PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Neuroscience. Author manuscript; available in PMC 2010 November 24.
Published in final edited form as:
PMCID: PMC2792745
NIHMSID: NIHMS157651

Assessing the Molecular Genetics of the Development of Executive Attention in Children: Focus on Genetic Pathways Related to the Anterior Cingulate Cortex and Dopamine

Abstract

It is well-known that children show gradual and protracted improvement in an array of behaviors involved in the conscious control of thought and emotion. Non-invasive neuroimaging in developing populations has revealed many neural correlates of behavior, particularly in the developing cingulate cortex and fronto-striatal circuits. These brain regions, themselves, undergo protracted molecular and cellular change in the first two decades of human development and, as such, are ideal regions of interest for cognitive- and imaging-genetic studies that seek to link processes at the biochemical and synaptic levels to brain activity and behavior. We review our research to-date that employs both adult and child-friendly versions of the Attention Network Task (ANT) in an effort to begin to describe the role of specific genes in the assembly of a functional attention system. Presently, we constrain our predictions for genetic association studies by focusing on the role of the anterior cingulate cortex (ACC) and of dopamine in the development of executive attention.

Introduction: Placing genes in a developmental context

The ability to use attention to control the processing of information from the environment is a central concept in developmental psychology and the study of psychopathology (M. I. Posner et al., 2007). The now widespread application of genetic and neuroimaging methods to the study of executive control makes it possible, in principle, to fathom how certain genes function in the development of neural networks that carry out executive attention. This is because individual differences can be observed in assessments of psychological processes or constructs, and then correlated to individual differences in brain structure, neural activity and genetic variation (reviewed in (A. E. Green et al., 2008). For example, a VNTR polymorphism in exon III of the dopamine d4 receptor (DRD4), a gene that is reliably associated with risk for the developmental disability ADHD, shows a correlation with cortical thinning in young children (P. Shaw et al., 2007) and interacts with parenting style to influence temperament (B. E. Sheese et al., 2007) suggesting a biological link between cortical development and cognitive development. Other developmental disabilities such as phenylketoneuria, Angelman syndrome and fragile-X mental retardation reveal the profound way in which single genes alter the normal trajectory of brain and cognitive development (G. Scerif and A. Karmiloff-Smith, 2005). Some genes may even function very early in embryogenesis, but only cause noticeable disruptions rather late in behavior. The forkhead box a2 (FOXA2) gene, for example, regulates the earliest stages of the birth of dopamine neurons but contributes to a Parkinsonian form of neural degeneration when the organism reaches late adulthood (R. Kittappa et al., 2007). Lastly, we know that genes act under the influence of the environment during development, as evidenced by the interaction of early childhood stress with the MAOA and 5HTT genes increasing the risk of aggressive behavior and depression later in life (A. Caspi et al., 2002; A. Caspi et al., 2003). Furthermore, early maternal stress can lead to epigenetic modification of the fetal genome, as seen in promoter of the glucocorticoid receptor gene (M. J. Meaney and M. Szyf, 2005). Hence, it is of interest to specify the role of genetic variation in terms of its influence on structural and cognitive change during development.

Choosing appropriate psychological constructs: Convergence on attentional control

In previous studies on executive attention, we have reported that several genetic polymorphisms were related to variation in performance as well as to the activity of brain regions known to function as nodes in larger neural networks that carry out attention (J. Fan et al., 2003). Since these studies were conducted with adult participants, we are unable to discern whether the role of specific genes has been one limited to the maintenance of and/or homeostatic regulation of the mature networks, or perhaps a developmental role in the early assembly of the attention system. To begin to assess the possible developmental roles of genes associated with executive attention, we seek to utilize tasks that can measure the efficiency of the attention system at different age ranges, such as a child-friendly version of the ANT. In choosing this strategy, we acknowledge a few of the many complexities in choosing suitable behavioral assessments for child populations. Firstly, it is important that the behavioral assessments are consistent with an established psychological model or framework. Practically speaking, it is also important to consider tasks that can be adapted to a neuroimaging environment and to consider tasks where performance can be compared across a wide range of ages by parametrically manipulating task difficulty. Substantial evidence points to a unified conceptual framework for the development of executive control where central attentional mechanisms are involved (A. Miyake et al., 2000; M. K. Rothbart & Posner M.I., 2001; M. C. Davidson et al., 2006) as well as dissociable processes such as inhibition and working memory (A. Diamond, 2002). Therefore, we expect attention, and moreover, genes that contrinute to the development of the attention system to play a central, but limited, role in the development of executive function.

A number of studies have already adapted the central construct of attention, upon which the ANT is built, to studies of attentional function in infants, toddlers and young children. Infants show a propensity to gaze longer during incorrect trials and show increased negative activity on measurements of event related potentials (A. Berger et al., 2006) while 2–3 year olds demonstrate correlation in their ability to resolve stimulus-response conflict and anticipatory eye movements (M. K. Rothbart et al., 2003). A few behavioral performance measures have been found to relate to aspects of temperament using scales that are appropriate for infants and children. In infants, the orienting of attention was found to relate to positive affect and function as a means to distract children from distress and reduce negative affect (C. Harman, Rothbart, M. K., & Posner, M. I. , 1997). In young children, effortful control was related to executive attention as measured by the child-friendly ANT (M. K. Rothbart, & Rueda, M. R. , 2005). By age 7, executive attention as measured by performance on the ANT appears stable (M. R. Rueda et al., 2004). However, performance on many traditional executive function tasks involving component functions such as working memory and inhibition (e.g., the Wisconsin Card Sorting, Tower of Hanoi and N-back) continues to improve in adolescence and into early adulthood.

Our use of the child version of the ANT is based on earlier work of Rueda and colleagues (M. R. Rueda et al., 2005) as well as the work of Konrad and colleagues who have reported functional activations in children who performed the ANT in the MRI scanner (K. Konrad et al., 2005). Each trial begins with a cue (or a blank interval, as in the no-cue condition) that informs the child either that a target will appear soon, and/or where it will appear on the screen. The target always occurs either above or below fixation, and consists of a central arrow (in the shape of a smiling fish), surrounded by flanking arrows (smiling fish). The flankers point either in the same direction as the target arrow (congruent) or in the opposite direction (incongruent) and a subtraction of RTs of congruent from incongruent target trials provides a measure of conflict resolution that assesses the efficiency of the executive attention network. The data presented in Figure 1, were obtained from a collection of 110 healthy children somewhat evenly distributed in gender and ages 5–13 years old who performed the task while under adult supervision. A view of both the distribution of raw and of normalized (conflict RT/overall RT) shows that children experience a reaction time cost of about 90ms related to the resolution of stimulus-response conflict and that there is little change in the efficiency of conflict resolution as children mature beyond this age range. This is largely in agreement with previous reports (E. Mezzacappa, 2004; M. R. Rueda et al., 2004) and suggest that genetic correlates of performance on the child-friendly ANT may relate to developmental processes that occur before the age of 5.

Figure 1
Executive attention scores of children 5–13 years of age

Choosing appropriate neural correlates: Convergence on dopamine and the anterior cingulate cortex

The next step in our research strategy has been to choose appropriate candidate genes, and variants within those genes, suitable for behavioral- and imaging-genetic association studies in mixed populations of healthy volunteers. Since there are several million common genetic variants in the human genome and thousands of voxels in both white and gray matter in the human brain, we seek to avoid inherent type-I statistical limitations by crafting hypotheses that are as specific as possible. Within the scope of our interest in the development of executive attention, the robust and reliable brain activity observed in the cingulate cortex of healthy children suggests that the anterior cingulate cortex (ACC) may be an ideal region of interest (G. Bush et al., 2005). For example, event related potential recordings in infants as young as 6–9 months of age show increased negativity in response to conflict that is spectrally very similar to the error-related negativity seen in adults, a neural process that has been localized to the ACC (A. Berger et al., 2006). In a population of children ages 7–11, Casey et al., reported that the blood-oxygen-level-dependent (BOLD) response in the ACC varied as a function of increased number of errors on a go no-go task and demonstrated that activity in ventral prefrontal regions was correlated with accuracy (B. J. Casey et al., 1997). This type of error-related activity in the ACC is suggested to inhibit dopaminergic function via projections to the striatum which, in-turn, supports learning of the no-go response (M. J. Frank et al., 2007). Like the prefrontal cortex, the cingulate cortex, however, does show evidence for a somewhat prolonged time-course of development (K. R. Ridderinkhof et al., 1997; E. R. Sowell et al., 2004; X. Guo et al., 2007). A recent functional connectivity study in children showed that correlated activity among voxels across the ACC was found to be weaker than in other areas of the brain (A. M. Kelly et al., 2008). Therefore, in the ongoing construction of specific hypotheses for our cognitive- and imaging-genetic studies, we constrain the scope of the research by limiting our focus to the development of the ACC.

Another strategy to focus and constrain the scope of our gene association studies, is to consider the role of dopamine (DA) in the development of executive attention. In the seminal work of Diamond, infants show marked improvement in tasks that require them to both inhibit a pre-potent response, and also hold-in-mind the location of a target (A. Diamond, 1991) and performance on these measures was shown to be dependent on the frontal cortex in monkeys (A. Diamond and P.S. Goldman-Rakic, 1985). Animal models of phenylketoneuria show that cognitive disruptions associated with PKU were dependent on reductions of DA in the prefrontal cortex (A. Diamond et al., 1994) which is consistent with Goldman-Rakic and colleagues who, using a primate model, found that the reduction of DA was equally as effective in diminishing executive function as were lesions to the frontal cortex (T. J. Brozoski et al., 1979). In primate models, age-related improvement on executive control tasks is paralleled by post-natal increase in DA levels (P. S. Goldman-Rakic, 1981) and an increase in DA receptor gene expression (M. S. Lidow et al., 1991). Within the developing brain, DA participates in cellular changes such as myelination, synaptogenesis and pruning (W. Shen et al., 2007; C. Fasano et al., 2008; Y. Feng, 2008) but may mainly subserve executive function as a modulator of excitability of recurrent synaptic inputs (D. J. Surmeier et al., 2007). The time course of these processes may be quite variable and outside the range of our present study population (5–13 years old) however, as, for example, synaptogenesis and myelination in the ACC may peak before age 5, while pruning may continue well after age 13 (E. R. Sowell et al., 2004).

Constructing hypotheses centered on specific genes: Pathways for ACC development

In the design of cognitive- and imaging-genetic associations studies, hypotheses should be generated that specify a particular allele of a candidate gene and its relation to brain and/or behavior. The choice of candidate gene then, should stem from converging evidence that implicates that gene in the development or function of targeted neural correlates (the ACC and dopamine in our case). The choice of allele is a more practical consideration of its frequency in human populations across ethnic backgrounds and whether there is some functional change in the encoded protein or in the abundance of the gene product that is conferred by the polymorphism. In mammals, a number of genetic pathways are known to regulate the development of the frontal midline. The most well-studied are genes that lead to holoprosencephaly, a genetic disorder where the ACC fails to develop due to poor division of the double lobes of the embryonic forebrain (T. Takahashi et al., 2003). Mutations in sonic hedgehog (SHH) and several downstream factors including 7-dehydrocholesterol reductase (DHCR7), patched (PTCH), zic family member 2 (ZIC2), Kruppel family member gli2 (GLI2) all give rise to midline deformities in humans (reviewed (M. M. Cohen, Jr., 2003)). Gene expression profiles for 3 types of interneurons and 2 types of projection neurons (layer 5 and layer 6) in the ACC show enriched expression of secreted frizzled-related protein 2 (SFRP2) natriuretic peptide precursor C (NPPC), endothelin converting enzyme-like 1 (ECEL1), tachykinin, precursor 1 (TAC1) and neurexophilin 3 (NXPH3) among other genes (K. Sugino et al., 2006). TAC1 is of particular interest since it shows associations with depressive illness in humans and depression- and anxiety-related behaviors in mice (A. Bilkei-Gorzo et al., 2002). Lastly, the development of the dopaminergic system in the frontal cortex is regulated by a number of members in the FGF family of proteins. For example, mice with a conditional inactivation of fgf2 as well as a null cnp1 background display no obvious anatomical abnormalities, but display hyperactivity that can be suppressed by dopaminergic antagonists (Y. Kaga et al., 2006), while the growth factor fgf20, which promotes dopaminergic cell survival, has been implicated in psychiatric illness (S. Murase and R. D. McKay, 2006). Frontal identity has been shown to be imparted by fibroblast growth factor-8 (FGF8) (T. Fukuchi-Shimogori and E. A. Grove, 2001).

In principle, each of these genetic factors would be ideal for future cognitive- and imaging-genetic experiments where measurements of executive attention and measurements of ACC activity and structure were obtained. While ongoing work in the laboratory proceeds in this direction, presently, however, no converging evidence exists in the cognitive- or imaging-genetic literature. We therefore begin our analysis of candidate genetic data by comparing our findings to existing converging evidence. The dopamine transporter gene (DAT1 or SLC6A3), which encodes a protein that is the molecular target of methylphenidate, a therapeutic used to treat ADHD and hyperactivity (S. Dresel et al., 2000) contains a 40-bp repeat polymorphism in the 3’ untranslated region. The 10-repeat allele of this polymorphism, has been associated with ADHD (S. V. Faraone et al., 2005) and structural aspects of the striatum (S. Durston et al., 2005), which is the same area to which methylphenidate is known to bind. Also, Rueda and colleagues reported that children who were homozygous for the long (10/10) genotype showed lower scores on the child ANT (M. R. Rueda et al., 2005). When this evidence is tested using our population, the data shown in Figure 2 also reveal this trend where children ages 5–7 who carry the long/long (10-repeat homozygotes) genotype are somewhat more efficient at resolving conflict (P=0.05). The shifting trend in the data among 10-repeat homozygotes toward higher scores with age is also consistent with previous findings that (10/10) adults are less efficient in the resolution of incongruent trials during the ANT (J. Fossella et al., 2002). The shift from more to less efficient conflict resolution with age may be connected to age-dependent changes in DAT1 levels (N. D. Volkow et al., 2001) that could alter the organism’s sensitivity to the inverted-U-shaped dose/response dependency for dopamine. Also, the paucity of DAT1 in the frontal cortex and ACC suggest that the relationship between performance on the ANT and ACC function could be indirectly mediated through dopamine d1 receptor (DRD1) which are stimulated by extrasynaptic dopamine in the cortex (S. R. Sesack et al., 1998; R. M. Bilder et al., 2004).

Figure 2
Relationship of DAT1 3' UTR polymorphism to executive attention scores in children 5–13 years of age

Concluding remarks

We have noted that it is important to understand the function of genetic variation in the context of brain and cognitive development. We describe a strategy for the design of hypothesis-driven cognitive and imaging-genetic studies in developing populations. To overcome the expense and statistical limitations faced in exploratory studies, we have adopted a hypothesis-driven investigative approach where gene- and allele-specific predictions are rooted in evidence obtained from structural and functional studies in humans, mice, and cell-based systems. We are focused on the developmental biology of executive attention networks and, most recently, on the role of the anterior cingulate cortex (ACC) and dopaminergic modulation within the ACC. In presenting our strategy, we are also mindful of several limitations. Firstly, there are a wide range of genetic factors that are expressed in circuits that carry out attention and executive control, and, in principle, any type of genetic variant, such as a single nucleotide polymorphism (snp) a variable number tandem repeat (vntr) or a large deletion of genetic material could impact brain development at any stage of fetal, neonatal or child development. We also acknowledge that we are probing a specific node (ACC) in a wider set of complex neural networks whose functional connectivity is organizing and re-organizing during development. Within this node, many of the biological changes that we seek to measure vis-a-vis genetic markers that index dopaminergic modulation, synaptogenesis, myelination and/or pruning may have largely occurred by age 5, or perhaps occur later than age 13. Lastly, we acknowledge that children may use a mix of alternate networks to carry out task demands, which therefore may confound the interpretations of the cognitive- and imaging-genetic assessments.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

REFERENCES

  • Berger A, Tzur G, Posner MI. Infant brains detect arithmetic errors. Proc Natl Acad Sci U S A. 2006;103:12649–12653. [PubMed]
  • Bilder RM, Volavka J, Lachman HM, Grace AA. The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes. Neuropsychopharmacology. 2004;29:1943–1961. [PubMed]
  • Bilkei-Gorzo A, Racz I, Michel K, Zimmer A. Diminished anxiety- and depression-related behaviors in mice with selective deletion of the Tac1 gene. J Neurosci. 2002;22:10046–10052. [PubMed]
  • Brozoski TJ, Brown RM, Rosvold HE, Goldman PS. Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science. 1979;205:929–932. [PubMed]
  • Bush G, Valera EM, Seidman LJ. Functional neuroimaging of attention-deficit/hyperactivity disorder: a review and suggested future directions. Biol Psychiatry. 2005;57:1273–1284. [PubMed]
  • Casey BJ, Trainor R, Giedd J, Vauss Y, Vaituzis CK, Hamburger S, Kozuch P, Rapoport JL. The role of the anterior cingulate in automatic and controlled processes: A developmental neuroantomical study. Developmental Psychobiology. 1997;30:61–69. [PubMed]
  • Caspi A, McClay J, Moffitt TE, Mill J, Martin J, Craig IW, Taylor A, Poulton R. Role of genotype in the cycle of violence in maltreated children. Science. 2002;297:851–854. [PubMed]
  • Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, McClay J, Mill J, Martin J, Braithwaite A, Poulton R. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science. 2003;301:386–389. [PubMed]
  • Cohen MM., Jr The hedgehog signaling network. Am J Med Genet A. 2003;123:5–28. [PubMed]
  • Daly G, Hawi Z, Fitzgerald M, Gill M. Mapping susceptibility loci in attention deficit hyperactivity disorder: preferential transmission of parental alleles at DAT1, DBH and DRD5 to affected children. Mol Psychiatry. 1999;4:192–196. [PubMed]
  • Davidson MC, Amso D, Anderson LC, Diamond A. Development of cognitive control and executive functions from 4 to 13 years: evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia. 2006;44:2037–2078. [PMC free article] [PubMed]
  • Diamond A. Frontal lobe involvement in cognitive changes during the first year of life. In: Gibson K, Petersen AC, editors. Brain maturation and cognitive development: comparative and cross-cultural perspectives. New York: Aldine de Gruyter; 1991. pp. 127–180.
  • Diamond A. Normal development of prefrontal cortex from birth to young adulthood. Cognitive functions, anatomy and biochemistry. In: Knight DSR, editor. Principles of frontal lobe function. New York: Oxford University Press; 2002. pp. 466–503.
  • Diamond A, Ciaramitaro V, Donner E, Djali S, Robinson MB. An animal model of earlytreated PKU. J Neurosci. 1994;14:3072–3082. [PubMed]
  • Diamond A, G-R PS. Evidence for involvement of prefrontal cortex in cognitive changes during the first year of life: comparison of performance of human infant and rhesus monkeys on a detour task with transparent barrier. Abstr Soc Neurosci (Part II) 1985;11:832.
  • Dresel S, Krause J, Krause KH, LaFougere C, Brinkbaumer K, Kung HF, Hahn K, Tatsch K. Attention deficit hyperactivity disorder: binding of [99mTc]TRODAT-1 to the dopamine transporter before and after methylphenidate treatment. Eur J Nucl Med. 2000;27:1518–1524. [PubMed]
  • Durston S, Fossella JA, Casey BJ, Hulshoff Pol HE, Galvan A, Schnack HG, Steenhuis MP, Minderaa RB, Buitelaar JK, Kahn RS, van Engeland H. Differential effects of DRD4 and DAT1 genotype on fronto-striatal gray matter volumes in a sample of subjects with attention deficit hyperactivity disorder, their unaffected siblings, and controls. Mol Psychiatry. 2005;10:678–685. [PubMed]
  • Fan J, Fossella J, Sommer T, Wu Y, Posner MI. Mapping the genetic variation of executive attention onto brain activity. Proc Natl Acad Sci U S A. 2003;100:7406–7411. [PubMed]
  • Faraone SV, Perlis RH, Doyle AE, Smoller JW, Goralnick JJ, Holmgren MA, Sklar P. Molecular genetics of attention-deficit/hyperactivity disorder. Biol Psychiatry. 2005;57:1313–1323. [PubMed]
  • Fasano C, Poirier A, DesGroseillers L, Trudeau LE. Chronic activation of the D2 dopamine autoreceptor inhibits synaptogenesis in mesencephalic dopaminergic neurons in vitro. Eur J Neurosci. 2008;28:1480–1490. [PubMed]
  • Feng Y. Convergence and divergence in the etiology of myelin impairment in psychiatric disorders and drug addiction. Neurochem Res. 2008;33:1940–1949. [PubMed]
  • Fossella J, Sommer T, Fan J, Wu Y, Swanson JM, Pfaff DW, Posner MI. Assessing the molecular genetics of attention networks. BMC Neurosci. 2002;3:14. [PMC free article] [PubMed]
  • Frank MJ, D'Lauro C, Curran T. Cross-task individual differences in error processing: neural, electrophysiological, and genetic components. Cogn Affect Behav Neurosci. 2007;7:297–308. [PubMed]
  • Fukuchi-Shimogori T, Grove EA. Neocortex patterning by the secreted signaling molecule FGF8. Science. 2001;294:1071–1074. [PubMed]
  • Goldman-Rakic PS. Prenatal formation of cortical input and development of cytoarchitectonic compartments in the neostriatum of the rhesus monkey. J Neurosci. 1981;1:721–735. [PubMed]
  • Green AE, Munafo MR, Deyoung CG, Fossella JA, Fan J, Gray JR. Using genetic data in cognitive neuroscience: from growing pains to genuine insights. Nat Rev Neurosci. 2008 [PubMed]
  • Guo X, Chen C, Chen K, Jin Z, Peng D, Yao L. Brain development in Chinese children and adolescents: a structural MRI study. Neuroreport. 2007;18:875–880. [PubMed]
  • Harman C, Rothbart MK, Posner MI. Distress and attention interactions in early infancy. Motivation and Emotion. 1997;21:27–43.
  • Kaga Y, Shoemaker WJ, Furusho M, Bryant M, Rosenbluth J, Pfeiffer SE, Oh L, Rasband M, Lappe-Siefke C, Yu K, Ornitz DM, Nave KA, Bansal R. Mice with conditional inactivation of fibroblast growth factor receptor-2 signaling in oligodendrocytes have normal myelin but display dramatic hyperactivity when combined with Cnp1 inactivation. J Neurosci. 2006;26:12339–12350. [PubMed]
  • Kelly AM, Di Martino A, Uddin LQ, Shehzad Z, Gee DG, Reiss PT, Margulies DS, Castellanos FX, Milham MP. Development of Anterior Cingulate Functional Connectivity from Late Childhood to Early Adulthood. Cereb Cortex. 2008 [PubMed]
  • Kittappa R, Chang WW, Awatramani RB, McKay RD. The foxa2 gene controls the birth and spontaneous degeneration of dopamine neurons in old age. PLoS Biol. 2007;5:e325. [PubMed]
  • Konrad K, Neufang S, Thiel CM, Specht K, Hanisch C, Fan J, Herpertz-Dahlmann B, Fink GR. Development of attentional networks: an fMRI study with children and adults. Neuroimage. 2005;28:429–439. [PubMed]
  • Lidow MS, Goldman-Rakic TS, Gallager DW, Rakic P. Distribution of dopaminergic receptors in the primate cerebral cortex: quantitative autoradiographic analysis using [3H] raclopride, and [3H] SCH23390. The Journal of Neuroscience. 1991;40:657–671. [PubMed]
  • Meaney MJ, Szyf M. Environmental programming of stress responses through DNA methylation: life at the interface between a dynamic environment and a fixed genome. Dialogues Clin Neurosci. 2005;7:103–123. [PMC free article] [PubMed]
  • Mezzacappa E. Alerting, orienting, and executive attention: developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Dev. 2004;75:1373–1386. [PubMed]
  • Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex "Frontal Lobe" tasks: a latent variable analysis. Cognit Psychol. 2000;41:49–100. [PubMed]
  • Murase S, McKay RD. A specific survival response in dopamine neurons at most risk in Parkinson's disease. J Neurosci. 2006;26:9750–9760. [PubMed]
  • Posner MI, Rothbart MK, Sheese BE, Tang Y. The anterior cingulate gyrus and the mechanism of self-regulation. Cogn Affect Behav Neurosci. 2007;7:391–395. [PubMed]
  • Ridderinkhof KR, van der Molen MW, Band GPH. Sources of Interference from irrelevant information: a developmental study. Journal of Experimental Child Psychology. 1997;65:315–341. [PubMed]
  • Rothbart MK, Ellis LK, Rueda MR, Posner MI. Developing mechanisms of temperamental effortful control. J Pers. 2003;71:1113–1143. [PubMed]
  • Rothbart MK, Rueda MR. The development of effortful control. In: Mayr EA U, Keele SW, editors. Developing individuality in the human brain: A tribute to Michael I. Posner. Washington, DC: American Psychological Association; 2005.
  • Rothbart MK, Posner MI. Mechanism and variation in the development of attention networks. In: Luciana CNM, editor. Handbook of developmental cognitive neuroscience. Cambridge, MA: MIT Press; 2001. pp. 353–363.
  • Rueda MR, Rothbart MK, McCandliss BD, Saccomanno L, Posner MI. Training, maturation, and genetic influences on the development of executive attention. Proc Natl Acad Sci U S A. 2005;102:14931–14936. [PubMed]
  • Rueda MR, Fan J, McCandliss BD, Halparin JD, Gruber DB, Lercari LP, Posner MI. Development of attentional networks in childhood. Neuropsychologia. 2004;42:1029–1040. [PubMed]
  • Scerif G, Karmiloff-Smith A. The dawn of cognitive genetics? Crucial developmental caveats. Trends Cogn Sci. 2005;9:126–135. [PubMed]
  • Sesack SR, Hawrylak VA, Matus C, Guido MA, Levey AI. Dopamine axon varicosities in the prelimbic division of the rat prefrontal cortex exhibit sparse immunoreactivity for the dopamine transporter. J Neurosci. 1998;18:2697–2708. [PubMed]
  • Shaw P, Gornick M, Lerch J, Addington A, Seal J, Greenstein D, Sharp W, Evans A, Giedd JN, Castellanos FX, Rapoport JL. Polymorphisms of the dopamine D4 receptor, clinical outcome, and cortical structure in attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2007;64:921–931. [PubMed]
  • Sheese BE, Voelker PM, Rothbart MK, Posner MI. Parenting quality interacts with genetic variation in dopamine receptor D4 to influence temperament in early childhood. Dev Psychopathol. 2007;19:1039–1046. [PubMed]
  • Shen W, Tian X, Day M, Ulrich S, Tkatch T, Nathanson NM, Surmeier DJ. Cholinergic modulation of Kir2 channels selectively elevates dendritic excitability in striatopallidal neurons. Nat Neurosci. 2007;10:1458–1466. [PubMed]
  • Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci. 2004;24:8223–8231. [PubMed]
  • Sugino K, Hempel CM, Miller MN, Hattox AM, Shapiro P, Wu C, Huang ZJ, Nelson SB. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat Neurosci. 2006;9:99–107. [PubMed]
  • Surmeier DJ, Ding J, Day M, Wang Z, Shen W. D1 and D2 dopamine-receptor modulation of striatal glutamatergic signaling in striatal medium spiny neurons. Trends Neurosci. 2007;30:228–235. [PubMed]
  • Takahashi T, Kinsman S, Makris N, Grant E, Haselgrove C, McInerney S, Kennedy DN, Takahashi T, Fredrickson K, Mori S, Caviness VS. Semilobar holoprosencephaly with midline 'seam': a topologic and morphogenetic model based upon MRI analysis. Cereb Cortex. 2003;13:1299–1312. [PubMed]
  • Volkow ND, Wang G, Fowler JS, Logan J, Gerasimov M, Maynard L, Ding Y, Gatley SJ, Gifford A, Franceschi D. Therapeutic doses of oral methylphenidate significantly increase extracellular dopamine in the human brain. J Neurosci. 2001;21:RC121. [PubMed]