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Neurobiology of Attention-Deficit/Hyperactivity Disorder in Preschoolers

Valera, Eve M. PhD; Seidman, Larry J. PhD

Editor(s): WOLRAICH, MARK L.

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Abstract

ATTENTION-DEFICIT/HYPERACTIVITY DISORDER (ADHD) is characterized by age-inappropriate symptoms of inattention, and/or hyperactivity or impulsivity which occurs for at least 6 months in at least 2 domains of life, beginning prior to the age of 7 (American Psychiatric Association, 1994). It is estimated that ADHD affects between 8 and 12% of school-aged (6–12 years) children (Faraone, Sergeant, Gillberg, & Biederman, 2003) and 5% of adults (Faraone & Biederman, 2005; Faraone, Biederman, & Mick, 2006; Kessler et al., in press). However, these numbers have varied considerably, depending on the population and the region studied, diagnostic criteria used, and measures of assessment (Barkley, 1998). In addition, although the typical age of onset of ADHD is 3–4 years (Palfrey, Levine, Walker, & Sullivan, 1985), most children are not formally diagnosed until the school-aged years (American Psychiatric Association, 1994). Thus, the prevalence of ADHD in preschoolers (age 3–5 years) is less clear and has been less frequently examined.

Establishing a “true” ADHD diagnosis in preschoolers is difficult, in part, because the behavior of children of this age is often much more variable than that of older children, and the behaviors of typical pre–school-aged children commonly include the core features of ADHD, namely, inattention, hyperactivity, and impulsivity (American Psychiatric Association, 1994; Connor, 2002). Some recent work has demonstrated reasonable test-retest reliability in preschoolers (see Egger, Kondo, & Angold, 2006, for a review of diagnostic issues) and shown 3-year predictive validity of DSM-IV criteria in children diagnosed at ages 4–6 years (Lahey et al., 2004). However, others have found that, of the preschoolers whose behaviors are serious enough to warrant a diagnosis of ADHD, only 48% retain the diagnosis by later childhood or early adolescence (Barkley, 1998). To address the uncertainty of whether “hard to manage” hyperactive, inattentive, and impulsive preschoolers actually have ADHD, some researchers have avoided using the standard checklist and categorical diagnosis of ADHD when studying preschoolers. Instead, some have chosen to assess ADHD in preschoolers on the basis of ADHD symptom severity (Sonuga-Barke, Dalen, Daley, & Remington, 2002) or to develop other age-appropriate diagnostic methodology (Dalen, Sonuga-Barke, Hall, & Remington, 2004). The challenge and uncertainty of an accurate ADHD diagnosis is critical to note in the context of understanding the results of research on preschoolers with “ADHD.” Nonetheless, obtaining an understanding of the neurobiology of ADHD in preschoolers is important especially in the context of clinical management and treatment, and in understanding the evolving course of the disorder.

Some of the most direct ways to assess the neural substrates of a disorder are through structural or functional imaging techniques.* Unfortunately, because of the difficulties of imaging very young children and, in particular, preschoolers aged 3–5 years, there are no neuroimaging studies of children with ADHD in this age range. Structural imaging studies of ADHD extend down only to age 5, and functional imaging studies extend only to age 6.

Despite the absence of structural or functional imaging studies of preschoolers aged 3–5 years, there are neuropsychologic assessment studies of preschoolers in this age range that can be used to infer underlying brain dysfunction. Although neuropsychologic tests provide less “direct” measures of neuroanatomical and functional substrates of a disorder, these tests were used long before the development of neuroimaging to infer brain abnormalities and/or damage in various patient populations. By identifying cognitive and neuropsychologic deficits with standardized tests, one can posit brain abnormalities in regions known to be associated with the functions being assessed. Also important to consider with respect to the neurobiology of a disorder are the etiologic mechanisms contributing susceptibility to that disorder. Knowledge of etiologic mechanisms provides a basis for understanding when and how neurobiologic abnormalities occur. Finally, given the lack of “direct” neuroimaging data on preschoolers aged 3–5 years, it is important to understand the course and temporal stability of the disorder in order to use data obtained with older samples to make inferences about earlier developmental processes.

Therefore, in this review, we will provide the following: (1) a brief review of neuropsychologic studies of preschoolers with ADHD; (2) a brief summary of structural and functional neuroimaging studies of ADHD in older children as they might relate to findings in preschoolers; (3) an empirically based theory of the neurologic substrate of ADHD, in particular, the cerebellar-prefrontal-striatal network; (4) an overview of the evidence for the etiology of ADHD; and (5) support for the idea that the neurobiology of ADHD is largely stable over time, such that we can infer abnormalities in preschoolers on the basis of data from older children with ADHD.

PUTATIVE BRAIN REGIONS INVOLVED IN THE PATHOPHYSIOLOGY OF ADHD

Early investigators of ADHD noted that there were similarities between the ADHD phenotype (ie, impulsivity, hyperactivity, inattention) and patients with frontal lobe lesions (Mattes, 1980). This observation led researchers to believe that ADHD was a frontal lobe disorder, and consequently they focused many of their studies on frontal brain regions. This idea was later expanded to include the potential involvement of brain regions with projections to the frontal cortex, and the term frontal-subcortical was used to describe the putative brain abnormalities in ADHD. Treatment studies have since provided an abundance of evidence for the involvement of dopaminergic and noradrenergic systems, which are particularly prominent in the frontal-subcortical regions (Pliszka, 2005). Evidence from neuropsychologic, neuroimaging, and animal studies has also supported the notion that frontal structures along with their connecting regions are implicated in ADHD (Seidman, Valera, & Makris, 2005). Nonetheless, there is abundant evidence providing support for the involvement of numerous other brain regions as well. In fact, the entire brain and all lobes of the cortex have been shown to be smaller in children with ADHD than in controls (Castellanos et al., 2002). In the next section, we will review the neuropsychologic data in preschoolers and then briefly summarize structural and functional neuroimaging findings for older individuals with ADHD. We conclude with a review, supporting a modified fronto-subcortical hypothesis, namely the cerebellar-prefrontal-striatal network hypothesis.

Neuropsychologic assessment in preschoolers

Although there are hundreds of studies examining neuropsychologic functioning of ADHD in childhood and adulthood (for reviews, see Frazier, Demaree, & Youngstrom, 2004; Hervey, Epstein, & Curry, 2004), there are only a handful of studies examining such functioning in preschoolers with ADHD aged 3–5 years. Overall, most of these studies have found that the neuropsychologic deficits found in older children and adults with ADHD extend down into the preschool years. Nonetheless, the special challenges of accurately interpreting data from studies assessing “ADHD” in this age range need to be considered when reviewing these data. First, study results need to be interpreted in the context of how the ADHD sample was diagnosed or defined (eg, using levels of ADHD symptoms or strict DSM diagnoses). Second, differences in cognitive or motor abilities between older children and preschoolers may necessitate tailoring standard neuropsychologic tests to fit this younger age group. This underscores the importance of obtaining convergent data among tests. Third, the emergence of certain cognitive processes during the preschool years may result in smaller differences between preschoolers with ADHD and controls, making it more challenging to conduct an adequate assessment.

Illustrating some of these points nicely, Sonuga-Barke and colleagues (2002, 2005) have done work, testing an ADHD model pertaining to neurobiologic pathways and ADHD symptoms. They have proposed a dual pathway model of ADHD development in which one pathway involves executive deficits associated with abnormalities in frontodorsal striatal circuits, and the other pathway involves delay aversion associated with abnormalities in frontoventral striatal circuits (Sonuga-Barke, 2002, 2005). In their work, they have assessed the relationship between ADHD symptoms (rather than strict DSM diagnosis) and delay aversion, inhibitory control, or other executive function deficits. In an earlier study, Sonuga-Barke et al. (2002) assessed planning, working memory, and inhibition, and found that only inhibition was associated with ADHD symptoms, suggesting that inhibitory control rather than more general executive function deficits (which are found in older children with ADHD) was impaired in preschoolers with ADHD. Later, Sonuga-Barke, Dalen, and Remington (2003) used a more comprehensive neuropsychologic battery including tests of working memory, set shifting, planning, delay of gratification, and preference for delay rewards, which they subjected to a principal components factor analysis. The analysis revealed 2 factors, executive dysfunctions (EDFs) and delay aversion (DA), each of which made significant independent contributions to predictions of ADHD symptoms. In addition, there was a high correlation between age and the EDF factor, but no relationship between age and the DA factor, suggesting that executive functions may be emerging during this time period whereas DA may be more of a fixed characteristic. Thus, it appears that although the quality of deficits in preschoolers with ADHD is similar to that found in older children, the effect of ADHD on some cognitive processes might be more subtle until the process fully emerges.

In other studies, relative to healthy preschoolers, 3- to 5-year-old preschoolers with ADHD have been shown to display more inhibitory deficits and be more delay aversive (Dalen et al., 2004), perform more poorly on tests of visual search cancellation task (Byrne, Bawden, DeWolfe, & Beattie, 1998; Byrne, DeWolfe, & Bawden, 1998; DeWolfe, Byrne, & Bawden, 1999), visual and/or auditory vigilance tasks (Byrne, Bawden et al., 1998; DeWolfe et al., 1999), motor control, working memory, goal directed persistence (Mariani & Barkley, 1997), and tasks of preacademic skills including tests of memory, reasoning, and conceptual development (DuPaul, McGoey, Eckert, & VanBrakle, 2001). Byrne, Bawden et al. (1998) also demonstrated improved performance in 4- to 5-year-old preschoolers with ADHD on a visual and auditory preschool vigilance test as well as on a visual-search preschool cancellation test after treatment with stimulant medication. On the whole, these neuropsychologic deficits are consistent with the data observed in older children and adults with ADHD.

It should be noted that, in part due to differences in school systems with respect to typical age of entrance, there are also a number of neuropsychologic studies of “preschoolers” with ADHD aged 5–6 years (eg, Berlin & Bohlin, 2002; Kalff et al., 2002) or 5–7 years (eg, Hanisch, Konrad, Gunther, & Herpertz-Dahlmann, 2004). These studies also provide results consistent with EDFs and inhibitory deficits. For example, these older preschoolers with ADHD performed more poorly than controls on tasks of visuomotor ability, working memory, and attention (Hanisch et al., 2004; Kalff et al., 2002). Some studies also demonstrated that the cognitive processes assessed were related to levels of hyperactivity and attention (Berlin & Bohlin, 2002; Harper & Ottinger, 1992). Similarities in functioning found in 3- to 5- and 5- to 7-year-olds in these cross-sectional studies support the hypothesis that neurocognitive deficits are persistent over time across these younger ages. Nonetheless, given the developmental differences between 3- to 5-year-old children and 5- to 7-year-old children, an analysis or synthesis of neuropsychologic data in preschoolers should be done with careful attention paid to the actual age range and mean age of the children being assessed in the study, as well as how differences in age could affect either diagnostic or assessment issues.

Brain imaging in older children and adolescents

As noted earlier, there are no neuroimaging studies conducted on 3- to 5-year-old preschoolers with ADHD. We are, therefore, not able to “directly” examine which brain regions are either structurally or functionally different from those of normal control preschoolers. However, we have reasonable evidence with which to make predictions based on both the neuropsychologic deficits that have been observed in preschoolers, and the likelihood (rationale discussed below) that the abnormalities found in older children and adolescents will also be found in preschoolers once examined. As reviewed recently (Bush, Valera, & Seidman, 2005; Seidman, Valera et al., 2005), structural and functional neuroimaging data in ADHD have begun to provide a reasonably consistent picture about the brain regions involved with this disorder. (See Figs. 1 and 2 for brain regions implicated in the pathophysiology of ADHD.) Structural imaging data have most consistently shown volumetric reductions in total cerebral volume (Castellanos et al., 1996, 2001, 2002; Filipek, Semrud-Clikeman, Steingrad, Kennedy, & Biederman, 1997), cerebellum (Berquin et al., 1998; Bussing, Grudnik, Mason, Wasiak, & Leonard, 2002; Castellanos et al., 2001, 2002; Durston et al., 2004; Mostofsky, Reiss, Lockhart, & Denckla, 1998),

Figure 1
Figure 1:
Sagittal view of the brain depicting structures implicated in the pathophysiology of ADHD. From Neuroanatomy primer: Color to learn, 1st ed., by M. Evelyn McNeill, p. 3, Copyright 1997 for Illustrative Art. Adapted and reprinted with permission from East Carolina University School of Medicine.
Figure 2
Figure 2:
Three-dimensional reconstruction of the caudate (a striatal structure) depicting its head, body, and tail. From The human brain: An introduction to its functional anatomy, 4th ed., by John Nolte, p. 65, Copyright 1999, Mosby. Reprinted with permission from Elsevier.

prefrontal cortex (Castellanos et al., 1996; Durston et al., 2004; Filipek et al., 1997; Hynd, Semrud-Clikeman, Lorys, Novey, & Eliopulos, 1990), striatal structures (caudate and pallidum: Aylward et al., 1996; Castellanos et al., 1996, 2002), and the splenium of the corpus callosum (Hynd et al., 1991; Semrud-Clikeman et al., 1994). Also, meta-analytic results of these structural imaging data indicate that frontal and cerebellar regions show the largest standardized mean difference scores (Valera, Faraone, Murray, & Seidman, 2005). Functional imaging studies have demonstrated abnormalities in similar regions including prefrontal regions (Rubia et al., 1999; Zametkin et al., 1990), striatum (Durston et al., 2003; Rubia et al., 1999; Teicher et al., 2000; Vaidya et al., 1998), dorsal anterior cingulate (Bush et al., 1999; Durston et al., 2003; Rubia et al., 1999; Zametkin et al., 1990), and more recently the cerebellum (Anderson, Polcari, Lowen, Renshaw, & Teicher, 2002; Kim, Lee, Shin, Cho, & Lee, 2002; Valera, Faraone, Biederman, Poldrack, & Seidman, 2005).* Thus, the abnormalities identified by structural and functional imaging studies are consistent with what one might predict on the basis of the neuropsychologic deficits found in this population, namely, deficits in attention, working memory, response inhibition, planning and other “executive functions,” motor control, and reward/motivation. These data provide clear evidence for a widespread network of abnormalities in the brains of individuals with ADHD. This widespread nature of the abnormalities is expected given the clinical heterogeneity of the disorder. In fact, we suspect that there are at least 1 or more networks of regions that are important for different ADHD subtypes or for heterogeneous ADHD features (eg, the absence or presence of cognitive deficits).

One such network is the cerebellar-prefrontal-striatal network (Giedd, Blumenthal, Molloy, & Castellanos, 2001). A consistent finding in structural imaging studies of children with ADHD has been volumetric reductions in the cerebellum (eg, Berquin et al., 1998; Bussing et al., 2002; Castellanos et al., 2001, 2002; Mostofsky et al., 1998). In fact, as noted above, reduction of the cerebellar vermis is one of the largest effect sizes in a meta-analysis of structural imaging findings in ADHD (Valera, Faraone, Murray et al., 2005). In addition, research in cerebrocerebellar circuitry shows that there are reciprocal connections between the cerebellum and the prefrontal cortex (among other cortical regions; Middleton & Strick, 2001), creating an anatomical substrate for the interactions between these 2 regions. Furthermore, though traditionally considered to be primarily involved in motor control (Dow & Moruzzi, 1958), recent data indicate that the cerebellum is also involved in other processes, such as cognition and affect (see Cabeza & Nyberg, 2000; Desmond & Fiez, 1998; Schmahmann & Sherman, 1998, for a review). These data, combined with earlier notions that ADHD is a frontosubcortical/frontal-striatal disorder, provide support for a growing notion that ADHD pathophysiology may involve a cerebellar-prefrontal-striatal network (Giedd et al., 2001).

Two recent neuroimaging studies also provide support for the role of this network in the pathophysiology of ADHD. First, in a recent study from our group, we used functional magnetic resonance imaging to assess neural activation in adults with ADHD during performance on a verbal working memory task (Valera, Faraone, Biederman et al., 2005). We found that, relative to controls, adults with ADHD demonstrated decreased activation in the left cerebellar hemisphere with a trend for decreased activation in the right (contralateral) dorsolateral prefrontal region.* Although these data cannot definitively identify neural connections, these findings would be consistent with an abnormality in the aforementioned cerebellar-prefrontal-striatal network. More direct support, however, comes from another recent study, using diffusion tensor imaging DTI. DTI is a newer imaging technique that can be used to assess fractional anisotropy (FA) of the white matter (WM) tracts in the brain. FA is a measure that reflects the orientation of WM and thus allows one to infer information about WM organization and integrity. WM that has more fibers oriented in the same direction will have higher FA values than will WM that has fibers oriented in more diffuse directions. In short, lower values of WM FA would indicate alterations in WM fiber orientation and integrity. In this first DTI study in children with ADHD, Ashtari et al. (2005) found that relative to controls, children with ADHD had decreased FA in several areas including right premotor, right striatal, left cerebellar peduncle, and left cerebellum. In addition, they found that decreased FA values in the cerebellar region were associated with increased severity of inattentive subscale scores of the Conners' Attention Deficit Scale.

Cerebellar involvement in the pathophysiology of ADHD is also consistent with a growing literature showing timing abnormalities in children with ADHD. First, empirically based theories indicate that the cerebellum is involved in timing of motor and/or cognitive processes (Hallett & Grafman, 1997; Ivry, 1997). Second, ADHD studies examining motor timing (paced finger tapping; eg, Rubia, Noorloos, Smith, Gunning, & Sergeant, 2003), duration discrimination (eg, Toplak, Rucklidge, Hetherington, John, & Tannock 2003), duration reproduction (eg, Barkley, Murphy, & Bush, 2001), verbal time estimation (eg, Smith, Taylor, Rogers, Newman, & Rubia, 2002), and anticipation tasks (eg, Rubia et al., 2003) tend to show that performance of children with ADHD is either less accurate or more variable than that of control children. In addition, associations have been found between attentional ratings and increased variability in both tapping and anticipation tasks (Rubia et al., 1999), and methylphenidate has been found to significantly reduce variability of performance on such temporal processing tasks (Rubia et al., 2003). Notably, a review by Castellanos et al. (2005) emphasizes the importance of assessing intraindividual variability in ADHD assessments.

The cerebellum is also proposed to play a critical role in establishing the temporal patterns of motor programs required for muscle control, and thus, cerebellar abnormalities could lead to motor difficulties. Up to 50% of children with ADHD have been found to have motor difficulties (Pitcher, Piek, & Barrett, 2002). In addition, relative to control children, children with ADHD have been found to perform more poorly on both fine and gross motor tasks including goal-directed arm movements (Eliasson, Rosblad, & Forssberg, 2004), handwriting (Barkley, 1990), motor timing and force output (Pitcher et al., 2002), “motor leg movement” (Nigg, Hinshaw, Carte, & Treuting, 1998), performance on the Purdue Pegboard Task (Pitcher, Piek, & Hay, 2003), dynamic balance and diadochokinesis (Kroes et al., 2002), manual dexterity skills (Piek, Pitcher, & Hay, 1999), and motor overflow (Denckla & Rudel, 1978).

Animal models of ADHD have also provided support for cerebellar involvement in ADHD pathophysiology. Although the spontaneously hypertensive rat model has been most commonly studied, a model of developmental cerebellar stunting has been examined as well (for reviews, see Davids, Zhang, Tarazi, & Baldessarini, 2003; Paule et al., 2000). Experimental studies have demonstrated age-dependent effects on the immature rat cerebellum (Ferguson, 1996; Ferguson, Paule, & Holson, 1996). Specifically, lesion studies have typically shown that insults to the cerebellum at very early stages (birth to postnatal day 4) produce severe cerebellar pathology that would typically be thought of as a classic cerebellar syndrome with severe motor abnormalities and learning deficits. However, insults produced later on (postnatal days 5–12) result in much less severe neuropathology and produce a mild hyperactivity with only a few learning deficits (Ferguson, 1996; Ferguson et al., 1996). These results suggest that developmental cerebellar stunting in the rat could serve as a model for the hyperactivity and mild cognitive impairment seen in individuals with ADHD.

In summary, these neuropsychologic, structural, and functional imaging findings, as well as clinical phenomenology and animal models, provide a strong foundation for a network, or possibly several networks, of neurobiologic abnormalities resulting in ADHD symptoms. The widespread nature of the structural and functional abnormalities is consistent with the heterogeneous symptoms and suggests several etiologic pathways causing early, neurodevelopmental damage. In fact, there is now ample evidence that higher rates of ADHD can be accounted for by genetics, intrauterine environmental conditions, perinatal complications, preterm births, and low birth weight (LBW). We discuss these etiologic mechanisms in the next section.

GENETIC INFLUENCE

A substantial number of family, twin, and adoption studies have provided support for the familiality and heritability of ADHD and ADHD-related symptoms (eg, hyperactivity). First, family studies have consistently demonstrated that ADHD runs in families (Biederman, Faraone, Knee, & Munir, 1990; Frick, Lahey, Christ, & Green, 1991), and the risk for ADHD in first-degree relatives (eg, siblings, parents) of children with ADHD has been shown to be 2 to 8 times that of a non-ADHD child (for a review, see Faraone, 2004). Second, twin studies also indicate high rates of heritability for ADHD. Mean heritability rates for ADHD and related symptoms have been shown to range from 0.77 (Biederman, 2005) to 0.80 (Faraone, 2004), indicating that up to 80% of the variance associated with the ADHD phenotype can be attributed to genetic factors. Importantly, heritability rates are not 1.0, which means that the etiology of ADHD is at least partially caused by nongenetic or environmental factors. As noted below, a number of biologic environmental factors (eg, pre- and perinatal complications) are associated with an increased risk for ADHD.

Adoption studies, though relatively few (and some of which are older), have also provided support for a genetic influence on the etiology of the ADHD phenotype. These studies (eg, Cantwell, 1975; Morrison & Stewart, 1973; Sprich, Biederman, Crawford, Mundy, & Faraone, 2000) have shown that relatives of adopted children with ADHD and ADHD symptoms have lower rates of ADHD or related symptoms than do biologic relatives of (nonadopted) children with ADHD or related symptoms.

Finally, 1 of the 2 approaches used in molecular genetics studies has provided strong evidence for the involvement of particular genes in the etiology of ADHD. The 2 main approaches used in molecular genetics studies are the candidate gene and the genome scan approach. In the candidate gene approach, theory and/or empirical data drive the selection and examination of just 1 or 2 genes thought to contribute to ADHD vulnerability. Alternatively, in the genome scan approach, all locations on the chromosome are examined without any a priori theory or data about which genes may contribute to ADHD vulnerability. A number of candidate gene studies have identified several genes that are likely involved in the etiology of ADHD (Barr et al., 2001; Faraone, Doyle, Mick, & Biederman, 2001). Not surprisingly, many of these studies were driven by the pharmacotherapy of ADHD, which clearly implicates the dopaminergic and noradrenergic systems in the pathophysiology of ADHD. In a recent review, Faraone et al. (2005) reported that for genes for which the same variant has been examined in at least 3 case control- or family-based studies, 7 of 8 genes (ie, DRD4, DRD5, DAT, DBH, 5-HTT, HTR1B, and SNAP-25) show significant evidence of association with ADHD on the basis of the pooled odds ratio. Nonetheless, though significant, these odds ratios are small, suggesting that the genetic susceptibility to ADHD is complex and likely influenced by a number of relatively small effects. In contrast to the candidate gene approach, the genome scan has been used only in a few studies (eg, Arcos-Burgos et al., 2004; Fisher et al., 2002; Smalley et al., 2002) and has produced inconsistent results.

PRE- AND PERINATAL COMPLICATIONS, PRETERM BIRTHS, AND LBW

Increased risk for ADHD has been associated with a number of pre- and perinatal complications. For example, Milberger, Biederman, Faraone, Guite, and Tsuang (1997) demonstrated that maternal illness/ infection, neonatal medical problems, and maternal substance use were the pre- and perinatal complications most associated with ADHD. Specific to maternal substance use, studies (Mick, Biederman, Faraone, Sayer, & Kleinman, 2002; Milberger, Biederman, Faraone, Chen, & Jones, 1997) have reported a significant association between maternal cigarette smoking or alcohol exposure during pregnancy and a diagnosis of ADHD in their offspring. Relative to non-ADHD controls, individuals with ADHD were 2.1 times more likely to be exposed to cigarettes and 2.5 times more likely to be exposed to alcohol while in utero (Mick, Biederman, Faraone et al., 2002). Confounding factors did not account for these effects of prenatal exposure to alcohol or cigarettes, and these effects were independent of each other (Mick, Biederman, Faraone et al., 2002). Other work has shown that children with exposure to alcohol in utero have been described as hyperactive, impulsive, and having short attention spans (Nanson & Hiscock, 1990).

Increased risk for ADHD has also been found in children born preterm. In a meta-analysis, Bhutta et al. (2002) demonstrated that children born preterm had a 2.64-fold risk for developing ADHD as well as a significantly higher prevalence of attention problems than did non-ADHD control children. Another study found that children aged 7–8 years who were born preterm were more likely to have inattention and impulsivity as well as a diagnosis of ADHD (Foulder-Hughes & Cooke, 2003).

Numerous studies have demonstrated an association with LBW and ADHD or ADHD-related symptoms. These studies have shown that children born with LBW including “extremely low” birth weight (<1000 g; Szatmari, Saigal, Rosenbaum, Campbell, & King, 1990), “very low” birth weight (<1500 g; Botting, Powls, Cooke, & Marlow, 1997), or “low” birth weight (<2500 g; eg, Breslau et al., 1996) have an increased risk of ADHD and ADHD-related features. As one might imagine, individuals with LBW might also have other confounding risk factors that could be associated with ADHD such as alcohol or cigarette exposure in utero. To address this potential confound, Mick, Biederman, Prince, Fischer, and Faraone (2002) examined the impact of LBW on ADHD while controlling for potentially confounding factors such as exposure to alcohol or cigarettes, adult ADHD, social class, or parental or child comorbid disruptive behavior disorders. They found that ADHD cases were still 3 times more likely to have been born LBW than non-ADHD controls. These data strongly support the notion that the origins of some forms of ADHD clearly occur prior to birth.

STABILITY OF NEUROBIOLOGIC CHANGES IN ADHD OVER TIME

In the absence of direct assessment of potential neurobiologic abnormalities in preschoolers with ADHD, one could look to other data for evidence of whether findings in older children and adults would extend down to preschoolers. The data we present below provide such evidence and suggest that, for the most part, the neurobiologic abnormalities in individuals with ADHD are stable over time and, as also noted above, occur early on in development or in utero. Thus, we would predict that the structures that are putatively involved in older children and adolescents with ADHD are also involved in preschoolers.

In thinking about the developmental course of the neural substrate of ADHD, it could be tempting to argue for a shift of dysfunction in striatal regions associated with hyperactivity and impulsivity in the earlier years, to prefrontal regions associated with executive functions in the later years. This is based on observations that hyperactive and impulsive symptoms are most prominent in the younger ADHD years such as preschool, whereas inattentive symptoms tend to become more obvious later in school and persist into adulthood. For example, Biederman, Mick, and Faraone (2000) demonstrated that inattentive and hyperactive symptoms declined at different rates with inattentive symptoms declining more slowly than either hyperactive or impulsive symptoms. In addition, in one study (Castellanos et al., 2002), group differences in caudate size (a striatal structure thought to be associated with hyperactivity) between children with ADHD and controls in early adolescence were shown to disappear by midadolescence, which is close to the time when hyperactive symptoms tend to diminish. These data suggest that the neurobiologic substrate of ADHD might change over time. However, there are several other pieces of data that provide support that most neurobiologic changes occur early on and are stable over the life of an individual with ADHD.

First, the neuropsychologic data collected on preschoolers as young as 3 years are largely consistent with data for older children and adults. In addition, although preschooler data were not included in some recent meta-analyses of neuropsychologic findings in ADHD, the results indicated general similarities rather than differences over age. In particular, a meta-analysis of adult ADHD studies covering a broad range of neuropsychologic tests (Hervey et al., 2004) showed deficits similar to those found in children across all but a couple of tests. Another meta-analysis (Frazier et al., 2004), including neuropsychologic studies across a wide span of ages (5–37), found that the age of participants with ADHD did not influence effects on full-sale IQ. Also, in our study examining neuropsychologic functioning of individuals with ADHD ranging in age from 9 to 17 years (Seidman, Biederman et al., 2005), we found no effects of age on group differences in neuropsychologic performance. Overall, these data provide support for the continuity of ADHD cognitive deficits and consequently for inferring similar neuroanatomical substrates across development.

Other support for the stability of anatomical differences from older children to preschoolers is provided in a landmark article by Castellanos et al. (2002). They examined volume for the total cerebrum, cerebellum, caudate, and the 4 major lobes of the cortex (frontal, parietal, temporal, and occipital) over a cross-sectional sample of children with ADHD (N = 152) aged 5–18 years and matched controls (N = 139). They also assessed a substantial number of these children at multiple times and created developmental growth curves to examine age-related changes over time. They found that although all the volumes were reduced for the children with ADHD relative to the controls, the growth curves for the brain structures for these 2 groups were parallel. This suggests that anatomical brain differences between individuals with ADHD and controls occur early on (at least prior to age 5) and are stable and nonprogressive (Castellanos et al., 2002). The caudate demonstrated the only exception to these parallel growth curves, with the difference between the 2 groups disappearing by the age of 15. In addition, regression analyses on meta-analysis results of childhood structural imaging studies indicated that the average age of children with ADHD (ranging from 9.4 to 14.6 years) did not influence the standard mean difference scores between children with ADHD and control children on total cerebral volume (Valera, Faraone, Murray et al., 2005). Although there was less power to adequately test for age differences in other brain regions that were examined in fewer studies, there was no evidence of any age effect for any other structures assessed.

Finally, it is important to consider that results may be influenced by the measures that are used to assess ADHD symptoms. For example, assessments using DSM criteria or behavioral observations have shown that inattention symptoms tend to develop later than hyperactive symptoms, and hyperactive and impulsive symptoms decline with age at a greater rate than do inattentive symptoms (eg, Barkley, 1998; Biederman et al., 2000; Loeber, Green, Lahey, Christ, & Frick, 1992). However, assessments using either neuropsychologic tasks or actigraphy measures (where bodily movements are directly assessed) show a potentially different picture. First, as noted above, preschoolers with ADHD perform more poorly on neuropsychologic tasks of attention (eg, DeWolfe et al., 1999), suggesting that what some see as an emergence of inattentive symptoms later on is possibly a reflection of the task demands of the child rather than a change in neurocognitive abilities per se. For example, sustained attention is not typically demanded of a pre–school-aged child (American Psychiatric Association, 1994). In addition, Teicher (1995) used actigraphy monitoring to show that adults with ADHD move more than controls during performance on a cognitive task. In fact, with a larger sample of adults with ADHD and controls, Teicher and colleagues have demonstrated that adults with ADHD move their heads 2-fold more and their extremities 3- to 4-fold more while performing attention tasks (M. H. Teicher, personal communication, December 7, 2004). These data raise the possibility that the apparent decline of hyperactive symptoms in adulthood could be a result of better behavioral management or a change in overt hyperactive behaviors rather than decreased levels of hyperactivity. Further research is needed to resolve these questions about the course of ADHD symptoms.

CONCLUSIONS

In summary, despite the difficulty in diagnosing ADHD in preschoolers and the subsequent dearth of neurobiologic studies on preschoolers with ADHD, enough data are available to provide support for the idea that (1) the etiology of ADHD occurs either in utero or very early on in life and (2) the alterations in the central nervous system are largely stable over time with differences in symptoms or deficits largely manifested as a reflection of the demands of the individual as he or she ages. Nevertheless, this must be considered a rudimentary framework as there are too few longitudinal or neurobiologic studies to posit firm conclusions.

There is a definite need for “direct” assessment of the neurobiologic substrate of ADHD in preschoolers aged 3–5 years. Future studies using structural and functional neuroimaging techniques will be helpful toward fulfilling this need. Nonetheless, until there are more data, we can hypothesize with a reasonable degree of confidence that the findings in older children and adolescents also hold true for the pre–school-aged child. It is recommended that we use this knowledge to guide our thinking and treatment approaches in these young children with ADHD.

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    *It should also be noted that neuropsychopharmacology data obtained from treatment studies are also informative for inferring underlying neural substrates of a disorder. These data are presented in detail in other articles in this issue and therefore will not be presented here.
    Cited Here

    *Although there are a handful of functional imaging studies in young children, many of the studies (especially the earlier ones) suffer from poor design with no or inadequate control groups, and should therefore be interpreted cautiously. See Bush et al. (2005) for a critical analysis of the functional data in ADHD.
    Cited Here

    *The cerebellum is connected with the rest of the brain in a contralateral fashion. Therefore, we would expect prefrontal abnormalities to be contralateral to the cerebellar abnormalities.
    Cited Here

    Keywords:

    ADHD; attention-deficit/hyperactivity disorder; neurobiology; neuroimaging; neuropsychology; preschoolers

    ©2006Lippincott Williams & Wilkins, Inc.