ADHD symptomatology is best conceptualized as a spectrum: a dimensional versus unitary approach to diagnosis.

ADHD Atten Def Hyp Disord DOI 10.1007/s12402-015-0171-4

REVIEW ARTICLE

ADHD symptomatology is best conceptualized as a spectrum: a dimensional versus unitary approach to diagnosis Rebeca Heidbreder1

Received: 18 January 2015 / Accepted: 18 April 2015 Springer-Verlag Wien 2015

Abstract The aim of this paper is to build a case for the utility of conceptualizing ADHD, not as a unitary disorder that contains several subtypes, but rather as a marker of impairment in attention and/or impulsivity that can be used to identify one of several disorders belonging to a spectrum. The literature will be reviewed to provide an overview of what is known about ADHD in terms of heterogeneity in symptomatology, neuropsychology, neurobiology, as well as comorbidity with other diseases and treatment options. The data from these areas of research will be critically analyzed to support the construct of a spectrum of disorders that can capture the great variability that exists between individuals with ADHD and can discriminate between separate disorders that manifest similar symptoms. The symptoms associated with ADHD can be viewed as dimensional markers that point to a spectrum of related disorders that have as part of their characteristics impairments of attention and impulsivity. The spectrum can accommodate symmetrically and asymmetrically comorbid psychiatric disorders associated with ADHD as well as the wide heterogeneity known to be a part of the ADHD disorder. Individuals presenting with impairments associated with ADHD should be treated as having a positive marker for a spectrum disorder that has as part of its characteristics impairments of attention and/or impulsivity. The identification of impairment in attention and/or impulsivity should be a starting point for further testing rather than being an endpoint of diagnosis that results in pharmacological treatment that may or may not be the & Rebeca Heidbreder [email protected] 1

PsychResearchCenter, LLC, 3669 Michaux Mill Drive, Powhatan, VA 23139, USA

optimal therapy. Rather than continuing to attribute a large amount of heterogeneity in symptom presentation as well as a high degree of symmetric and asymmetric comorbidity to a single disorder, clinical evaluation should turn to the diagnosis of the type of attentional deficit and/or impulsivity an individual has in order to colocate the individual’s disorder on a spectrum that captures the heterogeneity in symptomatology, the symmetrical and asymmetrical comorbidity, as well as subthreshold presentation and other variants often worked into the disorder of ADHD. The spectrum model can accommodate not only the psychophysiological profiles of patients, but is also consistent with what is known about the functional heterogeneity of the prefrontal cortex as well as the construct that cognitive processes are supported by overlapping and collaborative networks. Keywords ADHD Heterogeneity Spectrum disorder Diagnosis Prefrontal cortex Comorbidity Neural circuitry Neural networks Attention Impulsivity Mental health disorders

Introduction The century-long observation that certain children exhibit a behavior pattern that is characterized by fidgetiness and/or frank hyperactivity and that often presents along with a lack of attention or focus as well as impulsivity (Anastopoulos et al. 1994; Barkley and Peters 2012; Lange et al. 2010; Taylor 2011) has resulted in decades of research about the disorder now known as Attention Deficit Hyperactivity Disorder (ADHD) [Diagnostic and Statistical Manual of Mental Disorders 5th ed., American Psychiatric Association 2013, (DSM V)]. This disorder has been

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characterized in terms of types and traits (Fair et al. 2012; Sonuga-Barke 2002), age (Feldman and Reiff 2014; Volkow and Swanson 2013; Wolraich 2006), gender (Arnett et al. 2014; Skogli et al. 2013, Berry et al. 1985), treatment (Bolea-Alamanãc et al. 2014; Cunill et al. 2015; Ollendorf et al. 2013; Evan et al. 2014; Stevenson et al. 2014), duration (Faraone 2005; Mattfeld et al. 2014; van Lieshout et al. 2013), comorbidity with other syndromes (Biederman et al. 1991; DSM V 2013; Patel et al. 2012; Pliska et al. 1999; Williamson et al. 2014), as well as differences in neuroanatomical structure and function (Cao et al. 2014; Valera et al. 2007; for review see Rubia et al. 2014a). The DSM V (2013) characterizes ADHD as a single disorder the diagnosis of which can be made more specific in terms of the presence or absence of inattentiveness or hyperactivity to varying degrees as a function of the fulfillment of several criteria across different settings. There is growing evidence, however, that ADHD is a much more heterogeneous disorder than such a diagnosis is able to capture not only in children and adolescents (Fair et al. 2012; Bala´zs and Kereszteńy 2014; Koziol and Budding 2012; Sonuga-Barke 2002; Williamson et al. 2014), but also, and perhaps to an even greater extent, in adults (De Quiros and Kinsbourne 2001; Kessler et al. 2011). Furthermore, it has even been proposed that the presentation of inattentiveness as the primary symptom in some individuals that are given the diagnosis of ADHD may be indicative of a similar, but separate disorder (Barkley 2001; Barkley 2013; Carlson 1986; Diamond 2005; Hinshaw 2001; Lee et al. 2014; Milich et al. 2001). ADHD is also highly comorbid with other psychiatric disorders with some estimates as high as 60–70 % (MTA Cooperative Group 1999; Patel et al. 2012). This comorbidity is bidirectional as well as in that not only will the ADHD-diagnosed patient have one or more psychiatric or learning disorders, but those patients with a primary diagnosis of one of several psychiatric disorders also meet criteria for ADHD at rates higher than the general population (Biederman et al. 1991; Pliszka 2015), further complicating the etiology of ADHD and adding to the great variability in the ADHD population. Given the increasing rate in the diagnosis of ADHD in children (Visser et al. 2014) and adults (Kessler et al. 2011) as well as the exponential increase in the treatment of ADHD with pharmacological agents over the last decade, it is increasingly becoming more important to refine the characterization of ADHD to allow health care workers to delineate specific dysfunctions and maximize efficacy of treatment. The aim of this paper is to build a case for the utility of conceptualizing ADHD not as a unitary disorder that contains several subtypes, but rather as a marker of impairment in attention and/or impulsivity that can be used to identify one of several disorders belonging to a spectrum.

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The present review will provide an overview of what is known about ADHD in terms of heterogeneity in symptomatology, neuropsychology, neurobiology, as well as comorbidity with other diseases and treatment options. The data from these areas of research will be critically scrutinized in order to build a case that the current DSM system used as the gold standard for the diagnosis of ADHD is insufficient either to capture the great variability that can exist between individuals in their manifestation of the disorder or to discriminate between separate disorders that manifest similar symptoms. The review will conclude with the suggestion that the construct of a spectrum is not only better able to handle individual variability and heterogeneity among the ADHD population in terms of external manifestations such as symptom presentation, neuropsychological assessment and comorbidity, but given what is known about the cytoarchitecture and heterogeneity of the prefrontal cortex (Chudasama 2011; Heidbreder and Groenewegen 2003; Kesner and Churchwell 2011) as well as strong evidence supporting that different cognitive processes are subserved by different subregions of the prefrontal cortex, such a model can better explain the overlap in symptoms and strong interrelations ADHD has with other psychiatric diseases.

Prevalence of ADHD There are many reviews on the historical evolution of ADHD (Anastopoulos et al. 1994; Barkley and Peters 2012; Lange et al. 2010; Matthews et al. 2014; Tarver et al. 2014) that reveal a disorder first characterized by a hyperkinetic aspect and then the inability to focus in the presence of excessive distractibility. Over the last few decades, research has concentrated more on the attention and impulsivity aspects of the disorder as the understanding of the neural substrates that govern these aspects of cognition has increased, and pharmacological treatment has become more widespread (Douglas 1972, 1994, 1999, 2005; Konrad et al. 2006; Leth-Steensen et al. 2000; Rubia et al. 2005, 2014a). This is particularly meaningful given that (1) the hyperkinetic portion of the disorder may diminish with age (Barkley 1990, 1997; AACAP 1997) and (2) the disorder may persist into adulthood when the obvious hyperactivity has all but disappeared (Volkow and Swanson 2013; Willoughby 2003). The fact that the disorder may manifest as a constellation of different symptoms depending on the individual has contributed to the variability in the estimation of the frequency of the disorder in the population. Whereas the DSM V (2013) states that the prevalence of ADHD across cultures is about 5 % in children and 2.5 % in adults, data from the most recent National Survey of Children’s Health (NSCH) (Visser et al.

ADHD symptomatology is best conceptualized as a spectrum: a dimensional versus unitary approach…

2014) indicate that as of 2011, the prevalence in children 4–17 years of age who had ever received a diagnosis of ADHD by a health care provider as reported by a parent in the USA is approximately 11 % (6.4 million children). The study also states that this figure represents a 42 % increase in parent-reported history of ADHD diagnosis since 2003. In addition, the national yearly rate at which ADHD has been diagnosed has increased by 5 % since 2003; however, the rate of diagnosis varies substantially by state, from a low of 5.6 % in Nevada to a high of 18.7 % in Kentucky. All told the NSCH reveals that, relative to 2003, in 2011 approximately 2 million more American children had received a diagnosis of ADHD. When Polanczyk et al. (2014) conducted a meta-analysis of the data found in 135 studies published from 1985 to 2012 and aimed at investigating the prevalence of ADHD worldwide, the results suggested that differences in the estimates of prevalence were significantly associated with the diagnostic criteria, impairment criterion and source of information. The authors concluded that accurate prevalence rates can only be estimated if standardized diagnostic procedures are implemented in representative samples of the community. In their study, standardized diagnostic criteria were defined as those studies that used the diagnostic criteria from The Diagnostic and Statistical Manual of Mental Disorders 3rd ed., American Psychiatric Association, (1980) (DSM III), Diagnostic and Statistical Manual of Mental Disorders 3rd ed., text rev. American Psychiatric Association (1987), (DSM III-R), Diagnostic and Statistical Manual of Mental Disorders 4th ed., text rev. American Psychiatric Association (2000) (DSM IV) and the International Statistical Classification of Diseases and Related Health Problems. World Health Organization, (1992) (ICD 10). The authors state that when such an approach is taken, not only does the variability in ADHD prevalence estimates disappear, but so does the increase over time (1994–2010) in the number of children who meet the criteria for ADHD. Wolraich et al. (2014) also set out to disentangle the epidemiology of ADHD using the strict definition provided by the DSM IV (2000), which as in the DSM V (2013) requires information from multiple informants and settings to establish the diagnosis. Their results yielded an overall prevalence more in line with the Visser et al. (2014) study than the Polanczyk et al. (2014). Both Wolraich et al. (2014) and Visser et al. (2014) also found geographical differences in the prevalence range within the USA. In contrast, Polanczyk et al. (2014) found no prevalence variations as a function of geographical regions of the world. Though the net objectives in the Polanczyk et al. (2014) study and the Wolraich et al. (2014) study were the same, that is, to use the DSM diagnostic criteria to provide an estimate of the prevalence of ADHD, the prevalence rates found by Wolraich et al. (2014) are more than 1.5 times

greater than Polanczyk et al. (2014). This difference could indicate that the suspicion and/or diagnosis of ADHD remains subjective even when formal diagnostic criteria established by the DSM are implemented. This subjectivity comes primarily from two sources. First, the presence or absence of symptoms necessary for diagnosis are provided by parents, teachers and the patient himself, which are then interpreted and evaluated by a clinician. Second, certain combinations of symptoms are more likely to prompt clinical intervention and a subsequent diagnosis. There are data supporting that it is the symptomatology associated with hyperactivity, behavior disruption and disorganization rather than inattention and even impulsivity that will prompt consideration for clinical or therapeutic intervention (Diamond 2005, Feldman and Reiff 2014; Nigg et al. 2005; Weiss et al. 2003). In addition, there is also evidence suggesting that girls with ADHD are underidentified (Berry et al. 1985; Bruchmu¨ller et al. 2012). This underidentification may be due to the qualitative differences between the genders in their presentation of ADHD, which include symptom severity, (Arnett et al. 2014), behavior differences (Gaub and Carlson 1997) and cognitive differences (Arnett et al. 2013). The qualitative gender difference may, at least in part, also be responsible for the significant quantitative gender differences in ADHD (Willcutt 2012). All of these factors can ostensibly interact with cultural/ individual perceptions of what is or is not considered symptoms of a behavior disorder. Therefore, the very same factors that would tend to encourage over diagnosis in some cases make it impossible to account for all of the possible cases that were not included in the studies because of differences in thresholds for seeking clinical help for a child with ADHD in other cases. This variability in threshold holds particularly true with respect to comparisons across geographical regions, and may account for the difference between the Polanczyk et al. (2014) study which failed to find a significant effect of geographical region and the variance in prevalence among the different geographical regions found by Visser et al. (2014) and Wolraich et al. (2014).

Diagnosis The historical evolution of ADHD since the second edition of the DSM (DSM II) (Diagnostic and Statistical Manual of Mental Disorders 2nd ed., American Psychiatric Association 1968) until the current fifth edition (DSM V) has seen ebb and flow in terms of subtyping in an attempt to capture the heterogeneity in the disorder evident to researchers and clinicians. As suggested by the work on prevalence (Polanczyk et al. 2014; Visser et al. 2014; Wolraich et al. 2014) reviewed in the previous section,

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variability in the identification of ADHD in a patient occurs not only as a function of differences in methodology, but between clinical evaluations even when there is adherence to the criteria of the DSM. Typically, the suspicion of a clinical problem begins when either the parent or the teacher starts to notice, among other things, that the child cannot sit still, struggles to focus and has behavioral issues, which may also negatively impact academic performance (Sibley et al. 2014). The physician who ultimately sees the child may reach a diagnosis of ADHD simply based on the anecdotal evidence from the parent and/or teacher without actually subjecting the child to a comprehensive assessment (Sciutto and Eisenberg 2007). The physician may rely on further interviews, including with the patient himself, rather than standardized assessment tools and may or may not adhere to the DSM criteria in order to reach their diagnosis and start the child on medication (Wasserman et al. 1999). Even many psychologists do not regularly follow assessment procedures that are consistent with the best practice guidelines, which can result in a false-positive diagnosis and the inception of pharmacological therapy (Handler and DuPaul 2005). These scenarios seem to indicate that clinical practitioners still view ADHD as a compartmentalized disorder that presents with a concrete and highly recognizable set of symptoms (Bruchmu¨ller et al. 2012). This narrowed view on positive clinical presentation is particularly vexing in the area of what defines a clinically significant academic problem that is specific to the ADHD patient as compared to the academic problems observed in the normal age-matched population (Sibley et al. 2014). The lack of objective delineation not only muddies the waters, but can also lead to a failure to identify a child who actually has a clinical problem. For example, teachers may fail to detect children with symptoms of inattention because they exhibit no hyperactivity and are often quiet and less likely to act out in the classroom. These children may appear to be working, but they are often not paying attention to what they are doing. Conversely, some children who are actually exhibiting the clinical hyperactivity and impulsivity that are characteristic of some forms of ADHD in the classroom setting will be considered as merely having disciplinary problems, and rather than being evaluated for the disorder will simply be subject to punitive practices to correct their behavior. In their review, Nigg et al. (2005) point out that though most theorists do not believe that individuals with ADHD have a unitary disorder with a ‘‘common pathway to their problems,’’ there is still a need to address empirically the heterogeneity that everyone accepts as fact. Beyond possible differences in neuropsychological profiles that the authors were specifically addressing, the heterogeneity among the ADHD population can also be a function of comorbidity, the manner in which the symptoms that are

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part of the ADHD criteria for diagnosis combine and present in any one patient, as well as the ‘‘subthreshold’’ (Bala´zs, and Kereszteńy 2014) presentation of symptoms that does not lead to a true diagnosis of ADHD, but can still cause impaired function. Furthermore, data are building in support of the idea that some patients that receive the diagnosis of ADHD may actually be suffering from a disorder that is separate, but somehow similar to ADHD (e.g., Diamond 2005; Hinshaw 2001; Milich et al. 2001).

Subtype or separate disorder? The DSM V (2013) allows for the specification of ADHD along several dimensions based on the number of criteria that have been fulfilled in the categories of ‘‘Inattention’’ and ‘‘Hyperactivity and Impulsivity.’’ As a result, the patient with ADHD can be considered to have a Combined Presentation (the abbreviation ‘‘CT’’ will be used, which refers to the virtually identical ‘‘Combined Type’’ presentation from the DSM IV-TR) if sufficient criteria from both categories have been met, a Predominantly Inattentive presentation (PI) if sufficient criteria are met only for the ‘‘Inattention’’ category, or a Predominantly hyperactive/ impulsive type (HI) if sufficient criteria are met only for the ‘‘Hyperactivity and impulsivity’’ category. The DSM V (2013) further suggests specifying the disorder in terms of the severity of presentation (mild, moderate and severe). Several studies have investigated the cognitive differences between these subtypes with varying results (e.g., Baeyens et al. 2006; Bonafina et al. 2000; Chhabildas et al. 2001; Fair et al. 2012; Faraone et al. 1998; Geurts et al. 2005; Lockwood et al. 2001; Nigg et al. 2002; Riccio et al. 2006; Song and Hakoda 2014). However, it has been proposed that the differences between the CT subtype and the PI subtype are significant enough and that rather than being two different subtypes of the same disorder, the CT and PI subtypes should be considered as two distinct disorders (Baeyens et al. 2006; Barkley 2001; Diamond 2005; Hinshaw 2001; Milich et al. 2001). Both Milich et al. (2001) and Diamond (2005) provide detailed reviews of the qualitative differences between the two subtypes, which make a strong case for positing CT and PI as nearly diametrically opposite conditions. Although the PI subtype represents about 25–30 % of the ADHD cases seen in the clinic (Faraone et al. 1998; Weiss et al. 2003), in the community it is the more prevalent subtype (Carlson and Mann 2000) by nearly double relative to the CT subtype (Gaub and Carlson 1997). The CT subtype has almost exactly the opposite prevalence profile, namely in the clinic the CT subtype is seen nearly twice as much as the PI subtype (Faraone et al. 1998). Furthermore, whereas gender differences in prevalence ratios for the CT subtype in


the community and in the clinic are significantly different, the PI subtype has practically the same gender distribution in both the community and the clinic. This discrepancy suggests that not only are individuals with the PI subtype receiving clinical care at a lower frequency than the CT subtype, but the CT subtype is associated with symptoms that are more readily identifiable as being part of a disorder. It is likely that the very character attributes that distinguish the CT from the PI subtypes are what prompt the visit to the clinician for further evaluation. Diamond (2005) reminds us that while individuals with the CT subtype are hyperactive, fidgety, impulsive, talkative, disinhibited, disorganized and impatient, the PI subtype is characterized by the inability to pay close attention to detail, difficulty in planning and remembering to do what is required, and disorganization. Diamond (2005) also points out that though both the PI and CT subtypes could have the qualifier ‘‘easily distracted’’ added into their characterization; the difference between them has to do with the type of distractibility that they experience. Whereas the individual with CT subtype could become easily distracted by another stimulus that enters his sphere of awareness, the individual with the PI subtype becomes distracted because she loses interest in the activity she is engaging in. This difference can be qualified as an external versus internal mode of distractibility. According to Weiss et al. (2003), an individual suffering from the constellation of symptoms associated with the CT subtype is also more functionally impaired than someone suffering from the symptoms associated with the PI subtype. Furthermore, the individual with the CT subtype has a higher likelihood of not only being medicated, but being medicated at a higher dose (Barkley 2001; Diamond 2005; Milich et al. 2001; Weiss et al. 2003). In fact, Faraone et al. (1998) report that the CT subtype is associated with a more clinically severe syndrome than either the PI subtype or the HI subtype. Finally, although both subtypes appear to respond to treatment with stimulants, the CT subtype has a higher probability of being rated as improved relative to the PI subtype (Weiss et al. 2003). Although the data are compelling, there is as yet no absolute consensus regarding the separation of the PI and CT subtypes into two separate disorders. However, the research that has set in motion part of this debate has inadvertently opened the metaphorical ‘‘Pandora’s Box,’’ which has postulated the possibility of splitting the PI subtype itself into two different disorders of attention (Carlson and Mann 2002). The idea that ‘‘sluggish cognitive tempo’’ (SCT) could be indicative of an impairment that is separate from the PI subtype was proposed by Carlson and Mann (2002) as an extension to the findings from McBurnett et al. (2001). In their view, it may be possible to distinguish two different disorders in patients

who have no symptoms of hyperactivity and impulsivity, but who manifest problems with attention. Namely, one disorder is characterized by the Inattentive criteria outlined in the DSM, and the second disorder, seen only in a subset of PI patients, is characterized by specific traits including hypoactivity, dreaminess and forgetfulness. There has been a recent resurgence in the number of investigations aimed at providing evidence for the validity of qualifying SCT as a separate disorder (Barkley 2013; Bauermeister et al. 2012; Becker et al. 2014; Lee et al. 2014; Saxbe and Barkley 2014). The utility of these and future investigations is not only in their ability to reach consensus, but more importantly to increase the understanding in psychopathological problems of attention. As Becker et al. (2014) point out, ‘‘SCT may be useful as a transdiagnostic construct,’’ which could fit in neatly with the reminder from Barkley (2001) that ‘‘attention is multidimensional such that several distinct disorders of attention are likely to be identified besides ADHD.’’ With respect to the CT and HI subtypes, there is evidence to suggest that these may not be separate conditions that remain stable over time, but rather subsets of each other. On the one hand, investigators such as Lahey et al. (2005) suggest that HI is a less severe form of CT, perhaps due to the fact that hyperactivity reduces or disappears with age. On the other hand, Barkley (2007) suggests that the HI form of ADHD is simply a precursor to the CT form. Data to support either position only adds greater weight to the proposition that ADHD may not really be one disorder with dimensional presentations, but rather several disorders that are tied together by ‘‘etiological vicinity.’’ Finally, the diagnostic criterion of impulsivity has been used to subcategorize patients diagnosed with ADHD for at least two decades. However, the question about how to define impulsivity in particular with respect to psychiatric illness still continues to be debated (for review see Moeller et al. 2014). The list of ‘‘Hyperactivity/Impulsivity’’ criteria in the DSM V (2013) fails to capture the hallmark traits of delayed gratification, rapid and unplanned actions, as well as reduced sensitivity to negative consequences that have historically been associated with impulsivity. In fact, though ADHD is highly comorbid with conduct disorder and oppositional disorder (Biederman et al. 1991), the DSM V (2013) definition of the impulsivity associated with ‘‘Disruptive impulse control and conduct disorders’’ is very different from the impulsivity associated with ADHD; yet, when referring to the comorbidity with ADHD, the DSM V (2013) says that since ADHD individuals are impulsive, then it is not unusual that they should show this comorbidity. Although the paragraph on diagnostic features associated with ADHD does mention the hallmark traits associated with impulsivity, the examples provided for an impulsive action ‘‘darting into the street without looking’’

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or ‘‘taking a job without adequate information’’ only add to the ambiguity for several reasons. Whereas it is not difficult to envision an individual who cannot wait his turn, who is on the go and who runs about and intrudes and interrupts as capable of darting into traffic or making poor career choices, the individual who fits the profile of the PI subtype can find themselves engaging in these same actions for reasons that have nothing to do with impulsivity, but instead everything to do with the quality of the attentional processes these individuals may have access to. Though Barkley (1997) suggests that the differences in the attention difficulties between the PI and CT subtypes are related to problems with selective attention in the former and problems with disinhibition in the latter, it is also possible to look at both subtypes in terms of opposite ends of an attentional spectrum. Specifically, whereas the PI subtype may have an ‘‘attentional spotlight’’ that is too narrow, the CT and HI subtypes may have ‘‘attentional spotlights’’ that are too wide. The analogy of an ‘‘attentional spotlight’’ (Posner and Petersen 1989) or the fact that it may be too wide in ADHD is not new (Shalev and Tsal 2003); however, the idea that the subtypes may vary as a function of the breadth of the attentional spotlight is. On the one hand, the wide attentional spotlight of the CT and HI subtypes prevents those individuals from focusing selectively on any one stimulus for an interval long enough to make evaluative judgments. In other words, they are overwhelmed by stimuli, and as such, no one stimulus necessarily has more salience than another. In contrast, the narrowness of the PI subtype spotlight does not give them access to many other competing stimuli that may be available at any one time and are necessary to form an evaluative judgment. As a result, on the one hand, the person with CT or HI subtype may dart across the street or make a bad decision because they do not rest their focus long enough among the wide range of stimuli available to them to avoid making a decision that might have negative consequences. In other words, those individuals cannot stop moving their spotlight across the wide range of stimuli that are available to them. On the other hand, the PI subtype may have a stagnant spotlight, such that even when the appropriate information is available, they do not have the ability to access the information that would permit a better decision. In other words, they are not giving their attention to all that they can. Nevertheless, in both cases, the end result is the same. In sum, there is an abundance of data indicating that the variety in the symptomatology of patients seeking clinical help for suspected ADHD exceeds the current nosology for the delineation of this disorder. Furthermore, it would appear that the manner in which patients present these symptoms do not necessarily fall into clear-cut categories that are variations within only one disorder as proposed by the DSM.

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Comorbidity Beyond the recognized variety in symptomatology of ADHD, the accurate diagnosis of a patient can be confounded by the presence of one or more comorbid disorders. It is now well established that 50–75 % of patients diagnosed with ADHD will have at least one comorbid learning disorder, psychiatric disorder or neurodevelopmental disorder (Barkley 2005; Biederman et al. 1991; Jensen et al. 1997; Patel et al. 2012; Pliszka et al. 1999). In addition, an adult with ADHD throughout his lifetime will have six times the likelihood of developing another psychiatric disorder (Brown 2006). The extensive review by Biederman et al. (1991) tells us that ADHD is comorbid with conduct disorder at a rate of between 30 and 50 %, with oppositional defiant disorder at a rate of at least 35 %, with mood disorders at a rate ranging from 15 to 75 %, with anxiety disorders at a rate of at least 25 %, and with learning disabilities at a rate between 10 and 92 %. ADHD has also been associated with a greater risk for substance abuse (Wilens and Morrison 2011). We also know that comorbidity with ADHD can be asymmetrical, that is, while the rate of ADHD in certain disorders can exceed that in the general population, patients with ADHD do not necessarily show those same disorders at a rate higher than in the normal population (Pliszka 2015). For example, 60 % of Tourette patients will be concurrently diagnosed with ADHD; nevertheless, people with ADHD are not diagnosed with Tourette’s at a rate higher than what occurs in the general population (Jummani and Coffey 2015). Approximately 40 % of those diagnosed with borderline personality disorder will also have received a diagnosis of ADHD (Ferrer et al. 2010), but borderline personality disorder does not occur more frequently in those whose primary diagnosis is ADHD. Patients with bipolar disorder have comorbid ADHD at a rate of 60–90 %, but only 22 % of patients with ADHD as a primary diagnosis will have comorbid bipolar disorder (Wilens et al. 2003). Obsessive compulsive disorder is highly comorbid with ADHD particularly in boys in whom the rate of comorbidity is about 50 % (Geller 2006). Finally, there is even some evidence that the presence of ADHD symptoms may be indicative of premorbid schizophrenia or even be linked with a ‘‘distinct neurodevelopmental progression of schizophrenia’’ (Marsh and Williams 2006). Certain patterns of comorbidity are also more strongly correlated with ADHD than others (Greene et al. 1996; Jensen et al. 1997; Lynam 1996). For example, the relationship between ADHD, conduct disorder and oppositional defiant disorder often makes it difficult to distinguish one from the other (Biederman et al. 1991). Though large epidemiological studies do not usually


provide comorbidity rates in ADHD by subtype, Faraone et al. (1998) found that it is actually the CT form of ADHD that is associated with the highest rate of comorbidity with conduct disorder and oppositional defiant disorder. Furthermore, the difference in the rate of comorbidity between the CT and PI groups for these disorders, the so-called externalizing disorders, was significant. Though the relationship between internalizing disorders and the different subtypes is not as well defined (for review see Garner et al. 2013), children with the PI subtype tend to be more socially isolated and may be associated with a higher rate of comorbid internalizing disorders, including anxiety and depression, or at the very least are less prone to externalizing disorders (Diamond 2005). Schaughency et al. (1987) report that 43 % of individuals with ADD without hyperactivity, the DSM III (1980) subtype that is equivalent to the PI subtype of later editions, also received diagnoses of either anxiety or depression. Patients that have ADD with hyperactivity, the DSM III subtype that is equivalent to the CT subtype of later editions, were diagnosed with these disorders only at a rate of about 10 %. Faraone et al. (1998) also noted that the PI subtype was associated with a higher lifetime rate of major depressive disorder relative to the other subtypes. The study by Weiss et al. (2003) that involved comprehensive and multidisciplinary evaluations also revealed higher rates of anxiety and depression in the PI subtype than in the CT subtype. Finally, it may be the case that some of the impairment associated with ADHD subtypes may be more related to the influence of the comorbid disorder rather than to the disorder of ADHD itself (Milich et al. 2001). For example, Lockwood et al. (2001) were able to show significant neuropsychological differences at 80 % accuracy between the CT and PI types of ADHD when effect of presence of comorbidity was controlled. To summarize, there can be no doubt that the relationship that ADHD has with a variety of psychiatric disorders is a tangled and intricate one and many questions are left begging. There is still no clear understanding as to why there is such a high incidence of comorbidity between ADHD and other psychiatric diseases or how the diagnosis of ADHD can seemingly predispose an individual to another psychiatric disorder at a future date. One explanation may be that ADHD and its comorbid psychiatric disorders may share a common neural substrate involving the prefrontal cortex, which as a result of heterogeneity in its neural circuitry can account for a range of neurological, cognitive and psychiatric disorders (Chudasama 2011; Heidbreder and Groenewegen 2003; Kesner and Churchwell 2011). Recent evidence also suggests that there may be a common neural substrate across multiple mental illnesses (Goodkind et al. 2015).

Pharmacotherapy and alternative therapy for ADHD Although not an exhaustive review, the evidence provided so far argues against describing a patient as simply having ADHD or simply amassing together groups of patients that may have important differences at the level of symptom presentation, underlying psychiatric disease, and symptom severity leading to impairment under one disorder. Nevertheless, the prescription of pharmacotherapy to treat the symptoms of what appears to be ADHD often starts without an in-depth assessment of the true nature of the disorder in the patient. The finding by Wolraich et al. (2014) that 5.7 % of the children sampled in their study received a diagnosis of ADHD and were prescribed ADHD medication despite the fact that they did not meet the DSM IV-TR (2000) criteria for ADHD underscores the willingness to treat pharmacologically a set of symptoms that look like a disorder, but for which there is no objective evidence of its presence in the patient. Furthermore, even when a diagnosis is made using the criteria provided by the DSM, pharmacological treatment efficacy may vary significantly depending on the constellation of symptoms present in the patient (del Campo et al. 2011; Feldman and Reiff 2014; Hale et al. 2011). As of 2011, 1 million more children are taking ADHD medication than in 2003 (Visser et al. 2014). The very significant increase in use of ADHD pharmacological therapy is best described in the March 2014 Express Scripts Lab Report ‘‘U.S. MEDICATION TRENDS for ATTENTION DEFICIT HYPERACTIVITY DISORDER.’’ This report states that ADHD medication use among Americans has risen 35.5 % from 2008 to 2012. This increase has translated into a spending increase on ADHD medication of 14.2 % in 2012 and represents the greatest increase seen among any traditional drug category. ADHD medication sales are also forecast to continue to grow to nearly 25 % by 2015. The willingness to prescribe pharmacological therapy, specifically stimulants, for the patient presenting with ADHD symptoms comes in part from the awareness that this type of treatment seems to work best at alleviating the core symptoms of ADHD (Faraone and Buitelaar 2010), may be equally effective across subtypes (Solanto et al. 2009) and affects various aspects of cognition (Coghill et al. 2014) by exerting their effects on the frontal cortex (Rubia et al. 2014a). These findings are not surprising since it is well known that stimulants exert their effects on the dopaminergic network (for review see Segal and Kuczenski 1994; Kuczenski and Segal 1997), and these effects produce changes in behavior (Volkow et al. 1998). Given that even a single dose of methylphenidate can enhance cognitive performance in healthy volunteers and that there is a

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dose-dependent and a dose–response relationship that differs across a variety of cognitive domains (Linssen et al. 2014), pharmacotherapy as a global treatment for all patients diagnosed with ADHD without sensitivity to the array of symptoms and functional deficits in any one individual may be akin to taking a canon to an anthill. For example, though there may be an initial improvement in some of the symptoms, pharmacotherapy does not always ‘‘normalize’’ cognition and/or behavior (e.g., Be´dard et al. 2015; Gualtieri and Johnson 2008; Rapport et al. 1994; Hale et al. 2011; Rubia et al. 2009), and worse yet can end up precipitating far more serious symptoms, including psychosis if pharmacotherapy is begun when the relationship between the impairment due to any detected or undetected comorbid disorder and that due to the ADHD symptoms is unclear (Schaeffer and Ross 2002). Furthermore, as suggested earlier, though the evidence to date indicates that pharmacotherapy seems to eliminate the core symptoms of ADHD irrespective of subtype, there is evidence that the CT subtype has a higher probability of being rated as improved relative to the PI subtype (Weiss et al. 2003). Whether or not the differences in efficacy across the different presentations of ADHD as well as their associated effects on cognition and behavior are linked with differentiable changes in the aberration patterns of brain activity, and/or, for example, a related change in catecholamine availability (del Campo et al. 2011; Schmeichel and Berridge 2013) has yet to be fully clarified. Taken together all of these factors point to the great need for the refinement and deepening of the diagnostic process in order to identify a clear clinical presentation that can optimize the clinical outcome. In parallel to the increased use of pharmacotherapy to treat ADHD, there has also been an explosion in the search for alternatives to the pharmacological treatment of ADHD (Sonuga-Barke et al. 2013) even though many of these alternative treatments show no evidence of efficacy. The development of these alternatives has been fueled by variability in efficacy of pharmacotherapy (Faraone and Buitelaar 2010), the desire to avoid the known side effects (Graham et al. 2011; Bolea-Alamanãc et al. 2014; Clemow and Walker 2014) and the unknown consequences of longterm treatment with pharmacotherapy (Volkow and Insel 2003; Vitiello 2001) or simply by the desire to find a more holistic solution in lieu of the pharmacological one (Timimi 2004). One area that has received significant attention is the role of diet in ADHD either as the etiological basis for its symptoms (Wolraich et al. 1985; Wolraich et al. 1994) or as an option for treating the symptoms of the patient (Millichap and Yee 2012; Nigg et al. 2012). Recent reviews and meta-analyses of randomized controlled studies to investigate the effects of dietary treatments for ADHD

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(Stevenson et al. 2014; Sonuga-Barke et al. 2013) concluded that though supplementation with free fatty acid appears to have a small effect on ADHD-related behaviors, there is no definitive effect of dietary treatment on these behaviors. There have also been numerous studies that have looked at the effect of exercise on children with ADHD. A review by Kamp et al. (2014) on the effects of exercise on ADHDrelated behaviors in children under 14 years of age concluded that a wide variety of exercises, if implemented for 1–10 weeks, can have a positive effect on motor skills, social behavior and strength in this population. However, when Hoza et al. (2015) compared the effects of physical activity and sedentary activity before the start of school on the behavior of early elementary school age children at risk for ADHD, they were unable to assert conclusively that there was a difference between interventions. Though the benefits of exercise for the treatment of ADHD may still be unclear, there is sufficient preclinical as well as clinical data on nonADHD populations that show that exercise has a beneficial effect on neurobehavioral functioning and as such may be a direction worth pursuing (Halperin et al. 2014). Psychosocial interventions for children and adolescents with ADHD have a long and controversial history (see Antshel and Barkley 2011, for a historical review; Pelham and Fabbiano 2008). Evans et al. (2014) conducted a metaanalysis on 122 recent studies that investigated evidencedbased effects of training intervention and behavior management treatments. Their results confirm that traditional behavior management therapies have a well-established efficacy and that organization training also appears to be effective; however, improvement in function comes only with the implementation of behavior management techniques (Feldman and Reiff 2014). Other training interventions including neurofeedback (for review see Holtmann et al. 2014), cognitive training (for review see Sonuga-Barke et al. 2014) and social skills training (for review see Mikami et al. 2014) show little or no efficacy. There are at least two caveats to these findings. First, most studies do not look at effects of these alternative therapies by subtype. Mikami et al. (2014), however, did notice that social skills training seemed to be more effective for the PI subtype than for the CT subtype. Second, the addition of a behavioral therapy component to the pharmacological treatment does not appear to confer any advantage to the resolution of symptoms (Jensen et al. 2001a, b; Sonuga-Barke et al. 2013).

Neural substrates of ADHD The rationale behind why the core symptoms of ADHD improve with stimulants comes from decades of research that has revealed the effect of amphetamines on the


dopaminergic network (for review see Segal and Kuczenski 1994; Kuczenski and Segal 1997) and its consequent effects on behavior (Volkow et al. 1998). The effects of varying levels of dopamine in the brain have been linked specifically to the frontal cortex in normal subjects as well as in a variety of neurological and psychiatric disorders (for review see Clark and Noudoost 2014). In particular, levels of dopamine in the frontal cortex seem to modulate among other things impulsivity (Dagher and Robbins 2009; Volkow et al. 2009; for review see Sebastian et al. 2014), motivation (Volkow et al. 2011) and inattention (Rosa-Neto et al. 2005) all hallmark characteristics of ADHD. As a result, there has been a virtual explosion of studies aimed at uncovering differences in anatomy and function in the brains of ADHD patients relative to normal cohorts. The scientific impetus to conduct such studies stems presumably from a need to understand what may be dysfunctional or aberrant in the brain of the ADHD patient as well as the hope of finding a biomarker that could render a diagnosis conclusive (for review see Shaw et al. 2013). There are basically four main lines of research with this aim that have identified functional and anatomical differences in the brains of ADHD patients relative to age-matched normal controls: (1) analysis of brain volume and cortical thickness, (2) functional MRI (fMRI), (3) diffusion tensor imaging studies (DTI) and (4) most recently, connectomics. The combined results from these studies as well as a plethora of meta-analyses across such studies suggest that on average children and adolescents with ADHD have reduced brain volumes (Castellanos et al. 2002; Nakao et al. 2011; Lim et al. 2015), reduced cortical thickness (McLaughlin et al. 2014; Almeida et al. 2010; Shaw et al. 2013), show deactivation in function relative to controls predominantly in fronto-striatal, fronto-parietal and fronto-cerebellar regions (for review see Cubillo et al. 2011 and Rubia et al. 2014b) and appear to have abnormalities in functional connections (Cao et al. 2014; Tomasi and Volkow 2012; Sripada et al. 2014) primarily between these areas as well as a lag in brain maturation both in terms of structure (Shaw et al. 2007) and functional architecture (Sripada et al. 2014). The regions of brain that show decreased activation in the ADHD patient as measured by fMRI have historically been thought to support executive function (EF) given in part to the now well-documented finding that patients who have suffered lesions in these areas perform poorly on tasks that recruit EF such as, but not limited to the Stroop test, Stop-signal task, or Go/ no-go task (Shallice and Burgess 1991; Stuss et al. 2001). By extension, when a patient who does not have an observable injury of the brain performs poorly on such neuropsychological instruments, the

implication is that there is a correlated dysfunction in the areas of brain that support that task (Miyake et al. 2000). In other words, the rather circular argument states that objective evidence of injury to these areas is correlated with poor EF as measured by neuropsychological tests, and poor performance on those same tests is assumed to be the result of underlying abnormality in those same brain regions. Neuroimaging studies of ADHD patients that have required the performance of tasks that measure EF during image acquisition, ostensibly in order to activate the neural substrates which support task execution, have not reliably and consistently shown a significant correlation between performance on tasks of EF and the activation of the neural substrates supporting the task. Namely, some studies were successful in demonstrating a correlation between poor performance and hypoactivation (Booth et al. 2005; Konrad et al. 2006), but many have failed to show a correlation between performance on these tasks and deactivation of brain regions recruited during these tasks (Bell et al. 2006; Rubia et al. 2010; Cubillo et al. 2011; Rubia et al. 2011; Smith et al. 2006, Congdon et al. 2014; Konrad et al. 2006; Banich et al. 2009), or even to detect significant differences either in performance on the measure of EF or in the brain activation patterns (Congdon et al. 2014). Furthermore, data from resting state fMRI studies suggest that the hypoactivation seen on fMRI is unrelated to the performance of the task during image acquisition (Tian et al. 2008; Yu-Feng et al. 2007; Hoekzema et al. 2014; Li et al. 2014). Although the absence of a corroborative behavioral effect does not make neuroimaging findings irrelevant, it does beg the question as to which of the potential ‘‘cognitive algorithms’’ is the subject able and not able to recruit during the performance of the task, and how if at all are they linked to the ongoing detectable activity in the brain (Mostofsky and Simmonds 2008; for commentary see Wilkinson and Halligan 2004). With respect to ADHD, there are at least three issues that need to be evaluated before it will be possible to answer this question. First, is the variability in the corroboration between performance and activation in the associated neural substrates due to the neuropsychological task? Second, given the reviewed evidence for significant differences between subtypes, is the variability in the corroboration between performance and activation in the neural substrates a function of the heterogeneity in ADHD? Third, is the variability in the corroboration between performance and activation in the neural substrates a function of an incomplete understanding about the interaction between heterogeneity of cognitive processes and heterogeneity in the architecture of the frontal cortex?

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Is the variability in the corroboration between performance and activation in the associated neural substrates due to the neuropsychological task? One could argue that the sample size in the studies that failed to show a correlation between hypoactivation in brain regions and performance was too small (in general \ 20) to detect performance differences given that previous studies of purely cognitive or neuropsychological deficits in ADHD with similar sample sizes also failed to show effects associated with deficits in EF (Barkley 1997). However, there is also evidence showing that robustness in EF may not be a valid or reliable measure for the identification of ADHD patients (Barkley 2012; for review see Lange et al. 2014). For example, Wa˚hlstedt et al. (2009) report that 20–40 % of children with ADHD do not score in the clinically significant impairment range on a variety of EF measures. Duff and Sulla (2014) also make the case that although poor performance on an EF measure may be strongly suggestive of the presence of a clinical disorder, failure to demonstrate impairment on the measure does not belie the existence of a disorder. In addition, there seems to be a continuum effect such that there is a significant correlation between level of impairment on EF measures and negative behavioral symptoms. Furthermore, the use of any one ‘‘cold’’ cognitive construct such as those used in taskrelated fMRI studies may or may not be truly representative of EF functioning (Barkley 2012). For example, there are data suggesting that simply the addition of a working memory component, which requires the maintenance of an attentional set in order to complete the task, increases the probability of detecting differences in neural substrates that are correlated with the performance of a go/no-go task in normal adults, (e.g., Mostofsky et al. 2003; Mostofsky and Simmonds 2008), a color-word Stroop test in ADHD patients (Burgess et al. 2010) and an n-back test of working memory for spatial position (Be´dard et al. 2014). The inconsistency in the findings relative to the performance of neuropsychological tests by patients with ADHD may be related to the idea that the heterogeneity of symptom presentation does not warrant the assumption that the diagnosis of ADHD should lead to a homogeneous response pattern across patients during a neuropsychological assessment. An analysis conducted by Fair et al. (2012) clearly indicates that neuropsychological subtypes can be discerned with high precision not only in children with ADHD, but also in typically developing control youth. Furthermore, there appears to be an overlap between the heterogeneity found in children with ADHD and the variation found in typically developing children. With respect to the deficit in response inhibition that many studies on

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ADHD assume for purposes of their experiments, the Fair et al. (2012) analysis reveals that though valid at the group level, if individual results from the ADHD group are compared with the normal controls, the differences are significant only in about 50 % of the comparisons. A similar pattern was detected for working memory as well as temporal information processing. The finding that only about 50 % of the comparisons were significant when looked at individually rather than as a group follows the general trend that the percentage of children with ADHD who show clinically significant impairment varies within the ADHD population and also as a function of the task being used to measure EF (Wa˚hlstedt et al. 2009). Beyond just the individual versus group comparisons on neuropsychological tests, a neuroimaging study of the neural substrates of response inhibition in normal adults indicates that single-subject analysis reveals a pattern of activation associated with inhibitory response that is different from the pattern of activation produced in group analysis (Mostofsky et al. 2003). Koziol and Budding (2012) argue that precisely because of the highly heterogeneous character of ADHD, the subtypes provided by the DSM cannot capture the individual nature of the disorder at the neuropsychological level, and as such, cannot be used to characterize the disorder in terms of dysfunction of a specific set of neuroanatomical substrates. Rather, ADHD may perhaps best be described in terms of a continuum or spectrum (Haslam et al. 2006; for review see Bell 2011) a notion that is lent even more support from the observation that there are individuals with subthreshold ADHD. Bala´zs and Kereszteńy (2014) report a wide range (0.8–23.1 %) in the prevalence of subthreshold ADHD due to the lack of uniformity regarding the minimum number of symptoms that should be used to define subthreshold ADHD functional impairment. This is the case despite the fact that the DSM V (2013) includes both an ‘‘Other Specified Attention-Deficit/Hyperactivity Disorder’’ category as well as an ‘‘Unspecified AttentionDeficit/Hyperactivity Disorder’’ category meant to be used to identify individuals who have some of the symptoms characteristic of ADHD that cause clinically significant problems in academics or the work place. In addition to the heterogeneity in neuropsychological profiles within the ADHD population, there exists one other caveat that may be confounding the results on neuropsychological tasks of EF as well as neuroimaging during task execution. There is evidence suggesting that ADHD performance on many experimental tasks is subject to attentional inconsistency (Castellanos and Tannock 2002; Douglas 1999; Epstein et al. 2003; Leth-Steensen et al. 2000). This finding is not surprising given that lapses in attention can be, by definition, part of the ADHD disorder


(DSM V 2013). What is of much greater interest is how these lapses in attention present. Leth-Steensen et al. (2000) discovered abnormally slow reaction times in the tails, but not the leading edges of the distribution of the reaction times. Further analysis revealed that group differences disappeared when only leading edge reaction times for the ADHD group were compared with those of the control group. Thus, the attentional lapse may be occurring as a function of duration of the test, suggesting that the longer the test goes on, the poorer the performance of the subject. However, and in addition, each individual reaction time is made up of a constellation of several steps each of which may also be affected by attentional lapses. In other words, within any one response, there is the moment when the subject sees the response, decides what to do with it and then decides how to respond. Each of these steps could be subject to an attentional lapse, resulting in not only reaction time differences, but variability in the times at which areas of brain devoted to the execution of the steps are recruited. This is an especially important consideration when attempting to describe the neural substrates of the disorder. Namely, the differences measured during the performance of these tasks as well as the associated differences in activation of brain regions across groups may be the result of a comparison made between different portions of the sequence of operations required to complete the task. The assumption that any variability simply averages out particularly when such small sample sizes are used may inadvertently be washing out the very effect that is being sought. In sum, variability in the performance on neuropsychological tests of EF across the ADHD population can be attributable to at least four issues. First, it is still not clear how or if each of the neuropsychological tests used to measure EF in individuals with ADHD is actually measuring EF or at the very least what aspects of EF it is measuring. Second, there are not enough data to know how much of a cognitive load needs to be introduced into a neuropsychological test in order for performance differences relative to controls to be observed. Third, experimental designs do not take into account either the variability in performance as a function of test duration or the variability in performance across the span of the test. Both of these points are especially relevant when testing performance in subjects who are known to have variations in attention, focus and impulsivity over a span of time. Finally, there are little data regarding performance differences on neuropsychological tests as a function of ADHD subtype or severity of symptomatology. Given the evidence presented thus far in the review, there is not only sufficient support to warrant such investigations, but more importantly there is no rationale for assuming that patients in

which ADHD symptoms have been observed will all perform in a homogeneous manner.

Is the variability in the corroboration between performance and activation in the neural substrates a function of the heterogeneity in ADHD? A great majority of task-dependent neuroimaging studies do not make any distinctions in terms of symptom presentation or subtype of the ADHD subjects (Booth et al. 2005; Hart et al. 2013; Konrad et al. 2006; Rubia et al. 2005; Rubia et al. 2010; Simmonds et al. 2008). As has been discussed earlier in this review, there is converging evidence suggesting that not only are the differences between the variants of ADHD important, but in some cases, the differences are so remarkable that they may warrant being considered as separate disorders rather than subtypes of ADHD. Therefore, as our understanding about ADHD continues to grow, the evidence must confirm or deny the rationale for using ADHD as an umbrella term for the various subtypes and/or using only one subtype in experimental designs. The comparison of some of the data that have been collected to elucidate the neural substrates that support working memory exemplify the need to take into account the heterogeneity seen in ADHD. When adult patients with ADHD (collapsed across all subtypes) performed an n-back task during the acquisition of fMRI data, significant differences between adults with and without ADHD were found in the bilateral prefrontal cortices. However, only men with ADHD showed less activation in a network of brain regions that was lateralized to right frontal and subcortical regions and left occipital and cerebellar regions relative to controls. Adult females with and without ADHD were indistinguishable despite the fact that ADHD symptomatology in both the females and males was similar (Valera et al. 2010). Burgess et al. (2010) looked at the brains of adult males and females specifically with CT subtype ADHD with fMRI while they performed either a working memory digit span or spatial span task. They found decreased function in the left dorsolateral prefrontal cortex relative to normal controls; however, this study did not look at gender differences. Mills et al. (2012) found evidence for atypical thalamic connectivity with cortical structures in children with ADHD (collapsed across subtypes) related to their performance on a task of spatial span working memory using rs-fcMRI. Massat et al. (2012) also studied children with CT subtype ADHD using fMRI to investigate differences in activation patterns between that group and normal controls while performing an n-back

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working memory task, and their data found no evidence for differences in prefrontal areas between children with CT subtype ADHD and normal cohorts, but did find decreased activation in inferior parietal cortex, as well as decreased activity in the striatum and the cerebellum. When Lockwood et al. (2001) explicitly targeted the identification of differences between ADHD subtypes with respect to neuropsychological performance, all attempts were made to minimize methodological limitations including insuring that all participants were medication free, that clinical diagnosis was accurate, that there was no comorbidity and that there was equal representation across genders. Their results indicate that the PI subtype had greater difficulty selecting previously presented auditory information from prompted material. This finding agrees with the hypothesis discussed earlier in this review that the PI subtype is associated with a narrow focus of attention. In other words, one explanation for their findings could be that the discrimination between previously heard and currently heard material was difficult because the children stagnated their attention at different times during the previously presented stimuli and as such some of the information was not encoded into memory. In contrast, children with the CT subtype showed impaired response inhibition and reengagement as well as difficulty carrying out goaldirected activities. This finding also fits with the previously presented hypothesis that the CT subtype is associated with the inability to stop shifting focus, which in this case resulted in attending to inappropriate stimuli. The postulates from the Barkley (1997) model of ADHD that only the CT and HI subtypes, but not the PI subtype, are associated with EF deficits, have led to several studies aimed at investigating the validity of this claim. Though several studies did not find differences in EF across subtypes (e.g., see Duff and Sulla 2014 for review; Geurts et al. 2005; Riccio et al. 2006; Song and Hakoda 2014), the failure to find differences may be the result of assuming a binary response across a homogeneous test. That is to say, the expectation is that the response from individuals of one subtype will somehow be discrete from the other subtype. What is lacking in most of these studies, and serendipitously discovered in the Song and Hakoda (2014) study is that cognitive load, particularly with respect to working memory, may elicit the difference that is being investigated. In fact, Diamond (2005) suggests that the primary deficit of the PI subtype is in working memory. Furthermore, in their study on the comparison between children with low working memory and children with the CT subtype of ADHD, Holmes et al. (2014) report that children with a diagnosis of low working memory are almost indistinguishable to CT subtype children across many assessments, including EF and degree of inattentive behavior. However, what distinguished the two groups were

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the externalized behaviors, namely the hyperactivity and impulsivity associated with the CT subtype and the significantly slower response times in the low-workingmemory group. These findings led the authors to postulate that individuals diagnosed with low working memory may actually be manifesting the PI subtype of ADHD. Finally, there are preliminary data that provide some justification for taking into account the heterogeneity seen in ADHD when designing experiments that measure brain activity. Although their study did not seek to uncover taskrelated differences in brain using fMRI, Fair et al. (2013) did discover that resting-state functional connectivity MRI (rs-fcMRI) was able to differentiate between the CT and PI subtypes of ADHD. Another study using DTI uncovered a network of connections in the right frontal regions that was able to differentiate between the CT and PI subtypes; specifically, there was decreased connectivity in the CT subtype compared with the PI subtype (Hong et al. 2014). Both studies suggest that there could be neural signatures that are specific to at least two ADHD subtypes. As such, not taking into consideration the heterogeneity that exists in ADHD and mixing the results of qualitatively different groups can actually end up canceling out the differences one is searching for (Barkley 2001). Taken together, all of these results point to the conclusion that there is no rationale for using exclusively one subtype in an experimental design; or worse yet, to group together several subtypes under one umbrella category labeled as ADHD. Additionally, the Lockwood et al. (2001) study underscores the importance of using heterogeneous attentional measures if the aim is to assess neuropsychological differences between ADHD subtypes. Failure to do so could lead to the inaccurate collection of data and the erroneous interpretation of results. Finally, the data that do exist pertaining to the neural substrates associated with the heterogeneity found in ADHD actually provide some evidence that symptomatological variants may be supported by different brain circuits, and as such, could theoretically be considered as related, but separate disorders.

Is the variability in the corroboration between performance and activation in the neural substrates a function of an incomplete understanding about the interaction between heterogeneity of cognitive processes and heterogeneity in the architecture of the frontal cortex? The data pointing to impairment of EF in ADHD as well as an observed hypoactivation of the frontal cortex in patients with ADHD can lead to the assumption that EF is exclusively a function of the prefrontal cortex. However,


paraphrasing from both Dimond (1980) and Barkley (2012), the functions of the frontal lobe are as varied as it is large, and the dimensionality of EF is as broad as it is deep. Therefore, perhaps a better way to conceive of the prefrontal cortex is as a way station to different functions given its various interconnections with cortical and subcortical regions (Cubillo et al. 2011; Stuss and Benson 1986), particularly since there is strong evidence supporting that different cognitive processes are subserved by different subregions of the prefrontal cortex (Chudasama 2011; Heidbreder and Groenewegen 2003; Kesner and Churchwell 2011). In a meta-analysis of lesion and functional neuroimaging studies that investigated the role of the frontal lobes and EF, Alvarez and Emory (2006) concluded that the participation of the frontal lobes in EF is linked to the sensitivity of the different tests used to measure EF, each of which might be recruiting different cognitive processes. Their findings support the idea that EF is ‘‘fractionable’’ (Lopez-Vergara and Colder 2013; Miyake et al. 2000). However, as the review by Donohoe and Robertson (2003) points out, though there is evidence for the discrete neuroanatomical separation of the different ‘‘fractions’’ of EF, there remains an overlap between the different executive measures because they are correlated with each other and are causally related to each other. Furthermore, in their review on working memory and EF, Carpenter et al. (2000) provide converging evidence for a system in which rather than there being an association between each fraction of EF and a single cortical area, each cortical region has more than one function, and the functions of each of the regions may overlap each other. The authors suggest further that each task may prompt a different combination of several brain regions depending on the requirements of the task. Since this ‘‘nondiscreteness of specialization’’ proposed by Carpenter et al. (2000) is probably a reality of cortical organization in general, it can also serve as a point of departure for a novel way of conceptualizing ADHD.

Do ADHD symptoms point to a spectrum instead of a disorder? If the DSM is correct, then there are more than 70 million children and more than 60 million adults worldwide that have ADHD. These estimates have been made despite the fact that as Brown (2006) so succinctly puts ‘‘There is no one test that can determine whether a given individual meets DSM diagnostic criteria for ADHD. This is a diagnosis that depends on the judgment of a skilled clinician who knows what ADHD looks like and can use data from multiple aspects of the individual’s daily life over protracted periods of time to differentiate it from other possible causes of impairment.’’ What is so interesting about

Brown’s statement is that, on the one hand, he acknowledges that there is no single test to determine if someone has ADHD, but at the same time suggests that skilled clinicians nevertheless somehow ‘‘know’’ what ADHD looks like. Brown’s comment is actually corroborated by what the team of scientists from Johns Hopkins who won the ‘‘The ADHD-200 Global Competition’’ (http://fcon_1000.projects.nitrc.org/indi/adhd200/) discovered. They were able to demonstrate that there was a much greater probability of correctly discriminating between typically developing children and children with ADHD than there was for correctly identifying children with a true-positive diagnosis of ADHD. Interestingly, the Johns Hopkins team was also able to discern the subtypes of ADHD with high accuracy. This pattern of discrimination indicates that the individual who receives a diagnosis of ADHD is different from a normal individual, yet the diagnosis of ADHD is not associated with a signature that either renders the diagnosis unequivocable or is sufficiently unique that variations in presentation are not easily distinguishable. The primary aim of the present review was to elucidate whether or not there is something unique to ‘‘know’’ about ADHD such that every one of those tens of millions of individuals could somehow fit under the same single disorder; or rather, if there was some way to take what is known about ADHD and use it as a diagnostic tool for a spectrum of disorders that as Asperger (1979) said are ‘‘…at once so alike, but so different…’’ and yet the ‘‘…differences are so great…’’ The data on the prevalence, diagnosis, heterogeneity and comorbidity of ADHD described in this review all point to the struggle at trying to fit a wide variety of clinical presentations into the diagnostic criteria for a single disorder that usually leads to a debate about what should and should not be included. Though the observation of certain behavioral traits as indicated by the DSM V (2013) is undeniably necessary to identify individuals at risk, the diagnostic process is limited by the assumed need to fit a wide variety of symptoms under one rubric in part because of the limitation of therapeutic avenues, but more importantly because of the urgency to bring relief to an individual whose ability to engage in the everyday functions of life can be severely compromised. One way out of the conundrum of having to use a single diagnosis for so many clinical presentations is to think of ADHD as a ‘‘psychophysiological modality’’ that can be expressed across a spectrum that can accommodate the wide heterogeneity observed in ADHD. Earlier in this review, the idea of varying widths of attentional spotlights was introduced as a way in which to describe the differences in attention and hence impulsivity exhibited by the PI and CT subtypes of ADHD. Specifically, whereas the PI subtype was described as having a narrow spotlight of attention, the CT and HI

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subtypes were described as having wide spotlights of attention. The width of the spotlight dictated the degree to which an individual had access to information as well as the ability the individual had to focus on information. As a result of a narrow spotlight of attention, the patient with PI subtype fails to detect certain external stimuli because they are outside of his attentional focus. The extended spotlight width of the CT subtype creates a stimulus overload by allowing too many external stimuli to flood the attentional space, making it difficult for the patient to focus on any one of the myriad of external stimuli to which these patients have access. Each subtype can also be associated with an observed or perceived impulsivity. The PI subtype may appear impulsive only because decisions made were based strictly on information that the patient could access. On the other hand, the impulsivity of the CT subtype could be the result of an inability to focus on the necessary bits of information to which the patient has access. In other words, the difference between the two can be summarized as ‘‘not having access’’ versus ‘‘not knowing what to select.’’ Barkley (1997) suggests that problems with selective attention are associated with the PI subtype and inhibition was the problem with the CT subtype. The suggestion here is that both inhibition and selective attention are a problem for the CT subtype. That is, though the patient with the CT subtype may not be able to inhibit competing responses, they also cannot select the appropriate responses for evaluation. On the other hand, although the patient with PI subtype may have a deficit of selective attention, it is unclear whether or not the selection of what to attend to is under his voluntary control. As such, the CT and PI subtypes can be considered to lie on a spectrum that varies as a function of attention and impulsivity. A spectrum of attention and impulsivity could also accommodate the extremely high symmetrical and asymmetrical comorbidity linked with ADHD since so many of these comorbid disorders also show impairment in attention and/or impulsivity as part of their own clinical profiles. Although it is neither the intention nor the scope of this review to map out entirely what such a spectrum of disorders could look like, three separate psychiatric disorders that have been associated with ADHD (schizophrenia, obsessive compulsive disorder (OCD) and conduct disorder) will be used to illustrate how they could be colocalized on the spectrum. Schizophrenia is associated with a vast number of symptoms, including delusions, hallucinations, flat affect and inappropriate emotional expression that originate from completely within the schizophrenic patient rather than as a reaction to external stimuli. As a result, the schizophrenic can be viewed as having an extremely narrow attentional focus inasmuch as the patient is focused almost exclusively on internally generated stimuli, leaving little breadth of

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attention to dedicate to the external world. The schizophrenic patient also shows no impulsivity to the extent that he can be considered even risk aversive (Reddy et al. 2014). Thus, schizophrenia would be colocalized on the part of the spectrum that is associated with the most narrow attentional focus and the lowest degree of impulsivity, giving it a greater ‘‘etiological vicinity’’ to the PI subtype than the CT subtype. Conversely, the CT subtype and conduct disorder could be seen as having a very proximal ‘‘etiological vicinity’’ and would be extremely close in terms of their colocalization on the proposed spectrum. Both disorders are characterized by high levels of impulsivity and both have an overly wide attentional spotlight, making it difficult for patients with either disorder to focus on the appropriate stimuli that would lead to good decision making. Though their very high symmetrical comorbidity and similarity in characteristics invite the tendency to want to consider them as a single disorder (Biederman et al. 1991), there are sufficient fine differences, for example in the exhibition of defiance, that would separate these disorders on the spectrum. OCD would be colocalized somewhere between the PI subtype and schizophrenia and the CT subtype and conduct disorder. The patient with OCD is characterized by the presence of obsessions and/or compulsions with some patients showing relatively more impulsivity than others (Kashyap et al. 2012). OCD patients are also classified according to the degree of insight they have related to the nature of their disorder. The OCD patient who shows the greatest impulsivity also has the poorest insight where poor insight is defined by the patient’s belief that his OCD compulsions are warranted and true (Kashyap et al. 2012). Poor insight is thus considered a form of delusion, and since delusion is the result of internally generated beliefs, the attentional spotlight would be narrowed. However, because the internally generated beliefs are about external stimuli, and not an imagined construct as in schizophrenia, the attentional spotlight would not be as narrow as in schizophrenia. Thus, this type of OCD would be colocalized partly in the direction of the spectrum associated with some impulsivity and partly in the direction of a narrowing width of attentional spotlight. Conversely, some OCD patients have low impulsivity and high insight. This type of OCD would be colocalized in the direction of that part of the spectrum associated with little or no impulsivity and in the direction of a growing width of attentional spotlight. The spectrum approach to identifying disorders of attention and impulsivity avoids many pitfalls by permiting less subjectivity and more objectivity by shifting the focus to the quality of the defect rather than using the presence of the defect to position the disorder under one category or a subset of the same category. It is not inconceivable that such a system could lead to customizable treatment even as


medicine moves toward individual gene-based treatment approaches. The concept of looking at a group of psychiatric disorders as interrelated because they share a common psychophysiological modality that comprises varying degrees of two major cognitive functions in this case, attention and impulsivity, is plausible not only as part of psychological theory, but also as part of what we know about the function of the brain. The neurological construct supporting this idea would be similar to the one Carpenter et al. (2000) write about, that is, of a ‘‘cognitive process that spans multiple cortical sites with closely collaborative and overlapping functions’’. Data about the symmetric and asymmetric comorbidity associated with ADHD as well as the heterogeneity that have been observed within the disorder implicate the prefrontal cortex (PFC) as a major site for the dysfunction. There is an abundance of evidence indicating that the PFC is a heterogeneous structure that is divisible not only in terms of its cytoarchitectonics, (Heidbreder and Groenewegen 2003), but also on the basis of differences in connectivity patterns with structures such as the amygdala, the temporal cortex, the parietal cortex, thalamic nuclei, hippocampus, dorsal and ventral striatum, hypothalamus and midbrain, as well as with one another (Chudasama 2011). Furthermore, the manipulation of the different regions of the PFC produces distinct and dissociable cognitive deficits including impaired attention, poor working memory, dysregulated affect, abnormal decision making, impulsivity, inflexibility and profound disinhibition (Kesner and Churchwell 2011). Therefore, it is not difficult to envision a neural circuit governing attention and impulsivity that allocates dynamically to a vast number of other neural circuits that underlie any number of disorders. The contribution of this ‘‘attentional/impulsivity neural circuit’’ to the network of neural circuits associated with a particular disorder allow for that disorder to be colocated along the spectrum defined by the ‘‘attentional/impulsivity neural circuit’’. Finally, such a spectrum is not limited to clinically disordered states, but can also capture the heterogeneity in the ‘‘attentional/impulsivity’’ dimension seen in the general population that may affect behavior, but does not necessarily cause impairment. The benefit of characterizing comorbid disorders and interrelated impairments as varying along one dimension that spans a spectrum and using it as a starting point for diagnosis and treatment has several advantages. For example, in the case of ADHD, diagnosis is the result of a convergence of opinions (teachers, parents, clinicians and multiple settings) that confirm that a child is impaired from the point of view of attention and/or from the point of view of hyperactivity/impulsivity. In other words, the endpoint of the diagnosis is the confirmation of impairment in those domains. In contrast, that convergence of opinions could

become the starting point if we accept that the presence of those symptoms could point to any one of the disorders that make up part of the spectrum. The physician, therefore, will be prompted to start a deeper clinical investigation rather than simply halted after observing that a patient has a particular constellation of symptoms. Thus, the impairment in attention/hyperactivity/impulsivity will itself not be the disorder, but rather the marker for any of several disorders that are interrelated. This type of diagnostic also allows for greater flexibility in the identification of individuals who do not naturally come to mind when we think of the disorder ADHD, for example, patients who show an SCT profile or subthreshold patients. Additionally, if clinicians use the attentional/impulsivity marker as the start of their diagnosis, the diagnostic tools that will be used to refine their opinion about the patient could not only reduce the total number of patients treated pharmacologically, but also reduce the number of individuals who engage in the feigning and malingering to obtain a prescription for ADHD medication, a problem which is currently on the rise (Bagot and Kaminer 2014; Lensing et al. 2013; Quinn 2003; Rabiner 2013; Tucha et al. 2014). In order to develop better and more varied treatment options for people who have impairments in the ‘‘attention/ impulsivity’’ dimension, data sets generated with much greater scrutiny reliability and validity must be constructed. Data are lacking in the ability to discriminate among the heterogeneity in what today is known as ADHD. Few data have been collected under conditions that elucidate, for example, within subject differences across neuropsychological tests, or between subtype differences on the same neuropsychological test. There remains an urgent need to create experimental designs that carefully control for the different presentations of attention/impulsivity/hyperactivity, including as a function of age and gender. As the pieces of the puzzle come together not only will we be able to increase our understanding about how impairments in attention and impulsivity can present, but we will also elucidate how the different cognitive processes may be distributed across the neural networks that make up the heterogeneity of the prefrontal cortex. Data already exist that point to similarities in the underlying neural pathology associated with some psychiatric diseases and patients presenting with ADHD (e.g., for review see Banaschewski et al. 2005; Rubia et al. 2010); however, there is also evidence for shared neural substrates across diverse psychopathologies (Goodkind et al. 2015). Further clarification related to the neural networks that support the different disorders that lie across the spectrum will lead to both the prescription of the appropriate treatments that are available today as well as the identification of new treatments that target specific populations of neurotransmitters associated with the neural networks.

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In summary, precisely because the symptoms of inattention and impulsivity are not unique to ADHD and there is an exceedingly high and unexplained symmetrical and asymmetrical comorbidity of many psychiatric diseases with ADHD, it is reasonable to stop considering ADHD as a single disorder. Rather, future investigations should use what is known about ADHD to develop further the idea that individuals who show the symptoms typical of ADHD may be pointing to a different more specific disorder that is part of a larger spectrum.

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Is anxiety best conceived as a unitary condition? The benefits of lumping compared with splitting . . .

Whether "psychosis" is best conceptualized as a continuum or in categories is an empirical, practical and political question.

Childhood ADHD: a stepped diagnosis approach.

A different approach to rising rates of ADHD diagnosis.

A simple approach is often the best.

Transtibial Versus Anteromedial Portal ACL Reconstruction: Is a Hybrid Approach the Best?

Is early diagnosis best?

A best practices approach to caries management.

Oppositional Defiant Disorder Is Better Conceptualized as a Disorder of Emotional Regulation.

Self-Reported ADHD Symptoms and Interhemispheric Interaction in Adults: A Dimensional Approach.

Identification of best indicators of peptide-spectrum match using a permutation resampling approach.

Adult ADHD and comorbid disorders: clinical implications of a dimensional approach.

[Adult ADHD versus borderline personality disorder: criteria for differential diagnosis].

Robotic Versus Laparoscopic Hepatectomy: What Is the Best Minimally Invasive Approach?

ADHD is a social problem.

Want to avoid DFUs? A multidisciplinary team approach works best.

The role of the thalamus in ADHD symptomatology and treatment.

The role of β3 integrin gene variants in Autism Spectrum Disorders--diagnosis and symptomatology.

What is the Best Route to the Meckel Cave? Anatomical Comparison between the Endoscopic Endonasal Approach and a Lateral Approach.

nutritional approach to ADHD.

Radiation from a collapsing object is manifestly unitary.

Spectrum diagnosis and ego strength functioning as related to electrodermal activity in a conditioning paradigm.

A fuzzy approach to medical diagnosis.

Dysphagia. A practical approach to diagnosis.