Download Characterizing cognition in ADHD: beyond executive dysfunction

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TRENDS in Cognitive Sciences
Vol.10 No.3 March 2006
Characterizing cognition in ADHD:
beyond executive dysfunction
F. Xavier Castellanos1, Edmund J.S. Sonuga-Barke1,2, Michael P. Milham1
and Rosemary Tannock3
1
Institute for Pediatric Neuroscience, NYU Child Study Center, 215 Lexington Avenue, New York, NY 10016, USA
Department of Psychology, University of Southampton, Southampton SO17 1BJ, UK
3
Brain and Behavior Research Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada
2
The hypothesis that Attention-Deficit/Hyperactivity Disorder (ADHD) reflects a primary inhibitory executive
function deficit has spurred a substantial literature.
However, empirical findings and methodological issues
challenge the etiologic primacy of inhibitory and
executive deficits in ADHD. Based on accumulating
evidence of increased intra-individual variability in
ADHD, we reconsider executive dysfunction in light of
distinctions between ‘hot’ and ‘cool’ executive function
measures. We propose an integrative model that
incorporates new neuroanatomical findings and emphasizes the interactions between parallel processing pathways as potential loci for dysfunction. Such a
reconceptualization provides a means to transcend the
limits of current models of executive dysfunction in
ADHD and suggests a plan for future research on
cognition grounded in neurophysiological and developmental considerations.
Introduction
Attention-deficit/hyperactivity disorder (ADHD) is
characterized by pervasive behavioral symptoms of
hyperactivity, impulsivity and inattention, beginning in
childhood [1]. Despite its high prevalence and associated
lifelong impairment [2], ADHD remains controversial
because of the use of psychostimulants for treatment of a
‘behavioral condition’ that is diagnosed subjectively [3].
The heritability of ADHD is, however, both substantial
and solidly established. Although major risk genes have
yet to be identified, several genes related to monoaminergic neuromodulation are confirmed as minor contributors
to the overall phenotypic variance [4]. Moreover, there is
robust evidence of structural, functional and neurochemical brain differences in ADHD, in regions that support
vital cognitive functions [5]. Thus, a coherent and
comprehensive model of the cognitive substrates of
ADHD would be a highly desirable means of linking
genetic, neurobiological and phenotypic levels of analysis,
with the objective of improving diagnostic approaches and
therapeutic options.
Such a model should eventually encompass ADHD
throughout the lifespan. However, the ADHD literature is
Corresponding author: Castellanos, F.X. ([email protected]).
Available online 7 February 2006
preponderantly based on children of elementary school
age. As neuropsychological differences are most detectible
at this age [6], this brief review will generally exclude
studies on preschool-age-children or adults, despite
remarkable consistency between cognitive deficits across
these wide age ranges. We defer attempts to synthesize
initial efforts at linking cognitive and neuronal phenotypes to current candidate genes, all of which have minor
effects at best [4]. Similarly, active areas of potentially
related research, such as psychopharmacology [7] or
neuroimaging [8,9], which have been recently reviewed
elsewhere, are not covered, but will certainly contribute to
fully integrated models. Our purposes here are to reassess
currently dominant cognitive models of ADHD in light of
empirical and conceptual challenges and to highlight an
integrative model that incorporates emerging neuroanatomical perspectives. We believe that this approach will
provide a more robust framework for the translational
multidisciplinary efforts that are already underway in
laboratories throughout the world.
Evolving cognitive models of ADHD
The explicit criteria for ADHD that were first codified in
1980 (see historical review in [10]) emphasized inattention
as much as hyperactivity, based on robust objective
evidence of behavioral inattention and performance
deficits on a laboratory measure of attention, the
continuous performance task [11]. However, when specific
attentional processes were targeted (i.e. divided, selective,
sustained), investigators failed to observe specific diagnostic deficits [12]. Based on parallels between ADHD
symptoms and presumed cognitive deficits and those of
patients with frontal lobe disorders, researchers expanded
the scope of inquiry to higher-order cognitive processes
thought to be sub-served by the frontal lobes such as
inhibitory control, attentional regulation and working
memory – constructs grouped under the rubric of
executive function (EF) [13] (see Box 1). In 1997, Barkley
proposed a comprehensive theory of ADHD with deficient
inhibitory control as the core deficit that secondarily
disrupts other EF processes [14]. The explicit testable
prediction that inhibitory deficits and broad EF dysfunction should be observable in all children with ADHD,
taking into account measurement error, established the
EF deficit model as the dominant paradigm over the past
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Box 1. Executive function: is there a central executive?
There is no consensus definition of ‘executive function’ (EF). The
term is generally used to describe a broad range of ‘top-down’
cognitive processes and abilities that enable flexible, goal-directed
behavior. Examples of such processes include planning and
implementing strategies for performance, initiation and discontinuation of actions, inhibiting habitual or prepotent responses or taskirrelevant information, performance monitoring, vigilant attention
and set switching.
Researchers have struggled to understand whether the broad
range of ‘executive’ functions are supported by a single unitary
process or a diverse array of cognitive processes. Although some
have argued for the existence of a unitary process based in the
frontal lobes, neuroimaging and focal lesion studies fail to support
this notion. The frontal lobes have proven to be heterogeneous, with
numerous anatomically and functionally distinct subregions, each
associated with a different subset of executive functions [20]. As
such, current models favor a conception of EF as a collection of
higher-order cognitive control processes [21]. (See http://www.
aboutkidshealth.ca/ofhc/news/SREF/4144.asp for further developmental perspectives on EF.)
decade and catalyzed a burgeoning literature [15–19],
much of it focused on inhibition as the core deficit
in ADHD.
Testing inhibition as the primary executive deficit in
ADHD
Of several types of inhibitory processes, only executive
motor inhibition has clear replicated evidence in ADHD
[24]. The bulk of this support derives from Go/No-Go tasks
[25] and particularly, the Stop task [26] (see Box 2).
Converging lesion and imaging studies pinpoint the right
inferior prefrontal cortex as a crucial region for effective
Stop task performance [27,28]. Additionally, evidence of
familiality [29–31] suggests that the Stop task could be a
useful intermediate phenotype (endophenotype) for delineating risk genes corresponding to a neuropsychologically distinct subtype of ADHD.
The usefulness of the Stop task is also apparently
supported by a meta-analysis of 17 studies (nearly 1200
Box 2. Measuring executive inhibition: the Stop Task
The Stop Signal Task examines an individual’s ability to stop a
prepotent motor response and is unique among tasks used to
measure inhibitory control in that it permits an estimation of the
latency of the inhibitory process. It involves two tasks that differ with
respect to frequency, predictability, and stimulus parameters
(modality, intensity). The Go-Task, presented on every trial, is a
speeded forced-choice reaction time task that requires participants
to respond to a stimulus referred to as the Go-Signal. The Stop-Task,
occurring randomly and infrequently (e.g. 25% of Go trials), involves
presentation of a Stop Signal (SS; often an auditory tone) that
countermands the Go response requirements (i.e. participants are to
immediately inhibit the response to the Go-Signal).
The task assumes that ability to inhibit the Go response depends
on the outcome of a race between two independent processes
(response generation/execution, response inhibition) [22]. If the
inhibitory process wins, the planned action is stopped; otherwise,
the Go response continues to completion. Thus, inhibitory control
depends on the Go response time, the within-subject variability of
the Go response time, and the reaction time to the SS (SSRT). SSRT
is the primary performance variable, indicating the speed of the
inhibitory process; faster SSRT’s reflect efficient inhibitory
control [23].
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Vol.10 No.3 March 2006
children) reporting significant longer Stop Signal reaction
times (SSRT) in ADHD (Cohen’s effect size, dZ0.58) [18].
However, several potential confounds complicate the
interpretation of this difference. First, children with
ADHD also exhibit significantly slower RTs to Go stimuli
(dZ0.52) which may disproportionately influence the
calculation of the SSRT. Second, they demonstrate even
greater Go stimulus RT variability (dZ0.72) which
challenges the Stop task’s assumption of SSRT invariance
and undermines the feasibility of using a tracking
procedure to establish appropriate interval between Stop
and Go signals to produce equal proportions of successful
and failed inhibitions [18]. More generally, the Stop task
imposes subtle but continuous demands on stimulus
anticipation, response preparation, speed of stimulus
processing, and the ability to hold task instructions online [27] and children with ADHD may be impaired in each
of these processes [19,32,33]. The alternative of examining
motor inhibition with the simpler Go/No-Go task may also
be problematic. Electrophysiological studies of Go/No-Go
tasks in ADHD, which allow a more precise dissection of
effects, find nonspecific deficits that are not limited to NoGo trials [34–37]. Also casting doubt on the centrality of
inhibitory deficits is the largest study of stimulant naı̈ve
boys (nZ75) with severe pervasive ADHD (Hyperkinetic
Disorder) and matched controls (nZ70) which found
strikingly similar rates of No-Go errors and mean Go RT
on a computerized Go/No-Go task [38]. Thus, these
counterfactuals fail to support the strong hypothesis of
deficient inhibition as the primary cognitive deficit
in ADHD.
Is executive dysfunction intrinsic to ADHD?
When meta-analyses are extended to the broader domain
of executive function, a similar pattern of significant albeit
moderate associations emerges. A meta-analysis comprising 83 studies and over 6700 subjects found associations
between ADHD and executive dysfunction across all
domains tested (i.e. planning, vigilance, set shifting, and
verbal and spatial working memory) ranging in effect from
dZ0.4–0.7 [19]. A meta-analysis focused on working
memory examined a somewhat different subset of studies
and detected stronger effects (dZ0.85–1.14) when spatial
working memory manipulation was distinguished from
simple storage [33]. Thus, manipulation of spatial working
memory appears to offer the strongest evidence in ADHD,
but direct comparison studies have not yet been conducted. As in the case of response inhibition, EF effects are
difficult to interpret because most executive function tasks
fail to control for potential confounds ascribable to more
‘primitive’ cognitive or physiologic processes [39]. Two
recent studies highlight the seriousness of this issue by
demonstrating that when non-executive abilities are
accounted for by using appropriate control tasks, little
evidence of executive dysfunction remains [38,40].
The modest pattern of associations between a range of
EF deficits and ADHD is frequently interpreted as
providing evidence for a broader/weaker variant of the
Barkley EF hypothesis. This is the result of a failure to
understand the prediction of pervasive deficits, which has
not been adequately tested until recently. An
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TRENDS in Cognitive Sciences
approximation of such a test was performed by pooling
data from three different sites [41]. EF impairment was
defined in terms of performance exceeding the 90th
percentile cutoff (based on the control samples) on five
EF measures. On any individual measure, between 16%
and 51% of children with ADHD were classified as
impaired. When multiple deficits were compared, only
31% of children with ADHD, versus 9% of controls,
displayed pervasive impairment (deficits on 3 or more
measures) [41]. Only 10% of ADHD children showed
deficits across all five domains. By contrast, 21% of
children with ADHD (and 53% of controls) were unimpaired on all five measures. This combined analysis
recapitulates the conclusion that ‘EF weaknesses are
neither necessary nor sufficient to cause all cases of
ADHD’ ([19], p. 1336). Although not yet examined in metaanalyses, growing evidence links EF deficits to the
inattention dimension of ADHD, rather than to hyperactivity/impulsivity [42,43]. Clearly the strong predictions
of the EF deficit hypothesis of ADHD are not supported.
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Cognitive heterogeneity of ADHD: multiple pathway
models
The cognitive literature is thus incompatible with the
assumption of pathophysiological homogeneity – of a
single core deficit. Researchers have responded by
building models that accept heterogeneity [41,44,45] – in
which ADHD is regarded as an umbrella construct with
clinical value that subsumes multiple potentially dissociable but overlapping cognitive profiles. So far, too few
studies include measures from different functional
domains (EF, reinforcement/motivational, sensory/perceptual and motoric), so heterogeneity is largely inferred from
independent studies each working within a particular
functional domain.
One notable exception, designed by proponents of both
camps, contrasted EF and motivational models using a
Stop task and a Choice Delay task in which children chose
between small immediate and large delayed rewards [46].
This collaborative study produced two crucial findings:
first, choices of the small immediate reward (Delay
Aversion) were uncorrelated with SSRT – suggesting
that inhibitory deficits and Delay Aversion in ADHD
were dissociable processes. Second, performance on either
task was only moderately associated with ADHD but
together correctly classified nearly 90% of children with
ADHD. Similar results were found in preschool children
with ADHD [47] and in a study of the mediating pathways
between hydrocephalus and hyperactivity [48], highlighting that neither EF nor Delay Aversion models are
individually sufficient to account for neuropsychological
findings in ADHD.
associated with dorsolateral prefrontal cortex (DLPFC)
which are characterized as ‘cool’ as contrasted with
relatively ‘hot’ affective aspects of EF associated with
orbital and medial prefrontal cortex (OMPFC) [49,50]. In
this schema, ‘cool’ EF is elicited by relatively abstract,
decontextualized problems, such as most of the EF tasks
tested so far in ADHD (e.g. [15,16,19,24,33]), including
Stroop, flanker, Go/No-Go, Stop, continuous performance
and working memory tasks, which focus on the ability to
suppress automatic processes or prepotent responses and/
or maintain task instructions or representations in
working memory. ‘Hot’ EF ‘is required for problems that
are characterized by high affective involvement or
demand flexible appraisals of the affective significance of
stimuli’ ([49], p. 455). Based on the inarguable association
of moderately impaired performance on ‘cool’ EF tasks and
ADHD [19], Zelazo and Muller proposed that ADHD
should be considered a disorder of ‘cool’ EF [49]. We believe
this may be premature, reflecting the preponderance of
studies which have used just such a conclusion as a
starting point. Rather, we propose that inattention
symptoms may be associated with deficits in ‘cool’ EF,
whereas hyperactivity/impulsivity symptoms will be
found to reflect ‘hot’ EF deficits. This gives rise to the
possibility that some individuals with ADHD will manifest primarily ‘hot’ EF dysfunction, whereas others will
show mainly ‘cool’ EF deficits and others will have both
types. For example, risky decision making in the Iowa
gambling task – an index of ‘hot’ EF – was associated with
hyperactivity/impulsivity symptoms but not symptoms of
inattention, or ‘cool’ EF measures such as working
memory or IQ [51].
The increasing literature on the impact of reinforcement contingencies on ADHD comprising 22 studies with
nearly 1200 children was recently reviewed [52]. Although
the range of approaches used in these studies is too
disparate to allow quantitative meta-analyses, the
authors highlight clear evidence of delay aversion, and
some support for greater behavioral and lower psychophysiological sensitivity to reinforcements in ADHD [52].
Abnormalities or inconsistencies in maintaining instructional set (‘cool’) or motivational state (‘hot’) may also
account for the substantial variability in responding that
emerges in many cognitive tasks and in behavioral
descriptions of ADHD [39] (see Box 3). In summary,
whereas the literature surrounding executive dysfunction
in ADHD has mostly focused on ‘cool’ EF with modest
success, the presence of impairments in incentive,
motivational and reward-related processing suggests
that both ‘hot’ and ‘cool’ EF deficits should be assessed,
with particular attention to developmental aspects and
symptom subtypes [53–55].
Towards an integration of multiple pathway models
‘Hot’ and ‘cool’ executive functions
In considering potential synthetic linkages between EF
and processes related to motivation such as underlie Delay
Aversion, a line of work on the development of EF is
particularly pertinent. Noting the functional differentiations within frontal cortices, Zelazo and Muller
distinguish between more purely cognitive aspects of EF
Spiraling cortico-striato-thalamo-cortical circuits
A recent synthesis highlights the neuroanatomical substrates for interactions between motivational and cognitive
processes as potential loci of dysfunction in various forms of
psychopathology, including ADHD [56] (see Figure 1).
Dysfunction of distributed cortico-striato-thalamo-cortical
loops is implicated in all neurophysiological models of
psychopathology. However, models of parallel reciprocal
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Vol.10 No.3 March 2006
Box 3. Intra-individual variability and ADHD
Ironically, one of the most consistent manifestations of ADHD is the
high prevalence of ‘moment-to-moment variability and inconsistency
in performance. Such response variability is the one ubiquitous
finding in ADHD research across a variety of speeded-reaction-time
(RT) tasks, laboratories and cultures’ ([45], p. 624). Such variability can
be quantified by decomposing RT responses into the sum of a
normally distributed random variable, m (Figure Ia), and an independent exponentially distributed variable, t (Figure Ib), which accounts
for the positive skew of ‘ex-Gaussian’ RT distributions (Figure Ic).
Children with ADHD who had almost identical m values to those of
controls differed markedly from controls in the skew of the RT
distributions and in the derived values of t (Figure Id), yielding a
remarkable diagnostic efficiency of 96% [60].
The implication that children with ADHD exhibit two distinct
response modes, one of which is indistinguishable from normal
responses, affects the interpretation of all speeded cognitive tasks. For
example, although subjects with ADHD are deficient in timediscrimination and/or time-reproduction tasks, most timing findings
in ADHD are confounded with significantly greater variability,
particularly on time-reproduction tasks [61–67]. In some cases, the
increased variability is striking (e.g. F ratio of variancesO300 for
(a)
reproduction of 2 s intervals in one report [62]), supporting the
conclusion that putative deficits in temporal processing cannot be
evaluated straightforwardly without first accounting for increased
variability. Intra-individual variability of Go-RT exhibits moderate
heritability in ADHD [68]. Increased variability or inconsistency is
encountered as a correlate of superior and dorsolateral prefrontal
brain lesions in adults, but interestingly is not associated with inferior
medial prefrontal lesions [69]. These regionally specific findings have
been confirmed in healthy adult subjects performing a Go/No-Go task
using event-related functional imaging [70].
Returning to boys with ADHD, fluctuations in RT at very low
frequencies (between 0.02–0.07 Hz) that parallel oscillations in basal
ganglia neuronal firing rates are significantly more prominent compared
with controls [39]. Each of these pioneering studies requires replication,
but they independently validate the notion that intra-individual
variability in ADHD needs to be examined in more detail [39,69]. In
some paradigms, intra-individual variability may have greater explanatory power than the hypothesized alternatives, such as inhibition or
timing deficits. Alternatively, accounting for the quantitative effects of
between-group differences in variability should provide greater statistical power in head-to-head tests of competing cognitive models.
(b)
Exponential
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Probability density
Probability density
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Figure I. Reaction-time distributions can be modeled as the sum of (a), a normally distributed (Gaussian) random variable, m, and (b) an exponentially distributed
variable, t, which is independent of m. (c) The convolution of the Gaussian and exponential distributions results in positively skewed ex-Gaussian distributions. (d)
Comparison of children with ADHD and controls, showing the greater skew that is diagnostic for ADHD.
loops have failed to address ‘how information can be
transformed across functional regions to help implement
the learning and adaptability that is necessary in the
development of goal-directed behaviors’ ([50], p. 322). The
delineation of complex non-reciprocal pathways consisting
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of striato-nigral-striatal and thalamo-cortico-thalamic networks provides the anatomic basis through which emotion/
motivation related OMPFC pathways influence ‘cool’ EF
DLPFC pathways, which, in turn, influence motor pathways
[50]. The unidirectional nature of information flow through
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121
the inability to adapt appropriately to external and internal
cues, are key deficits in basal ganglia diseases which affect
these aspects of motor control, cognition and motivation’
([50], p. 325). In contemplating Haber’s spiraling circuits
through the cortico-striato-thalamo-cortical loops, we
acknowledge the temptation to converge again on a single
model, albeit one that links motivational, cognitive and
motoric domains. Such an interpretation would be inconsistent with the existing data [46]. It would also miss the
point that the Haber model highlights likely loci of neuronal
vulnerability that are hypothetically linkable to dissociable
cognitive profiles. For example, we hypothesize that
OMPFC and ventral striatal differences will be implicated
in Delay Aversion and abnormalities in sensitivity to
performance incentives and environmental factors [52].
We expect that pervasive ‘cool’ EF deficits, which are present
in a minority of children with ADHD [41], will be more
closely linked to DLPFC, dorsal anterior cingulate and
anterior striatal dysfunction. Developmental Coordination
Disorder, which occurs in up to half of children with ADHD
[57], should reflect abnormalities in premotor prefrontal
cortex, dorsal striatum and cerebellum.
Figure 1. Diagram of the organization of striato-nigro-striatal (SNS) projections [56].
The colored gradient in rostral and caudal schematics of the striatum illustrates the
organization of functional corticostriatal inputs (redZlimbic, yellow and greenZ
associative, blueZmotor). The shell (S) receives forebrain input primarily from the
amygdala, hippocampus and cortical areas 25 and Ia. The core receives input from
the entire OMPFC. The DLPFC projects to the central striatum and premotor and
motor cortex projects to the dorsolateral striatum. Midbrain projections from the
shell target both the ventral tegmental area (VTA) and ventromedial substantia
nigra pars compacta (SNc) (red arrows). Midbrain projections from the VTA to the
shell form a ‘closed’, reciprocal SNS loop (red arrow). Projections from the medial
SN feedforward to the core, forming the first part of a spiral (orange arrow). The
spiral continues through the SNS projections (yellow and green arrows) with
pathways originating in the core and projecting more dorsally (blue arrows). In this
way ventral striatal regions influence more dorsal striatal regions via spiraling SNS
projections. DLPFC, Dorsolateral prefrontal cortex; IC, internal capsule; OMPFC,
orbital and medial prefrontal cortex; SNc, substantia nigra, pars compacta; SNr,
substantia nigra, pars reticulata; VTA, ventral tegmental area. Reproduced from Ref.
[50], by permission of Elsevier q 2003.
the non-reciprocal components of these spiraling circuits
suggests a hierarchy of emotion/motivation affecting cognitive processing which can regulate motor outputs and which
appears to be particularly applicable to ADHD and related
disorders. However, as Haber points out, ‘parallel circuits
and integrative circuits must work together, so that the
coordinated behaviors are maintained and focused (via
parallel networks), but also can be modified and changed
according to appropriate external and internal stimuli (via
integrative networks). Indeed, both the inability to maintain
and to focus in the execution of specific behaviors, as well as
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Conclusion
The EF/inhibition model of ADHD set the stage for an
impending paradigm shift in the neuropsychology of
ADHD. ADHD is undeniably associated with ‘cool’ EF
deficits at a group level of analysis, but the predicted
central role of inhibitory and executive deficits is not
supported for a substantial proportion of children with
ADHD. ‘Hot’ EF deficits have been examined less
frequently and yet may be more relevant from the
perspective of functional outcomes and real-world decision
making. Additionally, the neuroanatomical substrates of
cortico-striato-thalamo-cortical circuitry are now revealed
to include spirals of one-directional information flow from
‘hot’ ventro-medial/orbitalfrontal/ventral striatal regions
to dorsolateral/superior medial/anterior striatal ‘cool’
regions to even ‘cooler’ premotor and motor circuits. This
neuroanatomical circuitry provides a framework within
which to embed continuing exploration of functional
differences in ADHD through behavioral, cognitive and
neuroimaging approaches. Such analyses will be more
effective if they take into account the potential effects of
intra-individual variability in the interpretation of task
results. Additionally, the analysis of such variability in
ADHD is worthy of further study in its own right.
Future directions
In response to the incontrovertible heterogeneity of the
disorder and its presumed multiple underlying psychopathological signatures, various multiple pathway causal
models are being proposed [44,58]. However, before being
able to distinguish between competing models with
sufficient statistical power, the following tasks will need
to be accomplished:
(i) Convergence on subsets of key psychometrically
validated dimensional measures that encompass both ‘hot’
and ‘cool’ EF. In the absence of ideal measures, the
cognitive batteries adopted by the NIH-funded MRI Study
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of Normal Brain Development and the NIMH-funded
International Multi-Center ADHD Genetics (IMAGE)
Project are likely to serve as de facto standards over the
next decade: the multidimensional datasets (high-resolution brain anatomy and high-resolution genotyping,
respectively, plus cognitive datasets in both studies) will
be made available to the wider scientific community
starting around 2008.
(ii) The dissemination of standardized computerized
batteries used by publicly funded consortiums should be
made a priority for the respective funding agencies, as this
would facilitate convergence on common methods and
approaches. The resulting loose networks of investigators
should function more as ‘confederations’ than as contractually-regulated bodies. Encouraging such developments
will maximize flexibility in rapidly advancing fields, and
have benefits of increased statistical power at relatively
low costs.
(iii) As many investigators will continue to work
independently, we will need to decompose candidate
cognitive measures into their most fundamental component processes, with particular attention to the
potential contributions of non-executive components by
using appropriate control tasks when assessing executive
function [38].
(iv) The largest effect in ADHD appears to be associated
with tasks that require manipulation of spatial working
memory [33]. The extensive knowledge of the neuronal
substrates of spatial working memory in humans and
animals, and the possibility of minimizing verbal confounds, make this construct close to ideal for integrative
examination [45]. Individual studies in numerous centers
examining genetic effects, neuroimaging correlates, symptom subtypes, and diagnostic co-morbidity patterns of
working memory are either underway or have been
recently completed. The next stage in cognitive and
functional neuroimaging studies will need to contrast
working memory, inhibition, decision-making and motor
coordination in ‘cool’ and well as ‘hot’ contexts, as a means
of constructing neurobiological dimensional profiles of
ADHD and related disorders that would improve on
current symptom-based distinctions.
(v) Accounting for the potential effects of differences in
intra-individual trial-by-trial variability [39,59] on executive and non-executive tasks, including tasks of inhibition,
timing performance, perceptual, physiological and motor
processes requires a paradigm shift from focusing on point
estimates of means and variability to attending to timeseries analyses. This approach is unfamiliar outside the
field of functional imaging, but it could provide a closer link
to neurophysiological processes than traditional neuropsychological measures, at the same time as improving our
estimates of the variance associated with such
traditional constructs.
Iteratively and collaboratively developing such
measures, techniques and approaches will accelerate the
momentum towards a fuller understanding of the cognitive neuroscience of ADHD, its relation to molecular
genetic markers, and how these play out in the brain as
reflected by the next generation of neuroimaging studies.
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Vol.10 No.3 March 2006
Acknowledgements
The authors appreciate the assistance of Daniel Margulies in preparing
the manuscript, Suzanne Haber for providing the figure, and Phil Zelazo
for helpful comments on an earlier version. Supported by grants to F.X.C.
from NIMH (MH066393), The Stavros S. Niarchos Foundation, NARSAD,
and the Leon Lowenstein Foundation, Inc.
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