Accepted Manuscript

EEG alpha power during maintenance of information in working memory inadults with ADHD and its plasticity due to working memory training: a random-ized controlled trial

Zhong-Xu Liu, Daniel Glizer, Rosemary Tannock, Steven Woltering

PII: S1388-2457(15)00988-8DOI: http://dx.doi.org/10.1016/j.clinph.2015.10.032Reference: CLINPH 2007635

To appear in: Clinical Neurophysiology

Accepted Date: 7 October 2015

Please cite this article as: Liu, Z-X., Glizer, D., Tannock, R., Woltering, S., EEG alpha power during maintenanceof information in working memory in adults with ADHD and its plasticity due to working memory training: arandomized controlled trial, Clinical Neurophysiology (2015), doi: http://dx.doi.org/10.1016/j.clinph.2015.10.032

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EEG alpha power during maintenance of information in working memory in adults with

ADHD and its plasticity due to working memory training: a randomized controlled trial

Zhong-Xu Liu1,2, Daniel Glizer1,3, Rosemary Tannock1,4, Steven Woltering1,5

1 Applied Psychology and Human Development, Ontario Institute for Studies in Education,

University of Toronto, Toronto, Ont., Canada

2 Rotman Research Institute of Baycrest Centre, University of Toronto, Toronto, Ont., Canada

3 Brain and Mind Institute, The University of Western Ontario, London, Ont., Canada

4 Neurosciences and Mental Health Research Program, SickKids Hospital, Toronto, Ont., Canada

5 Educational Psychology, Texas A&M University, College Station, TX, USA

* Correspondence to:

Steven Woltering

Department of Educational Psychology, Texas A&M University, 718B Harrington Office

Building, 77843-4225, College Station, TX, USA

Tel.: +1.979.862.8973

E-mail: swolte@tamu.edu

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Highlights

1. Randomized controlled clinical trial investigating neural changes in alpha power after working

memory training

2. Differences in alpha power between ADHD and non-ADHD students, and changes after

treatment, were marginally significant and had low effect sizes.

3. Findings suggest that alpha power could not reliably distinguish between ADHD and non-

ADHD, nor trace treatment effects, in the present sample.

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Abstract

Objective The present study examined whether neural indices of working memory maintenance

differ between young adults with ADHD and their healthy peers (Study 1), and whether this

neural index would change after working memory training (Study 2). Methods Study 1 involved

136 college students with ADHD and 41 healthy peers (aged 18 to 35 years) and measured their

posterior alpha activity during a visual delayed-match-to-sample task using

electroencephalography (EEG). Study 2 involved 99 of the participants with ADHD who were

randomized into a standard-length or shortened-length Cogmed working memory training

program or a waitlist control group. Results The ADHD group tended to be less accurate than the

peers. Similarly, the ADHD group exhibited lower posterior Alpha power at a trend level

compared to their healthy peers. There were no training effects on participants’ performance and

only marginal increases in posterior alpha power in training groups compared to the waitlist

group. Conclusions Considering that the training effects were small and there was no load and

dose effect, we conclude that the current study provides no convincing evidence for specific

effects of Cogmed. Significance These findings provide unique insights into neuroplasticity, or

lack thereof, with near-transfer tasks in individuals with ADHD.

Keywords: Working memory; training; ADHD; EEG; alpha power.

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1. INTRODUCTION

1.1. Improving working memory difficulties in ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is considered a neurobiological

disorder often characterized by difficulties with working memory functioning (Barkley, 1997;

Martinussen et al., 2005). The symptoms of a poor working memory resemble many of the

everyday life problems individuals with ADHD experience with organization, distraction, and

sustained concentration (Gray et al., 2015; Hervey et al., 2004). To improve working memory

functioning in ADHD, software programs were developed to specifically target working memory

using adaptive and intensive training (Klingberg et al, 2005). Although the current literature has

shown that working memory improvements from such programs do not appear to transfer or

generalize to other domains of functioning in ADHD populations, they have been found to

improve performance in working memory tasks similar to those practiced – in other words, near

transfer effects (Rapport et al., 2013; Mawjee et al., 2014; Melby-Lervåg and Hulme, 2013).

However, research into the effects of working memory training on the brain’s response to

near transfer effects has been scarce. It is possible that neural measures are better able to capture

subtle processing differences between individuals, or that result from training, which may not be

detected by performance measures alone. Examining neural training effects can contribute to our

basic understanding of neural plasticity related to working memory.

1.2. Study goals: individual differences and changes with treatment in neural activity during the

maintenance phase of working memory in ADHD

We conducted two studies. The objective of study 1 was to compare neural activity

during the maintenance phase of working memory in young adults with and without ADHD;

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whereas the objective of study 2 was to determine whether intensive computerized working

memory training (Cogmed Working Memory Training, CWMT) would alter the neural activity

during the maintenance phase in those with ADHD. To do so we used EEG with a visual delayed

match-to-sample task which constitutes a type of span task that was comparable to, but not

identical with those used as CWMT training tasks and likely relies on similar or overlapping

neural networks. Accordingly, it may be conceptualized as a ‘near transfer’ working memory

task.

1.3. Examining alpha power in a delayed match-to-sample task

A delayed match-to-sample task (see Haenschel et al., 2009; Kim et al., 2014) was used

to investigate changes in neural responses during the maintenance phase of working memory.

The task was visual in nature and manipulated the amount of representations that had to be held

in mind. In this sense, this task was similar to the working memory processes consistently trained

in the CWMT program. The vast majority of the CWMT tasks focus on increasing working

memory capacity for visuo-spatial tasks by training the amount of information that can be

maintained in working memory for several seconds (see Mawjee et al., 2014, for more details).

Although this task has not been used previously in CWMT studies, we believed it was likely that

it would be sensitive to near transfer effects. This task was used before in a previous study of

college students with ADHD that focused on the encoding stage (Kim et al, 2014).

Recent theoretical insights on the role of attention in working memory, spurred by

neuroscientific findings, have suggested that working memory performance may not solely

depend on capacity limits viewed as a storage space, but also on how efficient brains are able to

protect representations through attentional processes that filter out distractors (Awh and Vogel,

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2008; Vogel et al., 2005). As a neural index, we therefore choose to focus on posterior alpha

power because EEG oscillations in alpha frequency bands (9-14 Hz) are considered to play a key

role in gating and protecting internal representations from distraction (Bonnefond and Jensen,

2012; Jensen and Mazaheri, 2010; Palva et al., 2011; Sauseng et al., 2009). Alpha power is

considered to be particularly important for the sustained maintenance of working memory item

information (Kundu et al., 2015; Hsieh et al., 2011). Previous research has shown that increased

alpha in parietal-occipital regions during a delayed match-to-sample task was associated with

better working memory performance (Jensen et al., 2002). Moreover, previous EEG studies

using an identical delayed match-to-sample task (Haenschel et al., 2009) and studies using other

tasks manipulating working memory (Crespo-Garcia et al., 2013; Michels et al., 2008) have

found that alpha power increased after the offset of the stimulus during working memory

maintenance. Our previous study using the same ADHD post-secondary education (PSE)

population found strong evidence for reduced alpha power in the ADHD compared to a

comparison group during a resting state (Woltering et al., 2012). These effects were interpreted

as the ADHD group experiencing a lack of inhibition over sensory stimuli. As such, alpha power

may be involved in the active inhibition of external/distracting stimuli, which could, if

desynchronized, explain some of the attentional problems experienced by ADHD.

1.4. Studying working memory training of ADHD students in postsecondary education

We choose postsecondary education (PSE) students with ADHD as they constitute a

relatively understudied subgroup of ADHD. They are unique in that they are relatively high-

functioning despite ongoing impairments (DuPaul et al, 2009). In our previous study involving

participants in the present sample, standardized and normed scores did not show clinical levels of

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impairment on executive function and neuropsychological tasks, but their scores were lower

compared to their PSE peers. Moreover, strong evidence for impairment was found in their

qualitative, self-report, and measures specific to functional impairment in PSE (see Gray et al.,

2015).

Studies investigating alpha power during a delayed match-to-sample working memory

tasks in ADHD populations, however, are scarce and have mostly been conducted in child

populations. Group difference studies in child populations thus far have been ambiguous; one

study found no difference with peers (Gomarus et al., 2009; note this was a verbal working

memory) whereas another found higher alpha for the ADHD group (Lenartowicz, et al., 2014),

which was explained as overcompensation. Therefore, the first goal (Study 1) was to compare

PSE students with and without ADHD in terms of posterior alpha power during working memory

maintenance to determine whether it would differentiate the two groups. Our second goal (Study

2) was to ascertain whether CWMT would influence (increase) posterior alpha power in these

PSE students with ADHD. To do so we conducted a randomized controlled trial (RCT) in which

participants with ADHD were randomly assigned into three treatment arms: a standard-length

CWMT group (45 minutes/day), a shortened-length CWMT group which trained at one third of

the standard-length intensity level (i.e., 15 minutes/day; see also Mawjee et al., 2015); and a

waitlist group that received the same amount of contact with a training coach but did not receive

CWMT. This design has a distinct advantage of controlling for engagement and ‘expectancy for

improvement’ (Melby-Lervåg and Hulme, 2013). Furthermore, the presence of a dose effect

would be strong evidence for the notion that the training caused any observed changes (e.g., the

standard-length group would do better than the shortened-length group).

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1.5. Hypotheses

We formulated two main hypotheses:

Hypothesis study 1: Consistent with our previous findings in PSE students with ADHD (Kim et

al., 2014; Woltering et al., 2012), we predicted lower posterior alpha and worse performance in

our ADHD group compared to our Comparison group; and

Hypothesis study 2: We predicted posterior alpha power to increase (move in the direction of

normalization) in ADHD in the training groups compared to the waitlist control group. More

specifically, we expected a dose effect, whereby the increase in posterior alpha would be greater

in the standard-length compared to the shortened-length training group, which in turn would

show a greater increase compared to the waitlist who would show no change in posterior alpha

power.

2. METHODS

2.1. Participants

Participants with ADHD were recruited to participate in Study 1 and 2 through listserv

emails sent from Student Disability Services. Registration with Student Disability Services in

Canada requires comprehensive documentation or a new assessment to confirm their diagnosis.

In addition, semi-structured telephone interviews were conducted to assess current eligibility and

validate current ADHD symptomatology. Inclusion criteria were as follows: 1) registered with

Student Accessibility/Disability Services with a confirmed diagnosis of ADHD, 2) current

symptoms consistent with diagnostic criteria for ADHD as indicated by telephone interview and

meeting the criterion scores on the 6-item Adult ADHD Self-Report Scale Part A (ASRS-A); 3)

current enrollment in PSE institution; and, 4) between 18 to 35 years of age. Exclusion criteria

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included: 1) major neurological dysfunction or psychosis, 2) current use of sedating or mood

altering medication other than stimulant medication provided for ADHD, 3) uncorrected sensory

impairment, 4) motor or perceptual handicap that would prevent use of a computer program, or

5) a history of concussion or traumatic brain injury prior to ADHD diagnosis, 6) limited

proficiency in English language, 7) not being scheduled for the EEG assessment (this was the

case for certain cohorts), and 8) for Study 2, having usable data for the delayed-match-to-sample

task at their first visit. Exclusion criteria 1-6 were ascertained from self-report during the intake

interview. The comparison group was recruited through flyers around campuses as well as

listservs.

Study 1: A total of 286 participants with ADHD were assessed for eligibility for study 1

amongst who 136 met our inclusion criteria and completed the first visit (for a more detailed

breakdown, see the CONSORT diagram in Supplementary Figure S1). A total of 48 typically

developing students were recruited of whom 41 had usable EEG data. Thus, for the group

difference analyses, 136 (62 males) participants with ADHD were compared to 41 (22 males)

peers. However, for the high load condition in the EEG task, data for four participants in the

ADHD group and one in the comparison group were discarded because they did not have enough

artifact-free EEG data. Table 1 provides information on demographics for the ADHD and

comparison group. Detailed comorbid psychopathologies measured using the Symptom

Assessment-45 (SA-45; Maruish, 1999) is presented in Supplementary Table S1. Of the ADHD

group, 52.2% (N = 71) took medication, primarily psychostimulants (97.2%). Medication was

not an experimental manipulation in the present study so we cannot determine whether any

differences found with medication use are due to the medication itself or participant

characteristics that lead to medication treatment. To fully describe its effects, we presented the

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outcomes broken down for medication status in the Supplementary Figure S3 and Supplementary

Table S2). Note that the ‘medication type’ and ‘change of medication during medication’

variables, which were used as covariates, were entered as dichotomous variables (with/without

medication or medication change).

<< INSERT TABLE 1 HERE >>

Study 2: For the present CMWT study, we had a priori planned to combine participants

from three samples to maximize statistical power in finding neural effects and create a buffer for

any potential data loss due to EEG artifacts. Across these samples, participants were PSE

students with ADHD registered with disability services that underwent an identical CMWT

treatment program, and used identical outcome measures. The delay-matched-to-sample task, as

opposed to some of the other neural tasks in our study, was left unchanged across samples. We

will refer to these samples in chronological order as the Cogmed sample (see Gropper et al.,

2014), the Engage-pilot sample (see Mawjee, et al., 2014), and the Engage sample (see Mawjee

et al., 2015). Major differences between samples were, 1) the Cogmed sample was randomized

only to the Standard and Waitlist condition, 2) there was a 1-year gap in data collection between

Cogmed and remaining Engage samples, 3) different sets of psychological battery and neural

measures were used (none are relevant to this manuscript). Detailed information on the numbers

for each sample is indicated in the CONSORT diagram (Supplementary Figure S1) and

respective publications. Procedural differences, when relevant, can be found in the remainder of

this method section. The Cogmed and Engage samples did not differ in age, sex, education, level

of ADHD symptomatology (as measured by ASRS) or working memory functioning (Digit

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Span), p’s = .35 ~ .95. There were also no significant interactions between Sample and Training

Group (the waitlist and standard-length group) on any of these measures (p’s = .23 ~ .75).

For the CWMT study, combining all samples, 136 ADHD participants were randomized

into a standard-length (N = 52), shortened-length (N = 38, Engage-pilot and Engage only), and

waitlist control group (N = 46). However, only those subjects who had completed the program as

well as the pre- and post- lab sessions (N = 102) were included in the analyses of training effects

with 35 participants in the standard-length group, 31 participants in the shortened-length group,

and 36 participants in the waitlist control group. The dropouts were not significantly different

from the participants who completed training in their level of ADHD symptomatology as

measured by the ASRS and working memory functioning as measured by the digit span. We note

that for the high load condition in the EEG task, one participant from the standard-length group

(resulting in N = 34) and two participants from the waitlist group (resulting in N = 34) were

excluded due to insufficient artifact-free data. Table 2 describes the demographics and

participant characteristics in each group. Supplementary Table S3 lists comorbid

psychopathologies measured using SA-45. Supplementary Figure S1 shows the flow of

participants in compliance with CONSORT guidelines.

<< INSERT TABLE 2 HERE >>

2.2. Procedure

The present studies (e.g., Cogmed & Engage) were approved by the Institutional Ethics

Boards of the participating universities as well as by the community agency providing the

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CWMT program. Prior to entering the studies, informed written consent was obtained from all

participants.

For study 1, all participants with and without ADHD came to the lab and completed a

behavioral assessment, which included a battery of neuropsychological tests and behavioral

rating scales (see Gropper et al., 2014; Mawjee, et al., 2014, and Mawjee et al., 2015) which

lasted up to 3 hours, and an assessment in which participants underwent electroencephalography

(EEG) while completing a number of tasks including a resting state condition (see Woltering et

al., 2012) and a 30-minute visual-spatial working memory task (not yet reported). After a short

break, participants conducted the delayed match-to-sample task, which lasted about 25 minutes

(see also, Kim et al., 2014) and a Go-NoGo that took about 10 minutes (see Woltering et al.,

2013). Participants were compensated $20 at their first visit (pre-training) and $150 at their

second (post-training).

In study 2, the participants who were assigned to training groups had lab visits 3 weeks

after training; those in the waitlist group were also assessed twice with the same interval as for

the two training groups. Due to the nature of the treatment arms, it was not possible to keep

participants or CWMT Coaches blind as to which group participants had been randomized once

the treatment started.

2.3. Working memory training (Study 2)

The CWMT program (CogMed Cognitive Medical Systems AB; Stockholm, Sweden)

was provided by a licensed community psychology-services agency. It is important to note that

the intervention team was independent from the research team. The standard RM version of

CWMT consists of 12 auditory-verbal and visual-spatial working memory tasks. The difficulty

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level is automatically and continually adjusted to each individual by an adaptive algorithm to

ensure that each participant’s working memory is always maximally challenged. The CWMT

program and its variants are widely used in research (Klingberg, 2010).

Prior to beginning training, participants participated in an individually based

telephone start-up session with the CWMT coach to become familiarized with the working

memory training program (Note that the Cogmed sample received group start-up sessions). The

program requires 25 training sessions to be completed in 5 days per week for a period of 5-6

weeks. To ensure compliance and address training challenges with the program, weekly calls (by

telephone) were completed by a certified CWMT coach. Participants in the standard-length

group engaged in 45 minutes of training per day. Those in the shortened-length group engaged in

an identical program, however, only trained for 15 minutes per day. Participants in the waitlist

group did not undergo any training. All three groups received coach calls once a week to monitor

and discuss training progress (for the two CWMT groups) or progress at PSE and to provide

information and resources about working memory (for the waitlist group) to control for possible

effects of attention and motivation. We note that standardized protocols for coach calls were

introduced in the Engage study. More details on the training, tasks, and the entire battery of

measures used during this project, can be found in Mawjee et al. (2015, but also see Mawjee et

al., 2014).

Participants were run in cohorts of 15-20 to ensure that the CWMT coach was able to

provide each participant with adequate individual attention. Randomization, with stratification

for sex and age, was carried out separately for each cohort and study (cogmed, engage-pilot,

engage) before the treatment started. Participants were not informed as to their randomization

until after the first assessment.

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2.3. Measures

2.3.1. Neuropsychological tasks and questionnaires used to characterize participants

Three neuropsychological tasks were used to measure working memory performance and

other aspects of cognitive functioning and were used for both Study 1 and 2. To assess verbal

working memory ability, we used the Digit Span subtest from the Wechsler Adult Intelligence

Scale (WAIS - 4th Ed.) (Wechsler, 2008). Visual-spatial working memory functioning was

assessed using two subscales on the Cambridge Neuropsychological Testing Automated Battery

(CANTAB). Participants in this task needed to remember the spatial sequence of squares that

briefly flashed on the screen in the order they appeared (CANTAB-F) (Fray and Sahakian,

1996). Last, as an estimate of general intelligence, the Vocabulary and Matrix Reasoning

subscales from the Wechsler Abbreviated Scale of Intelligence were used (WASI – 2nd Ed.,

Wechsler, 1999).

Participants completed the Adults ADHD Self-report Scale (ASRS v1.1; Adler et al.,

2012; Gray et al., 2014), the Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982;

Wallace et al., 2002), and the Symptom Assessment-45 (SA-45; Maruish, 1999) to assess current

ADHD symptomatology, everyday impairments in cognitive functioning, and additional

psychopathology, respectively.

2.3.2. Delayed Match-to-Sample-task

The delayed match-to-sample task used for the neural recordings was adapted from

Haenschel et al. (2009) and featured in both Study 1 and 2. This task was found to be sensitive in

detecting neural differences using P3 ERPs between ADHD students and healthy controls in a

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previous study investigating the encoding phase (Kim et al., 2014). Furthermore, CMWT’s

training focuses on enhancing the maintenance of information, as opposed to encoding, and we

therefore thought that maintenance phase of this task would be sensitive to both training effects

and individual differences (encoding phase training effects as well as their relation to

maintenance may be investigated in a future manuscript). The task had 144 trials and featured a

low (72 trials, 2 stimuli) and high load (72 trials, 3 stimuli) condition. Our task differed from

Haenschel et al., (2009) in that it was self-paced; this reduced the loss of trials due to artifacts

and optimized participant readiness. We note that a lack of self-pacing would have confounded

the results, particularly in subjects with ADHD (see Dalby et al., 1989), making this task better

suited to measure working memory maintenance processes in the present sample. Inter-trial

intervals were typically short and, as shown in a previous study, were not different between

ADHD and comparison groups (Kim et al., 2014). Including inter-trial interval, the average trial

time varied between 4 and 6 seconds for low load and 5–7 seconds for the high load.

Trials were presented in blocks of 12 trials each. Before each block (either a high or low

load condition, in alternating order), participants were shown which condition to expect. After

each block, feedback on accuracy was provided. Participants initiated each trial by pressing the

spacebar on a keyboard. A clear time of 400 ms was introduced before the stimulus was

presented (Figure 1). Each target stimulus appeared in the center of the screen for 600 ms (visual

angle, 1.34 degrees) with an inter-stimulus interval of 400 ms. Two seconds after the last target

stimulus of the trial disappeared, a probe stimulus was shown for two seconds. Participants were

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

by pressing the keys labelled as ‘Same’ or ‘‘Different’’ on the keypad using their dominant hand.

Stimuli were chosen from a library of thirty-six different abstract figures. Response accuracy was

15

emphasized, but to maintain a reasonable pace of the experiment participants were required to

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

incorrect.

E-prime 1.2 software (Psychology Software Tools, Inc.) was used to control stimulus

presentation and timing as well as to record the accuracy and latency of responses.

<< INSERT FIGURE 1 HERE >>

2.5. EEG data acquisition and preprocessing

Data acquisition, processing, and analysis were identical for Study 1 and 2. The EEG was

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

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

to filter (FIR, 0.1 -100 Hz, 60 Hz notch) and segment the data (400 ms before and 400 ms after

the maintenance time period).

Segments containing artifacts were removed using automatic algorithms to detect eye

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

when the vertical eye channels exceeded a threshold of 150 µv (max–min) within a 160 ms

(moving) time window within each trial after running a 20 ms moving-average smoothing

algorithm across the entire trial period. Eye movements were detected when horizontal eye

channels exceeded a threshold of 100 µv (max–min) over a 200 ms time window. Channels were

automatically marked bad when they exceeded a transition threshold of 200 µv over the entire

segment (max-min). Segments containing more than 20% bad channels were automatically

removed. In addition, research assistants who were blind to the hypotheses visually inspected all

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epochs. Bad channels were replaced by values interpolated from neighboring channel data using

spherical splines. A research assistant blind to the study objectives or hypotheses verified the

results manually. Data were then average referenced corrected for the polar average reference

effect (PARE correction; Junghöfer et al., 1999).

2.6. EEG data Analysis

2.6.1. Alpha power calculation

First, the single-trial EEG signal was decomposed using short-time Fast Fourier

Transformation (FFT) with a 512 ms moving Hanning window, as implemented in EEGLAB

(Delorme and Makeig, 2004). Short-time transformations were applied since it would provide

insights into the temporal dynamics. Output frequencies of the FFT ranged from 0.98 Hz to 49.8

Hz, divided into 51 linearly-spaced frequency bands. In the time domain, the FFT output 600

time points covering the time range from 143.8 ms before to 2143.8 ms after the starting point of

the maintenance (i.e., the disappearance of the last stimulus). EEG power at time t and frequency

f was calculated as:

where N is the total trial number, Fk(f,t) is the spectral estimate (i.e., the Fourier transform) of the

EEG data at trial k, and | | denotes the absolute value (i.e., the complex norm). The power values

were in decibel units (dB). The baseline period was defined as -100 – 0 ms, within which the

averaged power value was calculated and subtracted from the EEG power at each time point

during working memory maintenance. Baseline power was not statistically different between

groups and treatment conditions.

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Alpha band power was calculated around 9-14 Hz by averaging the 5 frequency bands at

9.8, 10.7, 11.7, 12.7, and 13.7 Hz. The first 500 ms of the maintenance phase were excluded

from analysis because the last stimulus had just disappeared and maintenance was not considered

to be as effortful. Thus, the average alpha power in the 500-2000 ms window after the onset of

the last target stimulus was used to calculate the maintenance phase.

2.6.2. Univariate analysis

First, for Study 1 we conducted ANOVAs to examine group differences in alpha power

during the maintenance phase between the ADHD and Comparison group prior to commencing

working memory training. We then tested training effects in Study 2 by comparing alpha power

differences between pre- and post-training sessions among the 3 ADHD groups randomized to

standard-length, shortened-length, and waitlist group. Based on the visual-spatial nature of this

working memory task and the relevant literature (e.g., Jensen et al., 2002), we focused on the

posterior electrodes in all our univariate analyses. Because the alpha power during the

maintenance phase was stronger in the right hemisphere (e.g, see Figure 1), we selected seven

posterior electrodes based on the grand average plot of all participants for our calculation of the

average alpha power. Therefore, electrode selection for these univariate (ANOVA) analyses was

both data-driven and hypothesis-driven.

Age and sex were always entered as covariates. For study 1, we did not separate the

ADHD group according to participants' medication status, because medication was not an

experimentally manipulated condition. However, in the Supplementary Material we present

results that examined ADHD subjects with and without medication separately. For study 2, the

effect of medication would be a within-subject effect and, to control for its influence, we had

18

included medication treatment and medication changes from pre- to post-training as covariates in

our ANOVAs.

2.6.3. Multivariate partial least square (PLS) analysis

We also included an additional multivariate PLS analysis to confirm, further explore, and

nuance, our results obtained from the univariate analysis used to test training effects. This

multivariate analysis can yield novel insights with regards to the scalp-localization of effects as

the univariate analysis was based largely on a hypothesis driven electrode-selection whereas the

multivariate approach would be a data-driven examination of these training effects across all

electrodes. This excluded the possibility that our results were due to the data-driven method used

to select the electrodes in the univariate analysis.

PLS is a multivariate analysis that uses a singular value decomposition (SVD) method to

decompose the covariance or correlations between a task design (e.g., condition contrasts) matrix

and a dependent variable data matrix (our neural data). The task design matrix consists of

contrasts among the 3 training conditions (e.g., dummy coding for standard-length, shortened-

length, and waitlist groups). The dependent data matrix represents the pre-post EEG alpha power

changes at all selected 105 electrodes for these training conditions. PLS analysis produced a set

of paired latent variables for the two sets of multivariate dataset such that the associations (e.g.,

covariance/correlations) between the corresponding latent variables of the two datasets are

maximized. Thus, this analysis informs us what multivariate patterns in one dataset (e.g., task

design contrasts) has the strongest relationship with specific multivariate patterns of the other

dataset (e.g., neural data).

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In this study, we chose to use mean-centered task PLS analysis (McIntosh and Lobaugh,

2004; Krishnan et al., 2011), implemented in the PLS software toolbox in the Matlab

environment (MathWorks, Inc), to examine how patterns of alpha power post- minus pre-

changes among selected multiple channels differed among the 3 training conditions (for technical

details on the PLS method, we refer to, Krishnan et al., 2011). Here, the three training conditions

for all participants were first dummy coded to produce a condition contrast matrix C (e.g., with

33 + 33 + 31 = 97 rows and 3 columns for the high load condition). Then, individuals’ alpha

power changes (post - pre) for all participants were stacked in the order that corresponded to the

row of the contract Matrix C to produce a neural data matrix D (e.g., with 97 rows and 105

columns in the high load condition, each row representing data from one participant and each

column representing data from one electrode). When the two load levels were examined

together, the contrast and data matrix were expanded accordingly. The dot product of the C and

D was then computed, i.e., CTD, where T denotes the transposed matrix. After the columns of the

matrix CTD was subtracted by column means, CTD was decomposed using SVD, i.e., CTD =

USVT. V and U are the left and right singular vectors for CTD and serve as new orthonormal

bases to best characterize the task design and brain data pattern, respectively. S is the singular

value diagonal matrix in which the ranked singular values indicate the strength of the

associations between the latent variables for the condition contrast and alpha power datasets. The

latent variables for condition contrast were calculated as CU and called design scores in PLS

terminology. Latent brain alpha power variables were calculated as DV and called brain scores.

Thus, (CU)TDV = UTCTDV, where CTD = USVT, therefore, (CU)TDV = UTUSVTV; Because

UTU and VTV are identity matrix, then (CU)TDV = S. Thus, singular values in S reflect the

covariance of the design contrast and alpha power latent variables.

20

To test whether the latent variables obtained from the PLS analysis were statistically

significant, we used a permutation test in which the correspondence between task contrast matrix

and brain data matrix was reshuffled 500 times to produce a null distribution for singular values.

The probability of the original true singular values was calculated using this distribution. To test

the stability of the contribution of the individual electrodes to each latent variable set, we used

bootstrapping analysis in which participants were resampled 500 times with replacement to

produce a null distribution for these brain scores. Then Z scores were calculated for each

electrode using the bootstrapped standard deviation to reflect how stable each electrode

contributed to the corresponding latent variable. All the computation was executed using PLS

software.

2.6.4. Statistical threshold and effect sizes

We set our significance p-value to .05 for all analyses. Effects with p > .05 and ≤ .1 were

treated as marginal and interpreted only when in predicted directions. For both neural and

behavioral data, we winsorized (Wilcox, 2012; P30) potential outliers that were below 2.5 and

above 97.5 percentile. Post hoc analyses were corrected for multiple comparisons using the

Bonferroni correction. Partial eta-squared values (Eta2) were computed to ascertain effect size.

According to Vacha-Haase and Thompson (2004), an effect size based on Eta2 = .01 corresponds

to a small effect, Eta2 = .10 corresponds to a medium effect, and Eta2 = .25 represents a large

effect.

3. RESULTS

3.1. Study 1: Group difference effects

21

3.1.1. Behavioral data

To calculate each participant’s accuracy in the working memory task, we subtracted the

false alarm rate from the hit rate. Using a two-way ANOVA (Group x Load) with age and sex as

covariates, we found marginally significant main effects of Load, F(1, 173) = 3.24, p = .074,

partial Eta square = .018 (90% CI = [0 .064]), and Group, F(1, 173) = 3.22, p = .074, partial Eta

square = .018 (90% CI = [0 .064]). No significant interactions were found between the two

factors (p = .229). It is worth mentioning that without covariates, load main effects were

significant (p < .001). Therefore, as expected, accuracy in the delayed match-to-sample task was

significantly higher in the low load condition than in the high load condition for all participants,

thereby confirming the robustness of the experimental manipulation. The main effect of Group

indicated that the Comparison group tended to be more accurate in performing the task than the

ADHD group (Figure 2). Although these effects were weak, they confirmed the validity of our

working memory load manipulation and supported the notion that our ADHD participants were

underperforming in the working memory task.

Detailed descriptive data are given in Supplementary Table S4, in which we also

presented hit rate, false alarm, as well as reaction time data. We noted that similar results were

found for hit rate measure (see Supplementary Table S4). Reaction time was also found to be

significantly faster in the low and high load condition, further suggesting differences between the

load conditions. However, because response speed was not emphasized in this task, and multiple

factors could underlie variation in reaction time, we did not focus on this measure hereafter. Data

and statistical tests on our reaction time measure can be found in the Supplementary Material.

<< INSERT FIGURE 2 HERE >>

22

3.1.2. EEG data: Alpha power

To investigate differences in neural processing during working memory maintenance, we

conducted a Group by Load (2x2) ANOVA with sex and age as covariates on posterior alpha

power. This analysis revealed a trend level main effect of group, F(1,168) = 3.18, p = .077,

partial Eta square = .019 (90% CI = [0 .065]), suggesting that the ADHD group had lower alpha

power than the comparison group. There were no significant Load (F(1,168) = .82, p = .367) and

Load-by-Group interactions (F(1,168) = 1.37, p = .243). Figure 3 shows the ANOVA bar graphs,

topography, and time course graphs of alpha power comparing the ADHD and the comparison

group for each load condition. The time-frequency plots are shown in the Supplementary

Material (see Supplementary Figure S2).

<< INSERT FIGURE 3 HERE >>

3.1.3. Supplemental Analysis: Medication

It is worth mentioning that in an additional analysis, we separated the ADHD group into

subgroups with and without current medication treatment and compared them with the

comparison group. The pattern of results from this analysis resembled the main findings reported

in the main text, however, as can be seen from Supplementary Figure S3, the trend level group

differences were mainly due to the differences between the medicated ADHD group and the

comparison group. When alpha power from all electrodes was considered simultaneously in

multivariate PLS analysis, we found a significant different alpha pattern (p = .048) between the

ADHD group with medication vs. Comparison and the ADHD without medication group

23

(Supplementary Figure S3 D). We acknowledge that the design of the current study cannot

resolve whether it was medication itself or a potentially more serious disorder in this subgroup

that exaggerated an alpha activity deficit.

3.2. Study 2: Training Effects

3.2.1. Behavioral data

To test the effect of training on working memory accuracy, we conducted a 3 x 2 x 2

mixed ANOVA (3 training Groups by 2 Sessions by 2 Load conditions) with age, sex,

medication, and medication change as covariates. As can be seen from Figure 4, there was no

significant Group-by-Session (F(1, 95) = .681, p = .509) or Group-by-Session-by-Load (F(1, 95)

= .067, p = .934) interaction effects. No significant main effects of training Group (F(2, 95) =

.23, p = .978), Session (F(1, 95) = 2.26, p = .136), or Load (F(1, 95) = .58, p = .447) were found.

The interaction between load and medication changes as well as the 3 way interaction among

Load, Session, and Sex were significant (F(1, 95) = 5.62, p = .02, and F(2, 95) = 4.11, p = .045,

respectively). Therefore, we also examined load effects using planned contrasts, which revealed

that working memory accuracy was higher in the low load than high load condition (p < .001).

These results suggest that working memory training did not lead to improvements in

performance on this working memory task.

Detailed descriptive data are given in Supplementary Table S5, in which we also included

hit rate, false alarm, as well as reaction time data. We noted that similar results were found for

hit rate measure (see Supplementary Table S4).

<< INSERT FIGURE 4 HERE >>

24

3.2.2. EEG data univariate analysis: Alpha power

To investigate whether CWMT affected alpha power during working memory

maintenance, we conducted a 3 x 2 x 2 (group-by-session-by-load) mixed ANOVA on posterior

EEG alpha power during working memory maintenance (averaged from 7 posterior electrodes,

as mentioned in the Method section), with session and load as within-subject factors and group

(waitlist, shortened-length, and standard-length group) as a between-subject factor. Age, sex,

medication, and medication changes from pre- to post-training were entered as covariates. This

analysis revealed a marginally significant group-by-session 2-way interaction F(2, 92) = 2.71, p

= .072, partial Eta square= .056 (90% CI = [0 .133]). Post hoc analysis showed that only in the

standard-length training group was there a marginally significant increase in alpha power from

the pre to post session (p = .093). No other main effects or interactions were significant, p’s >

.15. Figure 5 shows the bar graphs of the alpha power for different conditions. Time course and

scalp topography plots of alpha power are also presented in Figure 5 for each load condition and

treatment group. To save space, the time-frequency plots are shown in the Supplementary

Material (see Supplementary Figure S4).

We note that alpha power before training differed between the three training groups.

Specifically, a 2 x 3 ANOVA (load-by-group) on pre-training alpha power with age, sex, and

medication as covariates showed a significant main effect of group, F(2, 93) = 4.12, p = . 019,

partial Eta square= .082 (90% CI = [.008 .170]). Post hoc paired comparisons revealed that alpha

power was significantly lower in the standard-length training group than the waitlist group (p =

.015). No difference was found between the shortened-length training and waitlist group (p =

.56), or between shortened-length and standard-length group (p = .43). Although the standard-

25

length group, as predicted, showed the largest increase in alpha power, the pre-training

difference was hard to explain other than a chance effect that occurred in the randomization. We

followed a randomization procedure to assign participants into different training conditions and

all participants were unaware of these training conditions during pre-training assessment. For

Study 2, we note that our randomization was not completely strict as thirteen participants were

used from a period in which participants with ADHD (n = 13) were only randomized to a

standard and waitlist group and that there were small procedural differences during the treatment.

Excluding these participants did not change the pre-test condition difference. Interestingly,

excluding these 13 participants did result in a significant treatment effect: Session-by-Group,

F(2, 79) = 3.78, p =.027, partial Eta square= .087 (90% CI = [.006 .183]), as opposed to

marginally significant. The implications for our results are discussed in the Discussion section.

<< INSERT FIGURE 5 HERE >>

3.2.3. EEG data multivariate analysis: Alpha power

To ensure that the training effects obtained using the above-mentioned univariate

analyses were not biased by our partially data-driven electrode selection, we also conducted a

multivariate PLS analysis, which considered alpha power from 105 electrodes simultaneously.

This type of analyses can reveal the pattern of training effects among all the 105 electrodes. As

mentioned in the Method section, we first calculated the alpha power changes from pre-training

to post-training session in each of 105 electrodes for the 3 groups (using all participants) in the

low and high load condition. After the effects of age, sex, medication, and medication changes

were partialled out, we subjected this multivariate dataset to a PLS analysis. The mean-centered

26

PLS revealed only one significant latent variable that reflected the contrast between the two

training groups and the waitlist group (p = .002), indicating that the pattern of the alpha power

changes in the two training groups were significantly different from those in the waitlist group.

As can be seen in Figure 6, the significant training vs. waitlist contrast was contributed by the

two load conditions from the shortened-length training group and the high load condition from

the standard-length training group. Bootstrapping tests with threshold of z > 1.96 (p < .05)

showed that the alpha power from posterior and lateral frontal and temporal electrodes

contributed reliably to the condition differences (Figure 6). It is worth mentioning that only using

participants in the strict 3-arm randomization (i.e., the Engage cohort participants), yielded

similar significant results (p < .004).

<< INSERT FIGURE 6 HERE >>

4. DISCUSSION

4.1. Summary of results

We set out to investigate whether alpha oscillation power during working memory

maintenance was different between college students with ADHD and their healthy peers (Study

1), and whether this neural response would change after working memory training (Study 2). Our

results showed suggestive evidence for small effects showing lower accuracy and lower alpha

power for the ADHD compared to the comparison group during working memory maintenance.

Within the ADHD sample, no training effects were present for behavioral performance on our

delayed match-to-sample task and there was only minimal neural evidence for training effects

suggesting an increase in posterior alpha power in the standard-length training group compared

27

to the shortened-length and waitlist groups. Multivariate PLS analysis revealed distributed alpha

power increases for both training groups, compared to the waitlist group, providing some

convergent evidence for changes with treatment at a neural level.

4.2. Study 1: Group difference analysis

Our hypothesis, i.e., predicting lower alpha in the ADHD sample compared to their peers

was confirmed only at a marginally significant level, which was consistent with the behavioral

data. However, since the group differences were in our predicted directions, we will cautiously

interpret these effects. These results seem in line with earlier findings in this sample showing low

posterior alpha power during a resting state (Woltering et al., 2012). Reduced alpha power

during working memory maintenance has also been found in patients with schizophrenia

(Bachman et al., 2008). Typically, increased alpha power has been observed during working

memory maintenance in the posterior part of the brain, especially when working memory load is

high (e.g., Haenschel et al., 2009, Crespo-Garcia et al., 2013; Michels et al., 2008, Hsieh et al.,

2011; Anderson et al., 2014). In line with the ‘gating by inhibition’ hypothesis (Jensen and

Mazaheri, 2010), the finding of lower alpha power seems to suggest that the brains of our

participants with ADHD were less efficient at protecting representations in working memory

during the retention period. Although we focused on alpha activity measured from the posterior

part of the brain, studies have shown that alpha oscillation involves interactions among

prefrontal, parietal, and occipital cortex as well as thalamic nuclei in controlling visual attention

(Saalmann et al., 2012; Bollimunta et al., 2011; Kundu et al., 2015). Our results provide evidence

supporting the idea that the attentional control network in adults with ADHD may function less

optimally compared to their healthy peers and may suggest a possible neural mechanism

28

explaining the distractibility people with ADHD experience. This also fit our ADHD

participants’ subjective reports that they often feel overwhelmed finishing assignments during

busy exam periods when multiple different assignments are due and why several students report

a large dependency on external organizers to offload working memory (Gray et al., 2014).

Our findings seem to differ from those described in Lenartowicz et al. (2014), who found

that children with ADHD showed stronger alpha activity during working memory maintenance.

The discrepancy was likely due to a different choice of baseline correction method in calculating

maintenance-related alpha power. In the current study we used the 100 ms time window right

before the offset of the last stimulus as a baseline, whereas Lenartowicz et al., (2014) used a pre-

trial period (e.g., before the first fixation cross appeared, several seconds before the maintenance

phase would begin). Although each choice of a baseline period may carry advantages and

disadvantages, we opted to use the period before the offset as it was similar to Haenschel et al.,

(2009) who used an identical task as ours, and most importantly, we explicitly tested for

differences between conditions in alpha power during this baseline period and did not find any. It

is possible, from studying the plots, that if Lenartowicz et al would have used a baseline

correction period similar to ours, they might also have observed a higher net increase in alpha

power for their comparison group because alpha power during their encoding period was higher

in the ADHD compared to the comparison group. Alternatively, it is possible that these highlight

developmental differences as we tested adults and Lenartowicz et al tested children, because,

e.g., adults with ADHD may show fewer cognitive deficits than children (Mostert et al., 2015).

It is worth mentioning that our supplementary analyses revealed that only the ADHD

group with medication showed lower posterior alpha power, compared to their healthy peers

(Univariate analysis; Supplementary Figure S3 B and C), or showed lower distributed alpha

29

power compared to both their healthy peers and ADHD group without medication (multivariate

analysis; Supplementary Figure S3 D). Although it is possible that the medicated group had more

severe ADHD or impairment than the non-medicated group, the design of the current study does

not allow us to disentangle effects due to medication itself or to more serious disorder.

Moreover, future studies are also needed to determine whether neural oscillations in other

frequency bands can differentiate ADHD with or without medications from their healthy peers.

That our group difference effects, albeit suggestive, were present in the right hemisphere above

parietal electrode sites seemed in line with the recent work from Sigi Hale and colleagues (Hale

et al., 2014, 2015) who proposed abnormalities in right-parietal brain function to be implicated in

ADHD.

4.3. Study 2: Training analysis

Our hypothesis, i.e., predicting that power in alpha oscillations would change with

CMWT, was not robustly confirmed. Similar to Study 1, effects were marginally significant, but

in predicted directions: posterior alpha power increased for those participants in the standard-

length group whereas this was not the case for ADHD participants in the shortened-length or

waitlist control groups. Using only the Engage samples which had a more strict 3-arm

randomization confirmed a statistically significant training Session by Group interaction, which

corroborated our interpretation of the originally trend level effects. Moreover, multivariate

analyses also showed significantly larger pre-post alpha power increase distributed among

different electrodes. For these reasons we will interpret these training effects, however, we

caution the reader that the analyses as conducted per our original analysis plan were only

marginally significant and highlight the limitations of our study, described below.

30

These findings of training-related brain plasticity in general seem to be in line with the

current literature (e.g., May, 2011; Zatorre et al., 2012; Klingberg, 2010; Keshavan et al., 2014,

for recent reviews). For example, it has been found that in healthy participants, extensive training

on music instruments (Paraskevopoulos et al., 2012), video games (Granek et al., 2010; Kühn et

al., 2014), executive functions (Chein & Schneider, 2005; Erickson et al. 2007; Kuhn et al.

2013), self-regulation skills (Woltering et al., 2011; Woltering et al., 2015), or motor skills (Hu

et al., 2011; Draganski et al., 2004; Draganski et al., 2006; Scholz et al., 2009) can lead to brain

functional or anatomical changes. Neural plasticity induced changes specific to cognitive training

have also been documented in patient with dyslexia (Krafnick et al., 2011; Keller and Just, 2009;

Kujala et al., 2001), schizophrenia (Subramaniam et al., 2012; Vinogradov et al., 2012), or mild

cognitive impairment/Alzheimer's disease (Hampstead et al., 2012; Simon et al., 2012; Clare &

Woods, 2004; Spironelli et al., 2013; Paasschen et al., 2013). Specifically for individuals with

ADHD, Hoekzema and colleagues have examined the brain’s functional and anatomical changes

after two-weeks of training on multiple executive function tasks (Hoekzema et al., 2010;

Hoekzema et al., 2011). These studies found evidence that brain activation in prefrontal and

cerebellar regions was enhanced during inhibition and attention tasks and that volumetric gray

matter increased after the training. Furthermore, a study by Olensen, Westerberg, & Klingberg

(2004) found changes in fronto-parietal systems after CWMT, albeit with a very low sample

(n<9). The current study, using a larger sample size and a randomized control trial, contributed to

this line of research showing suggestive evidence that alpha oscillation power in adults with

ADHD during working memory maintenance may be modified after intensive cognitive training.

However, the current EEG study cannot pinpoint the precise anatomical structure of the

training effects on alpha oscillation. Previous studies have shown that alpha oscillation involves

31

interactions among prefrontal, parietal, and occipital cortex as well as thalamic nuclei in

controlling visual attention (Saalmann et al., 2012; Bollimunta et al., 2011; Kundu et al., 2015).

Neuroimaging studies consistently found that executive brain regions such as the prefrontal-

parietal network and basal ganglia are crucial for working memory processing (Collette and Van

der Linden, 2002; D’Esposito and Postle, 2015; Fuster and Bressler, 2012; Watanabe and

Funahashi, 2012). Moreover, it has been found that working memory training can affect these

brain regions functionally, anatomically, and neurochemically (Brehmer, et al., 2011; Landau, et

al., 2004; Lövdén, et al., 2010; McNab, et al., 2009; Olesen, Westerberg, & Klingberg, 2004;

Schneiders et al., 2011; Takeuchi, et al., 2010). Therefore, it is possible that the alpha power

enhancement caused by training reflects improved functioning in these executive brain regions.

This seems consistent with our multivariate analysis results which found that the increase in

alpha power in the training groups compared to the waitlist group was not limited to the posterior

part of the brain. As can be seen from our PLS analysis (Figure 6), alpha power changes also

occurred in temporal and frontal electrodes, which may suggest distributed changes due to the

training.

More importantly, our results may suggest that the training related alpha power changes

are less likely to be directly related to improving working memory capacity per se. This is

because, first, in the current study the neural training effects were found in absence of any

behavioral training effects. This lack of a behavioral transfer effect with our delayed match-to-

sample task underscores how narrow transfer effects in these types of tasks are. Since there were

no measurable behavioral benefits to the training, we have to conclude that our neural effects

may only reflect evidence of plasticity but one that would not be strong enough to be manifested

in behavioral change. Similar conclusions have been drawn from recent meta-analyses and

32

review of behavioral studies (Dunning, Holmes, & Gathercole, 2013; Karbach and Verhaeghen,

2014, Melby-Lervåg and Hulme, 2012, Shipstead et al., 2012; Thompson et al., 2013). In the

same group of participants, we found intensive training only improved criterion measures of

working memory, i.e., changes on tasks that closely resemble the training tasks (Mawjee et al.,

2015; but also see Mawjee et al., 2014). This means that this task, even though it directly targets

working memory span and is similar to those directly trained, was still different enough to not

show any behavioral transfer.

Moreover, we did not find strong load modulation effects. Considering that the training

effects also existed in the low load condition in which these participants only need to maintain

two stimulus in their working memory during our EEG task, we think that the training effects

that we found may be related less to expanding working capacity per se but more so to improved

general attentional processing, such as gating or filtering information, during working memory

maintenance, which was not strong or specific enough to affect working memory performance.

Furthermore, the training effects that we found did not differ between the standard-length and

shortened-length training groups. Considering that the daily training load was very low for the

shortened-length group, no training effect differences between the two active training groups

also indicates the training effects that we observed are related less to expanding working capacity

per se, but more so to improved general executive functioning mentioned above.

We conclude this section by clarifying that the neural training effects we described do not

assume clinical relevance nor support the efficacy of the training program. First, consistent with

the recently published behavioral findings from the randomized controlled trial study from our

lab (Mawjee et al., 2015), there was no evidence in the present study of improvements in

participants at a behavioral level in the standard treatment versus the control conditions. Second,

33

the neural treatment effects, even if they were interpreted as if they were statistically significant,

only showed weak effect sizes. Third, it is unlikely the treatment improved working memory

capacity as CMWT originally intended because we found no evidence for load effects within the

task nor did we find intensity effects between treatment conditions. Last, we deemed the delay-

match-to-sample task to be similar to the tasks trained in the CMWT program. The effects in the

present paper therefore cannot speak strongly to transfer, i.e., the improvement of functioning in

areas outside of the treatment program’s context, which would have had the most clinical

relevance.

4.4. Limitations & future directions

A limitation of this study (Study 2) is that the training groups differed in alpha power at

the pre-training assessment. As mentioned earlier, we followed a randomization procedure to

assign participants into different training conditions. We were confident that none of the

participants were aware of their randomization assignment during pre-training assessment but the

pre-training alpha power differences across training groups may suggest that the training effects

may be dependent on individuals’ neurophysiological status when they entered the training

program: participants who had lower alpha power before training may be more responsive to the

training. Relatedly, because of the pre-training group differences, we cannot exclude the

possibility that “regression-to-mean” effects (Barnett et al. 2005) played a role in the pattern of

our results. In other words, we cannot exclude that changes from pre-training to post-training for

the standard-length group is purely a statistical phenomenon reflecting a natural variation for

lower scores to return to mean range.

34

Another limitation is that most of the effects were marginally significant which prohibits

strong conclusions. The most likely reason for this is that the working memory training was not

effective in providing enough transfer to our working memory task, as evidenced by the lack of

behavioral training effects and a weak separation of standard-length and shortened-length

training. Nevertheless, although our neural effects are weak and had small effect sizes, they were

in predicted directions. For that reason, we do think the present results demonstrated promising

directions and hypotheses for future research which may utilize other, or adjusted, training types

with a broader range of transfer.

Finally, we alert the reader that the present sample consisted of relatively high-

functioning individuals with ADHD which limits the external validity of the study. As shown in

previous studies in a similar sample, ADHD college students were not showing clinical levels of

impairment on standardized tests (Gray et al., 2015). It is therefore possible that stronger effects

may have been obtained if this study was conducted with more typical ADHD adult populations

or children with ADHD. Another consideration, potentially limiting the external validity, was

that about half the students who were assessed for eligibility did not meet the inclusion criteria.

Although no statistics were collected, the most common reason for rejection was that they were

students whose diagnosis of ADHD for some reason was not accepted and approved by

Disability Services. A strict enforcement of our inclusion criteria was necessary as a registration

ensured that students had an ADHD diagnosis.

4.5. Implications & Conclusion

Our findings did not provide strong evidence that alpha activity of adults with ADHD

differed from their healthy peers in how well visual representations were protected while being

35

maintained in working memory. Furthermore, only minimal evidence was found for a pattern

suggesting a normalization of brain activity with training. We concluded that there was no

convincing evidence for intensive working memory training changing the underlying neural

networks specifically supporting working memory functioning in a near-transfer task.

36

TRIAL REGISTRATION

www.clinicaltrials.gov ‘Working Memory Training in ADHD (The Engage Study)’ #

NCT01657721.

CONFLICT OF INTEREST STATEMENT

Authors report no conflict of interest.

ACKNOWLEDGEMENTS

We thank Dr. Corinna Haenschel and Nikolaus Kriegeskorte for letting us use the task stimuli.

This research was supported financially in part by a CIHR Operating Grant (# 245899, Tannock

& Lewis) and by the Canada Research Chair program (Rosemary Tannock). We also want to

acknowledge the Jewish Vocational Services for their openness in collaborating with us.

37

REFERENCES

Adler LA, Shaw DM, Spencer TJ, Newcorn JH, Hammerness P, Sitt DJ, et al. Preliminary

examination of the reliability and concurrent validity of the Attention-Deficit/Hyperactivity

Disorder Self-Report Scale v1.1 Symptom checklist to rate symptoms of Attention-

Deficit/Hyperactivity Disorder in adolescents. J Child Adolesc Psychopharmacol.

2012;22(3):238–44.

Anderson DE, Serences JT, Vogel EK, Awh E. Induced alpha rhythms track the content and

quality of visual working memory representations with high temporal precision. J Neurosci.

2014;34(22):7587–99.

Awh E, Vogel EK. The bouncer in the brain. Nat Neurosci. 2008;11(1):5–6.

Bachman P, Kim J, Yee CM, Therman S, Manninen M, Lönnqvist J, et al. Abnormally high EEG

alpha synchrony during working memory maintenance in twins discordant for schizophrenia.

Schizophr Res. 2008;103(1):293–7.

Barkley RA. Behavioral inhibition, sustained attention, and executive functions: Constructing a

unifying theory of ADHD. Psychol Bull. 1997;121(1):65–94.

Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: what it is and how to deal

with it. Int J Epidemiol. 2005; 34(1):215–20.

38

Bollimunta A, Mo J, Schroeder CE, Ding M. Neuronal mechanisms and attentional modulation

of corticothalamic alpha oscillations. J Neurosci. 2011;31(13):4935–43.

Bonnefond M, Jensen O. Alpha oscillations serve to protect working memory maintenance

against anticipated distracters. Curr Biol. 2012;22(20):1969–74.

Brehmer Y, Rieckmann A, Bellander M, Westerberg H, Fischer H, Bäckman L. Neural

correlates of training-related working-memory gains in old age. NeuroImage. 2011;58(4):1110–

20.

Broadbent DE, Cooper PF, FitzGerald P, Parkes KR. The cognitive failures questionnaire (CFQ)

and its correlates. Br J Clin Psychol. 1982;21(1):1–16.

Chein JM, Schneider W. Neuroimaging studies of practice-related change: fMRI and meta-

analytic evidence of a domain-general control network for learning. Cogn Brain Res.

2005;25(3):607–23.

Clare L, Woods RT. Cognitive training and cognitive rehabilitation for people with early-stage

Alzheimer’s disease: A review. Neuropsychol Rehabil. 2004;14(4):385–401.

Collette F, Van der Linden M. Brain imaging of the central executive component of working

memory. Neurosci Biobehav Rev. 2002;26(2):105–25.

39

Crespo-Garcia M, Pinal D, Cantero JL, Díaz F, Zurrón M, Atienza M. Working memory

processes are mediated by local and long-range synchronization of alpha oscillations. J Cogn

Neurosci. 2013;25(8):1343–57.

Dalby KN, Kinsbourne M, Swanson JM. Self-paced learning in children with attention deficit

disorder with hyperactivity. J Abnorm Child Psychol . 1989: 17(3): 269-275.

Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG

dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

D’Esposito M, Postle BR. The cognitive neuroscience of working memory. Annu Rev Psychol.

2015;66:115–42.

Draganski B, Gaser C, Busch V, Schuierer G, Bogdahn U, May A. Neuroplasticity: Changes in

grey matter induced by training - Newly honed juggling skills show up as a transient feature on a

brain-imaging scan. Nature. 2004;427(6972):311–2.

Draganski B, Gaser C, Kempermann G, Kuhn HG, Winkler J, Buchel C, et al. Temporal and

spatial dynamics of brain structure changes during extensive learning. J Neurosci.

2006;26(23):6314–7.

Dunning DL, Holmes J, Gathercole SE. Does working memory training lead to generalized

improvements in children with low working memory? A randomized controlled trial. Dev Sci.

2013;16(6):915–25.

40

DuPaul GJ, Weyandt LL, O’Dell SM, Varejao M. College students with ADHD: Current status

and future directions. J Atten Disord. 2009;13(3):234–50.

Erickson KI, Colcombe SJ, Wadhwa R, Bherer L, Peterson MS, Scalf PE, et al. Training-induced

functional activation changes in dual-task processing: An fMRI study. Cereb Cortex.

2007;17(1):192–204.

Fray P, Robbins T, Sahakian B. Neuropsychiatric applications of CANTAB. Int J Geriatr

Psychiatry. 1996;11(4):329–36.

Fuster JM, Bressler SL. Cognit activation: A mechanism enabling temporal integration in

working memory. Trends Cogn Sci. 2012;16(4):207–17.

Gomarus HK, Wijers AA, Minderaa RB, Althaus M. Do children with ADHD and/or PDD-NOS

differ in reactivity of alpha/theta ERD/ERS to manipulations of cognitive load and stimulus

relevance? Clin Neurophysiol. 2009;120(1):73–9.

Granek JA, Gorbet DJ, Sergio LE. Extensive video-game experience alters cortical networks for

complex visuomotor transformations. Cortex. 2010;46(9):1165–77.

41

Gray SA, Fettes P, Woltering S, Mawjee K, Tannock R. Symptom Manifestation and

Impairments in College Students With ADHD. J Learn Disabil. 2015. DOI:

10.1177/0022219415576523.

Gray S, Woltering S, Mawjee K, Tannock R. The Adult ADHD Self-Report Scale (ASRS):

Utility in college students with Attention-Deficit/Hyperactivity Disorder. PeerJ. 2014;2(1):e324.

Gropper RJ, Gotlieb H, Kronitz R, Tannock R. Working Memory Training in College Students

With ADHD or LD. J Atten Disord. 2014;18(4):331–45.

Haenschel C, Bittner RA, Waltz J, Haertling F, Wibral M, Singer W, et al. Cortical oscillatory

activity is critical for working memory as revealed by deficits in early-onset schizophrenia. J

Neurosci. 2009;29(30):9481–9.

Hale TS, Kane AM, Tung KL, Kaminsky O, McGough JJ, Hanada G, Loo SK. Abnormal

parietal brain function in ADHD: replication and extension of previous EEG beta asymmetry

findings. Front Psychiatry. 2014; 5:87.

Hale TS, Wiley JF, Smalley SL, Tung KL, Kaminsky O, McGough JJ, et al. A parietal

biomarker for ADHD liability: As predicted by The Distributed Effects Perspective Model of

ADHD. Front Psychiatry. 2015; 6:63.

42

Hampstead BM, Stringer AY, Stilla RF, Giddens M, Sathian K. Mnemonic strategy training

partially restores hippocampal activity in patients with mild cognitive impairment. Hippocampus.

2012;22(8):1652–8.

Hervey AS, Epstein JN, Curry JF. Neuropsychology of adults with Attention-

Deficit/Hyperactivity Disorder: A meta-analytic review. Neuropsychology. 2004;18(3):485–503.

Hoekzema E, Carmona S, Ramos‐Quiroga JA, Barba E, Bielsa A, Tremols V, et al.

Training‐induced neuroanatomical plasticity in ADHD: A tensor‐based morphometric study.

Hum Brain Mapp. 2011;32(10):1741–9.

Hoekzema E, Carmona S, Tremols V, Gispert JD, Guitart M, Fauquet J, et al. Enhanced neural

activity in frontal and cerebellar circuits after cognitive training in children with attention-

deficit/hyperactivity disorder. Hum Brain Mapp. 2010;31(12):1942–50.

Hsieh L-T, Ekstrom AD, Ranganath C. Neural oscillations associated with item and temporal

order maintenance in working memory. J Neurosci. 2011;31(30):10803–10.

Hu Y, Geng F, Tao L, Hu N, Du F, Fu K, et al. Enhanced white matter tracts integrity in children

with abacus training. Hum Brain Mapp. 2011;32(1):10–21.

43

Jensen O, Gelfand J, Kounios J, Lisman JE. Oscillations in the alpha band (9-12 Hz) increase

with memory load during retention in a short-term memory task. Cereb Cortex. 2002;12(8):877–

82.

Jensen O, Mazaheri A. Shaping functional architecture by oscillatory alpha activity: gating by

inhibition. Front Hum Neurosci. 2010;4:186.

Junghöfer M, Elbert T, Tucker DM, Braun C. The polar average reference effect: a bias in

estimating the head surface integral in EEG recording. Clin Neurophysiol. 1999;110(6):1149–55.

Karbach J, Verhaeghen P. Making working memory work a meta-analysis of executive-control

and working memory training in older adults. Psychol Sci. 2014;25(11):2027–37.

Keller TA, Just MA. Altering cortical connectivity: Remediation-induced changes in the white

matter of poor readers. Neuron. 2009;64(5):624–31.

Keshavan MS, Vinogradov S, Rumsey J, Sherrill J, Wagner A. Cognitive training in mental

disorders: update and future directions. Am J Psychiatry. 2014;171(5):510–22.

Kim S, Liu Z, Glizer D, Tannock R, Woltering S. Adult ADHD and working memory: neural

evidence of impaired encoding. Clin Neurophysiol. 2014;125(8):1596–603.

Klingberg T. Training and plasticity of working memory. Trends Cogn Sci. 2010;14(7):317–24.

44

Klingberg T, Fernell E, Olesen PJ, Johnson M, Gustafsson P, Dahlström K, et al. Computerized

training of working memory in children with ADHD-a randomized, controlled trial. J Am Acad

Child Adolesc Psychiatry. 2005;44(2):177–86.

Krafnick AJ, Flowers DL, Napoliello EM, Eden GF. Gray matter volume changes following

reading intervention in dyslexic children. NeuroImage. 2011;57(3):733–41.

Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for

neuroimaging: A tutorial and review. NeuroImage. 2011;56(2):455–75.

Kuhn S, Gleich T, Lorenz RC, Lindenberger U, Gallinat J. Playing Super Mario induces

structural brain plasticity: gray matter changes resulting from training with a commercial video

game. Mol Psychiatry. 2014;19(2):265.

Kühn S, Schmiedek F, Noack H, Wenger E, Bodammer NC, Lindenberger U, et al. The

dynamics of change in striatal activity following updating training. Hum Brain Mapp.

2013;34(7):1530–41.

Kujala T, Karma K, Ceponiene R, Belitz S, Turkkila P, Tervaniemi M, et al. Plastic neural

changes and reading improvement caused by audiovisual training in reading-impaired children.

Proc Natl Acad Sci. 2001;98(18):10509–14.

45

Kundu B, Chang J-Y, Postle BR, Van Veen BD. Context-specific differences in fronto-parieto-

occipital effective connectivity during short-term memory maintenance. NeuroImage.

2015;114:320–7.

Landau SM, Schumacher EH, Garavan H, Druzgal TJ, D’Esposito M. A functional MRI study of

the influence of practice on component processes of working memory. NeuroImage.

2004;22(1):211–21.

Lenartowicz A, Delorme A, Walshaw PD, Cho AL, Bilder RM, McGough JJ, et al.

Electroencephalography correlates of spatial working memory deficits in attention-

deficit/hyperactivity disorder: vigilance, encoding, and maintenance. J Neurosci.

2014;34(4):1171–82.

Lövdén M, Bodammer NC, Kühn S, Kaufmann J, Schütze H, Tempelmann C, et al. Experience-

dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia.

2010;48(13):3878–83.

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

impairments in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc

Psychiatry. 2005;44(4):377–84.

Maruish ME. Symptom Assessment–45 Questionnaire (SA-45). Use Psychol Test Treat Plan

Outcomes Assess. Mahwah, NJ: Lawrence Erlbaum; 1999. p. 725–57.

46

Mawjee K, Woltering S, Lai N, Gotlieb H, Kronitz R, Tannock R. Working Memory Training in

ADHD Controlling for Engagement, Motivation, and Expectancy of Improvement (Pilot Study).

J Atten Disord. 2014. DOI: 10.1177/1087054714557356.

Mawjee K, Woltering S, Tannock R. Working memory training in post-secondary students with

ADHD: a randomized controlled study. PLoS One. 2015: 10(9), e0137173.

May A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn Sci.

2011;15(10):475–82.

McIntosh AR, Lobaugh NJ. Partial least squares analysis of neuroimaging data: applications and

advances. NeuroImage. 2004;23:S250–63.

McNab F, Varrone A, Farde L, Jucaite A, Bystritsky P, Forssberg H, et al. Changes in cortical

dopamine D1 receptor binding associated with cognitive training. Science. 2009;323(5915):800-

2.

Melby-Lervåg M, Hulme C. Is working memory training effective? A meta-analytic review. Dev

Psychol. 2013;49(2):270–91.

Michels L, Moazami-Goudarzi M, Jeanmonod D, Sarnthein J. EEG alpha distinguishes between

cuneal and precuneal activation in working memory. NeuroImage. 2008;40(3):1296–310.

47

Mostert JC, Onnink AM, Klein M, Dammers J, Harneit A, Schulten T, et al. Cognitive

heterogeneity in adult attention deficit/hyperactivity disorder: A systematic analysis of

neuropsychological measurements. Eur Neuropsychopharmacol. 2015.

Doi:10.1016/j.euroneuro.2015.08.010.

Olesen PJ, Westerberg H, Klingberg T. Increased prefrontal and parietal activity after training of

working memory. Nat Neurosci. 2004;7(1):75–9.

van Paasschen J, Clare L, Yuen KS, Woods RT, Evans SJ, Parkinson CH, et al. Cognitive

rehabilitation changes memory-related brain activity in people with Alzheimer disease.

Neurorehabil Neural Repair. 2013;27(5):448–59.

Palva S, Kulashekhar S, Hämäläinen M, Palva JM. Localization of cortical phase and amplitude

dynamics during visual working memory encoding and retention. J Neurosci. 2011;31(13):5013–

25.

Paraskevopoulos E, Kuchenbuch A, Herholz SC, Pantev C. Evidence for training-induced

plasticity in multisensory brain structures: an MEG study. PloS One. 2012;7(5):e36534.

Rapport MD, Orban SA, Kofler MJ, Friedman LM. Do programs designed to train working

memory, other executive functions, and attention benefit children with ADHD? A meta-analytic

review of cognitive, academic, and behavioral outcomes. Clin Psychol Rev. 2013;33(8):1237–

52.

48

Saalmann YB, Pinsk MA, Wang L, Li X, Kastner S. The pulvinar regulates information

transmission between cortical areas based on attention demands. Science. 2012;337(6095):753.

Sauseng P, Klimesch W, Heise KF, Gruber WR, Holz E, Karim AA, et al. Brain oscillatory

substrates of visual short-term memory capacity. Curr Biol. 2009;19(21):1846–52.

Schneiders JA, Opitz B, Krick CM, Mecklinger A. Separating Intra-Modal and Across-Modal

Training Effects in Visual Working Memory: An fMRI Investigation. Cereb Cortex.

2011;21(11):2555–64.

Scholz J, Klein MC, Behrens TE, Johansen-Berg H. Training induces changes in white-matter

architecture. Nat Neurosci. 2009;12(11):1370–1.

Shipstead Z, Hicks KL, Engle RW. Cogmed working memory training: Does the evidence

support the claims? J Appl Res Mem Cogn. 2012;1(3):185–93.

Simon SS, Yokomizo JE, Bottino CMC. Cognitive intervention in amnestic Mild Cognitive

Impairment: A systematic review. Neurosci Biobehav Rev. 2012;36(4):1163–78.

Spironelli C, Bergamaschi S, Mondini S, Villani D, Angrilli A. Functional plasticity in

Alzheimer’s disease: effect of cognitive training on language-related ERP components.

Neuropsychologia. 2013;51(8):1638–48.

49

Subramaniam K, Luks TL, Fisher M, Simpson GV, Nagarajan S, Vinogradov S. Computerized

cognitive training restores neural activity within the reality monitoring network in schizophrenia.

Neuron. 2012;73(4):842–53.

Takeuchi H, Sekiguchi A, Taki Y, Yokoyama S, Yomogida Y, Komuro N, et al. Training of

working memory impacts structural connectivity. J Neurosci. 2010;30(9):3297–303.

Thompson TW, Waskom ML, Garel K-LA, Cardenas-Iniguez C, Reynolds GO, Winter R, et al.

Failure of Working Memory Training to Enhance Cognition or Intelligence. PLoS One.

2013;8(5):e63614.

Vacha-Haase T, Thompson B. How to estimate and interpret various effect sizes. J Couns

Psychol. 2004;51(4):473–81.

Vinogradov S, Fisher M, de Villers-Sidani E. Cognitive training for impaired neural systems in

neuropsychiatric illness. Neuropsychopharmacology. 2012;37(1):43–76.

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

controlling access to working memory. Nature. 2005;438(7067):500–3.

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

and Correlates. J Gen Psychol. 2002;129(3):238-56.

50

Watanabe Y, Funahashi S. Thalamic mediodorsal nucleus and working memory. Neurosci

Biobehav Rev. 2012;36(1):134–42.

Wechsler D. Wechsler abbreviated scale of intelligence. New York, NY: Harcourt Brace; 1999.

Wechsler D. Wechsler Adult Intelligence Scale. Fourth. San Antonio, TX: Pearson Assessment;

2008.

Wilcox RR. Introduction to Robust Estimation and Hypothesis Testing, Third Edition. Third.

Amsterdam; Boston: Academic Press; 2012.

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

in children with externalizing problems. Biol Psychiatry. 2011;70(9):873–9.

Woltering S, Jung J, Liu Z, Tannock R. Resting state EEG oscillatory power differences in

ADHD college students and their peers. Behav Brain Funct. 2012;8(1):60–60.

Woltering S, Liao V, Liu Z-X, Granic I. Neural rhythms of change: Long-term improvement

after successful treatment in children with disruptive behavior problems. Neural Plast. 2015:1–

11.

51

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

between adults with ADHD and their peers. Neuropsychologia. 2013;51(10):1888–95.

Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in

brain structure during learning. Nature Neurosci. 2012; 15(4): 528-536.

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Tables

Table 1: For Study 1: participant information: ADHD vs. Comparison

Comparison group

ADHD group

Group difference statistics

M SD N

M SD N

t p

Age 23.2 3.83 41

24.21 4.08 136

1.42 0.158

PS Education

2.82 1.49 33

2.47 1.46 136

1.22 .063

Cantab-F 0.641 1.08 31

0.139 1.08 135

2.32 0.022

WAIS-SS 9.87 2.53 38

8.81 2.82 135

2.08 0.039

ASRS-T 22.11 9.74 36

48.59 9.21 135

15.14 0.001

CFQ-T 28.21 9.19 34

57.64 13.1 117

12.24 0.001

SA45-G 53.41 8.98 27

61.63 7.81 134

4.87 0.001

Note: Cantab-F: Cambridge Neuropsychological Testing Automated Battery spatial span

(forwards) standardized score; WAIS-SS: Wechsler Adult Intelligence Scale digit span total

standardized score; ASRS-T: Adults ADHD Self-report Scale total raw scores; CFQ-T:

Cognitive Failures Questionnaire total raw scores; SA45-G: Symptom Assessment-45 global

severity standardized score. PS Education: Years of post-secondary education.

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Table 2: For Study 2: Questionnaire and demographic information (no group differences for all

measures as measured by ANOVAs)

Standard-length group

Shortened-length group

Waitlist group

M SD N

M SD N

M SD N

Males 18(52.9%)

12(38.7%)

16(47.1%)

Females 16(47.1%)

19(61.3%)

18(52.9%)

LD 10(29.4%)

7(22.6%)

3(8.8%)

University 20(80%)

27(87.1%)

21(70%)

College 5(20%)

4(12.9%)

9(30%)

Medication 19(55.9%)

14(45.2%)

16(47.1%)

Age 24.59 3.70 34

23.87 3.62 31

23.74 3.46 34

PS Education

2.46 1.54 35

2.65 1.31 31

2.25 1.36 36

Cantab-F -.004 1.04 34

.26 1.31 31

.22 1.05 34

WAIS-SS 8.00 2.56 34

9.30 3.21 30

8.71 2.55 34

WASI-IQ 114.76 10.89 25

110.81 11.60 31

114.23 13.7

9 30

ASRS-T 46.85 8.81 34

48.26 8.26 31

47.21 9.84 34

CFQ-T 55.65 11.88 31

57.96 12.32 25

55.78 14.3

9 27

SA45-G 61.18 6.90 34

61.48 7.90 31

61.34 8.07 33

Note: Cantab-F: Cambridge Neuropsychological Testing Automated Battery spatial span

(forward) standardized score; WAIS-SS: Wechsler Adult Intelligence Scale digit span total

standardized score; WASI-IQ: Wechsler Abbreviated Scale of Intelligence vocabulary and

matrix reasoning subscale standardized score; ASRS-T: Adults ADHD Self-report Scale total

raw scores; CFQ-T: Cognitive Failures Questionnaire total raw scores; SA45-G: Symptom

Assessment-45 global severity standardized score. PS Education: Years of post-secondary

education.

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Figure Legends

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

Each of the three stimuli was shown for 600 ms with an inter-stimulus interval of 400 ms during

encoding. After a 2000 ms delay, a probe stimulus was presented for the participants to judge

whether they saw the target among the initial set of the stimuli or not. Participants initiated each

trial by pressing the space bar. 200 ms of clear time was allowed before each trial. Two stimuli

were given in the low load condition. This working memory paradigm was adopted and modified

from Haenschel et al (2007). This study focused on the working memory maintenance phase.

The embedded figure shows the posterior electrodes that were used for alpha power analyses.

Figure 2: Working memory accuracy (hit rate – false alarm rate) in the high and low load

condition for the comparison and ADHD group with standard error bars.

Figure 3: A. EEG alpha power during working memory maintenance (averaged from 500-2000

ms) for the healthy comparison and ADHD group. Standard error bars are also presented. B. The

time course of the alpha power during working memory maintenance. In A and B, alpha power

was calculated from predefined posterior electrodes as indicated in C. C. Topoplot of alpha

power during working memory maintenance (averaged from 500-2000 ms) for the two groups

and their differences (Comparison – ADHD).

Figure 4: Pre- and post-training working memory accuracy (hit rate- false alarm rate) in high (H)

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and low load (L) for waitlist (black line), standard-length (red line), and shortened-length (blue

line) group. Standard error bars are also presented.

Figure 5: A. Pre- and post-training alpha power during working memory maintenance (averaged

from 500-2000 ms) in high (left) and low (right) load condition for the waitlist, shortened-length,

and standard-length group. Standard error bars are also presented. A 3 x 2 x 2 (group by session

by load) mixed ANCOVA revealed only a marginally significant group by session interaction (p

= .072), indicating a weak training effect. B. Topopot of pre- and post-training alpha power, as

well as post-pre differences, during working memory maintenance (averaged from 500-2000 ms)

from the 3 groups in high (left) and low (right) load conditions. Black dots on the difference plot

indicate the electrodes that were used to calculate averaged posterior alpha power in A and C. C.

The time course of pre- and post-training alpha power during working memory maintenance for

the 3 training groups.

Figure 6: Coefficient scores and corresponding pattern of alpha power differences (z scores) of a

latent variable (LV, p = .002) from partial least square (PLS) analysis. Coefficient scores (left)

indicate the weight of each training group in each load condition (L: low load; H: high load) that

contributed to the significant LV. The bootstrapping Z score topoplot (right; z > 1.96, p < .05)

depicts the pattern of differences among the 3 training groups in the two load conditions

(specified by the corresponding coefficient scores) of training induced alpha power changes. The

red color indicates the area (i.e., electrodes) where the alpha power increase was larger for the

groups and conditions with a positive LV coefficient (i.e., the training groups) than the ones with

a negative LV coefficient (i.e., the waitlist group). The blue color indicates the area (i.e.,

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electrodes) where the alpha power increase was larger for the groups and conditions with a

negative LV coefficient (i.e., the waitlist group) than the ones with a positive coefficient (i.e., the

training groups). Electrodes used for the PLS analysis (black squares) are also depicted.

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