Behavioral and Neural Target Engagement for ADHD Executive Working Memory Training
Phase: NA
Conditions: ADHD
Sponsor: Hartford Hospital

 
 
Target ADHD Executive Working 
Memory Training Replication Study 
 
 
Study Protocol 
IRB Approval Date: 9/22/2023 

1 
 
 
 
Behavioral and Neural Target Engagement for ADHD Executive Working Memory Training  
 
 Phase II 
 
 
This proposal is based on converging evidence that suggests prior ADHD working memory training studies might 
have produced lackluster clinical outcomes because they focused on enhancing the wrong cognitive and 
neurobiological targets.  The proposed study will replicate target engagement as assessed in the first phase while 
also determining if it correlates with clinically meaningful improvements in ADHD dysfunction.  The full project 
was designed so that if these study aims are successful, the data we obtain will be adequate to support a future 
preliminary efficacy clinical trial. 
 
Many investigators have attempted to show that using intensive cognitive training to increase Attention-
Deficit/Hyperactivity Disorder (ADHD) patients’ capacity to store information within WM might also reduce 
symptom severity or functional impairment.  Those studies were based on strong evidence for WM behavioral 
and neural dysfunction in ADHD, a widely-held belief that WM-related brain dysfunction is etiologically 
significant, and arguments that impaired WM contributes to the maintenance of the disorder.  But despite 
promising early evidence for WM storage training potential efficacy, a decade of equivocal and negative 
outcomes from several well-designed randomized clinical trials have left investigators wondering why 
meaningful, replicable ADHD clinical improvements are not always achieved when WM storage capacity is 
reliably enhanced.  Because few investigators want to mistakenly abandon such a promising intervention 
premise, there is a search for novel working memory-related treatment targets that focus on something other 
than simple WM storage capacity.  In this proposal, we synthesize published findings and provide our own pilot 
neuroimaging evidence to argue that a better WM training target might be brain regions that specifically 
underlie the executive aspects of WM.  Such executive working (EWM) memory operations represent patients’ 
ability to effectively use the information represented in WM for behavioral and cognitive control.  These 
abilities likely are more meaningful to ADHD behavioral impairment than merely how large patients’ general 
WM storage capacity is.  An extensive theoretical framework supports that the ‘working’ part of WM involves a 
small handful of discrete cognitive operations that flexibly manipulate information within WM, or stabilize 
WM representations in the face of different types of environmental distraction.  Dozens of prior fMRI studies 
reliably link the four primary types of these EWM operations to activity within specific brain regions – the 
superior frontal sulcus and mid-lateral prefrontal cortex.  Converging evidence including our pilot evidence 
shows these regions function abnormally in ADHD, predict ADHD symptom severity, and can be enhanced by 
practicing EWM tasks.  NIMH’s R61/R33 experimental therapeutics mechanism is ideal to learn if a typical 5-
week, course of intensive training that uses these EWM exercises can engage the proposed brain targets linked 
to both EWM ability and to ADHD dysfunction.  As the neuroscience-guided, initial 2-year R61 study phase 
achieved its milestones, this R33 study phase will seek to replicate, and then show the relevance of target 
engagement to ADHD clinical function.  If both R61/R33 aims are achieved, a series of secondary analyses will 
re-examine the trial outcome data.  These efforts will inform the design of any subsequent preliminary efficacy 
study, facilitating a rapid transition into larger-scale efficacy testing. 
 
 

2 
 
Over a decade ago, considerable interest emerged in whether intensive practice of working memory (WM) tasks 
designed to expand one’s overall capacity to store information briefly in mind might benefit patients with 
Attention-Deficit/Hyperactivity Disorder (ADHD).  WM training has a convincing neuroscientific foundation and 
there was exciting preliminary evidence that prompted WM training intervention development.  Nearly all clinical 
trials to date that have focused on increasing simple short-term WM storage capacity have been disappointing.  
Investigators remain perplexed why ADHD patients make very reliable, usually persistent WM gains, but simply 
having a larger WM capacity does not convincingly result in ADHD symptom improvements.  Our pilot studies 
suggest WM training might live up to early expectations if investigators switch focus to a new training target – 
‘executive working memory’ (EWM).  EWM refers to a few selective attention-based cognitive processes that 
operate within WM either to flexibly allow information to be manipulated, or to stabilize and protect mental 
representations when they are needed to guide behavior.  Converging evidence supports EWM as a potentially 
better training target than WM storage capacity.  Importantly, our pilot data also show EWM practice alters 
ADHD brain function in regions linked to ADHD symptom severity in our prior research.  This proposal includes 
a 2-year milestone-driven R61 initial test of target engagement using a new EWM training intervention in n=62 
ADHD-diagnosed adolescents randomized to a ‘sham training’ placebo or to train on 4 different EWM tasks per 
session 4 times each week.  Exercise difficulty levels in the active intervention increased across 5 weeks to 
continually challenge EWM ability.  EWM training used a novel, remotely-supervised ‘at home’ computerized 
training approach.  Since the R 61 EWM training target engagement milestones were met, a 3-year R33 phase 
will begin.  The R33 will replicate target engagement in another randomized placebo-controlled trial of n=90 new 
ADHD adolescents and attempt to establish a convincing link between the hypothesized targets and ADHD 
symptom expression.  It also will characterize ADHD brain activity or EWM ability changes relative to typical 
levels seen in an n=40 non-ADHD control group. 
 
 
 
R33 AIM 1  Replicate and Extend ADHD EWM Training Target Engagement Relative to Placebo  
Another randomized, placebo-controlled trial of n=90 new ADHD adolescents will provide evidence needed to 
support a subsequent efficacy trial – replication of target engagement and confirmation of clinical relevance. 
 
R33 Hypotheses 1-2  Compared to a ‘sham training’ placebo for which we expect no EWM target change: 
 
1) We will replicate both SFS/mid-latPFC brain activity/connectivity and behavioral changes seen in the R61. 
2) We will see evidence of ‘near transfer’ of EWM behavioral training gains to non-trained EWM tests. 
 
R33 AIM 2  Establish a Link Between Target Engagement and ADHD Symptom Expression   
Data will be used to test assumptions the proposed neural targets are relevant to ADHD clinical impairment. 
 
R33 Hypotheses 3-4  There will be a significant association between EWM engagement and ADHD symptoms: 
 
3) Before training, ADHD symptom severity will correlate with EWM brain function and behavior (r>.35). 
4) The degree of fMRI-measured EWM training target engagement will significantly correlate with ADHD 
outcome measured by ADHD symptom severity rating scales and secondary functional inventories. 

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Unmet ADHD Treatment Need  DSM 5 Attention-Deficit/Hyperactivity Disorder (ADHD) 1 affects 5-7% of 
children and adolescents 2-4.  It is marked by excessive impulsivity/hyperactivity and inattention causing 
significant academic 5-11, employment, legal or psychosocial problems and risk for other psychopathology and 
substance disorder 7, 12, 13 despite the best-supported treatments 14-16.  Stimulant-based medications are first-line 
recommended ADHD treatments 17, but have meaningful limitations.  Medications manage immediate problem 
behavior, but do not prevent poorer ADHD long-term educational, vocational, or social outcomes 18-24.  Roughly 
¼ of ADHD patients fail to show any beneficial response 25.  Medications can be very unpopular with some 
patients and their parents 26, 27 due to intolerance of side effects 28, 29 or parental concerns of misuse 30-33 from a 
high abuse potential 30-32, 34 and fears about growth and health risks 35, 36.  Only about half of ADHD-diagnosed 
children 37 and adolescents 38 use medications.  As such, at least half of ADHD patients could benefit from an 
effective alternative, non-pharmacological treatment to help reduce the $143-266 billion annual estimated 
economic burden of ADHD in the U.S. from healthcare costs, lost workforce productivity, and similar factors 39.  
An effective alternative ideally would not be another temporary palliative, but instead offer sustained benefits. 
 
Why Train Working Memory to Treat ADHD?  Over the past decade, considerable effort has been poured 
into studying a non-pharmacological cognitive training approach for ADHD that seeks to improve working 
memory (WM) through weeks of continually challenging training sessions.  Such approaches are based upon a 
well-supported premise that intensive practice reliably induces long-term synaptic connectivity and brain 
structure changes 40-42 (i.e., experience-dependent ‘neuroplasticity’) in networks that underlie the specific ability 
that has been trained 43-51.  A strong appeal of cognitive training is that it might offer enduring therapeutic benefits 
for ADHD patients from neuroplastic changes – particularly if those changes occur early to optimize 
neurodevelopmental trajectories into adulthood.  The ultimate goal of these intervention development efforts has 
always been to reduce ADHD symptom severity and associated clinical dysfunction.  The premise of WM training 
for ADHD rests on a reasonable conceptual foundation that links WM performance on brief WM information 
storage tasks to attention abilities 40, 52, 53, general aptitude (IQ 54-56, reasoning ability 57, or SAT performance 58), 
and academic achievement 54, 58-64.  Moreover, ADHD patients’ performance on some WM tasks predicts ‘real 
world’ ADHD-like behaviors in non-ADHD persons 65, 66, e.g., multi-tasking 67, 68, mind wandering 69, and 
following directions 70, 71.  Meta-analyses of neuropsychological studies also show WM deficits are among the 
most consistently found cognitive impairments in ADHD 72-75.  It has been proposed that WM training might work 
by directly remediating WM-related ADHD brain dysfunction believed to be etiologically-significant for the 
disorder 72, 76-84.  Not only is WM a proposed endophenotype for common genetic risk in ADHD 85, 86, recent 
studies have begun to demonstrate WM’s etiological importance by showing ADHD patients’ WM ability levels 
mediate links between ADHD polygenic risk scores and ADHD symptom expression 85, 87 in ways not found for 
other cognitive deficits 88.  In the WM training earliest studies, pioneering approaches (e.g., CogmedTM) were 
found not only to increase ADHD WM storage capacity on behavioral tests, but in non-ADHD also to alter grey 
and white matter brain structure, dopaminergic function, and brain activity 89.  Indeed, numerous fMRI studies of 
young 90-94 and older adults 90, 95, and fMRI 91, 96, 97 and EEG 98 functional connectivity studies done after WM 
training show brain network 99-102 connectivity reconfiguration and strengthening within the frontoparietal system 
engaged for WM, and with key WM-specialized regions in other parts of the brain.  As such, there was great 
excitement we ultimately might find these neural effects converged with evidence for WM-related brain 
dysfunction that has been found in ADHD in over a dozen fMRI studies 103-106. 
 
WM Storage Training Fails Expectations  Nearly all ADHD WM training research to date has focused on 
increasing WM storage capacity.  These efforts have fallen short of expectations.  On the one hand, qualitative 
107-113 and meta-analytic 114-117 reviews conclude WM training reliably improves ADHD WM capacity, with gains 
that persist over time 118-120.  Disappointingly however, the evidence these WM capacity gains translate into 
improved ADHD symptom severity or better functioning is marginal and inconsistent.  The earliest clinical trials 
found improved parent-, teacher-, or self-rated ADHD severity 53, 121-125 or objectively-measured ADHD behavior 
(e.g., actigraph-measured motor restlessness or classroom time-on-task 53, 126), with some evidence that gains 
might persist for months 53, 123, 125.  However, this evidence came almost entirely from ‘open label’ trials where 
expectancy effects typically inflate perception of clinical benefit.  Subsequent, better-designed randomized 
clinical trials (RCTs) have not strongly supported WM storage training efficacy in ADHD.  While several RCTs 
found WM training can produce modest clinical improvement, WM training groups typically do not outperform 
active treatment control groups.  A recent meta-analysis has concluded RCT blinding reduces the promising 
effects seen in the earlier open label trials to clinically insignificant levels 127.  Perplexingly, those RCTs often 
failed to replicate the same small positive outcomes 107, 125, 126, 128.  By and large, efforts to train other executive 

4 
 
abilities (e.g., response inhibition, sustained attention, etc.) (e.g., 129, 130; see meta-analysis 116), or to train several 
executive abilities together 131, or to individualize training exercises to ADHD patients’ pre-existing executive 
deficits 132 also have not held up against placebo control.  Most reviewers of this literature have concluded WM 
training still holds considerable promise in ADHD 108, 109, 112, 113, 115, 117, 133-138.  But they also make it clear new ideas 
are needed for how to modify training to have stronger and more reliable benefits for ADHD symptoms.  We 
believe meaningful ADHD clinical improvement will require not only an entirely new cognitive training focus, but 
also a neuroscience-informed research formulation that identifies brain-based training targets that have been 
empirically shown to predict ADHD symptom expression. 
 
Executive Working Memory  This proposal builds on converging theory and evidence that ‘executive working 
memory’ (EWM) likely represents a better context for ADHD WM training than WM storage capacity, with highly 
specific brain-based training targets of clear relevance to ADHD.  EWM describes cognitive operations that 
account for what Baddeley’s seminal WM theory described as the ‘central executive’ – a system that controls 
information flow within WM.   Although early WM studies tended to conceptualize WM’s central executive as a 
single construct, neuroscience evidence suggests that instead there likely are a small number of behaviorally 
dissociable basic EWM operations supported by partially overlapping brain systems.  Bledowski et al. 139 has 
proposed some of these EWM operations promote flexibility of selective attention to WM contents (e.g., 
measured by tasks that require attention Shifting among information held briefly in WM or Updating WM 
content with new information).  Others lend stability to WM representations (e.g., Filtering any extraneous 
distracting information during WM maintenance, or Suppression of proactive interference from uninformative 
information during retrieval of information from WM) 140.  These attention-based EWM basic operations emerge 
directly from Cowan’s 54, Oberauer’s 141, and Engle’s 142, 143 current theories about WM’s central executive and 
reflect a basic, well-supported dichotomy between maintenance vs. disengagement via selective attention 
processes 144.  Each of these EWM operations seem to be suitable candidates for cognitive training exercises.  To 
justify combining them all into a unified EWM training intervention, we examined their relationship to each other 
in N=160 adults who completed online EWM tasks on Amazon’s mTurk internet worker platform 145, 146, and 
whose data met rigorous sampling and data quality control criteria 147-149.  These participants completed the same 
verbal and nonverbal versions of the EWM Updating, Shifting and Distractor Filtering/ Suppression tasks we 
propose to use in this project (See Approach).  Confirmatory factor analyses of EWM task accuracy found 
acceptable model fit for both a 1-factor model where all EWM accuracy scores contributed to a single latent 
EWM construct (i.e., GFI=.947, RMSEA=0.072), and a 4-factor model showing each type of EWM ability had 
unique variance (GFI=.971, RMSEA=.049, and a non-significant CMIN test p=.155).  This combination of shared 
and unique variance supports that they all fit underneath an umbrella EWM construct, but also that none of the 
EWM operations could be considered redundant.  As such, all seem to be viable candidates for EWM training 
exercises. 
 
Neurobiological Basis of EWM  EWM operations have 
been extensively studied with fMRI 151-174.  Meta-analysis of 
EWM-specific fMRI studies 
140 (Fig. 1) find EWM 
operations overlap with a core frontoparietal WM 
representation network for information encoding, storage 
and retrieval (Fig. 2) found by meta-analysis of literally  
hundreds of fMRI studies, but engage a more extensive set 
of brain regions throughout lateral/medial PFC and 
parietal cortices.  Current ‘state representation’ WM 
neural models describe how the core WM network is 
integrated with these EWM-linked regions via selective 
attention mechanisms that activate and integrate 
information processed in distal regions.  This engages 
information in an active form that can be manipulated and 
used to guide behavior (sometimes termed ‘primary 
memory’ 112, 175-177).  Within this state representation 
theoretical context, the so-called ‘working’ parts of WM are simply these various specific attention-based EWM 
processes – Shifting, Updating, Filtering, and Suppression.  D’Esposito and Postle 178 recently gave a cogent 
summary of the considerable neurobiological evidence for how WM arises from such attention-related network 
connectivity changes during EWM task performance.  That is, functionally heterogeneous 179-182, hierarchically-
Fig. 1:  Nee et al. meta-analysis 140  of n=36 fMRI WM 
studies showing activation during 4 different types 
of EWM tasks. 
Fig. 2:  Frontoparietal domain-general ‘core’ WM 
system localized in an ALE meta-analysis of 189 
fMRI studies 150. 

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organized 183-185 prefrontal cortex (PFC) regions engage sustained activation 186-188 during WM that biases activity 
in other PFC brain regions or other parts of the brain in a ‘top-down’ way 189-191 via synchronized activation 192, 193 
to support WM state representations for information manipulation.  Two brain regions seem to be particularly 
important for these EWM operations – caudal superior frontal sulcus (SFS) linked to feature prioritization  
within WM 140, and a mid-lateral PFC region of the 
inferior frontal junction (mid-latPFC), immediately 
below and posterior to BA 9/46 linked to selecting among 
competing contexts or rules.  Meta-analysis shows that 
SFS and mid-latPFC are engaged for all EWM operations, 
possibly because they are key nodes in ‘where’ and ‘what’ 
WM information selection systems 140.  We demonstrated 
the importance of these regions in pilot Dynamic Causal 
Modeling (DCM) 194, 195 analyses of EWM fMRI task data 
that characterized causal relationships among EWM 
task-engaged brain regions (Fig. 3).  While extensive 
details are not necessary to illustrate the key point, SFS 
and mid-latPFC exert direct excitatory causal influences 
on other prefrontal and posterior brain regions engaged 
for EWM.  Those influences also are strongly correlated 
with behavioral accuracy.  The position of SFS and mid-
latPFC near the top of EWM network hierarchy and their role as ubiquitously-engaged information processing 
nodes for many types of EWM operations make them promising EWM training target candidates. 
 
Support for EWM as a Training Focus in ADHD  Here, we review neuropsychological and neuroimaging 
evidence that supports EWM is meaningful in ADHD and that SFS and mid-latPFC EWM-related brain regions 
have a plausible relationship to symptom expression.  First, meta-analysis shows EWM deficits are by far the 
strongest WM deficits in ADHD.  Meta-analytic statistical comparison finds they EWM deficits are significantly 
stronger than WM storage 72 in ADHD (p<.037 for verbal; p<.022 nonverbal).  Moreover, if one re-arranges the 
64 ADHD deficits Hedge’s g effect sizes from this meta-analysis 72,  the simplest short-term storage tasks’ have 
an average g=0.54 (“medium” deficit), while low executive demands like reversing information before recall 
g=0.72 (“large”).  The greatest ADHD effect size impairments (average g=1.13; very “large”) were seen for very 
high executive WM demands, such as switching attention among concurrently held information sets, etc.  This 
EWM effect size also is notably larger than any other cognitive impairment linked to ADHD (e.g., response 
inhibition, set shifting, timing, etc. 74, 196-198).  So perhaps one reason prior ADHD WM storage training trials have 
failed to benefit clinical symptoms is they have not targeted the most-impaired WM abilities that have the greatest 
likelihood of being related to the brain dysfunction central to ADHD. 
Second, fMRI EWM studies offer clear treatment 
targets because they reliably converge on specific ADHD 
brain abnormalities. Functional MRI study reviews and 
meta-analyses fail to precisely localize abnormalities in 
ADHD brain regions activated by simple WM information 
storage tasks 76, 199, 200.  Close examination shows no two 
ADHD fMRI WM storage studies 103-106 have localized 
ADHD abnormality to exactly the same brain region.  In 
contrast, fMRI studies consistently find evidence for 
ADHD dysfunction in brain regions specialized for EWM 
operations 201-210.  For instance in McCarthy et al.’s meta-
analysis 211 of 4  fMRI studies that used an N-Back WM task 
(which engages EWM operations on each trial – Shifting, 
Updating, and Suppression 212), ADHD hypofunction was reliably found in the same bilateral SFS and medial 
PFC regions.  To date, only 1 ADHD fMRI study has attempted to localize neural dysfunction to a specific, isolated 
EWM operation (i.e., distractor filtering 106).  It also found findings consistent with our conceptual model – lower 
ADHD intra-PFC functional connectivity, and blunted WM load-dependent activity increases in left ventrolateral 
PFC, right insula and right medial PFC.  Again, these are mostly regions implicated in EWM instead of the ‘core’ 
WM representation network.  Fig. 4 shows pilot study results that bolster confidence that impaired ADHD 
 
Fig. 3A:  Dynamic Causal Modeling results in n=15 
adults during EWM Updating.   GREEN dashed lines 
show excitatory intrinsic causal influences; RED 
inhibitory influences (Cohen’s d > .50).   
Fig. 3B:  Specific DCM matrix B modulatory 
connections that are + or – correlated r > .35 
(‘medium’ effect size) with Updating task % accuracy. 
88% of connections involved mid-latPFC (IFJa) or 
SFS (55b) parcels. 
Fig. 4.  Comparison of non-ADHD (n=5) > ADHD 
(n=5) executive WM fMRI Cohen’s d effect size maps 
for activation and connectivity.  Maps thresholded 
at d > 0.50 (“medium” effects).  

6 
 
activation in SFS, mid-latPFC and other EWM-linked brain regions is found during all EWM operations we 
propose for the new intervention.  (Note, this pilot study used fMRI analysis methods and a cortical atlas 
developed by the Human Connectome Project (HCP) that refers to discrete brain regions as HCP atlas ‘parcels’.)  
During WM Updating, ADHD had less activity in bilateral dlPFC parcel p9-46v and parcel PF (in the 
supramarginal gyrus; SMG).  ADHD had lower activation to parcel AAIC (insula) to recall probes on the WM 
Suppression condition – an effect made more prominent in the presence of proactive interference.  For 
Distractor Filtering, ADHD had less activation of parcel 7PL (superior parietal lobule) and parcels in both dorsal 
and ventral regions of bilateral PFC.  Several bilateral dACC parcels were hypofunctional during Distractor 
Filtering and Updating.  ADHD functional connectivity estimated by graph theory-based methods 213-216 was 
weaker connectivity in parcel 55b (caudal SFS) in the Suppression task, parcel PGi (SMG) for Updating, and 
tasks in SPL parcels 7PC and 7AL for Filtering.   
Third, if the ultimate goal of ADHD WM training research is to reduce 
ADHD symptoms, we suggest the most suitable brain treatment targets 
are those with a convincing association with ADHD symptom expression.  
Very often, only a subset of brain abnormalities found to be abnormal 
within a given psychiatric diagnostic phenotype has a direct association 
with the severity that disorder’s symptom expression.  In the largest study 
of ADHD brain structure abnormalities ever undertaken (N=2,246 ADHD 
patients), the ENIGMA consortium found throughout all cortex that only a 
superior frontal gyrus region (r=-0.19, p=0.01) (i.e., encompassing our 
proposed SFS target) and rostral cingulate (r=-0.18, p=0.03) surface area 
measurements were associated with ADHD Hyperactive/Impulsive 
symptom severity.  Surprisingly few published ADHD fMRI studies 
examined brain activity correlations with symptom severity in post hoc 
analyses (only 6 of >6 dozen reports we reviewed).  Nevertheless, there 
is piecemeal evidence that links ADHD symptom severity to lateral PFC.  
One study found a correlation between symptom severity and right lateral 
PFC activation during WM for emotional stimuli 207.  Another found a link 
between 
lower 
ADHD 
symptoms 
and 
methylphenidate-induced 
normalization of Stroop task-elicited left dlPFC activity  217.  Another type 
of evidence for a link between WM change and ADHD symptom change 
recently was found in a prospective study that showed normative 
improvements in ADHD patients’ WM ability from childhood to early 
adolescence directly predicted lower ADHD symptoms in patients 218 .  
This was a specific relationship, as similar developmental gains in 
response inhibition and sustained attention ability did not predict symptom levels.  All these indications are 
suggestive, but admittedly limited.  However, even stronger evidence exists linking ADHD symptom reductions 
to activity changes in our specific proposed EWM-related brain targets.  In our recent R21HD061915 study, we 
examined the effect of 5 weeks of CogmedTM WM storage training on ADHD adolescent brain activation elicited 
by a WM fMRI probe 103.  After training, ADHD PFC activity elicited during WM storage improved to non-
impaired levels (Fig. 5).  However, those WM storage-related neural gains did not predict clinical improvement.  
Instead, when meaningful clinical improvement was seen (in 61% of the ADHD sample), symptom reductions 
correlated with increased activity in brain regions known to be functionally specialized for EWM 139 (e.g., bilateral 
SFS, ventrolateral PFC, pre-SMA, and IPL/SMG and SPL parietal lobe regions).  These findings not only provide 
clues as to why WM storage training fails to reduce ADHD symptoms (i.e., it targets the wrong brain regions), it 
offers a strong indication that EWM brain region change underlies ADHD symptom remission.  Because 
CogmedTM exercises have minor executive demands, our observations likely resulted from an unintended, 
coincidental strengthening of EWM-related neural circuits despite CogmedTM targeting of WM  
Fig. 5. WM Training effects on WM 
Maintenance (whole brain p<.05 
corrected).  Data from 103. 

7 
 
storage.  Interestingly, our findings showed only the 
caudal SFS gains correlated with improvements in both 
Hyperactive/Impulsive 
and 
Inattentive 
symptoms, 
suggesting it might have relatively greater therapeutic 
potential than other regions. 
 
Finally, we conducted a proof-of-concept pilot study to 
show EWM training alters ADHD activity and functional 
connectivity in the proposed brain treatment targets.  N=7 
ADHD-diagnosed adolescents drilled on all EWM 
operations in exercises over a brief, 2-week course of 
practice (6 sessions).  Fig. 6 depicts widespread increased 
brain functional connectivity to the right SFS parcel in 
different EWM task contexts after EWM practice.  Both 
‘large’ and ‘very large’ effect size connectivity increases 
were seen, within ‘core’ WM and EWM-specialized 
regions, as well as with other brain regions.  Interestingly, different connectivity changes were seen when brain 
function was elicited by different EWM tasks, consistent with WM state representation theories that the key SFS 
and mid-latPFC regions will dynamically reconfigure their connectivity strength for different EWM demands.  
Therefore, we are likely to have the most widespread effect on EWM neural network function if we include all 4 
different EWM operations as training exercises.  Although this is strong evidence of these regions’ malleability 
and responsiveness to EWM practice, the next step of this research is to give patients a typical course of WM 
training (e.g., 20-25 sessions over 5 weeks) to assess the extent of brain target engagement with fMRI analyses 
of activation and network connectivity.  In this pilot, behavioral gains were not expected because participants 
only performed rote practice.  Actual EWM training will adaptively increase task difficulty and include intervention 
design elements that ensure engagement and motivation.  These are essential ingredients generally believed 
necessary to alter behavioral performance 43. 
 
Synthesis and Potential Impact  There generally is increasing enthusiasm and optimism for using cognitive 
training to treat psychiatric disorders as more and more studies discover its principles of application, and 
document its ability to re-sculpt brain networks through experience-dependent neuroplasticity 219.  Although the 
basic premise underlying some sort of WM training in ADHD still seems sound, a WM storage training behavioral 
target has been proven ineffective.  Broadly speaking, it makes sense that any link between WM and ADHD-
related behavioral impairment is likely to be more greatly related to patients’ ability to effectively use the 
information represented in WM in the service of cognition and behavioral control instead of merely how much 
information can be temporarily held in WM.  Guided by a careful synthesis of neuroscience evidence from the 
past decade, this proposal argues for new EWM-focused exercises as a vehicle for modifying specific new brain-
based targets. To recap, we conceptualize EWM impairments as the behavioral manifestation of a dimensional, 
RDoC-like 220-224 neurobiological abnormality in patients diagnosed with ADHD.  Although not all ADHD patients 
show every cognitive impairment that has been linked to the diagnostic phenotype 64, 77, 225-228, EWM tests have 
the strongest effect size of any cognitive impairment in ADHD.  This suggests EWM impairments are more 
important, or perhaps expressed in a greater proportion of ADHD patients than WM storage or other executive 
deficits.  Cognitive neuroscience neuroimaging studies point to SFS and mid-latPFC as ubiquitously important 
for all EWM operations; they direct the dynamic reconfiguration of EWM neural network function when accessing 
or protecting information held in mind to direct behavior that predict EWM task accuracy.  Clinical neuroscience 
studies also show these regions have abnormal activation in ADHD during EWM task performance and are linked 
to ADHD symptom expression.  As such, these two regions represent potential intervention targets whose 
engagement likely have a stronger potential to produce ADHD symptom severity or functional level 
improvements that have been so elusive in prior WM storage training research (e.g., CogmedTM).  We contend 
brain-based training targets ultimately will prove better than behavioral performance-based targets because they 
are more proximal to putative disease mechanism.  In this context, EWM behavioral change remains an important 
focus of cognitive training interventions, but primarily for interpretive reasons (i.e., to ensure training exercises 
have the intended effect).  Furthermore, our consideration of EWM brain region inter-connectivity to evaluate 
target engagement is consistent with the most recent ADHD neurobiological models of the disorder stemming 
from neural network connectivity abnormalities 229-237.  It also is noteworthy that success here is potentially 
relevant for other disorders whose putative etiology is linked to WM (e.g., schizophrenia and bipolar illness 238-
Fig. 6.  ADHD brain regions that increased their 
functional connectivity with right Superior Frontal 
Sulcus (SFS) HCP ‘seed’ parcel [55b] (outlined in 
turquoise) after 6 EWM training sessions.  Maps are 
thresholded at d > 1.0 (‘large’ effect sizes). 

8 
 
240, TBI 241, MS 242, stroke 243).  A successful target engagement demonstration here might open new therapeutic 
avenues for any disorder that shares diagnostically cross-cutting abnormalities in EWM abilities. 
The R61/R33 phased grant mechanism for efficient experimental therapeutics development is ideal for the 
current stage of this research.  The obvious next step is to repackage the EWM probes used in prior neuroscience 
research into a multi-week, intensive drilling format commonly used to maximize participant engagement and to 
continually challenge EWM ability.  Two carefully designed clinical trials will let us determine if the SFS and mid-
latPFC targets are indeed engaged by EWM training, to replicate findings, and assess if target engagement has 
clinical relevance – all necessary to support future clinical efficacy testing.  There should be optimism for success 
because a small handful of EWM training trials already have shown some success in both ADHD 244, 245 and non-
ADHD samples 92, 136, 246-249, at least at the behavioral performance level.  Those studies trained comparable 
EWM operations to what we propose to study here.  But they typically did not ensure training on all possible 
EWM operations linked to the proposed SFS/mid-latPFC brain targets.  Moreover, we focus here on both brain 
and behavioral levels.  The R33 will examine a larger replication sample. It will test ‘near-transfer’ generalization, 
and consider training delivery and patient characteristics as potential outcome moderators (e.g., pre-existing WM 
impairments vs. strengths, sex, age, ADHD subtype, etc.).  These analyses will ensure target engagement 
evidence meets all theoretical expectations and is robust to the likeliest potential confounds.  By the end of this 
R61/R33 project, we either will know to abandon this proposed new line of inquiry, or we will be positioned to 
conduct one of several possible ‘next step’ studies.  Ideally, we expect to be ready to propose a well-informed 
preliminary clinical efficacy trial.  Alternatively, we may learn we need to optimize the intervention delivery based 
upon the results of some proposed R61/R33 supplemental analyses that will determine if target engagement is 
moderated by the pace of EWM gains, exposure to training (i.e., ‘dose’), or patient characteristics. 
 
Innovation  The primary study proposal innovations are the new EWM treatment target and its primary focus 
on neurobiological targets presumably closer to ADHD etiology.  Despite strongly converging evidence, none of 
these EWM operations have been explored in depth in ADHD at the brain level, either in ADHD itself or within a 
cognitive training context.  Another important novel feature is our successfully-piloted remotely-supervised ‘at 
home’ EWM training that effectively substitutes for in-person appointments.  Not only should this delivery 
approach greatly enhance protocol adherence and retention, it provides a potentially paradigm-shifting option 
for a way to deliver personalized cognitive training that should prove to be more practical for typical families in 
future efficacy trials.  There also are many advantageous clinical trial design elements and analyses we describe 
in the Approach that were not used in most prior ADHD WM training trials.  These should overcome common 
clinical trial pitfalls to help ensure the most definitive possible answers to our target engagement hypotheses. 
 
APPROACH - Methodology in the R33 Project Phase 
 
Participants  ADHD participants in the R33 (n=90) trial will be representative of adolescents with a primary 
diagnosis of DSM 5 ADHD who have common minor secondary psychopathology (see the Eligibility Criteria 
section).  The R33 also will recruit n=40 demographically-equivalent non-ADHD participants for a useful 
comparison of ADHD EWM performance and brain function against a typical normative sample.  We focus on 
adolescent ADHD rather than children in part to avoid pre-pubertal neural differences and to continue our prior 
ADHD fMRI research (all of which focuses on teens). But also, adolescence represents an ideal developmental 
period for neuroplasticity-based interventions.  It has been well-established that post-pubertal neural system 
changes mostly involve experience-dependent refinements to a largely-established ‘adult-like’ brain organization 
(see our recent adolescent network connectivity review 250).  We are experienced from nearly a dozen current and 
recent R01s with large N recruitment for fMRI studies of adolescents.  As in our recent R21HD061915 and 
R01MH080956 ADHD fMRI studies, ADHD participants will be recruited from both clinical and community 
resources.  ADHD is ~4 times more common in boys 251, but we will over-sample ADHD females to assess sex 
effects on outcome, as per NOT-OD-15-102 (> ⅓ will be female).  A Note on ADHD Medications:  All ‘in person’ 
visits for ADHD fMRI/neurocognitive evaluations will require a 24-hour medication washout if patient 
symptoms are managed with typical, short half-life psychostimulants.  This is a common, widely-accepted 
research practice to ensure test results cannot be attributed to the temporary, palliative effects of those drugs on 
the brain on assessment days (see Eligibility Criteria section for detailed discussion of the issues involved and 
list of permitted medications). 
 
 
 
 
 

9 
 
Clinical 
Assessment 
 
This 
evaluation will fully assess all the 
study exclusion criteria and quantify 
ADHD 
symptom 
severity 
and 
functional ability.  Table 1 lists 
evaluation instruments, noting when 
multiple respondents will provide 
subjective ratings.  All measures are 
validated for adolescents. As per K-
SADS-5 guidelines, collateral parent 
interview 
will 
inform 
diagnosis 
decisions.  Diagnostic interviews will 
be performed by clinically-trained 
postdoctoral staff supervised in a weekly diagnosis consensus meeting by PI Stevens (a licensed clinical 
psychologist with >16 years’ experience supervising K-SADS interviews for NIH-funded studies).  Staff 
interviewing training will include 10 “modeled” interviews, direct observation of the first 10 interviews, and 
quarterly review of all interview videotapes by other clinically-trained research staff to control for assessment 
‘drift’.  We also will assess participants’ age, race/ethnicity, education level, handedness 262, and socioeconomic 
status 263.  Parents will detail family psychiatric history 264 and confirm child’s medical history.  These clinical 
evaluations usually last between 2 - 3 hours. 
 
Neurocognitive Assessment  Cognitive testing will 
exclude WASI FS IQ 271 <80.  It also will detail common 
WM processes, assess EWM tests of ‘near transfer’ to 
test the generalizability of EWM training outside of 
trained tasks, and test ‘far transfer’ of training effects 
to other abilities often impaired in ADHD.  Table 2 lists 
tests along with their test-retest reliability.  Cognitive 
testing will require ~2 hours.  Co-I Dr. Hawkins will 
supervise 
staff 
training 
and 
cognitive 
testing 
procedures throughout the project. 
 
MRI Assessment Overview  MRI data will be 
collected using a Siemens 3T Skyra.  Subjects will wear  
hospital ‘scrubs’ to minimize the chance of introducing 
metal into the MRI.  They will provide a urine sample 
before MRI to test for drug metabolites, and pregnancy 
in females.  Positive drug results could defer MR data collection and will be noted for post hoc analyses.  
Caffeine/nicotine intake and cigarette smoking will be as normal to prevent acute withdrawal effects on brain 
activity, but restricted to >1 hour prior to MRI to avoid well-documented neural effects of recent use.   
 
fMRI Paradigms  There are two important differences between the fMRI tasks that will assess EWM ability 
before and after training and the EWM training exercises.  First, 4 separate tasks will be used during training to 
isolate each EWM operation, whereas fMRI uses 3 because WM Distractor Filtering and WM Suppression can 
be efficiently combined into a single paradigm.  WM Updating and WM Shifting cannot be evaluated by fMRI 
simultaneously because each operates differently on the stimuli set held in WM.  In contrast, EWM stability 
operations can be assessed in using the same trials because the EWM operations take place after WM short-term 
maintenance 272.  By combining them for fMRI, we lessen the fMRI scanning time burden.  Second, to avoid 
stimulus familiarity confounds the fMRI tasks will use stimuli sets never seen during EWM training (different 
counter-balanced sets between pre-/post-training MRI scans).  Because SFS and mid-latPFC engagement has 
proven by meta-analysis to be largely domain-general 140, we include both verbal and nonverbal versions of each 
task with the expectation they will be collapsed for stable measurements.  The  
Table 1.  Clinical Assessment (R61/R33) 
Task Name 
Domain 
Min 
K-SADS-5 252  
DSM Axis I diagnosis 
30-120 
Child Behavior Checklist (CBCL) 253 
Psychiatric/problem behavior 
10 
Conners 3 (P/S) 254 
ADHD symptom severity 
20 
* Weiss Func Impair Rating Scale (P/S)  255  
Psychosocial function 
5 
* Inven. of Parent and Peer Attach. (P/S) 256 
Social relationship quality 
10 
* Wide Range Achievement Test-5 
Academic Achievement 
45 
Beck Depression Inventory II 257 
Depression severity 
5 
Multidimen. Anxiety Disorder Scale 2 258 
Anxiety 
5 
Olin Health Care Questionnaire 
Health and substance use 
10 
Adol Alcohol/Drug Involv. Scale (AADIS) 259 
Alcohol/Drug involvement 
5 
Fagerstrom (FTND) 260 
Smoking status/nicotine use 
1 
Petersen Physical Develop. Scale 261 
Pubertal status 
5 
* Clinical severity measures to assess functional impairment; P – Parent, S – Self 
Table 2. Cognitive Tests (R61/R33) 
Domain/Task Name 
Ref Reliability Min 
Working Memory Capacity 
 
 
 
WISC-IV Letter-Number Sequencing 
265 
.90 
15 
WISC-IV (Integrated) Spatial Span 
265 
.79 - .81 
5 
WISC-IV Digit Span 
265 
.80 - .83 
5 
EWM ‘Near Transfer’ Tests 
 
 
 
Shifting – Refresh/Repeat Task 
156 
n/a 
12 
Updating – Spatial Updating Task 
266 
n/a 
14 
Filtering – Attend-Ignore Task 
172 
n/a 
4 
Suppression – Intr. Resistance Task 
160 
n/a 
12 
Executive/Other Abilities 
 
 
 
CPT-II Omissions (Sustain. Attention) 
267 
.84 
15 
CPT-II Commissions (Resp. Inhib.) 
267 
.65 
- 
CogState Detection (Process Speed) 
268 
.92 
2 
CogState Set Shifting Task (Shifting) 
268 
n/a 
5 
Temporal Estimation Task (Timing) 
269 
n/a 
6 
Stroop Color/Word (Cog Control) 
270 
0.97 
6 

10 
 
visuospatial versions will use fractal images that are not easily encoded 
verbally.  All were constructed with adequate delays for WM maintenance, 
but not so long to raise concerns over different WM decay times or possible 
use of undirected rehearsal strategies.  Because it is crucial we avoid 
exceeding typical WM storage span (which could result in our study testing 
WM storage capacity more than EWM), no exercise uses more than 4 stimuli 
at a time (i.e., this is the well-replicated limit of the primary memory span 54).  
Task visual layout and pacing are similar so any cross-task differences can be 
confidently attributed to the EWM operations themselves. 
 
WM Updating  This task measures replacement of items in WM.  It is based 
upon Murty et al. 273, simplified to 2 conditions.  Each trial begins with 4 
stimuli presented for 4 sec, followed by 2 to 4 successive presentations (2 sec 
each) of an asterisk as placeholder or a new stimulus in each position (Fig. 
7).  For asterisks, participants are instructed to retain the previously viewed 
stimulus in WM, but to update the contents of WM for any new stimulus.  
During a final 3 sec response phase, participants indicate if the presented 
stimuli match the contents of WM (half the probes are correct/half 
incorrect).  For fMRI, the primary contrast of interest is Updating > 
Maintenance.  Each of two fMRI runs (verbal or visuospatial) has 20 
Maintenance and 20 Updating trials.  Each run=11:39 min (23:18 total). 
 
WM Shifting  This task measures the ability to refocus selective attention to 
different stimuli held concurrently in WM without altering WM contents.  
Each trial (Fig. 8) begins with 4 stimuli (verbal or visuospatial) for 4 sec, then 
a 6 sec delay with no stimuli.  In a 4 sec probe phase, one location is 
highlighted and subjects are cued to respond to different Yes/No questions 
about item content (i.e., randomly chosen to ensure participants do not just 
encode answers instead of stimuli).  For verbal – “Alive?” vs. “3 Letters?” For 
visuospatial – “Color?” vs. “Fills Screen?”).  For Control trials, subjects are 
prompted to hit a randomly-chosen button.  For fMRI, the contrast of most interest is Shift > Control.  Each of 
two fMRI runs (verbal or visuospatial) has 12 Control and 12 Shift trials.  Each run=7:10 min (14:20 total). 
 
WM Distractor Filtering/Interference Suppression  This 
task assesses brain function engaged to a) suppress/inhibit 
irrelevant, distractor information from WM and b) suppress 
proactive interference during WM retrieval 272.  Participants 
are asked to retain a 4-item memory set in WM (Fig. 9).  For 
the verbal task, two stimuli are shown in red, two blue.  Our 
visuospatial version has a comparable format, except 
instructions are to retain the two top or bottom stimuli.  
After a PreCue retention-interval, a 2 sec Cue instructs participants to retain in WM only items of that color (i.e., 
50% red or 50% blue) throughout a PostCue WM retention-interval.  A probe word requires an affirmative 
dominant-hand response if it matches either of the items retained in WM (e.g., POOL and TILL).  On 50% of 
trials the probe matches one of the items that should be in WM (Valid probes), on 25% the probe matches one of 
the items that should have been inhibited from WM (Lure probes), and on the remaining 25% of the trials the 
probe does not match any WM items (Control).  For fMRI, the contrasts of interest for this study are PreCue > 
PostCue (efficacy of suppression) and Lure > Control probe (resolution of proactive interference).  Each fMRI 
run=5:28 min (21:52 total). 
 
Stimulus Delivery/Response Recording  E-Prime (Psychology Software Tools, Inc.) will implement fMRI 
tasks.  Stimuli will be projected via a 3200 ANSI lumens system onto a screen behind the patients head in MRI.  
Participants view this screen using a mirror on the head coil.  An MR-compatible 5-button fiber optic response 
device (Current Designs, Inc.) will acquire behavioral responses.  Each fMRI task is programmed to track 
accuracy and RT for conditions of interest for offline analysis.  Participants communicate with staff during MRI 
using an MR-compatible auditory sound system delivered by 30 dB sound-attenuating headphones. 
 
MRI Sequences  fMRI gradient EPI (TR/TE 900/35 msec, flip 60°, multi-band AF=7).  Fieldmaps (TR/TE 
8400/62 msec, flip 80°, AF=1, 0:25 min, run twice with reversed A>>P phase encoding) (EPI/fieldmap 
Fig 7.  WM Updating  Task trial. 
Fig 8.  WM Shifting Task Trial. 
Fig 9.  Layout of a Suppression WM Task trial. 

11 
 
sequences have 2.1 mm isotropic voxels, 70 interleaved slices, 228 mm FOV). T1-weighted (3D MPRAGE, 
TR/TE/TI=2400/2.07/1000 msec, flip 8°, FOV=256×256mm, 0.8 mm isotropic vox; 7:02 min). T2-weighted 
(TR/TE=3200/565, FOV=256x256, 0.8 mm isotropic vox; 6:45 min).  Images will be Radiologist-assessed to be 
free of macroscopic pathology.  Daily MR stability/QA will ensure equal scan quality throughout the project. 
 
HCP Pipeline MRI Processing  We have experience with Human Connectome Project (HCP) 274 pre-
processing pipelines 275 from several prior and ongoing large R01 MRI studies.  They provide highly-accurate, 
structural image-guided brain atlas normalization for fMRI data.  T1/T2 images will be ACPC-oriented, brain 
extracted, B0 inhomogeneity-corrected, mutually co-registered, distortion fieldmap-corrected, and finally 
MNI152 atlas-registered using FSL FLIRT+nonlinear FNIRT algorithms 276. FreeSurfer-based 277 registration, 
skull-stripping, and pial extraction on 1mm downsampled T1/T2 data will create structural volume/cortical 
ribbon files. After fMRI EPI data is motion-corrected, it will be registered to FreeSurfer output, resampled to 
atlas space, intensity normalized, smoothed (cortical ribbon surface mapping data @2mm FWHM), and written 
as timeseries in CIFTI format for analysis.  All fMRI analyses will examine brain regions localized to the cortical 
subdivisions identified by the recently released HCP multimodal-derived map of 180 cortical parcels 278.  We 
emphasize this point, as this atlas represents the highest-resolution cortical atlas to date, including 97 new 
regions.  It divides conventionally-recognized structures into numerous sub-regions defined by uniquely 
demarcated boundaries constructed using each brain’s functional connectivity and myeloarchitecture.  We also 
will use a recently-developed sub-cortical/cerebellar map that reveals 358 highly-organized parcels that align 
with these new cortical regions 279.  Together, these atlases represent the cutting-edge in brain localization. 
 
MRI and Head Motion  Total MRI session time is 1h 10 min, of which 50 min involves fMRI data collection.  
Although this scan duration can be challenging for adolescents who are prone to fidget (especially unmedicated 
ADHD participants), we have ample experience scanning ADHD teens (R01MH080956, R21HD061915, 
K23MH070036) and non-ADHD teens for these scan lengths.  A mock scanner will acclimate subjects.  
Framewise Integrated Real-Time MRI Monitoring (FIRMM; www.firmm.io 280) will prompt staff to discontinue 
obviously unusable scans (i.e., data with movement >1 2.1 mm voxel); these will be replaced with parallel task 
versions in the same session.  This will be more time/cost-efficient than a 2nd scan.  If needed, substitute scans 
at a proximal session will be done to avoid discarding an entire dataset.  Minor head motion or sharp spikes will 
be addressed using ICA-FIX 281, 282, which isolates/de-noises such signal artifacts and regresses linearly-
detrended motion parameters from the HCP-processed timeseries.  We have learned in 15+ years of pediatric 
fMRI experience >90% of adolescent non-ADHD and >80% of ADHD fMRI data meets QC criteria, while roughly 
10% of ADHD ultimately can be replaced by a second scan session with careful participant re-training. 
 
Conventional fMRI Analyses  Single-subject activation will be quantified with FSL FEAT with FMRIB’s 
Improved Linear Model, using geodesic Gaussian algorithms to estimate autocorrelation and smooth surface-
based fMRI data.  Explanatory Variables (EVs) will use a double-gamma HRF convolution to translate event 
onsets in conditions-of-interest into regressors to be fit to the BOLD timeseries.  EV contrasts (against other 
conditions) will create activation maps.  Task performance will be considered by 1) separately modeling correct 
vs. incorrect events, then collapsing across them if SFS/mid-latPFC measurements are unaffected by accuracy, 
and 2) examining the effect of accuracy for post hoc analyses at the group-level.  
 
Functional Connectivity Analyses  Graph theory (GT) offers a diverse variety of useful and informative 
network metrics.  In GT, networks are mathematical representations of real-world complex systems.  GT metrics 
parameterize these systems to describe relationships among fMRI-measured region activity profiles 283, 284.  GT 
metrics range from those that simply quantify how integrated a single brain region is with others in a network or 
sub-network, to others that capture more complex properties of network structure, integration, segregation, or 
resilience to insult 215.  Although it is possible to examine thousands of connections among SFS, mid-latPC and 
other relevant brain network regions, GT also can focus hypothesis-testing on individual brain regions to reduce 
multiple comparisons demands.  Our primary functional connectivity metric will be ‘weighted-degree,’ which 
quantifies the number of edges (connections) to a any network node of interest, weighted by their association 
strength.  In this way, we will have a single value per participant for SFS and mid-latPFC parcels that represents 
nodal functional connectivity strength.  When preparing data, we will follow detailed published recommendations 
to mitigate any head motion signal artifacts 285, 286 on fMRI timeseries data.  Because ICA-FIX already will have 
been applied and motion parameters regressed from the data from HCP processing, we additionally will regress 
white matter, CSF and global signal from the data, then apply a 0.8 Hz low-pass filter.  The residualized 
timeseries will use partial correlation to form adjacency matrices 287.  Un-thresholded, full adjacency matrices 
will avoid the arbitrariness of threshold selection and loss of information when binarizing connections. 
 

12 
 
EWM Training Trial Methodology Considerations  Although the primary purpose of the R61 and R33 
trials is a to assess target engagement, not clinical efficacy, the major criticisms of prior ADHD WM training 
efficacy trials noted in reviews of the available literature 109, 112, 113, 115, 134-137 provide design considerations that are 
germane to any cognitive training trial.  These issues are summarized in Table 1, along with our innovations to 
avoid the same limitations to enhance this project’s methodological rigor and potential reproducibility. 
 
 
 
Placebo Condition  The strongest criticisms of prior ADHD WM training RCTs are the absence of placebo 
control, as results could be driven by expectancy effects.  Although neural and neurocognitive measures of target 
engagement that are the focus of this project are less vulnerable to expectancy, we chose to add a placebo training 
group to both R61 and R33 for methodological rigor and to enhance the certainty of causality inferences.  Choice 
of placebo for WM training trials is challenging because some previously-used WM ‘active placebos’ have 
changed brain function 288.  We will use Mahjong and Bejeweled tile games for the placebo condition. These 
deliver an equivalent ‘training time’ exposure, are compatible with remote monitoring, the game elements can 
be presented as a feasible treatment to preserve the blind, and we provide the same coaching experience.  We 
only will use these tile ganes for placebo exercises as they have low EWM demands 196, 197. 
 
Executive WM Training Sessions  Each ~50 min EWM training session will include sixteen 3 min EWM 
exercises (counter-balancing the order of EWM task types and verbal vs. nonverbal versions).  To avoid stimulus 
familiarity effects, the stimulus set for all exercises will be unique each training day, drawn randomly from a 
large pool.  Importantly, EWM training task difficulty will be adaptively increased across the 5 weeks of training 
based on session-to-session performance.  Day 1 EWM training will begin with relatively easy 2-item memory 
sets.  These will quickly ramp up to 4-item stimuli sets across the first 2-3 sessions as each participant attains 
80% accuracy for each 3 min exercise.  After this familiarization/confidence-building period across the 5 Week 
1 sessions, EWM task difficulty manipulations will begin using an adaptive staircase design.  Each EWM exercise 
will manipulate difficulty in different ways.  For WM Updating, the number of updates per trial will increase 
from 1 to 4.  If a participant achieves proficiency when challenged by 4 WM updates, the speed of each update 
will be shortened by 100 msec until behavioral gains plateau or a minimum of a 1 sec response window.  For WM 
Shifting, the number of shifts per trial will be increased from 1-4, then duration of each response window 
successfully shortened by 100 msec.  For WM Distractor Filtering, the number of possible colors used for 
directed forgetting will be increased from 2 to 4, then response window shortened by 100 msec.  For WM 
Suppression, more proactive interference probes will be given at once (from 1 to a max of 4), then 100 msec 
response window will decreases once consistent optimal performance on 4 probes is attained. Patient differences 
between stimulus manipulation difficulty level and fastest response window size will provide improvement 
indices to characterize each subject.  All adjustments will be based on running weighted averages of trial accuracy 
and reaction time, continually saved/updated by the E-Prime stimulus presentation software.   
 
Engagement/Motivation Cognitive training gain are believed to be optimized when patients approach trials with 
consistent high motivation 43.  We have incorporated social and minor financial incentives for engagement (rapid 
response time consistency) and improvements in each session. It is hoped this also will partially reduce boredom.  
The coach also will assess participants’ subjectively-rated motivation on a 10-point Likert scale. 
 
Remotely-Supervised EWM Training  Our EWM training delivery is designed to maximize compliance.  For 
a typical family with late middle- or high-school age children who have many, often-conflicting priorities, visits 
Table 3.  Methodological Criticisms of Previous WM Training Clinical Trial Design or Interpretation 
Issue 
Solution 
Active placebos in RCTs also can produce clinical improvement, 
possibly undermining clear understanding of WM training efficacy 
Use a carefully-chosen placebo arm, and supplement with other 
methods for ensuring any gains after EWM training are valid 
RCTs do not examine how treatment compliance affects outcome 
CACE analysis of the effect of compliance on WM outcome metrics 
EWM test performance at trial outcome might be contaminated by 
practice effects 
Ensure stimuli used for training are not used for pre- and post-
training fMRI assessment; for all WM neuropsych tests used for 
assessment, choose those with reliable parallel forms 
Near- and far-transfer inconsistently evaluated 
Include commonly-used tests for comparability across studies 
Not all previous studies evaluated psychiatric comorbidity (e.g., 
CD/ODD), ADHD DSM-IV clinical subtype, or LD 
Conduct full diagnostic interview and assess possible academic 
disparity with IQ; evaluate outcome moderation effects statistically 
The medication status of ADHD participants has been inconsistent 
and often un-evaluated across studies 
Limit to unmedicated ADHD and those on stable, short-acting 
psychostimulants; use 24 hour “washout”; assess moderator effect 
Not clear to what extent age influences one’s ability to benefit from 
executive WM training; possible pubertal confounds 
Avoid pre-pubertal children to circumvent major developmental 
confounds; examine age (or time since puberty onset) moderation 
Previous studies typically ignored any effects that individual 
differences in pre-training WM ability might have on outcome 
We will randomly assign participants to active/placebo treatment 
and examine the role of pre-training EWM ability on outcome 

13 
 
to the Olin NRC 4 times a week for 5 weeks will impossibly compromise study feasibility.  Consistent with 
guidelines we have proposed for other forms of remotely-supervised interventions 291, 292, WM training will be 
done ‘at home’ on a study-provided Lenovo TP X1 Tablet with a cellular modem and Verizon data plan.  Tablets 
will run GoToAssist’s ‘RemoteDesktop’ software so Olin NRC staff can remotely administer EWM practice 
sessions.  Study staff can remotely start the session (even when the tablet is powered down), initiate Skype 
videoconferencing for a ‘virtual presence’ during training, and load pre-configured E-Prime EWM task training 
exercises.  Training behavioral data are transferred daily to Olin NRC servers for archiving via FTP.  Each tablet 
is password-locked and will run Absolute software that provides GPS location and the ability to erase all data in 
case of loss or theft.  We successfully used these procedures for our n=7 EWM training ADHD pilot study.  This, 
and our other success with supervised ADHD WM training 103, validates the feasibility of the novel training 
delivery approach.  Coaching  During each session, a project staff member will interact with each participant 
through this virtual connection.  As in other types of WM training, such a ‘coach’ works closely with families to 
help problem-solve unanticipated barriers to regular practice.  
 
R61 and R33 Hypothesis-Testing General Comments  Missing data bias tests will use Little’s multivariate 
test, as well as univariate counterparts 293. Missing data will be dealt with using Full Information Maximum 
Likelihood algorithms 294 or pattern mixture modeling, as appropriate 295.  Outlier and specification error 
diagnostics will be examined.  All data will be examined to ensure they meet statistical test assumptions.  If not, 
we will use robust estimators based either on Huber-White estimation, bootstrapping, or methods described in 
Wilcox 296.  Group fMRI analyses will use PALM 297 – a CIFTI data format-compatible analysis tool that applies 
threshold-free cluster enhancement for surface-based fMRI analysis and multiple comparisons corrections with 
permutation-based inference.  All group analyses will use age as a covariate to account for any maturational 
differences.  We will examine sex differences as per NOT-OD-15-102. 
 
 
 
 
R33 Goals   The 3-year R33 study will seek to replicate R61 target engagement evidence in a larger sample, 
using analyses robust to factors that potentially might mitigate outcomes.  It also will determine if EWM training 
target engagement can be linked to clinically-relevant behavioral change.  We do not intend the R33 to be a 
formal efficacy test; it is intended only to inform a decision about the potential of an EWM training intervention 
for a subsequent efficacy trial testing.  Secondary goals are to inform the design of possible future efficacy 
studies by a) learning if ADHD EWM gains represent resolution of brain function deficits compared to non-ADHD, 
assessed by recruiting a demographically-matched n=40 non-ADHD comparison adolescent sample who will 
undergo 2 evaluations, also ~5 weeks apart, b) determining whether EWM gains generalize to non- 
trained EWM tasks, c) examining the effect of intervention non-adherence 
on target engagement, and d) identifying ADHD characteristics that might 
moderate EWM training outcomes which could influence the design of 
subsequent efficacy research. 
 
Gene Banking We envision a need to have whole-genome data in this richly 
phenotyped dataset to answer emerging questions. Saliva sampling costs were 
budgeted and samples will be stored at Olin for future use. 
 
 
R33 Trial Methodology  The R33 will use the same R61 EWM training 
intervention.  As such, it will have the same randomization, coaching, 
discontinuation rules, allocation concealment, 5-week baseline/endpoint 
assessment timeframe, target ‘dose’ of 20-25 EWM training or ‘sham 
training’ placebo sessions, and www.clinicaltrials.gov registration.  After 20% 
expected attrition, there should be 36 ADHD ‘completers’ in each arm (Fig. 11).  Unlike the R61, the R33 also 
will examine a primary clinical endpoint (ADHD symptom severity) and secondary outcomes (3 psychosocial 
function assessment measurements; see Table 1).  The R33 also will formally evaluate Training Non-Adherence 
using CACE analyses of compliance effects 300 on target engagement.  We define ‘protocol non-adherence’ as 
<80% session completion, with the goal of at least 20 sessions by an endpoint 5 weeks after trial start.  Any 
change to a participant’s type/dose of medications or adding another form of treatment (e.g., neurofeedback, 
etc.) also will be non-compliance.  ‘Procedural non-adherence’ is defined as poor quality engagement, as rated 
both subjectively and objectively by the training coach via virtual observation of each session (see appended 
Intervention Manual).   
 
Fig 11.  R33 CONSORT diagram 

14 
 
R33 Hypothesis-Testing  We remind reviewers PHS-CT supplement Section 4 contains the detailed 
hypothesis-testing plan, including several important supplemental analyses to greatly enhance study impact.   
 
R33 Power Analysis  Power for this study’s ANCOVA 
analyses of treatment outcome is primarily impacted by 
three factors – 1) sample size, 2) effect size d, and 3) the 
strength of association R2 between the dependent variable 
and any model covariates.  Figure 12 shows ANCOVA 
power analysis estimates for the expected 81 of 90 R33 
completers.  We see power will be at or greater than 80% 
for Cohen’s d > 0.57 effect sizes for any ANCOVA test 
where the R2 for the covariate terms alone falls above 
0.16.  These anticipated R2 values we tested are realistic.  
Quantile analysis found a convincing majority (85%) of the 
R2 distribution values across all the R61 trial’s functional 
connectivity ANCOVA models were well above 0.16.  In 
sum, the R33 design is adequately powered for a credible 
replication study.  Its power will suffice to confirm R61 
findings even if the new R33 ADHD sample shows the 
same result at a slightly lower effect size magnitude.  
Alternatively, we could decrease α to .01 for any specific 
brain connectivity replication test while maintaining 
sensitivity to a comparable d = 0.68 effect size magnitude 
(i.e., approximately the same as the smallest effects reported in the R61). 
 
R33 Impact on Decision to Continue Research  The R33 phase results will inform a decision about 
whether the intervention shows potential for improving clinical outcomes.  A large-scale efficacy trial will be 
recommended only if a) R61 behavioral and biological target engagement evidence is replicated in the new R33 
ADHD sample, and b) if there is evidence for a meaningful association between EWM training-induced target 
engagement and credible ADHD clinical changes over the brief 5-week R33 trial.  The magnitude of any clinical 
changes (great or small) also will be instructive in deciding whether or not this intervention development research 
should advance in future studies to efficacy testing.  Moreover, the specific endpoints that show improvement 
will be the focus of any subsequent RCT proposal. Secondary R33 analyses will confirm the remotely-supervised 
EWM training approach has the anticipated acceptability, tolerability, and retention. 
 
Key Study Design Choices to Maximize Robust Results 
1) Sample Sizes – Careful statistical power analyses were done to support adequate sampling for confidence that 
both R61 and R33 phase results would provide a definitive Yes/No answers about target engagement. 
2) Use of Placebo Control – Both R61 and R33 include random assignment to placebo for strongest inference 
possible about whether target engagement is due to the intervention.  Moreover, the behavioral and neural 
targets are objective and likely robust to any expectancy effects seen in prior research. 
3) How To Handle ADHD WM Deficits – Pre-existing cognitive ability has an equivocal relationship to training-
related cognitive gains 301-303 and there is no published guidance as to what effect ADHD deficits might have 
on training outcomes.  As such, our R33 will ensure any evidence for target engagement is robust to sample 
variability in pre-training EWM ability levels. 
4) Other ADHD Clinical Heterogeneity – There are many ADHD clinical characteristics (co-morbidities, 
medication histories, etc.) and suspected etiological biomarkers (including variable WM expression) that 
might influence EWM target engagement.  If the R61/R33 is successful, supplemental analyses will begin to 
assess possible moderation effects to inform future sampling plans and research design. 
5) EWM Tasks for Training vs. Brain Target Assessment? – The only way to be confident neural target 
engagement reflects EWM training effects is to use the exact same tasks for both fMRI and training.  However, 
it remains important to show EWM is training actual ‘ability’ and not just maximizing specific task performance.  
As such, we will examine ‘near transfer’ of training gains to 4 non-trained EWM Shifting, Updating, Distractor 
Filtering and Suppression tests (Table 2).  We also will conduct exploratory ‘far transfer’ analyses of simple 
WM short-term storage and any training effects on other executive function 304-306 tasks. 
6) Interpretation of EWM Training Effects – We will compare ADHD training effects to data from non-ADHD 
participants to better understand trial outcomes.  We are open to the possibility that brain changes either 
Fig 12.  R33 Power Analysis 

15 
 
resolve abnormalities, or alternatively that they build upon intact neural systems to enhance ability, thereby 
‘compensating’ for other deficits.  Either underlying mechanism would support future efficacy research. 
7) Theoretical Advances – PHS-CT Section 4 describes several supplemental analyses using pooled R61 and 
R33 trial outcome data that might refine our conceptual model of how the EWM intervention works. 
 
Other Study Design Features For Rigor/Reproducibility  This R61/R33 provides a built-in replication 
by design.  We use strong RCT design principles and we avoid many methodological pitfalls of prior ADHD WM 
training RCTs.  We base sampling attrition estimates on direct experience to ensure our final sample size will be 
adequate; statistical power was carefully confirmed for our planned hypothesis-testing, using conservative 
estimates.  Only a small handful of treatment targets will be tested for engagement, requiring a very specific, 
narrow path towards study milestone achievement.  The R61 and R33 use the same intervention to ensure 
continuity between the two study phases.  Protocol compliance will be closely monitored and its effects on target 
engagement will be formally evaluated in the R33 analysis plan.  Clinical and cognitive tests have high test-retest 
reliability and are validated for adolescents.  We use well-validated, piloted fMRI tasks with rigorous QC 
procedures to obtain high-quality, movement free MRI data.  HCP methods offer the most accurate localization 
of brain function.  We avoid the interpretive confounds of assessing ADHD patients’ EWM ability when they are 
medicated.  Data will be made available in NDA repositories so other investigators will be free to re-examine our 
results, or extend findings with new analyses. 
 

16 
 
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