM with adult ADHD. Our results may suggest, based on what is known about475

changes in P3 amplitudes in the ERP literature, that weak activation in attentional pathways might play476

a role in defective attention resource allocation to updating representations in working memory.477

Moreover, our results concerning the P1 versus P3 suggest a temporal as well as syndrome specific478

neural index of adult ADHD. These findings could have important implications for understanding the479

cognitive difficulties ADHD adults face, as well as intervention.480

481

482

483


22/33

WM encoding in ADHD

21

Acknowledgements484

We thank Dr. Corinna Haenschel and Niko Kriegeskorte for letting us use the task stimuli. Also, we485

appreciate Alex Lamey for his programming assistance with the E-prime task and Peter Fettes for486

helping with parts of the data analysis. We would like to thank Dr. Marc Lewis for the use of his487Canadian Foundation for Innovation (CFI #482246) funded EEG lab. This research was supported488

financially in part by a CIHR Operating grant (# 245899, Tannock & Lewis) and by the Canada489

Research Chair program (RT).490

491

492


23/33

WM encoding in ADHD

22

References493

Adler LA, Spencer T, Faraone SV, Kessler RC, Howes MJ, Biederman J, et al. Validity of pilot Adult494

ADHD Self- Report Scale (ASRS) to Rate Adult ADHD symptoms. Ann Clin Psychiatry495

2006;18:145-148.496

Awh E, Vogel EK. The bouncer in the brain. Nat Neurosci 2008; 11:56.497

Baddeley A. Working memory: Looking back and looking forward. Nat Rev Neurosci 2003; 4:829-839.498

Baddeley AD, Hitch GJ. Developments in the concept of working memory. Neuropsychology499

1994; 8:485.500

Barkley RA, Cook E, Diamond A, Zametkin A, Thapar A, Teeter A. International consensus statement501

on ADHD. Clin Child Fam Psych 2002; 5:89-111.502

Carrasco M. Visual attention. Vision Res 2011; 51:1484-1525.503

Cowan N. The magical number 4 in short-term memory: A reconsideration of mental storage capacity.504

Behav Brain Sci 2001; 24:87-114.505

Donchin E, Coles MG. Is the P300 component a manifestation of context updating? Behav Brain506

Sci1988:11:357-374.507

DuPaul G, Weyandt L, ODell S, Varejao M. College students with ADHD: Current Status and future508

directions. J Atten Disord 2009; 13:234.509

Fabiani M, Karis D, Donchin E. P300 and recall in an incidental memory paradigm.510

Psychophysiology 1986; 23:298-308.511

Faraone SV, Biederman J, Mick E. The age-dependent decline of attention deficit hyperactivity512

disorder: a meta-analysis of follow-up studies. Psychol Med 2006; 36:159-166.513

Ford JM, White P, Lim KO, Pfefferbaum A. Schizophrenics have fewer and smaller P300s: A single-514

trial analysis. Biol Psychiat 1994; 35:96-103.515

Frazier TW, Youngstrom EA, Glutting JJ, Watkins MW. ADHD and achievement meta-analysis of the516

child, adolescent and adult literatures and a concomitant study with college students. J Learn517


24/33

WM encoding in ADHD

23

Disabil 2007; 40:49-65.518

Haenschel C, Bittner RA, Haertling F, Rotarska-Jagiela A, Maurer K, Singer W, et al. Contribution of519

impaired early-stage visual processing to working memory dysfunction in adolescents with520

schizophrenia: a study with event-related potentials and functional magnetic resonance imaging.521

Arch Gen Psychiat 2007; 64:1229.522

Heiligenstein E, Guenther G, Levy A, Sevino F, Fulwiler J. Psychological and academic523

functioning in college students with attention deficit hyperactivity disorder. J Am Coll Health524

1999; 47:181-185.525

Helenius P, Laasonen M, Hokkanen L, Paetau R, Niemivirta M. Impaired engagement of the ventral526

attentional pathway in ADHD. Neuropsychologia 2011; 49:1889-1896.527

Hervey AS, Epstein JN, Curry JF. Neuropsychology of adults with attention-Deficit/hyperactivity528

disorder: a meta-analytic review. Neuropsychology 2004; 18:485.529

Karis D, Fabiani M, Donchin E. P300 and memory: Individual differences in the von Restorff effect.530

Cognitive Psychol 1984; 16:177-216.531

Kane MJ, Conway AR, Hambrick DZ, Engle RW. Variation in working memory capacity as variation in532

executive attention and control. Oxford: Oxford University Press, 2007.533

Kok A. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology534

2001; 38:557-577.535

Kok A, Ramautar JR, De Ruiter MB, Band GP, Ridderinkhof KR. ERP components associated with536

successful and unsuccessful stopping in a stopsignal task. Psychophysiology 2004; 41:9-20.537

Linden DEJ. The P300: where in the brain is it produced and what does it tell us? Neuroscientist538

2005; 11:56376.539

Luck SJ. An introduction to the event-related potential technique (cognitive neuroscience).540

Cambridge, MA: MIT Press, 2005.541

Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of542


25/33

WM encoding in ADHD

24

working memory impairments in children with attention-deficit/hyperactivity disorder. J543

Am Acad Child Adolesc Psychiatry 2005;44:377-384.544

Missonnier P, Hasler R, Perroud N, Herrmann FR, Millet P, Richiardi J, et al. EEG anomalies in adult545

ADHD participants performing a working memory task. Neuroscience 2013; 241:135-146.546

Polanczyk G, Jensen P. Epidemiologic considerations in attention deficit hyperactivity disorder: a547

review and update. Child Adol Psych Cl 2008; 17:245-260.548

Polanczyk G, Rohde LA. Epidemiology of attention-deficit/hyperactivity disorder across the lifespan.549

Curr Opin Psychiatry 2007;20:386-389.550

Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 2007; 118:2128-2148.551

Rutman AM, Clapp WC, Chadick JZ, Gazzaley A. Early topdown control of visual processing predicts552

working memory performance. J Cognitive Neurosci 2010; 22:1224-1234.553

Shaw-Zirt B, Popali-Lehane L, Chaplin W, Bergman A. Adjustment, social skills, and self-esteem in554

college students with symptoms of ADHD. J Atten Disord 2005; 8:109-120.555

Skodzik T, Holling H, Pedersen A. Long-Term Memory Performance in Adult556

ADHD: A Meta-Analysis. J Atten Disord 2013; doi 10.1177/1087054713510561.557

Szuromi B, Czobor P, Komlosi S, Bitter I. P300 deficits in adults with attention deficit558

hyperactivity disorder: a meta-analysis. Psychol Med 2011; 41:1529.559

Tabachnick BG, Fidell LS. Multivariate analysis of variance and covariance using multivariate statistics.560

Boston: Allyn and Bacon, 2001.561

Verleger R, Heide W, Butt C, Kmpf D. Reduction of P3b in patients with temporo-parietal lesions.562

Brain Res 1994; 2:103-116.563

Vogel EK, McCollough AW, Machizawa MG. Neural measures reveal individual differences in564

controlling access to working memory. Nature 2005; 438:500-503.565

Wallace JC, Kass SJ, Stanny CJ. The Cognitive Failures Questionnaire revisited: Dimensions and566

correlates. J Gen Psychol 2002; 129:238-256.567


26/33

WM encoding in ADHD

25

Woltering S, Granic I, Lamm C, Lewis MD. Neural changes associated with treatment outcome in568

children with externalizing problems. Biol Psychiat 2011; 70:873-879.569

Woltering S, Liu Z, Rokeach A, Tannock R. Neurophysiological differences in inhibitory control between570

adults with ADHD and their peers. Neuropsychologia 2013; 51: 1888-1895.571

572

573

574


27/33

WM encoding in ADHD

26

Figure Legends575

576

Figure 1.Schematic outline of the delayed match-to-sample task for low load condition.577

Working memory paradigm adopted and modified from Haenschel et al (2007). Two stimuli were578

presented in low load condition, and 3 stimuli were shown in high load condition for encoding for 600579

ms each with an inter-stimulus interval of 400 ms. After a 2000 ms delay, a probe stimulus was580

presented for the participants to judge whether they saw the target among the initial set of the stimuli or581

not. Participants initiated the next trial by pressing the space bar. 200 ms of clear time was allowed582

before each trial.583

584

Figure 2. ERP waveforms at site P3 (averaged across all P3 electrodes) for (A) Low and (B) High585

load conditions for each stimulus [(A) left: first stimulus, right: second stimuli (B) first, second586

and last stimuli].587

588

Figure 3. ERP waveforms atsite P3 (averaged across all P3 electrodes)and topoplots (250-500 ms)589

for (A) Low and (B) high load condition across the stimuli.590

591


28/33

Encoding (600ms per stimuli) Maintenance (delay: 2s) Retrieval (target:2s)

600ms 600ms 2000ms

ure 1


29/33

gure 2


30/33

gure 3


31/33

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.

* p< .05, ** p< .01, *** p< .001. p-values shown reflect one-way ANOVAs.

ADHD Comparison P-values

ASRS *** 44.97(9.12) 18.71(7.98) .00

CFQ *** 53.88(12.74) 27.56(8.09) .00

SA-45

Anxiety* 8.41(2.37) 6.75(1.83) .01

Depression 9.37(3.63) 8.05(3.58) .21

Obsessive Compulsive *** 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


32/33

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.

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

Span)

ADHDMean (SD)

ComparisonMean (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

Reaction Time

Low load 1260.60(232.85) 1176.17(166.47) .14

High Load 1309.96(268.49) 1241.70(208.08) .30

Intra-individual variability (RT)

Low load 494.32(241.75) 446.06(216.30) .44

High load 420.31(242.63) 452.05(228.98) .62

Total duration of the task 23.32(3.26) 21.81(2.57) .07

Neuropsychological tests

WAIS Digit Span 8.31(3.31) 9.95(2.94) .08

CANTAB SWM* -0.10(1.13) 0.51(0.62) .04

SSP 0.09(0.86) 0.60(1.14) .10


33/33

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

ADHD Control

Amplitude P1

Low load 3.27(1.30) 4.02(2.09)

1ststimulus 2.83(1.51) 3.83(2.35)2ndstimulus 3.70(1.54) 4.21(2.05)

High load 3.62(1.22) 4.30(2.08)1

ststimulus 3.20(1.52) 3.58(2.42)

2ndstimulus 3.87(1.70) 4.33(1.90)

3rdstimulus 3.83(1.13) 5.05(2.36)

P3

Low load 1.96(1.52) 3.02(2.06)1

ststimulus 1.59(1.70) 2.69(2.61)

2nd

stimulus 2.32(1.68) 3.39(1.85)

High load 2.02(1.45) 3.32(2.07)1ststimulus 1.80(1.42) 3.12(2.20)

2ndstimulus 2.18(1.60) 3.47(1.99)3rdstimulus 2.04(1.61) 3.29(2.18)

Latency P1

Low load 127.27(16.98) 127.92(17.05

1ststimulus 127.27(16.98) 127.92(17.05)2ndstimulus 124.47(16.19) 124.80(16.30)

High load 125.58(11.99) 125.65(11.66)1

ststimulus 124.73(15.21) 127.76(19.41)

2ndstimulus 124.53(13.47) 123.44(16.16)

3rdstimulus 126.55(14.19) 125.76(10.30)

P3

Low load 360.13(26.42) 357.40(22.59)1

ststimulus 350.27(42.41) 350.24(37.86)

2ndstimulus 370.00(38.08) 364.56(23.31)

High load 468.04(29.31) 362.21(18.94)1ststimulus 372.60(48.80) 363.52(31.73)

2ndstimulus 366.27(35.64) 360.72(24.66)3

rdstimulus 365.27(42.75) 362.40(25.23)

Documents

Clinical Neurophysiology Volume Issue 2013 [Doi 10.1016_j.clinph.2013.12.094] Kim, Soyeon; Liu, Zhongxu; Glizer, Daniel; Tannock, Rosemary; Wo -- Adult ADHD and Working Memory- Neural