Open access peer-reviewed chapter

Traditional Scales Diagnosis and Endophenotypes in Attentional Deficits Disorders: Are We on the Right Track?

Written By

Annabel Jiménez-Soto, Juan Pedro Vargas, Estrella Díaz and Juan Carlos López

Submitted: 05 October 2020 Reviewed: 15 October 2020 Published: 17 December 2020

DOI: 10.5772/intechopen.94507

From the Edited Volume

ADHD - From Etiology to Comorbidity

Edited by Hojka Gregorič Kumperščak

Chapter metrics overview

735 Chapter Downloads

View Full Metrics


The concept of ADHD has changed widely through the history of mental health classification manuals. In the past three decades, the number of ADHD diagnoses has hugely increased worldwide. One of the reasons to explain this fact could be the lack of precision, differentiation and adjust of the criteria and indicators of this disease. Research has detected as well, some subjectivity bias in the traditional assessment (based in questionnaires and behavioral scales), which is affecting to the precision in the diagnose and to the further adjustment to the treatment. In this regard, these diagnoses are based in symptoms but not in etiology of the disorder. Therefore, different disorders will share the same treatment, regardless etiology. A different approach is based on the study of vulnerable traits associated with impulsivity and attentional deficit. In a quantitative fashion; these traits could be used to define a specific endophenotype. This view would allow us a more precise medical/psychological assessment focus on patient along the life spam, avoiding a diagnostic based on the number of symptoms. Here, we discuss about the differences between traditional diagnosis scales and the possibilities to find endophenotypes in order to address a specific treatment.


  • ADHD
  • assessment
  • diagnosis
  • impulsivity
  • endophenotypes

1. Introduction

Attention Deficit Hyperactivity Disorder, better known as ADHD, is one of the most common diagnoses among children nowadays and its prevalence worldwide is estimated at 5% for children, and 2.5% for adults [1]. However, the prevalence showed in different studies and countries varies quite a lot and does so in wide ranges, which is commonly associated with theoretical and methodological approaches to understand and assess the disorder. Understood as a brain disorder, ADHD has been defined on multiple occasions trying to account for the symptom variability and heterogeneity present in people with ADHD [2, 3, 4, 5]. Due to the great symptomatic variability showed by these patients, the lack of biological or genetic markers of the disorder recognized by the APA itself and the heterogeneity expressed in the cognitive functioning of these individuals [1], explanatory models of the disease are increasingly abundant and diverse, making the diagnose a complicated, questioned and commonly criticized process.

There are different times in the history of medicine, psychology and psychiatry in which several authors identified ADHD as a syndrome for the first time [6, 7, 8], even though it is not until 1968 when the disorder was full described in one of the main manuals of mental health [9]. In its second edition, the diagnostic category for childhood and adolescence included a new syndrome that was termed as “hyperkinetic reaction”. The manual described this syndrome as a disorder characterized by overactivity behavior, restlessness, easy distraction and short attention span. It was more common in children and generally improved during adolescence [9]. Afterwards, the DSM III published for the first time a reference to the disorder as we know it currently. Named as “attention deficit disorder” and included into the behavioral category of diseases that occurs in the infancy or childhood, the diagnosis was subdivided into two subtypes: attention deficit hyperactivity disorder and attention deficit non-hyperactivity disorder. On the one hand, it is mentioned among the clarifications that the symptoms may not be directly observable by the clinician [10]. In addition, it included indicators among the criteria of the disorder such as “often does not seem to listen” or “often acts before thinking”, constructs that are hardly observable in an objective way.

In 1987, the American Psychiatric Association revised the text of the third manual that resulted in the DSM III-R. This review included ADHD as a discrete disorder, for which it was necessary to display both related symptoms, inattention and hyperactivity. If just inattention symptoms were present, but not the hyperactivity, the diagnosis was “undifferentiated attention deficit disorder” [11]. Despite the increased requirements for the diagnosis, the number of people being diagnosed kept growing. A few years after the fourth version of the diagnostic manual of mental disorders, published in 1994, described the basis of what we currently understand by ADHD. The disorder described affected three main axes: attention, hyperactivity and impulsivity. And it gave rise to different subtypes of the disorder that were classified into inattentive, hyperactive–impulsive, or combined [12]. Although it has been more than twenty years since this description, it is remarkable that an exhaustive differentiation between hyperactivity and impulsivity as constructs has not been made yet when ADHD is defined or assessed. Since then and until the last release of the Diagnostic Manual of Mental Disorders, there were no remarkable changes in the way of defining the disorder [13]. In contrast, the latest version of the manual, DSM 5 [1], makes some important changes in the disorder definition, its causes and specific characteristics. In this edition of the manual, ADHD is defined as a neurodevelopmental disorder, and in an inconsistent way, the age of symptoms onset was increased from seven to twelve years old. This change in the age of symptoms onset also applies to the ICD-11 versus ICD-10 versions [14, 15]. Furthermore, one of the most significant changes in DSM 5 for ADHD comes from the introduction of a paragraph that states: “The signs of the disorder may be minimal or absent when the individual receives frequent rewards for appropriate behavior, is under close supervision, in a new situation, participating in especially interesting activities, has constant external stimulation (e.g., with electronic displays), or is in situations where he or she interacts face-to-face with another person (e.g., in the clinician’s office)” [1]. What could that possibly mean? We do not know what is the meaning of this statement, but the result of DSM 5 changes is a sharply increased prevalence of the disorder. This has gone hand in hand with modifications in the DSM, since diagnostic criteria in the recent version remain vague. That is the reason why nowadays is easier to receive a diagnosis based on these criteria than twenty years ago. DMS 5 relativizes the importance of symptoms, which goes from assuming “clear evidence of clinically significant dysfunction in the social, academic or occupational sphere” [13] to “interfering with or reducing the quality of life of any of them” [1]. In addition, the number of symptoms needed to meet the criteria for inattention or hyperactivity-impulsivity was reduced from six to five for adolescents and adults over 17 years old.


2. ADHD traditional assessment: what could be wrong?

Nowadays, the most used instruments to assess ADHD are tests and scales of behavior, which are usually completed by parents and teachers. These tools are generally based on the diagnostic indicators of ADHD, describing some symptoms or behavior included in the disorder. Although behavior scales and tests currently remain the most widely used assessment method, the validity and reliability of these tools are not consistent in the literature [16]. Even when clinical practice guidelines on ADHD have recommended a neuropsychological assessment of symptoms, beyond the subjective reports provided by scales and tests, at present, this assessment is still considered dispensable to establish the diagnosis of ADHD [16, 17, 18, 19]. Fortunately, the use of neuropsychological tests to assess the effects of these patients is becoming increasingly common.

Perhaps one of the most important problems when assessing ADHD is the bias produced by the use of tests and behavioral scales. The subjective experience of responding to a survey can record biases such as social desirability, the anticipation of the hypothesis, and even false and premeditated responses. In addition, when relatives are the ones who report on the behavior of a third party, the problems of subjectivity are even greater [20]. This fact has been repeated for more than thirty years. Parents and teachers tend to rate up to 50% of healthy children as inattentive, distracted, restless or hyperactive [21]. This subjectivity is also sensitive to the halo effect, which has shown a bilateral effect between inattention and hyperactivity-impulsivity symptoms. That is, the greater the number of symptoms in one of the criteria, the higher the score in the other [22]. Moreover, depending on whether it is the mother, father or teacher who fill in the questionnaires, patients can be diagnosed with one or another subtype of the disorder [23], with parents reporting the most symptoms [24]. Besides, the correlation between parents and teachers’ reports is generally low [25].

Another assessment problem about ADHD is related to development. That is, evaluation and diagnosis do not take into account that the symptoms of the disorder are not stable during the life span [26]. Additionally, according to the scientific literature, the persistence of the disorder in adulthood varies between 4 and 66% [27]. These diagnostic differences between youths and adults are usually explained by the existence of undiagnosable subthreshold ADHD in adults [28], or the existence of two syndromes with different trajectories [29]. In this regard, a recent study that followed a sample of people diagnosed with ADHD for nine years found that when the participants were 18 years old on average, only 16.7% still met the criteria for the diagnosis of ADHD, and 11% were classified as having sub-threshold ADHD [30]. This means that 72.3% of the whole sample did not show any disturbance after nine years of follow-up.

The fact that the symptoms are not stable over time is something we have known for over two decades now [26]. Nevertheless, the diagnostic criteria and indicators are the same for all age groups. The only difference we can find is related to the number of indicators needed to meet the criteria, where adults need five instead of six indicators to meet the criteria of inattention or hyperactivity-impulsivity. Something similar happens concerning symptoms shown in people with ADHD. Despite the efforts to find functional subtypes within the disorder, findings have displayed a high diversity/heterogeneity in the symptoms and disturbances of the disorder [31, 32], that resembles the behavioral repertoire of these individuals [33]. This fact makes extremely difficult to accept the definition of the disorder as it is currently understood.

2.1 Comorbidity gets worse the current ADHD definition

Another great problem reported is the wide and diverse comorbidity commonly found in the disorder, such as the concurrent presence of a dissocial disorder or oppositional defiant disorder [34]. The problem is even greater if we consider that the three main axes of the disorder (hyperactivity, impulsivity and inattention) are affected in a wide variety of psychopathological conditions [35]. According to the DSM 5, there are up to 16 comorbidities frequently associated with the disorder, such as oppositional defiant disorder, autism spectrum disorders, anxiety, depression, intellectual disability, or other neurodevelopmental disorders [1]. They have also been assessed for their concurrent occurrence with ADHD, coordination disorders, substance abuse disorders and even Tourette’s syndrome [35].

Several authors have suggested that comorbidities are variable because they are specifically related to each subtype of the disorder. Thus, externalizing problems seem to be more comorbid in patients with hyperactive–impulsive or combined subtype, while internalizing is more common in people with inattentive subtype [36]. According to APA, ADHD combined subtype is associated with the oppositional defiant disorder in approximately 50% of the diagnosed cases and up to 25% of the inattentive subtype, whereas behavioral disorders among all the diagnoses are present in a quarter of them [1].

In this regard, people with ADHD generally display more symptoms associated with anxiety disorders or depression than general population, and these comorbid symptoms are also stable over time [37]. Concerning depression, a meta-analysis with more than 300,000 participants reported that ADHD was also comorbidly related to the occurrence of suicidal behavior [38]. These facts make that pure diagnosis of ADHD cases are very rare and complicated to find [35]. For instance, a study analyzed 1919 cases of diagnosed ADHD, finding that 66% of participants had at least one comorbidity with learning, sleep, anxiety or opposition disorders [39]. The overlap of symptoms in ADHD and other disorders is a quite important problem in the assessment, and it represents a challenge for its correct diagnosis [40]. This could be the reason why diagnostic criteria and indicators of the DSM 5 show a lack of validity. It is difficult to think of a child who is not “hard to keep up with” or “runs or talks too much.” That is, many times the symptoms described for ADHD define the usual and normal behavior of children [33].

We tried to assess how people interpret the measure of ADHD symptoms in a survey made by our laboratory. Using the diagnostic criteria to explore the symptoms of ADHD among the general population, participants indicate as present in their behavioral repertoire on average at least five symptoms of the disorder, being more frequent the symptoms of inattention than those of hyperactivity/impulsivity [41]. With these indicators, many children who do not suffer from this disorder, but show some behavior outside of what is considered “normal”, could easily be diagnosed with ADHD if just the DSM’s criteria were used to assess the disorder. Thus, the current overdiagnosis might be due, among other problems, to the lack of consensus in the evaluation criteria [42]. For some authors, this fact makes DSM not to be reliable enough for ADHD diagnosis [33], and therefore other criteria should be considered for an accurate ADHD diagnostic [43].

It might appear that symptoms described in the indicators of ADHD usually define the normal behavior of children [33]. This explains why the criteria for maintenance of the symptoms in time and the contexts in which they appear are so important to evaluate when assessing this disorder. Currently if a child presents some behavior related to the description of ADHD, it is more than likely that he or she will receive the diagnosis. Although DSM 5 and ICD 11 focus on specific situations and how long a symptom lasts, the environmental triggers are not usually assessed, addressing the assessing to behavior patient. What is most disturbing is the fact that diagnosis is also defined in light of these described behavior, which would lead to a problem of reification pointed out by several authors [44, 45, 46].

2.2 Hyperactivity and impulsivity: different concepts, same diagnose

Another problem of criteria and indicators is related to hyperactivity and impulsivity concepts. Although both are defined in the DSM with different indicators for the disorder (6 for hyperactivity and 3 for impulsivity), they share the diagnostic category of hyperactive–impulsive [1], giving rise to a single subtype of the disorder. However, we currently know that hyperactivity and impulsivity belong to different constructs and domains and they are not understood as parts of a continuum.

Impulsivity is defined as a multidimensional concept, and it includes problems in decision making processes regarding long/short term reinforcement, a lack of behavioral inhibition related to future consequences, and an inappropriate behavior [2, 47, 48]. In addition, impulsive behavior displays a lack of sensitivity to negative consequences, and fast and unplanned responses [49]. Thus, impulsive behavior is a kind of no reflective behavior defined as a failure in inhibitory processes. This might be due to lower development of executive functions, getting worse in specific context as familiar or academic situations [50, 51]. It is commonly agreed that an impulsive person is one who “usually speaks or acts without reflection or caution, allowing himself or herself to be carried away by the impression of the moment”. In contrast, we define hyperactivity as a “behavior characterized by excess activity”.

Although both are expressed through lack of control, impulsivity could be better understood as a lack of cognitive inhibition and hyperactivity as a lack of motor inhibition. To verify this, some authors have evaluated the levels of hyperactivity and impulsivity in a sample of more than 10,000 healthy children, concluding emphatically that the measures of hyperactivity and impulsivity address different constructs [52]. In this regard, it is not complicated to imagine a person of any age who can be very energetic, in terms of activity, and yet be extremely reflective in terms of decision making. In the same way, we can also imagine someone who is not very energetic in his or her daily activities and notwithstanding is very impulsive when it comes to decision making. In light of all these results, we can see that the assessment of ADHD is a rather complicated process. There are no specific diagnostic tests to assess the disorder objectively, and the current assessment process is not free of problems and biases, which makes diagnosis even more difficult. The assessment of ADHD cannot and should not be carried out exclusively using questionnaire reports and behavior scales [53]. Only the combined use of reports and neuropsychological tests would produce an adequate assessment of the disorder [54], since the scales and tests do not measure the same as the experimental tasks [55].

2.3 ADHD treatment: do all roads lead to Rome?

According to DSM 5, a diagnosis of ADHD can lead to three different subtypes of the disorder: predominantly inattentive, the criteria of inattention is met, but not those of hyperactivity-impulsivity; predominantly hyperactive–impulsive, the criteria of hyperactivity-impulsivity are met, but not those of inattention; and the third subtype would be a combined presentation of the disorder, where both traits of inattention and hyperactivity-impulsivity would be affected [1]. In addition, the manual also includes two diagnostic categories when the criteria for the main subtypes are not met: other specified attention-deficit/hyperactivity disorder and unspecified attention-deficit/hyperactivity disorder. Nevertheless, there are no main differences in the therapeutical interventions and psychopharmacological treatments for these ADHD subtypes, and all of them will probably receive the same health/medical cares.

ADHD is usually treated as a problem of neurochemical dysfunction, accordingly the treatment commonly used is the administration of psychostimulants, which in a high number of cases reduces the frequency and duration of symptoms [17]. That is, regardless of the subtype diagnosed, patients are usually treated, at least in the initial phase, with the same drug. However, we now know that, although the systemic administration of stimulants such as methylphenidate, a psychostimulant that acts by inhibiting the reuptake of dopamine and noradrenaline, improves the symptoms for a while in most cases, but not in the long term [56]. This situation causes the beginning of continuous changes in treatments. This fact clearly indicates that the problems associated with this disorder depend on several pathways. One of them seems to be dependent on prefrontal dopaminergic pathways, and at the other extreme, we can find the opposite profile, triggering a pattern probably independent on these prefrontal pathways. In any case, at present we do not have diagnostic tools that allow us to identify these different populations within the same diagnosis [41].

2.4 ADHD as a neurodevelopmental problem: is it possible to find an anatomical profile?

Although the ADHD is considered as a neurodevelopmental disorder, in the last years there is an increasing number of studies that point to the possibility that ADHD can emerge in adults without previous history of the disorder [57]. These studies may indicate that at least some of the ADHD cases are not related to neurodevelopmental issues. In any case, there are an overwhelming number of studies that suggest a link between the development of the central system and the ADHD. The disturbances related to ADHD are extremely complex, especially given the large number of brain structures involved in the affected processes. Brain imaging techniques have made a critical contribution to deepening our understanding of the neuroanatomical etiology of ADHD. Studies using magnetic resonance have provided us with precise information about the volume of gray matter, density, cortical thickness or integrity of white matter, as well as its connectivity [58, 59, 60]. Neuroanatomical development varies throughout childhood until reaching the adult stage, in some cases in a linear fashion, such as the increase in cortical white matter, and in others in an inverted U-shape from the pre-pubertal phase until entering the adult phase, as in the case of cortical gray matter thickness [58]. In addition, this maturation is also carried out unevenly between areas, generally beginning development earlier in the older phylogenetic zones than in the more recent ones, as occurs in the prefrontal cortex. This is also the case in the motor and sensory areas, where development begins earlier than in areas associated with more complex functions, such as those involving cognitive control or attention [58, 59]. These findings are important because an altered maturation process of these latter cortical areas has been identified in patients with ADHD [61], mainly in the frontal cortex [62]. In fact, it has been described that the symptomatology of ADHD in the general population is negatively correlated with total brain size [63], probably due to the decrease in the volume of gray matter in various subcortical structures such as accumbens, amygdala, caudate, hippocampus and putamen; and cortical structures such as the prefrontal cortex and parietal–temporal [63, 64, 65]. This decrease is also accompanied by a delay in maturation [66], and is usually more pronounced in childhood, with some persistent reductions in frontal areas in a subgroup of ADHD patients with symptoms lasting into adulthood.

At the functional level, it is observed some correlates that indicate variations in patients with ADHD in relation to patients without a diagnosis of this disorder. For instance, the cortical and subcortical areas described above fluctuate spontaneously when passing from a resting phase to an explicit task phase. The active networks in the resting state reduce their activity when they increase the focused attention processes, with both networks showing a process of negative feedback depending on attentional demands [67, 68, 69]. This inverse correlation between the networks of cognitive control and those of rest are diminished or absent in children and adults with ADHD, which is why they show the continuous problems of sustained attention [68, 70, 71]. Furthermore, it is possible, that the connectivity patterns of cortico-limbic, cortico-striatal, and thalamocortical loops are altered in children with ADHD [68, 72]. Specifically, a decrease in the activation of the network connecting the frontal and neostriatal areas next to the thalamus-parietal is observed in ADHD children in goal-directed tasks; although the most common alterations show patterns of hypoactivation of the frontoparietal network, a network related to executive functions [73]. Similarly, the motor inhibition tasks also produce consistent decreases in children diagnosed with ADHD in the associative and sensorimotor network, networks that include the supplementary motor area, anterior cingulate gyrus, and the putamen and caudate nuclei [73, 74].

2.5 Genetic profile in ADHD

The heritability for ADHD has been estimated between 70 to 90% from studies using families and twins with ADHD [75, 76]. By DSM 5, the heritability of ADHD is considerable, being more frequent among first-degree biological relatives of individuals with the disorder. Although ADHD is not associated with specific physical traits, the manual indicates that the presence of minor physical abnormalities such as hypertelorism, bowed palate and low ears, are common among these individuals [1].

Clinical research in ADHD has also identified several genes associated with the disorder which are related to the metabolism, transport and reception of certain neurotransmitters, especially dopamine, noradrenaline and serotonin. Among the genes that have been most frequently correlated with ADHD are the serotonin transport gene 5-HTT and the serotonin receptor gene HTR1B. Regarding dopamine: the DAT1 transporter gene and the DRD2, DRD4 and DRD5 receptor genes; and the gene responsible for the conversion of dopamine into noradrenaline DBH, among others [77, 78, 79, 80]. This genetic involvement at the neurotransmitter level would be at the basis of hypofrontality, or reduced activity in the frontal lobes in these patients [81, 82, 83]. The frontoparietal network is usually more affected in subjects with an inattentive subtype while in the hyperactive–impulsive type it is usually the frontostriatal network [84]. These dysfunctions have also been found in frontoparietotemporal, frontocerebellar and even frontolimbic circuits [85].

The approaches to the genetics of ADHD have been many and varied. Some studies have found an altered maturation of the cortex, characterized by a delay in cortical maturation in people with ADHD [61, 86]. A study with 366 subjects with a diagnosis of ADHD and a large control group (n = 1047), analyzed the genetics of the disorder by calculating the variation in the copy number of certain genes. The authors found a difference that they called rare in 50 of the 366 subjects with the disorder, 13.66% of the participants with ADHD. In the control group, the abnormality was present in 75 of 1047 cases (7.16%) [87]. It is curious to argue that these differences could be good predictors of the possible genetic origin of the disorder; however, they also found similar results with other diseases, as autism or schizophrenia. And recently, it has been published what is considered to be the largest genetic study of ADHD. Researchers from around the world have participated in the project and it has been proposed as the first gene map around ADHD that identifies variants surpassing genome-wide significance in 12 independent loci. These findings, described by the authors, “are compelling, but only capture a tiny fraction of common variant risk for ADHD” [88]. Furthermore, the contribution of these genes to ADHD heritability is very low [89] and recent studies suggest that we have to look at how the different genes interact each other and with environmental etiological factors [90].

Although technological progress in recent decades has enabled great and important advances in understanding the genetic disposition and physiological and behavioral functioning of many disorders, biological markers for the disorder are still lacking. According to APA “Although specific genes have been correlated with ADHD, these are neither necessary nor sufficient causal factors” [1].


3. Searching a specific phenotype across measurable traits

Above we have discussed about ADHD definition and the difficulties when we try to carry out an accurate diagnosis. In addition, we have indicated that, regardless the diagnosis, the medical care is usually the same. That is, we should not need to differentiate between diagnoses in order to choose a treatment. An alternative to this traditional view of current diagnostic scales could be the objective quantification of specific features that reflect a mental disorder. In this regard, we should give up the idea of how many symptoms are met per patient to be diagnosed of ADHD, and keen on objective scales of how deep is a symptom. In addition, data from genetic studies and brain activity could help us to define the profile of different disorders. This is important because it would allow us to link these measures with quantifiable cognitive/behavioral features in order to develop a possible endophenotype.

One important line of research has identified EEG-based markers as event-related potential and frequency analysis. The use of these markers may improve the diagnosis by offering an alternative to the present symptom-based system. At present, the benefit of using neurofeedback EGG on ADHD is controversial [91, 92]. Although studies on the use of EEG to identify biomarkers related to ADHD is promising, at the moment there are several difficulties. One of the main challenges is defining the ADHD population and the different sub-groups [93]. An alternative to EEG-based markers comes from studies from experimental psychology. For more than a century, the experimental psychology has described the basis of the psychological processes in multiple paradigms. As the ADHD is not a unified disorder, we propose to disentangle some traits related to the ADHD and apply the models from experimental psychology.


4. ADHD focused on impulsivity

Attention is a basic psychological process that facilitates the control of cognitive processes. Traditionally, the distinction made between different dimensions of attention in current models has been associated with different neural networks. Therefore, when we talk about attention we do not talk about a unitary concept [94, 95]. One of the most complete models about attention processes is the one proposed by Posner and Petersen [96]. In its different formulations, it has conceptually modified the basic principles of its original proposal and currently allows to distinguish between several attentional functions. Specifically, it proposes the existence of three networks: the orientation network, the alert network and finally the executive control network. Each one of them would have its own function, supported by an associated brain circuit and mediated by a different neuromodulator. This model has been extended and reformulated in several occasions, highlighting the value of studies that emphasize individual differences between groups of subjects [97].

The executive network has been directly related to goal-directed processes, frontality and action control. Not surprisingly, attention disorders and hyperactivity/impulsivity have gone hand in hand. Thus, when hyperactivity or impulsivity is suspected, its effect on attention is habitually analyzed. However, we do not currently know if the attentional disorders are cause, effect or simply part of shared circuits in ADHD.

The explanatory models range go from those that propose ADHD as the affectation of a single aspect related to the inhibition of response, to models that propose that the disorder is the sum of multiple deficits [98]. Single deficit proposals are related to low capacity in inhibitory control, state regulation and delay avoidance. Specifically, it is related to the lack of capacity to inhibit a response to an attractive and irresistible stimulus associated with immediate reinforcement [2]. In addition, this trait is associated with behavioral impairment of non-verbal working memory, language internalization, and self-regulation on which such inhibition is partially dependent. In other words, ADHD is probably not due to deficits in the processes, but rather to the regulation and control done over those processes.

Impulsive processes have usually been described in at least two different ways. One of them refers to a slow way that involves deliberation and action even considering its negative consequences. The other is the fast way; that is, without thinking of short- or long-term outcomes [99]. This has been translated into two models generally known as cool vs. hot, top-down vs. bottom-up, stopping vs. waiting, action restraint vs. action cancelation or motor vs. choice [100, 101]. Thus, the motor component reflects spontaneity or action without thinking, while unplanned impulsivity reflects a lack of reflection on future consequences. This activity is easily measurable in both human and animal models. In fact, it has been used to analyze patients with ADHD [102]. This view transforms the analysis of impulsive behavior in a quantitative dimension [103], which allows a quantification of the level of impulsivity of individuals, both in human and non-human models, depending on both internal and external variables. This is important because it allows us the study of models of impulsivity from the current theories of learning.

4.1 Impulse control and cognitive correlates in ADHD diagnose

Impulse control is one of the main and consistently affected function in the scientific literature on ADHD [82, 104, 105, 106, 107], and it is also one of the difficulties more often reported by parents of boys and girls with ADHD [25]. Accounted as one of the key affectations in ADHD [108, 109], good abilities in impulse control are considered decisive in the future development of individuals [110, 111]. Despite being a common problem among ADHD diagnosed patients, it is more frequently found in people with diagnosed of combined subtype [55].

In addition, these patients show general deficits on working memory [112], and is considered as one of the main alterations and possible axes of the disorder [2, 82]. The central executive appears to be one of the more affected dimensions [113], as well as visuospatial skills [81, 84, 114]. Some authors have found that the greatest deficits in working memory in ADHD were related to tasks involving the central executive [115], while others have proposed that lower performance level in tasks related to executive functions could be explained by deficits in discrimination of interferences in working memory [116]. Interference control is more compromised in subjects with ADHD than in typically developing individuals [117, 118] and visual perception is highly related to higher cognitive processes such as reading comprehension and arithmetic skills [119]. In addition, people with ADHD usually show deficits in tasks that assess visual working memory [120, 121], alterations in visuospatial skills related to working memory [122], and in the ability to process spatial information adequately [123]. However, we do not have standardized tests focused on quantification of the deficit, only a qualitative view of the deficit itself. This might be the reason why it has not found any differences between subtypes of the disorder for this domain yet [124].

Deficits in attention, impulse control, or working memory are just some of the neuropsychological findings in patients with ADHD. Planning and problem solving have also been pointed above as a common deficit in the disorder [82, 125, 126, 127], as well as timing skills [128, 129, 130], which have also been linked to problems with attention, language, reading and executive functions [131].

Despite the numerous findings identified in the neuropsychology of ADHD, on the one hand, the disturbances expected for the diagnosis not always are found in the assessment process [41]. In addition, they are not only present in this disorder but also in other conditions. Furthermore, the neuropsychological deficits profiles identified are also present in the general population, but in children with ADHD the values are utmost [132].

4.2 Starting the route from animal models

Research in animal models offers an in-depth approach to the possible etiology and development of some diseases, being considered invaluable for the preclinical evaluation of treatments and interventions [133]. In this way, it is generally agreed that those disorders that have previously been studied from the perspective of animal models are currently better understood than others [134]. In this regard, animal models of ADHD, as other brain disorders, need to meet certain characteristics of validity, essential to be considered as an adequate animal model. Similar behavioral characteristics of the disorder are usually known as “face validity”. When models show a theoretical consistency with the disorder or disease, we speak of “construct validity”. Finally, for being an adequate model, neuroscientific findings, for instance genetic and neurobiology data, should be similar to those displayed in clinical population of the disease in order to ensure “predictive validity” [135, 136].

One of the most analyzed traits studied in animal models has been impulsivity. This is an easy trait to measure at cognitive or behavioral level, besides being closely linked to attention. Recent studies have been able to link different behavioral procedures in order to define possible attentional deficit profile. And these procedures can be used in both, human and non-human animal models, allowing us a quantifiable view of symptoms, making access a deeper view of the disorder.

Autoshaping is currently considered as a valid animal model of impulsivity [137]. This model is based on the study of the motivational aspects of the relationships between the presentation of a conditioned stimulus (CS), the responses to the presence of this stimulus and, finally, the presentation of the unconditioned stimulus (US). The autoshaping, also denominated as sign-tracking, describes the progression of direct movements of orientation-approach to the key or CS that precedes the US [138, 139]. This model allows differentiating how each experimental subject attributes the motivational salience of the signal and the reward, and it is beginning to be used with the aim of assessing individual differences. López et at. [140], studied the behavioral profile in animals classified as sign (ST) and goal (GT) trackers in order to analyze the relationship between impulsivity and attentional processes. Results in prepulse inhibition (PPI), a procedure used to analyze early attentional gating mechanisms, showed a consistent decrease in PPI response in ST animals. That is, animals with a trend to show an impulsive behavior displayed a deficit in a preattentional phase, indicating the more impulsive behavior the higher deficits probability in early phases of attentional process.

Using the same paradigm, Serrano et al. [141] found processing differences of a CS in latent inhibition (LI), other easily quantifiable procedure. LI is a learning process associated to selective attention assessment. This procedure consists in a repeated display of a stimulus without consequences, and after a habituation period, the stimulus is associated to a reward/punishment. The CS-US association tends to be slower in this situation in general population. Yet, animals classified as impulsive (that is, ST) showed a low LI. That is, impulsive animals displayed a slower habituation rate to a neutral stimulus, indicating impulsive animals had paid attention to a higher number of irrelevant stimuli than the rest of population.

The procedures describe above are focused on the named hot model of impulsivity [139]. This kind of impulsivity is usually shown by ST animals, and it is closely related to dopamine neurotransmitter. In fact, nucleus accumbens shows higher levels of dopamine release in the presence of a CS [142]. Besides, D2 dopamine receptors stimulation reduces ST behavior selectively, in a similar way to medial prefrontal cortex lesion [140, 141]. Thus, this model integrates information at different level from this animal model that cover biochemical, anatomical and behavioral level. In this regard, all these data might indicate a higher vulnerability to attentional problems as described in ADHD in impulsive animals.


5. Conclusions

One of the major challenges in the treatment of psychological and psychiatric disorders is to understand the influence of individual differences or traits variability on the potential outcome of treatment. A future goal could be to tailor the treatment to fit the patient profile, rather than assuming a generic approach based on a broad diagnosis. Moreover, many ADHD patients change to more severe clinical diagnosis when they reached adolescence or late adolescence. In this regard the detection of risk factor is essential to focus on detecting what symptom could be a risk factor of possible mental illness in the future. The search of a specific drug for the treatment of different types of ADHD makes increasingly evident the necessity to measure multiple domains of brain and behavior. Here we highlight an alternative to traditional diagnosis scales that would allow a better accurate treatment, regardless of the general symptoms shown.



This research was supported by PID2019-110739GB-I00 (Ministerio de Ciencia, Innovación y Universidades, FEDER, UE).


  1. 1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington DC: APA; 2013.
  2. 2. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychol Bulletin. 1997; 121(1):65-94. DOI: 10.1037/0033-2909.121.1.65
  3. 3. Sonuga-Barke EJS, Taylor E, Sembi S, Smith J. Hyperactivity and delay aversion—I. The effect of delay on choice. J Child Psychol Psychiatry. 1992; 33(2):387-98. DOI: 10.1111/j.1469-7610.1992.tb00874.x
  4. 4. Nielsen M. ADHD and temporality: A desynchronized way of being in the world. Med Anthropol Cross Cult Stud Heal Illn. 2017; 36(3):260-72. DOI: 10.1080/01459740.2016.1274750
  5. 5. Sergeant J. The cognitive-energetic model: An empirical approach to Attention-Deficit Hyperactivity Disorder. Neuroscience and Biobehavioral Reviews. 2000; 24(1):7-12. DOI: 10.1016/S0149-7634(99)00060-3
  6. 6. Barkley RA, Peters H. The earliest reference to ADHD in the medical literature? Melchior Adam Weikard’s description in 1775 of “Attention Deficit” (Mangel der Aufmerksamkeit, Attentio Volubilis). J Atten Disord. 2012; 16(8):623-30. DOI: 10.1177/1087054711432309
  7. 7. Lange KW, Reichl S, Lange KM, Tucha L, Tucha O. The history of attention deficit hyperactivity disorder. ADHD. Atten Deficit Hyperact Disord. 2010;2(4):241-55. DOI: 10.1007/s12402-010-0045-8
  8. 8. Thome J, Jacobs KA. Attention deficit hyperactivity disorder (ADHD) in a 19th century children’s book. Eur Psychiatry. 2004; 19(5):303-6. DOI: 10.1016/j.eurpsy.2004.05.004
  9. 9. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 2nd ed. Washington DC: APA; 1968.
  10. 10. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 3rd ed. Washington DC: APA; 1980.
  11. 11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 3rd ed. Rev. Washington DC: APA; 1987.
  12. 12. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington DC: APA; 1994.
  13. 13. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Rev. Washington DC: APA; 2000.
  14. 14. World Health Organization. ICD-10: International Statistical Classification of Diseases and Related Health Problems. 10th Revision. Geneva: World Health Organization; 2004.
  15. 15. World Health Organization. ICD-11 for mortality and morbidity statistics. Available from: [Accessed: 2020-10-09]
  16. 16. American Academy of Pediatrics. Clinical practice guideline: Diagnosis and evaluation of the child with Attention-Deficit/Hyperactivity Disorder. Pediatrics. 2000; 105(5):1158-70. DOI: 10.1542/peds.105.5.1158
  17. 17. American Academy of Child and Adolescent Psychiatry. Practice parameter for the assessment and treatment of children and adolescents with Attention-Deficit/Hyperactivity Disorder. J Am Acad Child Adolesc Psychiatry. 2007; 46(7):894-921. DOI: 10.1097/chi.0b013e318054e724
  18. 18. Canadian Attention Deficit Hyperactivity Disorder Resource Alliance. CADDRA. Canadian ADHD Practice Guidelines. 3rd ed. Toronto: CADRA; 2011.
  19. 19. Scottish Intercollegiate Guidelines Network (SIGN). Attention deficit and hyperkinetic disorders in children and young people: A national clinical guideline. Edinburgh: SIGN; 2005.
  20. 20. Bruchmüller K, Margraf J, Schneider S. Is ADHD diagnosed in accord with diagnostic criteria? Overdiagnosis and influence of client gender on diagnosis. J Consult Clin Psychol. 2012; 80(1):128-38. DOI: 10.1037/a0026582
  21. 21. Whalen KC. Hiperactividad, problemas de aprendizaje y trastornos por déficit de atención. In: Ollendick T, Hersen S, editors. Psicopatología Infantil. Barcelona: Editorial Martínez Roca; 1986. p. 213-79.
  22. 22. Hartung CM, Lefler EK, Tempel AB, Armendariz ML, Sigel BA, Little CS. Halo effects in ratings of ADHD and ODD: Identification of susceptible symptoms. J Psychopathol Behav Assess. 2010; 32(1):128-37. DOI: 10.1007/s10862-009-9135-3
  23. 23. Fernández-Perrone AL, Fernández-Mayoralas DM, Fernández-Jaén A. Trastorno por déficit de atención/hiperactividad: del tipo inatento al tipo restrictivo. Rev de Neurología. 2013; 56(1):77-84. DOI: 10.33588/rn.56s01.2012651
  24. 24. Canals J, Morales-Hidalgo P, Jané MC, Domènech E. ADHD prevalence in Spanish preschoolers: Comorbidity, socio-demographic factors, and functional consequences. J Atten Disord. 2018; 22(2):143-53. DOI: 10.1177/1087054716638511
  25. 25. McCandless S, O’Laughlin L. The clinical utility of the Behavior Rating Inventory of Executive Function (BRIEF) in the diagnosis of ADHD. J Atten Disord. 2007; 10(4):381-9. DOI: 10.1177/1087054706292115
  26. 26. Applegate B, Lahey BB, Hart EL, Biederman J, Hynd GW, Barkley RA, et al. Validity of the age-of-onset criterion for ADHD: A report from the DSM- IV field trials. J Am Acad Child Adolesc Psychiatry. 1997; 36(9):1211-21. DOI: 10.1097/00004583-199709000-00013
  27. 27. Biederman J, Petty CR, Evans M, Small J, Faraone SV. How persistent is ADHD? A controlled 10-year follow-up study of boys with ADHD. Psychiatry Res. 2010; 177(3):299-304. DOI: 10.1016/j.psychres.2009.12.010
  28. 28. Faraone SV, Biederman J, Mick E. The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychological Medicine. Cambridge University Press. 2006; 36:159-65. DOI: 10.1017/S003329170500471X
  29. 29. Caye A, Rocha TBM, Anselmi L, Murray J, Menezes AMB, Barros FC, et al. Attention-Deficit/Hyperactivity Disorder trajectories from childhood to young adulthood: Evidence from a birth cohort supporting a late-onset syndrome. JAMA Psychiatry. 2016; 73(7):705-12. DOI: 10.1001/jamapsychiatry.2016.0383
  30. 30. Lecendreux M, Silverstein M, Konofal E, Cortese S, Faraone SV. A 9-Year follow-up of Attention-Deficit/Hyperactivity Disorder in a population sample. J Clin Psychiatry. 2019; 80(3). DOI: 10.4088/jcp.18m12642
  31. 31. Capdevila-Brophy C, Artigas-Pallarés J, Ramírez-Mallafré A, López-Rosendo M, Real-Gatius J, Obiols-Llandrich JE. Fenotipo neuropsicológico del trastorno de déficit atencional/hiperactividad: ¿existen diferencias entre los subtipos? Rev Neurol. 2005; 40(S01):S017-23. DOI: 10.33588/rn.40s01.2005074
  32. 32. Nigg JT, Blaskey LG, Huang-Pollock CL, Rappley MD. Neuropsychological Executive Functions and DSM-IV ADHD subtypes. J Am Acad Child Adolesc Psychiatry. 2002;41(1):59-66. DOI: 10.1097/00004583-200201000-00012
  33. 33. Corrigan MW. Debunking ADHD: 10 Reasons to Stop Drugging Kids for Acting Like Kids. New York: Rowman y Littlefield Publishers; 2014.
  34. 34. Ghanizadeh A. Psychiatric comorbidity differences in clinic-referred children and adolescents with ADHD according to the subtypes and gender. J Child Neurol. 2009; 24(6):679-84. DOI: 10.1177/0883073808331086
  35. 35. Brown TE. ADHD Comorbidities: Handbook for ADHD Complications in Children and Adults. Washingtong DC: APA; 2010.
  36. 36. Maedgen JW, Carlson CL. Social functioning and emotional regulation in the Attention Deficit Hyperactivity Disorder subtypes. J Clin Child Adolesc Psychol. 2000; 29(1):30-42. DOI: 10.1207/s15374424jccp2901_4
  37. 37. Michielsen M, Comijs HC, Semeijn EJ, Beekman ATF, Deeg DJH, Kooij JJS. The comorbidity of anxiety and depressive symptoms in older adults with attention-deficit/hyperactivity disorder: A longitudinal study. J Affect Disord. 2013; 148(2-3):220-7. DOI: 10.1016/j.jad.2012.11.063
  38. 38. Septier M, Stordeur C, Zhang J, Delorme R, Cortese S. Association between suicidal spectrum behaviors and Attention-Deficit/Hyperactivity Disorder: A systematic review and meta-analysis. Neurosci and Biobehavioral Reviews. 2019; 103:109-18. DOI: 10.1016/j.neubiorev.2019.05.022
  39. 39. Reale L, Bartoli B, Cartabia M, Zanetti M, Costantino MA, Canevini MP, et al. Comorbidity prevalence and treatment outcome in children and adolescents with ADHD. Eur Child Adolesc Psychiatry. 2017; 26(12):1443-57. DOI: 10.1007/s00787-017-1005-z
  40. 40. Katzman MA, Bilkey TS, Chokka PR, Fallu A, Klassen LJ. Adult ADHD and comorbid disorders: Clinical implications of a dimensional approach. BMC Psychiatry. 2017; 17:302. DOI: 10.1186/s12888-017-1463-3
  41. 41. Jiménez-Soto A. Batería Informatizada para la Evaluación Neuropsicológica de las Afectaciones Relacionadas con el TDAH: BIENART [thesis]. Sevilla: Universidad de Sevilla. 2020.
  42. 42. Lasa-Zulueta A, Jorquera-Cuevas C. Evaluación de la situación asistencial y recomendaciones terapéuticas en el trastorno por déficit de atención e hiperactividad. Informes de Evaluación de Tecnologías Sanitarias: OSTEBA. Vitoria-Gasteiz: Serv. Central de Comunicaciones del Gobierno Vasco; 2010.
  43. 43. García de Vinuesa F, González-Pardo H, Pérez-Álvarez M. Volviendo a la normalidad: La invención del TDAH y del trastorno bipolar infantil. Madrid: Alianza Editorial; 2014.
  44. 44. Batstra L, Nieweg EH, Hadders-Algra M. Exploring five common assumptions on Attention Deficit Hyperactivity Disorder. Acta Paediatrica. International Journal of Paediatrics. 2014; 103(7): 696-700. DOI: 10.1111/apa.12642
  45. 45. Pérez-Álvarez M. Más Aristóteles y menos Concerta®: Las cuatro causas del TDAH. Ulzama: Ned Ediciones; 2018.
  46. 46. Schwartz A. ADHD nation: Children, doctors, big pharma, and the making of an American epidemic. New York: Simon & Schuster; 2016.
  47. 47. Ainslie G. Specious reward: A behavioral theory of impulsiveness and impulse control. Psychol Bull. 1975; 82(4):463-96.
  48. 48. Eysenck HJ. The nature of impulsivity. In: McCown WG, Johnson JL, Shure MB, editors. The impulsive client: Theory, research, and treatment. Washington DC: American Psychological Association; 1993. p. 57-69. DOI: 10.1037/10500-004
  49. 49. Moeller FG, Barratt ES, Dougherty DM, Schmitz JM, Swann AC. Psychiatric aspects of impulsivity. American Journal of Psychiatry. 2001; 158(11):1783-93. DOI: 10.1176/appi.ajp.158.11.1783
  50. 50. Hofmann W, Friese M, Strack F. Impulse and Self-Control from a Dual-Systems Perspective. Perspect Psychol Sci. 2009; 4(2):162-76. DOI: 10.1111/j.1745-6924.2009.01116.x
  51. 51. Metcalfe J, Mischel W. A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychol Rev. 1999; 106(1):3-19. DOI: 10.1037/0033-295X.106.1.3
  52. 52. Allan DM, Lonigan CJ. Examination of the structure and measurement of inattentive, hyperactive, and impulsive behaviors from Preschool to Grade 4. J Abnorm Child Psychol. 2019; 47(6):975-87. DOI: 10.1007/s10802-018-0491-x
  53. 53. Gratch LO. El trastorno por déficit de atención (ADD-ADHD). Clínica, diagnóstico y tratamiento en la infancia, la adolescencia y la adultez. Buenos Aires: Médica Panamericana; 2009.
  54. 54. Rowland AS, Skipper B, Rabiner DL, Umbach DM, Stallone L, Campbell RA, et al. The shifting subtypes of ADHD: Classification depends on how symptom reports are combined. J Abnorm Child Psychol. 2008; 36(5):731-43. DOI: 10.1007/s10802-007-9203-7
  55. 55. Ramos-Galarza C, Pérez-Salas C. Control inhibitorio y monitorización en población infantil con TDAH. Avances en Psicología Latinoam. 2017; 35(1):117-30. DOI: 10.12804/
  56. 56. Molina BSG, Hinshaw SP, Swanson JM, Arnold LE, Vitiello B, Jensen PS, et al. The MTA at 8 years: Prospective follow-up of children treated for combined-type ADHD in a multisite study. J Am Acad Child Adolesc Psychiatry. 2009; 48(5):484-500.
  57. 57. Agnew-Blais J, Arseneault L. Late-onset ADHD: Case closed or open question? Am J Psychiatry. 2018; 175(5):481-482. DOI: 10.1176/appi.ajp.2018.17111240
  58. 58. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA. 2004; 101(21):8174-9. DOI: 10.1073/pnas.0402680101
  59. 59. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nature Neuroscience. 2003; 6(3):309-15. DOI: 10.1038/nn1008
  60. 60. Sowell ER, Trauner DA, Gamst A, Jernigan TL. Development of cortical and subcortical brain structures in childhood and adolescence: A structural MRI study. Dev Med Child Neurol. 2007; 44(1):4-16. DOI: 10.1111/j.1469-8749.2002.tb00253.x
  61. 61. Shaw P, Malek M, Watson B, Sharp W, Evans A, Greenstein D. Development of cortical surface area and gyrification in Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry. 2012; 72(3):191-7. DOI: 10.1016/j.biopsych.2012.01.031
  62. 62. Ambrosino S, De Zeeuw P, Wierenga LM, Van Dijk S, Durston S. What can cortical development in Attention-Deficit/Hyperactivity Disorder teach us about the early developmental mechanisms involved? Cereb Cortex. 2017; 27(9):4624-34. DOI: 10.1093/cercor/bhx182
  63. 63. Hoogman M, Rijpkema M, Janss L, Brunner H, Fernandez G, Buitelaar J, et al. Current self-reported symptoms of attention Deficit/Hyperactivity Disorder are associated with total brain volume in healthy adults. PLoS One. 2012; 7(2):e31273. DOI: 10.1371/journal.pone.0031273
  64. 64. Greven CU, Bralten J, Mennes M, O’Dwyer L, Van Hulzen KJE, Rommelse N, et al. Developmentally stable whole-brain volume reductions and developmentally sensitive caudate and putamen volume alterations in those with Attention-Deficit/Hyperactivity Disorder and their unaffected siblings. JAMA Psychiatry. 2015; 72(5):490-9. DOI: 10.1001/jamapsychiatry.2014.3162
  65. 65. Huang-Pollock C, Ratcliff R, McKoon G, Shapiro Z, Weigard A, Galloway-Long H. Using the Diffusion Model to explain cognitive deficits in Attention Deficit Hyperactivity Disorder. J Abnorm Child Psychol. 2017; 45(1):57-68. DOI: 10.1007/s10802-016-0151-y
  66. 66. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, et al. Subcortical brain volume differences in participants with Attention Deficit Hyperactivity Disorder in children and adults: a cross-sectional mega-analysis. The Lancet Psychiatry. 2017; 4(4):310-9. DOI: 10.1016/S2215-0366(17)30049-4
  67. 67. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: Anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences. 2008; 1124:1-38. DOI: 10.1196/annals.1440.011
  68. 68. Posner MI, Rothbart MK, Sheese BE, Voelker P. Developing attention: Behavioral and brain mechanisms. Adv Neuroscience. 2014; 2014(1):1-9. DOI: 10.1155/2014/405094
  69. 69. Raichle ME, Snyder AZ. A default mode of brain function: A brief history of an evolving idea. 2007; 37(4):1083-90. DOI: 10.1016/j.neuroimage.2007.02.041
  70. 70. Castellanos FX, Margulies DS, Kelly C, Uddin LQ, Ghaffari M, Kirsch A, et al. Cingulate-precuneus interactions: A new locus of dysfunction in adult Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry. 2008; 63(3):332-7. DOI: 10.1016/j.biopsych.2007.06.025
  71. 71. Sun L, Cao Q, Long X, Sui M, Cao X, Zhu C, et al. Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with Attention Deficit Hyperactivity Disorder. Psychiatry Res - Neuroimaging. 2012; 201(2):120-7. DOI: 10.1016/j.pscychresns.2011.07.001
  72. 72. Bos DJ, Oranje B, Achterberg M, Vlaskamp C, Ambrosino S, de Reus MA, et al. Structural and functional connectivity in children and adolescents with and without Attention Deficit/Hyperactivity Disorder. J Child Psychol Psychiatry. 2017; 58(7):810-8. DOI: 10.1111/jcpp.12712
  73. 73. Hart H, Radua J, Nakao T, Mataix-Cols D, Rubia K. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in Attention-Deficit/Hyperactivity Disorder: Exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry. 2013; 70(2):185-98. DOI: 10.1001/jamapsychiatry.2013.277
  74. 74. Liou YJ, Wei HT, Chen MH, Hsu JW, Huang KL, Bai YM, et al. Risk of traumatic brain injury among children, adolescents, and young adults with Attention-Deficit Hyperactivity Disorder in Taiwan. J Adolesc Heal. 2018; 63(2):233-8. DOI: 10.1016/j.jadohealth.2018.02.012
  75. 75. Larsson H, Dilshad R, Lichtenstein P, Barker ED. Developmental trajectories of DSM-IV symptoms of Attention-Deficit/Hyperactivity Disorder: Genetic effects, family risk and associated psychopathology. J Child Psychol Psychiatry Allied Discip. 2011; 52(9):954-63. DOI: 10.1111/j.1469-7610.2011.02379.x
  76. 76. Faraone S V, Larsson H. Genetics of Attention Deficit Hyperactivity Disorder. Molecular Psychiatry. 2019; 24:562-75. DOI: 10.1038/s41380-018-0070-0
  77. 77. Barr CL, Wigg KG, Bloom S, Schachar R, Tannock R, Roberts W, et al. Further evidence from haplotype analysis for linkage of the dopamine D4 receptor gene and Attention-Deficit Hyperactivity Disorder. Am J Med Genet - Neuropsychiatry. 2000; 96(3):262-7. DOI: 10.1002/1096-8628(20000612)96:3<262::AID-AJMG5>3.0.CO;2-8
  78. 78. Faraone SV, Khan SA. Candidate gene studies of Attention-Deficit/Hyperactivity Disorder. J Clin Psychiatry. 2006; 67(8):13-20.
  79. 79. Rowe DC, Stever C, Giedinghagen LN, Gard J, Cleveland HH, Terris ST, et al. Dopamine DRD4 receptor polymorphism and Attention Deficit Hyperactivity Disorder. Mol Psychiatry. 1998; 3(5):419-26.
  80. 80. Shook D, Brady C, Lee PS, Kenealy L, Murphy ER, Gaillard WD, et al. Effect of dopamine transporter genotype on caudate volume in childhood ADHD and controls. Am J Med Genet Part B Neuropsy Genet. 2011; 156(1):28-35. DOI: 10.1002/ajmg.b.31132
  81. 81. Brown TE. A new understanding of ADHD in children and adults: executive function impairments. Washington DC: Routledge; 2013.
  82. 82. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of Attention-Deficit/Hyperactivity Disorder: A meta-analytic review. Biological Psychiatry. 2005; 57(11):1336-46. DOI: 10.1016/j.biopsych.2005.02.006
  83. 83. Zang YF, Jin Z, Weng XC, Zhang L, Zeng YW, Yang L, et al. Functional MRI in Attention-Deficit Hyperactivity Disorder: Evidence for hypofrontality. Brain Dev. 2005; 27(8):544-50. DOI: 10.1016/j.braindev.2004.11.009
  84. 84. Diamond A. Attention-deficit disorder (Attention-Deficit/Hyperactivity Disorder without hyperactivity): A neurobiologically and behaviorally distinct disorder from Attention-Deficit/Hyperactivity Disorder (with hyperactivity). Dev Psychopatholy. 2005; 17(3):807-25. DOI: 10.1017/S0954579405050388
  85. 85. Rubia K, Alegría AA, Brinson H. Anomalías cerebrales en el trastorno por déficit de atención/hiperactividad: Una revisión. Rev Neurol. 2014; 58(1):3-18. DOI: 10.33588/rn.58S01.2013570
  86. 86. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, et al. Attention-Deficit/Hyperactivity Disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A. 2007; 104(49):19649-54. DOI: 10.1073/pnas.0707741104
  87. 87. Williams NM, Zaharieva I, Martin A, Langley K, Mantripragada K, Fossdal R, et al. Rare chromosomal deletions and duplications in Attention-Deficit Hyperactivity Disorder: A genome-wide analysis. Lancet. 2010; 376(9750):1401-8. DOI: 10.1016/S0140-6736(10)61109-9
  88. 88. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for Attention Deficit/Hyperactivity Disorder. Nat Genet. 2019; 51(1):63-75. DOI: 10.1038/s41588-018-0269-7
  89. 89. Hohmann S, Adamo N, Lahey BB, Faraone SV, Banaschewski T. Genetics in child and adolescent psychiatry: Methodological advances and conceptual issues. Eur Child Adolesc Psychiatry 2015; 24(6):619-634. DOI: 10.1007/s00787-015-0702-8
  90. 90. Cortese S, Coghill D. Twenty years of research on Attention-Deficit/Hyperactivity Disorder (ADHD): Looking back, looking forward Evidence-Based Mental Health 2018; 21(4):173-176. DOI: 10.1136/ebmental-2018-300050
  91. 91. Van Doren J, Arns M, Heinrich H, Vollebregt MA, Strehl U, Loo K. Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. Eur Child Adolesc Psychiatry. 2019; 28(3):293-305. doi:10.1007/s00787-018-1121-4
  92. 92. Schönenberg M, Wiedemann E, Schneidt A, Scheeff J, Logemann A, Keune PM, et al. Neurofeedback, sham neurofeedback, and cognitive-behavioural group therapy in adults with attention-deficit hyperactivity disorder: a triple-blind, randomised, controlled trial. The Lancet Psychiatry. 2017; 4(9):673-84. DOI: 10.1016/S2215-0366(17)30291-2
  93. 93. Miranda P, Cox CD, Alexander M, Danev S, Lakey JRT. In quest of pathognomonic/endophenotypic markers of Attention Deficit Hyperactivity Disorder (ADHD): Potential of EEG-based frequency analysis and ERPs to better detect, prevent and manage ADHD. Med Devices (Auckl). 2020; 13:115-137. DOI: 10.2147/mder.S241205
  94. 94. Castillo Villar MD. La Atención. Madrid: Pirámide; 2009.
  95. 95. De la Fuente-Arnanz J, Pousada M. La Atención. Barcelona: Editorial UOC; 2014.
  96. 96. Posner MI, Petersen SE. The attention system of the human brain. Annu Rev Neurosci. 1990; 13(1):25-42. DOI: 10.1146/
  97. 97. Petersen SE, Posner MI. The attention system of the human brain: 20 years after. Annu Rev of Neurosci. 2012; 35:73-89. DOI: 10.1146/annurev-neuro-062111-150525
  98. 98. Artigas-Pallarés J. Modelos cognitivos en el trastorno por déficit de atención/hiperactividad. Rev Neurol. 2009; 49(11):587-93. DOI: 10.33588/rn.4911.2009369
  99. 99. DeYoung CG. Impulsivity as a personality trait. In: Vohs KD, Baumeister RF, editors. Handbook of self-regulation: Research, theory, and applications. New York: Guildford Press; 2011. p. 485-502.
  100. 100. Castellanos FX, Sonuga-Barke EJS, Milham MP, Tannock R. Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences. 2006; 10(3):117-23. DOI: 10.1016/j.tics.2006.01.011
  101. 101. Lawrence A, Clark L, Labuzetta JN, Sahakian B, Vyakarnum S. The innovative brain. Nature. 2008; 456(7219):168-9. DOI: 10.1038/456168a
  102. 102. Solanto MV, Abikoff H, Sonuga-Barke E, Schachar R, Logan GD, Wigal T, et al. The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: A supplement to the NIMH multimodal treatment study of AD/HD. J Abnorm Child Psychol. 2001; 29(3):215-28. DOI: 10.1023/A:1010329714819
  103. 103. Bari A, Robbins TW. Animal models of ADHD. Current Topics Behavioral Neuroscience. 2011; 7(1):149-85. DOI: 10.1007/7854_2010_102
  104. 104. Ma I, van Duijvenvoorde A, Scheres A. The interaction between reinforcement and inhibitory control in ADHD: A review and research guidelines. Clinical Psychology Review. 2016; 44:94-111. DOI: 10.1016/j.cpr.2016.01.001
  105. 105. Puiu AA, Wudarczyk O, Goerlich KS, Votinov M, Herpertz-Dahlmann B, Turetsky B, et al. Impulsive aggression and response inhibition in attention-deficit/hyperactivity disorder and disruptive behavioral disorders: Findings from a systematic review. Neurosci and Biobehavioral Reviews. 2018; 90: 231-46. DOI: 10.1016/j.neubiorev.2018.04.016
  106. 106. Skogli EW, Egeland J, Andersen PN, Hovik KT, Øie M. Few differences in hot and cold executive functions in children and adolescents with combined and inattentive subtypes of ADHD. Child Neuropsychology. 2014; 20(2):162-81. DOI: 10.1080/09297049.2012.753998
  107. 107. Van Dessel J, Morsink S, Van der Oord S, Lemiere J, Moerkerke M, Grandelis M, et al. Waiting impulsivity: a distinctive feature of ADHD neuropsychology? Child Neuropsychol. 2019; 25(1):122-9. DOI: 10.1080/09297049.2018.1441819
  108. 108. Janssen TWP, Heslenfeld DJ, van Mourik R, Logan GD, Oosterlaan J. Neural correlates of response inhibition in children with Attention-Deficit/Hyperactivity Disorder: A controlled version of the stop-signal task. Psychiatry Res – Neuroimaging. 2015; 233(2):278-84. DOI: 10.1016/j.pscychresns.2015.07.007
  109. 109. Slaats-Willemse D, Swaab-Barneveld H, De Sonneville L, Van Der Meulen E, Buitelaar J. Deficient response inhibition as a cognitive endophenotype of ADHD. J Am Acad Child Adolesc Psychiatry. 2003; 42(10):1242-8. DOI: 10.1097/00004583-200310000-00016
  110. 110. Blair C, Raver CC, Finegood ED. Self-regulation and developmental psychopathology: Experiential canalization of brain and behavior. In: Cicchetti D, editor. Developmental Psychopathology. New Jersey: John Wiley & Sons; 2016. DOI: 10.1002/9781119125556.devpsy213
  111. 111. Ursache A, Blair C, Raver CC. The promotion of self-regulation as a means of enhancing school readiness and early achievement in children at risk for school failure. Child Development Perspectives. 2012; 6(2):122-8. DOI: 10.1111/j.1750-8606.2011.00209.x
  112. 112. Wells EL, Kofler MJ, Soto EF, Schaefer HS, Sarver DE. Assessing working memory in children with ADHD: Minor administration and scoring changes may improve digit span backward’s construct validity. Res Dev Disabil. 2018; 72:166-78. DOI: 10.1016/j.ridd.2017.10.024
  113. 113. Martinussen R, Hayden J, Hogg-Johnson S, Tannock R. A meta-analysis of working memory impairments in children with Attention-Deficit/Hyperactivity Disorder. J Am Acad Child Adolesc Psychiatry. 2005; 44(4):377-84. DOI: 10.1097/01.chi.0000153228.72591.73
  114. 114. Flores JC. Características de comorbilidad en los diferentes subtipos de Trastorno por Déficit de Atención con Hiperactividad. Psicothema. 2009; 21(4), 592-597.
  115. 115. Rapport MD, Alderson RM, Kofler MJ, Sarver DE, Bolden J, Sims V. Working memory deficits in boys with Attention-Deficit/Hyperactivity Disorder (ADHD): The contribution of central executive and subsystem processes. J Abnorm Child Psychol. 2008; 36:825-37. DOI: 10.1007/s10802-008-9215-y
  116. 116. Riccio CA, Homack S, Jarratt KP, Wolfe ME. Differences in academic and executive function domains among children with ADHD Predominantly Inattentive and Combined Types. Arch Clin Neuropsycholy. 2006; 21(7):657-67. DOI: 10.1016/j.acn.2006.05.010
  117. 117. Lansbergen MM, Kenemans JL, Van Engeland H. Stroop interference and Attention-Deficit/Hyperactivity Disorder: A review and meta-analysis. Neuropsychology. 2007; 21(2):251-62. DOI: 10.1037/0894-4105.21.2.251
  118. 118. Sebastian A, Gerdes B, Feige B, Klöppel S, Lange T, Philipsen A, et al. Neural correlates of interference inhibition, action withholding and action cancelation in adult ADHD. Psychiatry Res – Neuroimaging. 2012; 202(2):132-41. DOI: 10.1016/j.pscychresns.2012.02.010
  119. 119. Cui J, Zhang Y, Wan S, Chen C, Zeng J, Zhou X. Visual form perception is fundamental for both reading comprehension and arithmetic computation. Cognition. 2019; 189:141-54. DOI: 10.1016/j.cognition.2019.03.014
  120. 120. Ackermann S, Halfon O, Fornari E, Urben S, Bader M. Cognitive Working Memory Training (CWMT) in adolescents suffering from Attention-Deficit/Hyperactivity Disorder (ADHD): A controlled trial taking into account concomitant medication effects. Psychiatry Res. 2018; 269:79-85. DOI: 10.1016/j.psychres.2018.07.036
  121. 121. Matsuura N, Ishitobi M, Arai S, Kawamura K, Asano M, Inohara K, et al. Distinguishing between Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder by using behavioral checklists, cognitive assessments, and neuropsychological test battery. Asian J Psychiatry. 2014; 12(1):50-7. DOI: 10.1016/j.ajp.2014.06.011
  122. 122. Alderson MR, Kasper LJ, Hudec KL, Patros CHG. Attention-Deficit/Hyperactivity Disorder (ADHD) and working memory in adults: A meta-analytic review. Neuropsychology. 2013; 27(3):287-302. DOI: 10.1037/a0032371
  123. 123. Tamm L, Juranek J. Fluid reasoning deficits in children with ADHD: Evidence from fMRI. Brain Res. 2012; 1465:48-56. DOI: 10.1016/j.brainres.2012.05.021
  124. 124. Gallego-Martínez A, Fenollar-Cortés J, García-Sevilla J. Implicación de la memoria visoespacial y fonológica en la heterogeneidad clínica del Trastorno por Déficit de Atención con Hiperactividad (TDAH). Anales de Psicol. 2018; 34(1):16-22. DOI: 10.6018/analesps.34.1.289671
  125. 125. Aguiar A, Eubig PA, Schantz SL. Attention Deficit/Hyperactivity Disorder: A focused overview for children’s environmental health researchers. Environmental Health Perspectives. 2010; 118(12):1646-53. DOI: 10.1289/ehp.1002326
  126. 126. Rubiales J. Executive functioning in children with Attention Deficit Hyperactivity Disorder. Rev Iberoam Diagnóstico y Evaluación. 2014;2(38):31-54.
  127. 127. Barkley RA. Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment. 4th ed. New York: Guilford Publications; 2018.
  128. 128. Falter CM, Noreika V. Time processing in developmental disorders: A comparative view. In: Arstila V, Lloyd D, editors. Subjective time: The philosophy, psychology, and neuroscience of temporality. Cambridge: MIT Press; 2014. p. 557-97.
  129. 129. Toplak ME, Dockstader C, Tannock R. Temporal information processing in ADHD: Findings to date and new methods. J Neurosci Methods. 2006; 151(1):15-29. DOI: 10.1016/j.jneumeth.2005.09.018
  130. 130. Dahan A, Ryder CH, Reiner M. Components of motor deficiencies in ADHD and possible interventions. Neuroscience. 2018; 378:34-53. DOI: 10.1016/j.neuroscience.2016.05.040
  131. 131. Noreika V, Falter CM, Rubia K. Timing deficits in Attention-Deficit/Hyperactivity Disorder (ADHD): Evidence from neurocognitive and neuroimaging studies. Neuropsychologia. 2013; 51(2):235-66. DOI: 10.1016/j.neuropsychologia.2012.09.036
  132. 132. Drechsler R, Brem S, Brandeis D, Grünblatt E, Berger G, Walitza S. ADHD: Current concepts and treatments in children and adolescents. Neuropediatrics. 2020; 51(5):315-35. DOI: 10.1055/s-0040-1701658
  133. 133. Sharma N, Jamwal S, Bansal PK. Animal Models of Attention-Deficit Hyperkinetic Disorder (ADHD). In: Bansal PK, Deshmusk R, editors. Animal Models of Neurological Disorders: Principle and Working Procedure for Animal Models of Neurological Disorders. New York: Springer; 2017. p. 217-32.
  134. 134. Russell VA, Sagvolden T, Johansen EB. Animal models of Attention-Deficit Hyperactivity Disorder. Behavioral and Brain Functions. 2005; 1(9):1-17. DOI: 10.1186/1744-9081-1-9
  135. 135. Escorihuela RM, Fernández-Teruel A. Modelos animales en psicopatología y psicofarmacología: del análisis experimental de la conducta a la neurogenética. Psicología Conductual. 1998; 6(1):165-91.
  136. 136. Sagvolden T, Russell VA, Aase H, Johansen EB, Farshbaf M. Rodent models of Attention-Deficit/Hyperactivity Disorder. Biol Psychiatry. 2005; 57(11):1239-47. DOI: 10.1016/j.biopsych.2005.02.002
  137. 137. Vargas JP, Díaz E, Portavella M, López JC. Animal models of maladaptive traits: Disorders in sensorimotor gating and attentional quantifiable responses as possible endophenotypes. Front Psychol. 2016; 7(206):1-9. DOI: 10.3389/fpsyg.2016.00206
  138. 138. Flagel SB, Watson SJ, Robinson TE, Akil H. Individual differences in the propensity to approach signals vs goals promote different adaptations in the dopamine system of rats. Psychopharmacology. 2007; 191(3):599-607. DOI: 10.1007/s00213-006-0535-8
  139. 139. Robinson TE, Flagel SB. Dissociating the predictive and incentive motivational properties of reward-related cues through the study of individual differences. Biol Psychiatry. 2009; 65(10):869-73.
  140. 140. Lopez JC, Karlsson RM, O’Donnell P. Dopamine D2 modulation of sign and goal tracking in rats. Neuropsychopharmacology. 2015; 40(9):2096-102. DOI: 10.1038/npp.2015.68
  141. 141. Serrano-Barroso A, Vargas JP, Diaz E, O’Donnell P, López JC. Sign and goal tracker rats process differently the incentive salience of a conditioned stimulus. PLoS One. 2019; 14(9):1-16. DOI: 10.1371/journal.pone.0223109
  142. 142. Flagel SB, Clark JJ, Robinson TE, Mayo L, Czuj A, Willuhn I, et al. A selective role for dopamine in stimulus-reward learning. Nature. 2011; 469(7328):53-9. DOI: 10.1038/nature09588

Written By

Annabel Jiménez-Soto, Juan Pedro Vargas, Estrella Díaz and Juan Carlos López

Submitted: 05 October 2020 Reviewed: 15 October 2020 Published: 17 December 2020