The top and the bottom of ADHD: a neuropsychological perspective

Review

The top and the bottom of ADHD: a neuropsychological perspective

Joseph A. Sergeant*, Hilde Geurts, Stephan Huijbregts, Anouk Scheres, Jaap Oosterlaan

Department of Clinical Neuropsychology, Klinische Neuropsychologie, Vrije Universiteit, Van der Boechorststraat 1, 1081 BT Amsterdam, Netherlands

Received 25 March 2003; revised 4 August 2003; accepted 25 August 2003

Abstract

Five models of attention deficit/hyperactivity disorder (ADHD) are reviewed. It is proposed that the cognitive-energetic model provides a

reasonably comprehensive account of ADHD by incorporating the features of both the inhibition and delay aversion models. It is suggested

that ADHD can only be accounted for by an analysis at three levels: top-down control, specific cognitive processes and energetic factors. It is

argued that a refined and conceptually comprehensive neuropsychological battery is needed to advance research in ADHD. A widely

distributed neural network involving frontal, basal ganglia, limbic and cerebellar loci seem implicated in ADHD.

q 2003 Elsevier Ltd. All rights reserved.

Keywords: Attention deficit/hyperactivity disorder; Executive function; Working memory; Inhibition; Delay aversion; Selective attention; Higher function

autism; Reward

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583

2. Reward, delay and inhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584

3. Energetics: when top meets bottom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

4. WM: maintenance, manipulation and monitoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586

5. Distinguishing WM and executive attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

6. EF and neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

7. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590

1. Introduction

Several theoretical accounts of Attention Deficit/Hyper-

activity Disorder [ADHD] have emerged over the last 10

years. The purpose of clinical research is to understand,

diagnose and treat disorders efficiently. To achieve this goal,

theoretical models are used as a systematic guide in

conducting research. The current models of ADHD have

different emphases. Some stress the frontal cortex [1], which

we refer to here as the ‘top’ of the system. Others emphasise

subcortical and even brain stem loci, which are referred to

here as the ‘bottom’ of the system [2]. This variation in

which locus of the brain is thought to be the cause of ADHD

reflects, in part, the symptomatic heterogeneity of ADHD

and the high degree of comorbidity with other disorders.

The variation in the models also reflects, in part, the

theoretical bias of the model builder. Models of how ADHD

should be explained also differ with respect to which point

in the development of the disorder they emphasise.

Currently, there is no model which incorporates develop-

mental data and can predict the developmental trajectory of

ADHD. In addition, the differentiation of ADHD from

highly associated (comorbid) disorders such as oppositional

defiant disorder (ODD) and conduct disorder (CD) has

proved neuropsychologically more difficult than first

expected [3]. Likewise, differentiation of the attentional

dysfunction in ADHD from highly associated disorders such

as dyslexia and higher function autism (HFA) has met with

some, but mixed success [4]. Use of contrasting clinical

groups with either specific brain damage or known

biochemical disorders, such as Phenylketonuria, that allow

0149-7634/$ - see front matter q 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.neubiorev.2003.08.004

Neuroscience and Biobehavioral Reviews 27 (2003) 583–592

www.elsevier.com/locate/neubiorev

* Corresponding author.

E-mail address: [email protected] (J.A. Sergeant).

http://www.elsevier.com/locate/neubiorev

the comparison of the hypothesised dopamine deficiency in

ADHD with conditions with an established dopamine

deficiency are currently ongoing. [5,6]. Critical relation-

ships such as the role of punishment and reward in ADHD

and their ensuing influence upon higher cognitive processes

have only been modestly established. With this in mind, we

briefly review the current models and examine issues which

require clarification for further advancement in the field.

Currently, there are five models of ADHD: the Delay

aversion model [7], the Behavioural Inhibition/Activation

model [8], the Inhibition model [1], the executive function

(EF) model [9] and the cognitive-energetic model [2]. These

five models can be conceived as varying along a continuum

of explanatory power: some models are less and others more

comprehensive. In this paper the five models are treated as

being conceptually nested within the cognitive-energetic

model being considered to be the most comprehensive.

The models differ in how far higher, cognitive processes

are considered the key to ADHD. A key term in ‘top-down’

models of ADHD is EF. A second term is working memory

(WM), a third concept is attention and a fourth is inhibition.

These terms are inter-related and this can be a source of

confusion. A function of this paper is to discuss the

neuropsychological support for these concepts, in particular

for WM, and with reference to ADHD. This discussion may

help clinicians unfamiliar with the area to appreciate the

significance of this work both in terms of its diagnostic and

therapeutic significance. For example, some reflection is

required on the validity of the concepts; it is not self-evident

whether WM and attention are entirely or only partly

independent [10], suggesting that the discovery of a specific

process deficit in ADHD will require separating attentional

and WM effects in task performance.

Both EF, inhibition and WM are concepts which reflect

recent cognitive conceptions of brain function. These terms

have been used in ADHD research as mechanisms to explain

the ADHD deficit [1,11]. Is EF a sufficient explanation of

ADHD? This question has been answered elsewhere as ‘no’

and will not be dealt with here extensively [2,12]. It will be

concluded that there is evidence of EF deficits in ADHD,

although it is unclear whether the EF account of ADHD is

process specific enough to differentiate ADHD from other

childhood psychopathological disorders, such as HFA.

In contrast to the work focusing on higher cognitive

concepts, there is a body of research which has stressed

bottom-up processes such as the role of reward-punishment

and motivation in ADHD [8,13,14,15]. Barkley [1] noted

that several early studies demonstrated that ADHD children

become satiated more quickly than controls. Several

researchers have suggested that the performance of children

with AD/HD is dependent on the presence or absence of

response contingencies [16,17]). Although researchers seem

to agree about an unusual sensitivity to reward as a

characteristic of children with AD/HD, disagreement exists

as to whether these children are over-sensitive or under-

sensitive to reward. Some researchers argued and showed

that children with AD/HD are less sensitive to reward

[17–19]. However, others have argued that children with

AD/HD show an increased tendency to look for immediate

reward [20,21]). There are also studies that have failed to

show that reward affects the performance of children with

AD/HD differentially [22,23]. In short, although there is

disagreement as to whether children with AD/HD are less or

more sensitive to reward, results of several studies have

suggested that children with AD/HD react differently to

reward than do control children.

Of interest for the separation of ADHD from associated

disorders is the finding that CD children are sensitive to

reward. For example, Shapiro, Quay, Hogan, and Schwartz

[24] showed that children with CD were more sensitive to

reward than normal control children. Thus sensitivity to

reward may not be specific to ADHD but be a mechanism

common to both ADHD and CD.

The effects of reward and punishment have been

associated in the cognitive energetic model as being

critical to the operation of the effort pool [2,25]. The

energetic component of that model might be considered

to be a ‘bottom-up’ system which registers and gives

feedback to the orbital frontal cortex on whether a

particular stimulus–response relation is satisfying or

aversive for the organism [26].

This emphasis has two justifications. First, in the early

to preadolescent stage of ADHD, motor restlessness has

historically been considered a key element of the disorder

[27]. The mechanism of transition by which overactive

children become more normo-active as adults is unclear,

although overactivity can still distinguish ADHD persons

from controls in adult life [28]. While activity in sleep and

sleep pattern in ADHD has a chequered history [27,29],

there does seem to be an association of sleep and

behavioural overactivity [30]. Consequently, the models

that only emphasise higher meta-cognitive dysfunction

may be inappropriate for this subgroup of hyperkinetic

children. Secondly, the bottom-up system possibly contains

interconnections between activity and delay aversion [31].

Schweitzer and Sulzer-Azaroff [32] noted that in contrast

to ADHD children, controls adopted a rational strategy of

delaying their responses and gaining maximum reward.

The ADHD children in that study also showed increased

motor activity with task exposure. Various observational

studies have shown increased activity of ADHD children in

a vigilance task [33,34] which may depend upon energetic

allocation [2]. More recently, reward has been shown to

activate the nucleus accumbens, suggesting the ‘bottom-

up’ systems may be critical to behavioural deficits in

ADHD [35].

2. Reward, delay and inhibition

The first term of the above heading clearly refers to a

bottom-up process [35–37]. It is unclear whether delay is

J.A. Sergeant et al. / Neuroscience and Biobehavioral Reviews 27 (2003) 583–592584

only a bottom-up process or is also a top-down process [38].

Inhibition is considered the dominant EF function in ADHD

[1], whereas Quay [13] would also attribute to it a bottom-

up role. This illustrates the need for studies which

disentangle the conceptual issues addressed here.

Delay aversion is supported by research which suggests

that ADHD children are unwilling to delay their need for

gratification. Sonuga-Barke et al [31] found that, when

given a choice between a small immediate reward and a

large delayed reward, ADHD children chose immediate

reward but only when this led to shorter total task duration

irrespective of the amount of reward available. When trial

length was paced by the experimenter, ADHD children

waited for the larger delayed reward.

On the basis of these results (and others), Sonuga-Barke

[39] argued that ADHD children are ‘delay aversive’ but do

not have a failure of inhibition. More recently, this position

has changed. There is evidence that both the delay aversion

and the Inhibition models can account for independent

variance in ADHD symptoms [40]. This revised dual

pathway model recognises two distinct subtypes of

ADHD. In the first ADHD is the result of the dysregulation

of action and thought due to poor inhibitory control

associated with the meso-cortical branch of the dopamine

system projecting into the pre-frontal cortex [38]. The

second ADHD subtype is conceived of as a motivational

style characterised by an altered delay of reward gradient

linked to the meso-limbic dopamine branch associated with

the reward circuits e.g. nucleus accumbens. Note, however,

this revised model does not address the issue of how ADHD

does or does not differ from ODD or CD.

The behavioural inhibition/activation model accounts for

ADHD by arguing that ADHD children have an under-

responsive BIS and an overactive BAS [8,13]. According to

Gray [37], the BIS involves the septo-hippocampal area and

its connections to the frontal cortex. The BIS signals

whether the subject should anticipate pleasure or punish-

ment. Evidence for an under responsive BIS has been

provided by Iaboni, Douglas and Ditto [41], who showed

that ADHD children do not exhibit increased skin

conductance level and, hence remain at the same level of

arousal, during extinction as healthy controls. An under

responsive BIS has been associated with diminished

norepinephrine inputs from the locus coeruleus [37]. The

BAS signals the state of the organism which is required to

meet the task situation. The ADHD children in one study

[41] also displayed faster heart rate habituation to signals of

reward which is consistent with the prediction that ADHD

have an overactive BAS [8,13].

Importantly, the Behavioural Inhibition/Activation

model predicts that ADHD, ODD and CD have a common

‘disinhibitory’ deficit. The Behavioural inhibition/activa-

tion predicts that on an inhibitory task, such as the Stop

Signal Task, these groups cannot be distinguished. This

prediction has in fact been confirmed by a meta-analysis of

stop-signal task [42]. Thus this model explains disruptive

disorders as a group in terms of the balance between the BIS

and the BAS systems. As will be seen later, the Cognitive-

energetic model follows the behavioural inhibition/acti-

vation model in this respect. The BIS/BAS model differs

from the Cognitive-energetic model in not accounting for

the role of high cognitive processes such as set-shifting in

childhood disorders.

The Inhibition model attributes all deficits in ADHD

children to a failure of inhibitory control [1]. Barkley

claimed that poor behavioural inhibition is the central

deficiency in ADHD and argued that the inhibitory deficits

are responsible for secondary deficiencies in other EFs.

Thus this model places inhibition above all other EFs. His

extensive review of the existing literature envisaged

inhibition as also regulating arousal and other energetic

factors. In contrast, Nigg [3] suggested that cognitive

inhibition is necessary to protect WM. Nigg argued that the

observed inhibition deficit in children with HFA might be

related to deficits in WM. This places WM squarely in the

EF terrain. Nigg’s proposal clearly links inhibition to a WM

deficit as did an earlier review [9]. Thus following this line

of reason, inhibition is fundamentally a top-down process,

as opposed to a bottom-up one. Paradigms are required to

differentiate the activation patterns which would be

predicted to differentiate these two views, followed by

their application in distinguishing ADHD from control and

other psychopathological groups.

3. Energetics: when top meets bottom

The cognitive-energetic model draws attention to the fact

that ADHD has effects at three levels: cognitive mechan-

isms such as response output, energetic mechanisms such

activation and effort and control systems of EF [2]. The

model suggests that disruptive disorders have common

deficiencies in EF control systems [42], and may be possibly

differentiated either at an energetic level or at specific

elementary cognitive stages. Thus the cognitive-energetic

model is an attempt to encompass both top-down and

bottom-up processes. The cognitive-energetic model does

not suggest a single EF deficit in ADHD. It suggests that

disorders such as HFA will have communalities with ADHD

in terms of inhibition of responding. Conversely, it assumes

that a range of EF-functions can, in principle, differentiate

syndromes. The model suggests that differences between

ADHD and ODD/CD should be sought in terms of reward

mechanisms influencing inhibitory control [23,43]. Anxiety

disorders are predicted to be oversensitive to signals of

punishment or reward [23,42]. Hence, in specifying the

difference between ADHD and say HFA, the cognitive-

energetic model predicts that the EF deficits found will be

dependent upon both task parameters and the processing

state of the child.

The delay aversion and behavioural inhibition/activation

models are considered as part of the cognitive-energetic

J.A. Sergeant et al. / Neuroscience and Biobehavioral Reviews 27 (2003) 583–592 585

model in terms of how the ventro-medial frontal cortex

influences EF [26,44].

The primary top-down processing, EF, is associated with

five principle domains: inhibition, set shifting, planning,

fluency and WM. In addition, top-down processing includes

emotional and arousal regulation within the EF construct as

well as the executive control of attention [45]. Other

definitions of top-down processing include what may be

synonyms such as goal directed behaviour for planning,

further subdivisions such as inhibition of automatic

responses and conflict resolution for inhibition, or even

cognitive operations that do not appear to be fully captured

by the five principle domains such as error detection [46,

47]. Brain networks associated with executive control

include the anterior cingulate cortex (ACC) and supplemen-

tary motor area, the orbitofrontal cortex, the dorsolateral

prefrontal cortex (DLPFC), portions of the basal ganglia and

the thalamus [47].

As noted earlier, a primary task of research in ADHD is

to establish the relation between inhibition and WM,

particularly in response conflict studies, such as, the Stroop

and Flanker tasks. The importance of determining the

relation between inhibition and WM was noted earlier in

relation to Nigg’s [3] proposal that HFA inhibition deficits

might be explained by WM, whereas ADHD WM deficits

can be better explained by inhibition dysregulation [1]. The

idea that response conflict might differentiate ADHD

children from children with other disorders and controls

raises the question: what is the nature of the response

conflict? It might be due to poor mapping of stimulus-

response relationships held in WM, although then the

question arises whether problems result from stimulus-

inherent interference or from deficient organisation of the

S–R associations? On the other hand, response conflict

could, in part, be explained by an aberrant reward system in

ADHD, which fluctuates while performing a task [48].

Thus two levels of explanation may be required for some

ADHD deficits. The first is at the level of the maintenance of

stimulus-response relationships in WM. This point we will

further address in the discussion. The second level of

explanation involves the role of the reward-punishment

system which has been associated with structures such as the

nucleus accumbens and amygdala [26,49]. It cannot be ruled

out that these processes co-occur. We will now focus on the

level concerned with maintenance of stimulus-response

relationships in WM. The way in which inhibition may be

one aspect of WM will be described, i.e. be included in the

executive control aspect of WM and how the concept of

WM may be a better concept than inhibition in explaining

outcome of response conflict tasks in different contexts.

4. WM: maintenance, manipulation and monitoring

In this section, we address evidence concerning the

functional anatomy of WM and monitoring. Two types of

process in WM may be distinguished: ‘maintenance’ and

‘manipulation’ of information on-line, deal respectively,

with the hold function versus the manipulation and/or

transposing of information e.g., reversing the order of a list

of numbers. The maintenance function of WM includes

basic mnemonic processes such as active selection,

comparison, and judgement of stimuli held in short-term

and long-term memory, whereas the manipulation and

monitoring function is involved in ‘strategic’ or higher level

executive processes [50,51]. Smith and Jonides [52]

reviewed a number of neuroimaging dissociations between

maintenance and manipulation processes of WM. Spatial

maintenance WM is thought to be mediated by a network of

predominantly right-hemisphere regions that include areas

in the posterior parietal, occipital, and frontal cortices.

Smith and Jonides argued that inhibition is mediated by the

left-hemisphere prefrontal region and that it can be

dissociated from verbal maintenance and rehearsal pro-

cesses. Petrides [51] argued that the role of the mid-DLPFC

in visual WM is not confined to the maintenance of

information, but also involves the monitoring of

information.

Bunge, Ochsner, Desmond, Glover and Gabrieli [53]

found overlapping activations for maintenance and manipu-

lation during a verbal WM task bilaterally at the

ventrolateral and DLPFC, anterior insula, ACC and parietal

cortex. However, activation of the right middle frontal gyrus

and the left inferior gyrus was correlated with the ability to

resolve interference (manipulation) efficiently, whereas

ACC-activity was correlated with maintenance. Owen

et al. [54] found a significant change of blood-flow in the

right mid-ventrolateral frontal cortex, but not in the mid-

DLPFC, during maintenance in a ‘spatial span’ WM task.

During a spatial ‘2-back’ task, requiring subjects to

manipulate WM content, blood flow increases were

observed in both frontal regions. A direct comparison

between the two tasks revealed that only manipulation

results in significantly greater activity in the right mid-

DLPFC. Thus activation of DLPFC may be dependent on

how much a task requires the ‘manipulation’ process as

in both the N-back task and the self-order-pointing (SoP)

task [50].

The ‘manipulation’ process in WM is also dependent

upon the ACC [47,53,55–59]. ‘Manipulation’ seems also to

be dependent upon cognitive expectancies. Carter et al. [56]

varied subjects’ expectancies for congruent and incongruent

stimuli in a variant of the Stroop colour naming task. When

expectancy for incongruent stimuli was high, no response-

related increase in ACC-activity was present. In contrast,

expectancy for congruent stimuli resulted in an increase of

ACC-activity when an incongruent stimulus was presented.

It was concluded that, when incongruent stimuli were

expected, control processes were engaged to overcome the

prepotent tendency to read the word. This top-down control

seems particularly relevant to the issue briefly noted earlier:

delay aversion may be the result of a mismatch between


expectancies and predicted reward, which may be linked to

ACC functioning in ADHD children [43,60]. Consequently,

delay aversion may be dependent upon top-down processes

unaccounted for by the delay of gratification model but can

be explained by the cognitive energetic model.

What is the significance of this research for ADHD? It

firstly informs us that simple conflict tasks such as the

Stroop are more than just inhibition tasks. They also involve

WM. The specific form of WM which is used in a conflict

task depends upon the precise top-down process(es) tasks

require. Thus, from such measures and tasks there is task

impurity making it difficult to conclude specifically which

process or interaction between WM and inhibition is

accounting for the observed results. Since there is some

evidence that ADHD adults perform the Stroop by not

activating the ACC but bilaterally activating the insula [61],

studies of response conflict in children are urgently required

in order to determine whether this finding also occurs in

children with ADHD. If this were found to be the case, a

theoretical account of ADHD would have to incorporate

why the insula is active and why the ACC is inactive in

ADHD. One possible explanation may be that activation of

widely distributed brain networks reflects an inefficient

match between required cognitive control and selective

firing of the usual specific loci: too much activation is

uneconomic; producing too little output for too much brain

labour.

5. Distinguishing WM and executive attention

WM evokes the association of a mechanism associated

with purely memorial process. However, Baddeley and

Logie [62] noted that this understandable association is due

to how Baddeley and Hitch [63] approached the issue. Had

they approached the concept from an attentional point of

view, then it would probably have become termed ‘working

attention’. The host of models which describe WM, their

differences and similarities have been summarised by

Miyake and Shah [64]. For present purposes, a working

definition of WM would be active long-term memory,

responding to selective attention demands. In a study

investigating the relationship between visuospatial search

and object WM, Pollmann and von Cramon [65] showed

that the right DLPFC is significantly involved when

difficulty of the object-recognition process increases and

more attention is demanded. This finding suggests that the

right DLPFC is invoked in executive control of selective

attention/orienting. In support of this, Casey et al. [57]

demonstrated in a response conflict task, the flanker task,

that activation in both the ACC and DLPFC increased as a

function of interference from the response conflict induced

by the flanker stimuli with increasing top-down control.

This research indicates that there is clearly both specificity

of activation but also an overlap in processes subsumed

under the concepts of WM and selective attention. Further,

the activation observed is dependent upon the controlled

processing demands exerted by the task. Consequently,

researchers in ADHD will need to account for both

processes in the disorder. This may be particularly

important in studies with children who not only have

ADHD but also either dyslexia or language impairments

[66–68].

Selective attention has been defined as serial, effortful

controlled processing which can operate either in a focused

or in a divided mode [69]. Kane et al [45] refer to executive

attention to stress the attentional control component of

selective attention. Cowan [70] emphasised the role

of activating long-term memory and the temporary nature

of this activation in selective attention. It is clear that the

concept of WM is not simply attention and it is also not

simply memorial. The associated slave systems of the visual

scratch pad and the auditory loop reflect the temporary and

modality specific nature of activated long-term memory

stores [62]. Thus WM may be better conceived of as the

selective activation of long-term memory, which requires

executive attention. Executive attention appears to have the

property of inhibitory control [45], particularly in tasks

where shifts of covert and overt attention are required [57].

Executive attention is also required in what is termed

monitoring. The monitoring function may vary as a function

of the attentional load, or complexity of S–R relationships,

and possibly the length of the delay between stimulus

presentation and response execution. Since monitoring may

be at the heart of the inhibition model [1], clarity concerning

this concept and its operationalization would be helpful in

future ADHD research. Furthermore, and currently not well-

documented, is the fact that tasks commonly called WM

tasks may vary along a dimension of controlled-automatic

processing and generate different activation patterns. The

automatic-controlled dimension may be not only of

importance for brain activation patterns but also for the

deployment of attentional resources via an effort pool [2].

6. EF and neurotransmitters

EF research in relation to the biochemistry of ADHD

constitutes an invaluable contribution to theoretical

accounts of cognitive dysfunction in ADHD and may give

some future clues to the aetiology of the disorder. In order to

delineate exactly what occurs in ADHD, neurotransmitter

availability in specific brain regions must be investigated,

since some aspects of EF and WM might depend on

particular neurotransmitters. Allocation of energetic

resources is probably strongly associated with the avail-

ability of particular neurotransmitters. Methylphenidate is

the current pharmacological treatment of choice in ADHD

[71]. Volkow, Fowler, Wang, Ding, and Gatley, [72] have

documented that in the human brain therapeutic doses of

methylphenidate block more than 50% of the dopamine

transporters and significantly enhance extracellular DA, an


effect that appears to be modulated by the rate of DA

release. Volkow et al [72] postulate that methylphenidate’s

therapeutic effects are in part due to amplification of DA

signals, and that variability in responses is in part due to

differences in DA tone. Further, methylphenidate’s effects

may also be context dependent. Methylphenidate-induced

increases in DA are also associated with its reinforcing

effects but only when this occurs rapidly, as with

intravenous administration [72]. In order to have some

insight into the significance of neurotransmitters in

cognitive functioning and its potential for understanding

the nature of ADHD, a selective and very brief account is

given of EF studies in relation to neurotransmitters relevant

for ADHD. Since a hypofunction of dopamine may account

for ADHD [72,73], it is important to consider the

similarities and dissimilarities in EF between ADHD and

PKU, an inherited metabolic disorder which has been

established to represent a dopamine deficiency. We briefly

discuss the relation of dopamine with EF.

Both dopamine and norepinephrine are important

modulators of the attentional system [74]. There is evidence

for a relation between acetylcholine and orienting/selective

attention, norepinephrine and vigilance/sustained attention,

and dopamine and executive control [47]. Dopamine

availability has been found to influence some but not all

cognitive functions mediated by PFC. McDowell, Whyte,

and D’Esposito [75] administered a dopamine agonist,

bromocriptine, to patients with lesions in the PFC.

Interestingly On tasks requiring both manipulation and

monitoring of WM performance improved with bromocrip-

tine. In contrast, when maintenance of WM was manipu-

lated, performance did not improve with bromocriptine.

One study reported that bromocriptine improved perform-

ance on prefrontal measures only in subjects with lower

WM capacity, whereas it impaired performance in subjects

with higher WM capacity [76]. The results suggest that the

effect of a specific neurochemical agent may be dependent

on the type of cognitive operation used, the individual

differences and possible interactions between task and

cortical network affected by the neurotransmitters [77].

Thus, when the effects of methylphenidate on the cognitive

performance of ADHD children is investigated, it is

important to consider which aspects of cognition are

influenced by which component of the medication, i.e. do

dopaminergic and noradrenergic enhancements target

specific aspects of WM or does noradrenergic enhancement

change the level of vigilance which might result in overall

cognitive improvement? It has been suggested that inability

of ADHD children to delay their need for gratification [31]

may be influenced by methylphenidate. In this respect,

Mehta et al. [72] were able to dissociate maintenance from

manipulation in a spatial WM task of the Cambridge

Neuropsychological Test Automated Battery (CANTAB)

using methylphenidate. The maintenance function of this

task involved the online storage and active response

organisation based on the retrieval of information

from posterior cortical association systems. In contrast,

manipulation involved inhibition of previously learnt,

successful S–R associations. Maintenance resulted in

drug-induced changes in regional cerebral blood flow in

the posterior parietal cortex, whereas manipulation led to

methylphenidate induced changes in left DLPFC. Methyl-

phenidate has been shown to normalise CANTAB spatial

WM performance in ADHD [78,79]. More studies are

required to establish the availability of neurotransmitters

and their role in cognition in ADHD.

Comparison of ADHD performance with PKU children

is relevant here in view of the well-documented dopamine

disorder in PKU children. PKU children have difficulty in

sustained attention [5], poorer inhibition of a prepotent

responses, and poorer attentional flexibility [6]. However,

no deficit was found in PKU children for the maintenance

function of WM, neither in orienting nor in stimulus-

inherent interference suppression [6]. The contrast between

PKU and ADHD is a useful one in informing us of the

differences between an established and putative disorder of

dopamine in childhood.

7. Discussion

In this paper the main thrust has been to consider in detail

the implications of the top-down and the bottom-up

processes which may be implicated in ADHD and other

childhood psychopathological disorders. Memorial span

task requiring no other concurrent function were distin-

guished from span tasks requiring concurrent processing. In

addition, it was noted that concurrent processes such as

monitoring or selective attention are intimately associated

with WM [51]. Hence, as a working hypothesis, it is

assumed here that WM ¼ short term memory (or activated

long-term memory) þ selective attention [10,64].

In previous research with the Sternberg memory

scanning paradigm, differences between ADHD and control

children could not be accounted for by the increase in

memory load [80–82]. Interestingly, this effect has been

replicated in ADHD comparisons with normal controls

using a non-verbal visual WM task, the Self-ordered

Pointing task [4]. However, other WM tasks such as the

N-back task have differentiated ADHD children from

controls [83]. Shallice et al concluded that the EF

differences found in their study between ADHD children

and controls might be associated with a higher-level effort

mechanism and did not simply reflect only WM [2].

It has been noted that one may consider WM as the

combination of short-term memory plus selective attention

[64]. This definition stems, in part, from research originat-

ing in the controlled processing literature on selective

attention [69,84]. Selective attention was defined in that

literature as a limitation in the rate of controlled processing

located in short term memory. This definition led to a series

of studies using memory and visual search paradigms in


ADHD children [25]. The tasks used in that research

programme were memory maintenance tasks (with varying

attentional load) requiring a simple concurrent process:

target detection. From that research it was evident that the

maintenance function of WM was intact in ADHD children

both as a controlled process [80,81] and as an automatic

process [85]. The corollary of this is that, when attentional

demands are manipulated parametrically, ADHD children

do not have a deficit in selective attention. However, when

monitoring of errors and adjustment in performance was

required, ADHD children had a deficit of response

adjustment compared with controls [86]. This is where

ADHD children may differ from for example PKU children,

since error monitoring and adjustment appear not to be

problematic in children with PKU [6].

Engle et al [10] have addressed the relation between EF

and WM. Engle et al. suggested that, before one can draw

conclusions on the executive element of EF, one needs to

account for such factors as general and fluid intelligence and

short term maintenance memory. When these factors have

been partialled out, the remaining variance may be

considered as EF or WM-manipulation and monitoring.

To reach this goal, studies will need to employ structural

equation modelling in order to partial out the variance

shared between these various processes.

Specificity of dysfunction is generally achieved in

neuropsychology by a double-dissociation. Double-dis-

sociation requires that two clinical groups can be shown to

differ from one another such that one group shows deficits in

one task (A) but not the other (B), whereas the second group

shows deficits on test B but not A. An attempt to obtain a

double dissociation between HFA and ADHD has recently

been conducted by our group [4]. The comparison of HFA

with ADHD indicated that the EF deficit was more general

and severe in children with HFA. HFA children had deficits

in six cognitive domains: inhibiting a prepotent response,

interference control, visual WM, cognitive flexibility, verbal

fluency, and planning. In contrast, the ADHD group was

deficient in three areas of EF: inhibiting a prepotent

response, cognitive flexibility, and verbal fluency. Despite

the variety of differences between controls and HFA and

ADHD respectively, a striking finding from that study was

that the ADHD and HFA groups could only be differentiated

from one another on visual WM, whereby the HFA group

had greater visual WM deficits than ADHD children.

The results reported by Geurts et al [4] contrast to those

of Ozonoff and Jenssen [87] and of Nyden et al. [88].

Ozonoff and Jenssen concluded that children with autism

have deficits in planning and flexibility, but not in

inhibition. ADHD children in the Ozonoff and Jenssen

study exhibited deficits which were exactly the opposite. In

the Nyden study, children with ADHD had deficits in

flexibility and response inhibition. The autistic children had

deficits in response inhibition only. It is unclear whether the

differences between the studies may be explained in terms

of comorbid disorders, such as ODD or CD [9,12].

The importance of also assessing ODD and CD in

double-dissociation studies has been demonstrated by

Seguin, Boulerice, Harden, Tremblay, and Pihl [89]. Seguin

et al used the SoP [Petrides, 199]. Children with physically

aggressive behaviour had poorer visual WM than children

who were aggressive but not physical. Seguin et al showed

in their analysis that ADHD did not account for the EF

deficits in children with a history of physical aggression.

Further research is needed to establish the neuropsycholo-

gical commonalties and differences between ADHD, ODD/

CD and the comorbid groups [40,90–92].

This selective review of the literature suggests that the

discovery of a neuropsychological profile for each of the

childhood disorders will be dependent upon using sophis-

ticated processing batteries and analysis techniques. The

batteries need to control for on the one hand general effects

of short term memory, flexible intelligence and, on the other

hand, both modality effects as well as the distinction

between maintenance and manipulation. The existing data

suggest that childhood disorders can be differentiated only

quantitatively and not qualitatively in contrast to predictions

from the double dissociation paradigm. The target of

interest would seem to be WM for the differentiation

of HFA from ADHD as well as ODD/CD. Contrasting levels

of encoding: graphic, orthographic, semantic, appears a

fruitful approach for differentiation of dyslectic children

from children with pathological conditions. Comorbidity of

psychopathology with dyslexia may be expected to lead to

complex process interactions, which may not lend them-

selves easily for group differentiation.

Throughout this paper, there has been noted that the

reward-punishment relationship is essential to compre-

hending ADHD [36]. In addition, it has been argued here

that there is reason to believe that the differentiation of

ADHD and CD is to be found in this relationship [8].

Recently, it has been found that children rated to be

ADHD, when administered a punishment trial following a

period of reward trials, responded like controls to avoid

punishment [93]. This finding would seem a useful starting

point in attempting to discriminate ADHD children from

CD children. This finding contrasts with the position of

Newman and Wallace [94] that inhibitory deficits would be

more apparent in ADHD, when signals of punishment are

introduced. Two possible caveats here should be noted: the

period between signal and administration of reward/

punishment may be critical [32], since this is related to

dopamine functioning; secondly, the magnitude of sub-

jective expectation of reward/punishment may determine

performance [21].

We have suggested that comparison between the five

current models of ADHD is a fruitful exercise. It has

become evident that two models: inhibition and delay

aversion independently explain variance in ADHD symp-

toms. This finding indicates in itself that neither model is

sufficient to inform us what the ADHD deficit is. Therefore,

a model is needed which incorporates both models. We have


argued that the cognitive energetic model is currently the

most comprehensive account of ADHD. Nevertheless, there

remains much to be done to achieve the specificity required

to account for childhood psychopathological disorders. A

comprehensive account of ADHD in relation to the

associated disorders has yet to be assembled. In order to

get to the bottom of the disorder, not only top-down but

bottom-up processes need to be accounted for in ADHD.

Acknowledgements

The first author wishes to express his indebtedness to Drs

F.X. Castellanos and E. Sonuga-Barke in discussing their

work in relation to this review.

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