Annual research review: Rare genotypes and childhood psychopathology--uncovering diverse developmental mechanisms of ADHD risk.

Journal of Child Psychology and Psychiatry **:* (2014), pp **–**

doi:10.1111/jcpp.12374

Annual Research Review: Rare genotypes and childhood psychopathology – uncovering diverse developmental mechanisms of ADHD risk Gaia Scerif,1 and Kate Baker2 1

Department of Experimental Psychology, University of Oxford, Oxford; 2Department of Medical Genetics, Cambridge Institute of Medical Research, University of Cambridge, Cambridge, UK

Background: Through the increased availability and sophistication of genetic testing, it is now possible to identify causal diagnoses in a growing proportion of children with neurodevelopmental disorders. In addition to developmental delay and intellectual disability, many genetic disorders are associated with high risks of psychopathology, which curtail the wellbeing of affected individuals and their families. Beyond the identification of significant clinical needs, understanding the diverse pathways from rare genetic mutations to cognitive dysfunction and emotional–behavioural disturbance has theoretical and practical utility. Methods: We overview (based on a strategic search of the literature) the state-of-the-art on causal mechanisms leading to one of the most common childhood behavioural diagnoses – attention deficit hyperactivity disorder (ADHD) – in the context of specific genetic disorders. We focus on new insights emerging from the mapping of causal pathways from identified genetic differences to neuronal biology, brain abnormalities, cognitive processing differences and ultimately behavioural symptoms of ADHD. Findings: First, ADHD research in the context of rare genotypes highlights the complexity of multilevel mechanisms contributing to psychopathology risk. Second, comparisons between genetic disorders associated with similar psychopathology risks can elucidate convergent or distinct mechanisms at each level of analysis, which may inform therapeutic interventions and prognosis. Third, genetic disorders provide an unparalleled opportunity to observe dynamic developmental interactions between neurocognitive risk and behavioural symptoms. Fourth, variation in expression of psychopathology risk within each genetic disorder points to putative moderating and protective factors within the genome and the environment. Conclusion: A common imperative emerging within psychopathology research is the need to investigate mechanistically how developmental trajectories converge or diverge between and within genotype-defined groups. Crucially, as genetic predispositions modify interaction dynamics from the outset, longitudinal research is required to understand the multi-level developmental processes that mediate symptom evolution. Keywords: Rare genotypes, causal pathways, developmental mechanisms, ADHD risk.

Introduction Rare genotypes and psychopathology: Common phenotypes, contrasting multilevel mechanisms Neurodevelopmental disorders of known genetic origin can serve as multilevel models for understanding the origins of psychiatric syndromes that are currently defined at the behavioural level. Here, we choose to target causal pathways to one of the most commonly identified childhood behavioural syndromes, attention deficit hyperactivity disorder (ADHD). We aim to discuss both the promise and caveats emerging from the study of ADHD in children with neurodevelopmental disorders of known genetic aetiology. What can we learn about causal pathways to ADHD in the context of rare genotypes? Childhood psychopathology is characterized by complexity at all levels of description. A major advantage of investigating groups with identified genetic aetiologies is

Conflict of interest statement: none declared

that causal pathways can be delineated from the levels of molecular and cellular function through to neural networks and cognitive processing, and stable or changing profiles can be examined prospectively via early (in some cases as early as pre- or perinatal) diagnosis. We begin by defining the problem domain: the search for causal pathways in behaviourally defined childhood disorders including ADHD. We then review systematically the reported prevalence of ADHD diagnoses and symptoms across multiple genetic disorders, including those that are relatively frequent to rarer disorders identified more recently. We next evaluate the evidence for distinguishable cognitive and neural systems mechanisms leading to heightened ADHD risk in different genetic disorders. We then discuss the emerging understanding of molecular and cellular mechanisms in each genetic disorder. Finally, we draw attention to the developmental nature of genetic risk itself: in this context, rare genotypes associated with high risk but not inevitability of ADHD symptoms provide an invaluable prospective opportunity to study risk and resilience.

© 2014 Association for Child and Adolescent Mental Health. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA

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Gaia Scerif and Kate Baker

ADHD as target phenotype: Common, behaviourally defined, heterogeneous, developmental ADHD affects 2%–5% of children worldwide (Ford, Goodman, & Meltzer, 2003; Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). Diagnosis is attained through consensus on the presence of key symptom behaviours of variable severity along continua of hyperactivity and inattention. ADHD can be diagnosed in isolation, but recent changes to diagnostic criteria recognize that ADHD is frequently associated with other behavioural characteristics including autistic spectrum features or intellectual disability (APA, 2013). Heterogeneity at the levels of symptoms is further complicated by their changes over developmental time (Lahey, Pelham, Loney, Lee, & Willcutt, 2005). Recent large-scale investigations pinpoint the diversity of possible neurocognitive mechanisms within ADHD: some children and adolescents with ADHD demonstrate predominantly inhibitory deficits, motivational difficulties, challenges in sustained attention or working memory, or combinations of these and other impairments (Fair, Bathula, Nikolas, & Nigg, 2012; Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005). At the level of neural systems, a nearly exclusive focus on frontostriatal contributions to ADHD has recently been replaced by evidence of multinetwork involvement (Castellanos & Proal, 2012; Cortese et al., 2012). ADHD has been associated with structural and functional abnormalities of a distributed lateralized corticostriatal network (Batty et al., 2010; Durston et al., 2003; Groom et al., 2010), as well as global neuroanatomical atypicalities, for example a marked delay in attaining peak thickness across the cerebral cortex (Shaw et al., 2007). In essence, the search for a single core symptom, symptom profile, cognitive deficit or neuroanatomical correlate shared by all children with ADHD has been heavily criticized (e.g. Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005).

ADHD aetiology: Complex, heterogeneous, developmental, but increasingly identifiable ADHD is highly heritable, with estimates of the genetic contribution to risk ranging from 40%–90%. In addition to genetic aetiology, multiple lines of evidence point to complex gene-environment interactions (Thapar, Cooper, Eyre, & Langley, 2013; (Thapar & Harold, 2014). The search for a single or few genetic causes initially focused (unsuccessfully) on candidate genes influencing the dopamine and serotonin systems (Gizer, Ficks, & Waldman, 2009). No single gene has reached genome-wide significance in genome-wide association studies, despite the most promising meta-analytic approaches (Neale et al., 2010). There is mounting evidence that the high heritability of ADHD reflects a large and diverse

repertoire of rare and common risk-associated variants influencing multiple cellular functions (Stergiakouli et al., 2012). For many individuals, ADHD risk is polygenic encompassing a combination of variants of small effect. For other individuals, one or several variants with relatively large effect size (but never 100% penetrance) can be identified. Recent studies pinpoint an increased burden of rare (500 kb) copy number variants (CNVs) involving diverse loci (Elia et al., 2012; Stergiakouli et al., 2012; Williams et al., 2012). Network analyses of the genes within these highly variable loci highlight at least 15 functional clusters (Elia et al., 2012), including the metabotrobic glutamatergic receptor (“mGluR”) network, cell adhesion, neuron migration, neurite outgrowth, neuronal morphogenesis, and synaptic plasticity (Poelmans, Pauls, Buitelaar, & Franke, 2011; Yang et al., 2013). In its ensemble, the state-of-the-art highlights ADHD as a common, complex, heterogeneous, developmental, behavioural disorder arising from diverse causal factors. Rare but recurrent genotypes associated with ADHD risk represent identifiable minority groups that are potentially informative of heterogeneous developmental mechanisms.

Methods: Review search strategy This review intends to provide an overview of selected genetic disorders known to be associated with ADHD risk, and data pertinent to that specific psychopathology risk. We contrast several types of genetic disorder, including well-known chromosomal syndromes and Mendelian disorders, more recently identified recurrent chromosomal anomalies (CNVs), and rare inherited mutations associated with ADHD risk. We examine ADHD at multiple levels to appraise evidence for the diversity in mechanisms and developmental dynamics of ADHD risk across genetic disorders. We begin discussion at each level by presenting findings pertaining to fragile X syndrome (FXS), because this relatively common inherited cause of intellectual disability (ID) is associated with exceptionally high rates of ADHD, and has been a focus for a broad range of relevant research against which other disorders can be compared. Cross-syndrome comparisons will be examined to highlight the diversity of pathways to ADHD risk amongst rare genetic disorders. Studies investigating the variability in symptoms and their developmental trajectories within each syndrome group will be emphasized where such data is available.

Rare genotypes and multilevel diverse pathways to ADHD risk The behavioural symptom level Table 1 presents ADHD diagnostic prevalence and symptom profiles across multiple genetic disorders, selected according to the search strategy described above, and lists full references for the evidence summarized here. The first two rows of the Table are dedicated to a comparable synopsis for children with ADHD without identified genetic cause, with and without ID. © 2014 Association for Child and Adolescent Mental Health.

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disorders, types of social dysfunction vary from hypersociability in WS, to autism in FXS, and risk of oppositional behaviours in Trisomy 21. 22q11.2 deletion syndrome (22q11DS) is associated with inattention symptom rates on a par with FXS, but a uniquely high degree of association with mood and anxiety disorders in adolescence and psychosis in adulthood.

Developmental progression. Further pointers toward the diversity of pathways leading to ADHD risk across genetic disorders come from studies investigating the developmental trajectories of symptoms. Developmental time is of the essence in the presentation of ADHD symptoms across distinct genotypes. Specific profiles of inattention and hyperactivity vary with developmental stages in FXS, with high levels of persistent inattention, but age-related decline in hyperactivity through later childhood and adolescence (Wheeler et al., 2014). Even though WS and Trisomy 21 differ in ADHD prevalence as described above in late adolescence, earlier in childhood (4 – 8 years of age) both groups exhibited heightened hyperactivity, and this was indeed more extreme in the case of WS (Cornish, Steele, Rondinelli Cobra Monteiro, Karmiloff-Smith, & Scerif, 2012). Reports of postpubertal worsening hyperactivity in Trisomy 21 (Maatta, Tervo-Maatta, Taanila, Kaski, & Iivanainen, 2006) contrast with declining hyperactivity across childhood in both FXS and WS (Leyfer, Woodruff-Borden, Klein-Tasman, Fricke, & Mervis, 2006; Sullivan et al., 2006). In the context of 22q11.2DS, a strikingly late emergence of ADHD symptoms is reported in longitudinal studies, in some cases as late as after 11 years of age (Antshel et al., 2013). These tantalizing and as yet unexplained differences in longitudinal trajectories of ADHD symptoms across disorders highlight a further, as yet little explored, benefit of investigating rare genotypes. Given that the average age of diagnosis of, for example, FXS, is much earlier than that for ADHD diagnosis (typically confirmed after the age of 5 years), FXS affords the opportunity of studying high risk cases from early in development. This promise of genetic disorders with an early diagnosis is not unique to FXS: disorders like Williams syndrome are also characterized by high risk for ADHD-symptoms and receive a very early diagnosis (Rhodes, Riby, Matthews, & Coghill, 2011). Childhood onset of ADHD symptoms in these populations at high risk might allow for a well-defined longitudinal investigation of the mechanisms driving the onset of these symptoms, and possibly to distinguishable aetiological processes at different points in development. Caveats and limitations: Rare genotypes have sometimes been dismissed as uninformative of causal pathways for psychopathology in general. Much discussion has focused on whether symptoms of inattention and hyperactivity in the context of

identified genotypic differences are truly comparable to those encountered in cases of unidentified aetiology (e.g. Antshel, Phillips, Gordon, Barkley, & Faraone, 2006; Simonoff, Pickles, Wood, Gringras, & Chadwick, 2007), “idiopathic ADHD” henceforth. Similar debates have characterized the literature on autistic symptomatology within the context of genetic disorders like FXS (e.g. Hall, Lightbody, Hirt, Rezvani, & Reiss, 2010). Some caveats hinge on methodological flaws of the tools available to quantify ADHD diagnoses or symptoms, when these are applied to children with identified genetic disorders and ID: for example, the most commonly used scales quantifying inattention and hyperactivity, the Conners Teacher and Parent Behavior Rating Scales, have been criticized for inflating hyperactivity and impulsivity when applied to individuals with severe ID. At the other end of the continuum, difficulties in differentiating between mild ID and inattention have been highlighted across rare genotypes. Finally, the fact that inattention and hyperactivity in multiple rare genotypes co-occur with comorbid symptoms such as challenging behaviours and autistic spectrum disorders has sometimes led to their dismissal (e.g. see Oliver, Berg, Moss, Arron, & Burbidge, 2011; for a discussion of these issues), although comorbidity, as noted above, is increasingly recognized as common in idiopathic ADHD (Jensen et al., 2001). In conclusion, the cross-syndrome symptom prevalence, within-syndrome variability and distinguishable developmental trajectories of ADHD diagnoses and symptoms described above speak to this overall debate as to whether, and if so how, genetic disorders at risk for ADHD are informative of ADHD in the broader population. Appropriate methods of assessment that are reliable and valid for these groups exist (e.g. Einfeld & Tonge, 1996), are represented in multiple studies summarized in Table 1 and validate ADHD prevalence figures. Most importantly, ADHD risk is not equivalent in rates, symptom profiles or developmental trajectories across all genetic causes of ID, and therefore is not easily attributable simply to low IQ, but reflects specific neurodevelopmental trajectories.

The cognitive level What influences the developmental trajectories of some children with rare genotypes to increase risk of symptoms of ADHD, and why do some but not all at-risk children with each risk-associated genetic disorder exhibit these difficulties? Similarities and differences in neurocognitive mechanisms for hyperactivity and inattention underlying ADHD symptoms in the context of genetic disorders can further elucidate diverse pathways to ADHD risk. In this section we turn to the following question: How might the cognitive basis for ADHD symptoms differ across genotypes? Table 2 summarizes related neurocognitive data across our target genetic disorders. Inspection of Table 2 highlights the diversity of deficits © 2014 Association for Child and Adolescent Mental Health.

420 kb duplication within complex chromosomal region; several breakpoints leading to recurrent CNVs

15q13.3 duplication (~ 0.3% in controls, 0.4%–0.9% in ADHD cases)

Identified in individuals with average range IQ, mild ID and severe ID.

Penetrance for ADHD not known Williams, Driscoll, and Dagli (2010); Williams et al. (2010)

3 Mb deletion (90%), 1.5 or 2 Mb nested deletions

22q11.2 deletion syndrome (1 in 6000)

Not reported

Combined 11% Inattentive 23% Hyperactive 2.4%

Late emergence of ADHD symptoms, in some cases > age 11 Antshel et al. (2013) Low persistence in adulthood (16%) Not yet known

37% Schneider et al. (2014); international consortium data on 1,400 cases

Average IQ ~75. 40% average range, 60% borderline-mild ID; rarely severe ID.

Loss of expression of the maternal copy of UBE3A at 15q11.2 via deletion, uniparental disomy, or intragenic mutation

Angelman syndrome (1 in 12000-20000)

Hyperkinesia, not inattention or impulsivity Berry, Leitner, Clarke, and Einfeld (2005)

Very high rates of distractibility in adults Elison, Stinton, and Howlin (2010) Comorbidity with anxiety disorders Inconsistent date on hyperactivity prognosis Clayton-Smith (2001); Oliver et al. (2011)

Combined 18%, Inattentive 45%, Hyperactive 2.6% Leyfer et al. (2006)

65% Leyfer et al. (2006), Rhodes, Riby, Frazier et al. (2011), Rhodes, Riby, Matthews et al. (2011) 60% Williams, Driscoll, and Dagli (2010)

Average IQ 55, range 40-100 Martens, Wilson, and Reutens (2008)

1.55 Mb deletion (or smaller nested deletions) at 7q11.23

William syndrome (1 in 7500)

Severe ID in almost all cases

Early hypoactivity, then transient hyperactivity, then emergent inattention Wheeler et al. (2014)

Combined 14.8% Inattentive 31.5% Hyperactive 7.4% Sullivan et al. (2006)

60%–75% Turk (1998)

Average IQ: males 40 females 93 Loesch et al. (2002); Merenstein et al. (1996)

ADHD age of onset, prognosis

Trinucleotide CGG repeat expansion in the 50 untranslated region of the FMR1 gene at Xq27.3

ADHD (subtypes) diagnostic rates

Fragile X syndrome (1 in 4000 males, 1 in 6000 females)

ADHD (all) diagnostic rate

Global cognitive ability

Genomic abnormality associated with ADHD risk

Population at-risk of ADHD (prevalence)

Table 1 (continued)

Other diagnoses increase with age (mood/anxiety in adolescence, schizophrenia 40% in adults) Comorbid conduct disorder within ADHD sampleWilliams, Driscoll, and Dagli (2010); Williams et al. (2010). Wide range of other behavioural phenotypes van Bon et al. (2009)

Comorbidity with autism

Comorbidity with other behavioural disorders (group-wide)

No studies to date

No studies on stimulant medication. Ongoing treatment trials with levodopa/ carbidopa, and minocycline Bird (2014) MPH effective and safe, and improved prefrontal cognitive task performance Green et al. (2011)

Stimulants (esp MPH) may be more effective than in the non-FXS population: Roberts et al. (2011) Trials of nonstimulants and novel agents underway Torrioli et al. (2008, 2010); Jacquemont et al. (2014) MPH helpful in 75%, but with higher rates of irritability and potential cardiac risks Martens et al. (2013)

ADHD treatment

4 Gaia Scerif and Kate Baker

© 2014 Association for Child and Adolescent Mental Health.

No studies to date

No studies to date

Not known Not impulsive/ oppositional Loss-of-function mutations in the X-linked gene PAK3 PAK3 sequence variants ultrarare

ID, intellectual disability; OR, odds ratio; MPH, methylphenidate.

Mild to severe ID in males

Higher rates of inattention and hyperactivity than other X-linked causes of ID (Baker et al., under review)

Not yet known All combined or hyperactive/ impulsive Penetrance for ADHD not known Elia et al. (2012); Jarick et al. (2014) Not known, individuals with ID excluded from sample Deletions and duplications involving PARK2 at 6q26 PARK2 CNVs (0.47% in controls, 2.25% in ADHD cases)

Deletions in early-onset Parkinson Disease Recessive mutations in juvenile PD Kilarski et al. (2012) Case reports of persistent adult psychopathology Peippo et al. (2007)

ADHD age of onset, prognosis ADHD (subtypes) diagnostic rates ADHD (all) diagnostic rate Global cognitive ability Genomic abnormality associated with ADHD risk Population at-risk of ADHD (prevalence)

Table 1 (continued)

Comorbidity with other behavioural disorders (group-wide)

ADHD treatment

Rare genotypes and developmental mechanisms


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There are important limitations to comparing data across genotypes represented in Table 1: groups differ in ascertainment strategies, assessment tools and ages. Nonetheless, one key conclusion is that multiple lines of evidence point away from accounts of ADHD-symptomatology in genetic disorders as simply reflecting low IQ. Firstly, ADHD symptoms show marked variation between genetic disorders in rates, symptom profiles and developmental progression, despite similar ranges of ID severity. For example, males with FXS are usually severely intellectually impaired, often with an average IQ as low as 40, with severe communication difficulties. Approximately 70% of individuals with FXS fulfil criteria for an ADHD diagnosis, making ADHD the most commonly diagnosed behavioural condition in FXS (Sullivan et al., 2006; Turk, 1998). This contrasts with Trisomy 21, associated with similar range of IQ but considerably lower reported rates of ADHD (albeit with variability between studies). Secondly, genetic disorders with contrasting severity of ID can show similar prevalence of ADHD. For example, inattention symptoms are extremely common in William syndrome (“WS”) and are comparable to rates within FXS, despite WS being associated with higher verbal IQ on average than FXS. Thirdly, within syndromes it is unusual to find a correlation between IQ and ADHD symptoms – lower IQ does not predict the severity or profile of ADHD symptoms in young children with FXS (Cornish, Cole, Longhi, Karmiloff-Smith, & Scerif, 2012), neither is IQ associated with ADHD risk in Trisomy 21 (Ekstein, Glick, Weill, Kay, & Berger, 2011). Across all genetic disorders where data is available, within-genotype variation in IQ does not predict the presence of ADHD symptoms or diagnosis. Another key observation is that ADHD symptom expression differs between genetic disorders. For example, symptom profiles between FXS and Trisomy 21 are contrasting, with inattention dominating in FXS and hyperactivity dominating in Trisomy 21 (at least for older children and adolescents, but see below for considerations on cross-syndrome developmental trajectories). Informative cross-genotype differences also emerge for sex chromosome monosomy (Turner syndrome) and sex chromosomal trisomies (Leggett, Jacobs, Nation, Scerif, & Bishop, 2010; Ross, Zeger, Kushner, Zinn, & Roeltgen, 2009). High rates of ADHD are reported in all of these disorders, but individuals with XXX and XXY are characterized by high rates of inattention, whereas hyperactivity is most prevalent in XYY (Tartaglia, Ayari, Hutaff-Lee, & Boada, 2012). In turn, this suggests that there may be sex-linked genes involved in these differing pathways, as has been suggested in the context of divergence in language profiles across sex chromosomal trisomies including XXY (Bishop & Scerif, 2011; Bishop et al., 2011). Patterns of comorbidity for behavioural symptoms in addition to ADHD also differ between groups – whilst atypical social function is common to several

Rare genotypes and developmental mechanisms developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences of the United States of America, 109, 6769–6774. Farzin, F., Rivera, S.M., & Whitney, D. (2011). Resolution of spatial and temporal visual attention in infants with fragile X syndrome. Brain, 134, 3355–3368. Fenelon, K., Xu, B., Lai, C.S., Mukai, J., Markx, S., Stark, K.L., . . . & Gogos, J.A. (2013). The pattern of cortical dysfunction in a mouse model of a schizophrenia-related microdeletion. Journal of Neuroscience, 33, 14825–14839. Ford, T., Goodman, R., & Meltzer, H. (2003). The British child and adolescent mental health survey 1999: The prevalence of DSM-IV disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1203–1211. Fung, L.K., Quintin, E.-M., Haas, B.W., & Reiss, A.L. (2012). Conceptualizing neurodevelopmental disorders through a mechanistic understanding of fragile X syndrome and Williams syndrome. Current Opinion in Neurology, 25, 112– 124. Gath, A., & Gumley, D. (1986). Behavior problems in retarded-children with special reference to downssyndrome. British Journal of Psychiatry, 149, 156–161. Gizer, I.R., Ficks, C., & Waldman, I.D. (2009). Candidate gene studies of ADHD: A meta-analytic review. Human Genetics, 126, 51–90. Glaser, B., Hessl, D., Dyer-Friedman, J., Johnston, C., Wisbeck, J., Taylor, A., & Reiss, A. (2003). Biological and environmental contributions to adaptive behavior in fragile X syndrome. American Journal of Medical Genetics Part A, 117A, 21–29. Gothelf, D., Eliez, S., Thompson, T., Hinard, C., Penniman, L., Feinstein, C., . . . & Reiss, A.L. (2005). COMT genotype predicts longitudinal cognitive decline and psychosis in 22q11.2 deletion syndrome. Nature Neuroscience, 8, 1500–1502. Gothelf, D., Furfaro, J.A., Hoeft, F., Eckert, M.A., Hall, S.S., O’Hara, R., . . . & Reiss, A.L. (2008). Neuroanatomy of fragile X syndrome is associated with aberrant behavior and the fragile X mental retardation protein (FMRP). Annals of Neurology, 63, 40–51. Green, T., Weinberger, R., Diamond, A., Berant, M., Hirschfeld, L., Frisch, A., . . . & Gothelf, D. (2011). The effect of methylphenidate on prefrontal cognitive functioning, inattention, and hyperactivity in velocardiofacial syndrome. Journal of Child and Adolescent Psychopharmacology, 21, 589–595. Greer, P.L., Hanayama, R., Bloodgood, B.L., Mardinly, A.R., Lipton, D.M., Flavell, S.W., . . . & Greenberg, M.E. (2010). The Angelman syndrome protein Ube3A regulates synapse development by ubiquitinating arc. Cell, 140, 704–716. Groom, M.J., Scerif, G., Liddle, P.F., Batty, M.J., Liddle, E.B., Roberts, K.L., . . . & Hollis, C. (2010). Effects of motivation and medication on electrophysiological markers of response inhibition in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 67, 624–631. Grossman, A.W., Aldridge, G.M., Weiler, I.J., & Greenough, W.T. (2006). Local protein synthesis and spine morphogenesis: Fragile X syndrome and beyond. Journal of Neuroscience, 26, 7151–7155. Hall, S.S., Jiang, H., Reiss, A.L., & Greicius, M.D. (2013). Identifying large-scale brain networks in fragile X syndrome. JAMA Psychiatry, 70, 1215–1223. Hall, S.S., Lightbody, A.A., Hirt, M., Rezvani, A., & Reiss, A.L. (2010). Autism in fragile X syndrome: A category mistake? Journal of the American Academy of Child and Adolescent Psychiatry, 49, 921–933. Hayrapetyan, V., Castro, S., Sukharnikova, T., Yu, C., Cao, X., Jiang, Y.-H., & Yin, H.H. (2014). Region-specific impairments in striatal synaptic transmission and impaired instrumental learning in a mouse model of Angelman syndrome. European Journal of Neuroscience, 39, 1018–1025.


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Hazlett, H.C., Poe, M.D., Lightbody, A.A., Gerig, G., MacFall, J.R., Ross, A.K., . . . & Piven, J. (2009). Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism. Journal of Neurodevelopmental Disorders, 1, 81–90. Hazlett, H.C., Poe, M.D., Lightbody, A.A., Styner, M., MacFall, J.R., Reiss, A.L., & Piven, J. (2012). Trajectories of early brain volume development in fragile X syndrome and autism. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 921–933. Hennis, M.R., Marvin, M.A., Taylor, C.M., II, & Goldberg, M.S. (2014). Surprising behavioral and neurochemical enhancements in mice with combined mutations linked to Parkinson’s disease. Neurobiology of disease, 62, 113–123. Hessl, D., Tassone, F., Cordeiro, L., Koldewyn, K., McCormick, C., Green, C., . . . & Hagerman, R.J. (2008). Brief report: Aggression and stereotypic behavior in males with fragile X syndrome - Moderating secondary genes in a “Single Gene” disorder. Journal of Autism and Developmental Disorders, 38, 184–189. Hiroi, N., Takahashi, T., Hishimoto, A., Izumi, T., Boku, S., & Hiramoto, T. (2013). Copy number variation at 22q11.2: From rare variants to common mechanisms of developmental neuropsychiatric disorders. Molecular Psychiatry, 18, 1153–1165. Hoeft, F., Carter, J.C., Lightbody, A.A., Hazlett, H.C., Piven, J., & Reiss, A.L. (2010). Region-specific alterations in brain development in one- to three-year-old boys with fragile X syndrome. Proceedings of the National Academy of Sciences of the United States of America, 107, 9335–9339. Hoeft, F., Hernandez, A., Parthasarathy, S., Watson, C.L., Hall, S.S., & Reiss, A.L. (2007). Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome. Human Brain Mapping, 28, 543– 554. Hoeft, F., Walter, E., Lightbody, A.A., Hazlett, H.C., Chang, C., Piven, J., & Reiss, A.L. (2011). Neuroanatomical differences in Toddler boys with fragile X syndrome and idiopathic autism. Archives of General Psychiatry, 68, 295–305. Holzapfel, M., Barnea-Goraly, N., Eckert, M.A., Kesler, S.R., & Reiss, A.L. (2006). Selective alterations of white matter associated with visuospatial and sensorimotor dysfunction in Turner syndrome. Journal of Neuroscience, 26, 7007–7013. Hong, D.S., Hoeft, F., Marzelli, M.J., Lepage, J.-F., Roeltgen, D., Ross, J., & Reiss, A.L. (2014). Influence of the X-chromosome on neuroanatomy: Evidence from Turner and Klinefelter syndromes. Journal of Neuroscience, 34, 3509–3516. Hong, D.S., & Reiss, A.L. (2014). Cognitive and neurological aspects of sex chromosome aneuploidies. Lancet Neurology, 13, 306–318. Hooper, S.R., Curtiss, K., Schoch, K., Keshavan, M.S., Allen, A., & Shashi, V. (2013). A longitudinal examination of the psychoeducational, neurocognitive, and psychiatric functioning in children with 22q11.2 deletion syndrome. Research in Developmental Disabilities, 34, 1758–1769. Hooper, S.R., Hatton, D., Sideris, J., Sullivan, K., Hammer, J., Schaaf, J., . . . & Bailey, D.B., Jr (2008). Executive functions in young males fragile X syndrome in comparison to mental age-matched with controls: Baseline findings from a longitudinal study. Neuropsychology, 22, 36–47. Huang, W., Zhou, Z., Asrar, S., Henkelman, M., Xie, W., & Jia, Z. (2011). p21-activated kinases 1 and 3 control brain size through coordinating neuronal complexity and synaptic properties. Molecular and Cellular Biology, 31, 388–403. Iliff, A.J., Renoux, A.J., Krans, A., Usdin, K., Sutton, M.A., & Todd, P.K. (2013). Impaired activity-dependent FMRP translation and enhanced mGluR-dependent LTD in Fragile X premutation mice. Human Molecular Genetics, 22, 1180–1192.

Rare genotypes and developmental mechanisms Mulherkar, S.A., & Jana, N.R. (2010). Loss of dopaminergic neurons and resulting behavioural deficits in mouse model of Angelman syndrome. Neurobiology of Disease, 40, 586– 592. Munir, F., Cornish, K.M., & Wilding, J. (2000). A neuropsychological profile of attention deficits in young males with fragile X syndrome. Neuropsychologia, 38, 1261–1270. Neale, B.M., Medland, S.E., Ripke, S., Asherson, P., Franke, B., Lesch, K.-P., . . . & Psychiat, G.C.A.S. (2010). Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 884–897. Neece, C.L., Baker, B.L., Blacher, J., & Crnic, K.A. (2011). Attention-deficit/hyperactivity disorder among children with and without intellectual disability: An examination across time. Journal of Intellectual Disability Research, 55, 623– 635. Nigg, J.T., Willcutt, E.G., Doyle, A.E., & Sonuga-Barke, E.J.S. (2005). Causal heterogeneity in attention-deficit/ hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biological Psychiatry, 57, 1224–1230. Nithianantharajah, J., Komiyama, N.H., McKechanie, A., Johnstone, M., Blackwood, D.H., St Clair, D., . . . & Grant, S.G.N. (2013). Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nature Neuroscience, 16, 16-U37. van Nuenen, B.F.L., Weiss, M.M., Bloem, B.R., Reetz, K., van Eimeren, T., Lohmann, K., . . . & Siebner, H.R. (2009). Heterozygous carriers of a Parkin or PINK1 mutation share a common functional endophenotype. Neurology, 72, 1041– 1047. Oliver, C., Berg, K., Moss, J., Arron, K., & Burbidge, C. (2011). Delineation of behavioral phenotypes in genetic syndromes: Characteristics of autism spectrum disorder, affect and hyperactivity. Journal of Autism and Developmental Disorders, 41, 1019–1032. Ortiz-Abalia, J., Sahun, I., Altafaj, X., Andreu, N., Estivill, X., Dierssen, M., & Fillat, C. (2008). Targeting Dyrk1A with AAVshRNA attenuates motor alterations in TgDyrk1A, a mouse model of down syndrome. American Journal of Human Genetics, 83, 479–488. Paterson, S.J., Brown, J.H., Gsodl, M.K., Johnson, M.H., & Karmiloff-Smith, A. (1999). Cognitive modularity and genetic disorders. Science, 286, 2355–2358. Peippo, M., Koivisto, A.M., Sarkamo, T., Sipponen, M., von Koskull, H., Ylisaukko-oja, T., . . . & Jarvela, I. (2007). PAK3 related mental disabillity: Further characterization of the phenotype. American Journal of Medical Genetics Part A, 143A, 2406–2416. Pelc, K., Cheron, G., & Dan, B. (2008). Behavior and neuropsychiatric manifestations in Angelman syndrome. Neuropsychiatric disease and treatment, 4, 577–584. Peng, D.X., Kelley, R.G., Quintin, E.-M., Raman, M., Thompson, P.M., & Reiss, A.L. (2014). Cognitive and behavioral correlates of caudate subregion shape variation in fragile X syndrome. Human Brain Mapping, 35, 2861– 2868. Peters, S.U., Kaufmann, W.E., Bacino, C.A., Anderson, A.W., Adapa, P., Chu, Z., . . . & Wilde, E.A. (2011). Alterations in white matter pathways in Angelman syndrome. Developmental Medicine and Child Neurology, 53, 361– 367. Pignatelli, M., Piccinin, S., Molinaro, G., Di Menna, L., Riozzi, B., Cannella, M., . . . & Bruno, V. (2014). Changes in mGlu5 receptor-dependent synaptic plasticity and coupling to homer proteins in the hippocampus of Ube3A hemizygous mice modeling Angelman syndrome. Journal of Neuroscience, 34, 4558–4566. Poelmans, G., Pauls, D.L., Buitelaar, J.K., & Franke, B. (2011). Integrated genome-wide association study findings:


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Identification of a neurodevelopmental network for attention deficit hyperactivity disorder. American Journal of Psychiatry, 168, 365–377. Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., & Rohde, L.A. (2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry, 164, 942–948. Quintero, A.I., Beaton, E.A., Harvey, D.J., Ross, J.L., & Simon, T.J. (2014). Common and specific impairments in attention functioning in girls with chromosome 22q11.2 deletion, fragile X or Turner syndromes. Journal of Neurodevelopmental Disorders, 6, 5. Reiss, A.L., Eckert, M.A., Rose, F.E., Karchemskiy, A., Kesler, S., Chang, M., . . . & Galaburda, A. (2004). An experiment of nature: Brain anatomy parallels cognition and behavior in Williams syndrome. Journal of Neuroscience, 24, 5009–5015. Rhodes, S.M., Riby, D.M., Fraser, E., & Campbell, L.E. (2011). The extent of working memory deficits associated with Williams syndrome: Exploration of verbal and spatial domains and executively controlled processes. Brain and Cognition, 77, 208–214. Rhodes, S.M., Riby, D.M., Matthews, K., & Coghill, D.R. (2011). Attention-deficit/hyperactivity disorder and Williams syndrome: Shared behavioral and neuropsychological profiles. Journal of Clinical and Experimental Neuropsychology, 33, 147–156. Rhodes, S.M., Riby, D.M., Park, J., Fraser, E., & Campbell, L.E. (2010). Executive neuropsychological functioning in individuals with Williams syndrome. Neuropsychologia, 48, 1216–1226. Riday, T.T., Dankoski, E.C., Krouse, M.C., Fish, E.W., Walsh, P.L., Han, J.E., . . . & Malanga, C.J. (2012). Pathway-specific dopaminergic deficits in a mouse model of Angelman syndrome. Journal of Clinical Investigation, 122, 4544–4554. Roberts, J.E., Hatton, D.D., Long, A.C.J., Anello, V., & Colombo, J. (2012). Visual attention and autistic behavior in infants with fragile X syndrome. Journal of Autism and Developmental Disorders, 42, 937–946. Roberts, J.E., Miranda, M., Boccia, M., Janes, H., Tonnsen, B.L., & Hatton, D.D. (2011). Treatment effects of stimulant medication in young boys with fragile X syndrome. Journal of Neurodevelopmental Disorders, 3, 175–184. Ross, J.L., Zeger, M.P.D., Kushner, H., Zinn, A.R., & Roeltgen, D.P. (2009). An extra X OR Y chromosome: Contrasting the cognitive and motor phenotypes in childhood in boys with 47, XYY syndrome OR 47, XXY Klinefelter syndrome. Developmental Disabilities Research Reviews, 15, 309–317. Russell, H.F., Wallis, D., Mazzocco, M.M.M., Moshang, T., Zackai, E., Zinn, A.R., . . . & Muenke, M. (2006). Increased prevalence of ADHD in Turner syndrome with no evidence of imprinting effects. Journal of Pediatric Psychology, 31, 945– 955. Scerif, G. (2010). Attention trajectories, mechanisms and outcomes: At the interface between developing cognition and environment. Developmental Science, 13, 805–812. Scerif, G., Cornish, K., Wilding, J., Driver, J., & Karmiloff-Smith, A. (2004). Visual search in typically developing toddlers and toddlers with Fragile X or Williams syndrome. Developmental Science, 7, 116–130. Scerif, G., & Karmiloff-Smith, A. (2005). The dawn of cognitive genetics? Crucial developmental caveats. Trends in Cognitive Sciences, 9, 126–135. Scerif, G., Karmiloff-Smith, A., Campos, R., Elsabbagh, M., Driver, J., & Cornish, K. (2005). To look or not to look? Typical and atypical development of oculomotor control. Journal of Cognitive Neuroscience, 17, 591–604. Scerif, G., Longhi, E., Cole, V., Karmiloff-Smith, A., & Cornish, K. (2012). Attention across modalities as a longitudinal predictor of early outcomes: The case of fragile X syndrome. Journal of Child Psychology and Psychiatry, 53, 641–650.

Task performance not found to correlate with behavioural symptoms of ADHD (small study numbers to date)

No studies

No studies

Executive dysfunction predicts ADHD and other psychiatric symptoms Hooper et al. (2013)

Association between cognitive impairments and ADHD symptoms

Presymptomatic carriers for PD-associated PARK2 mutations have normal neuroanatomy but increased fMRI activation during motor activity – compensatory reorganization? van Nuenen et al. (2009) No studies

No studies

Global cortical and WM developmental trajectory differences, with additional abnormalities/atypical maturation emerging during adolescence.

Case–control MRI differences

No studies

No studies

No studies

Likely to interact with other genomic anomalies and other factors to explain variable penetrance Itsara et al. (2009) A developmental continuum of risk (early ADHD, late PD? no data yet)

SES and family environment may influence behavioural symptoms Allen et al. (2014); Shashi et al. (2010)

Longitudinal studies, moderators of ADHD risk

No studies

Frontal, cingulate and cerebellar grey matter volumes associated with attention/executive functions Shashi et al. (2010)

Association between MRI differences and ADHD symptoms/relevant cognitive impairments

FMRP, Fragile X protein; GM, grey matter; WM, white matter; fMRI, functional MRI; XLID, X-linked intellectual disability; PD, Parkinson disease; S, socioeconomic status.

PAK3 mutations

Increased accuracy on selective attention tasks compared to other causes of XLID (Baker et al., under review)

No studies

PARK2 CNVs

15q13.3 duplication

Delay in multiple neurocognitive trajectories, especially peripubertal “complex cognition” Executive control deficits, increasing with age Quintero et al. (2014) No studies

Case–control cognitive impairments

22q11.2 deletion syndrome

At-risk population

Table 2 (continued)




associated with attentional difficulties, their neural correlates and ultimately high ADHD risk across genetic disorders, whether studied in isolation or in a direct comparison across syndromes.

Cross-genotype cognitive comparisons – a focus on distinguishable attentional profiles. Attentional control skills are a set of related processes, that are nonetheless separable in their developmental trajectories, neural correlates, and overlap with executive processes (Amso & Scerif, in press; Scerif, 2010). Recent large-scale investigations pinpoint the diversity of cognitive mechanisms underpinning ADHD symptoms in idiopathic ADHD: some children and adolescent with ADHD demonstrate “classic” impulse control and inhibition difficulties, while others reveal a whole host of other cognitive characteristics, such as motivational difficulties, challenges in sustained attention or working memory, or, in a significant proportion of cases, their combination (e.g. Fair et al., 2012). Similarly, comparisons across genetic disorders with equivalently high ADHD risk point to distinguishable cognitive processes underpinning ADHD symptoms, with some similarities and some differences observed across genotypes. For example, as highlighted above, toddlers and young children with FXS or WS share high hyperactivity and inattention rates. They also share selective attention difficulties (Breckenridge, Braddick, Anker, Woodhouse, & Atkinson, 2013); Farzin, Rivera, & Whitney, 2011; Roberts, Hatton, Long, Anello, & Colombo, 2012; Scerif, Cornish, Wilding, Driver, & Karmiloff-Smith, 2004). Inhibitory control dysfunctions can present with concomitant memory impairments in both FXS (Hooper et al., 2008; Lanfranchi, Cornoldi, Drigo, & Vianello, 2009) and in WS (Rhodes, Riby, Fraser, & Campbell, 2011; Rhodes, Riby, Park, Fraser, & Campbell, 2010). However, in infants and toddlers with FXS, these difficulties are accompanied by inhibitory control deficits that extend across the visual and auditory modality (Scerif et al., 2004, 2005; Sullivan et al., 2007; Van der Molen et al., 2012). In contrast, in infants, toddlers and young children with WS, these difficulties are accompanied by difficulties in visual attention disengagement (Cornish, Scerif, & Karmiloff-Smith, 2007) and in visuo-spatial but not language-related response control (Breckenridge et al., 2013). These modality and domain differences set the two groups apart. Cross-genotype similarities and differences emerge for other groups associated with inattentive and hyperactive profiles. For example, recently, both shared and specific attentional difficulties have been established for girls with 22q11.2 deletion syndrome, FXS or Turner syndrome. All three groups demonstrate executive attention difficulties (Quintero, Beaton, Harvey, Ross, & Simon, 2014). Contrasting deficits include differences in visuo-spatial orienting, with girls with FXS being insensitive to visuo-spatial cues compared to the other groups. © 2014 Association for Child and Adolescent Mental Health.

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A clear challenge is to relate cross-syndrome differences in cognitive task performance to ADHD symptom risk. For some syndromes, with similar rates and types of ADHD, distinct cognitive impairments can be observed (for example contrasting visual attention deficits in FXS vs. WS, Cornish et al., 2007; Scerif et al., 2004; despite similar rates of inattention symptoms). In specific groups, individual differences in ADHD symptoms have been related to individual differences in attentional or executive impairments (e.g. Rhodes, Riby, Frazier et al., 2011; for WS; Scerif, Longhi, Cole, Karmiloff-Smith, & Cornish, 2012 for FXS). However, direct evidence comparing different cognitive processing parameters to superficially similar behavioural symptoms across genetic disorder has been limited. For other syndromes with superficially similar cognitive impairments (for example, executive functions deficits in trisomy 21 and in WS), contrasting behavioural symptoms can be observed (prominent hyperactivity vs prominent inattention). Such dissociations are crucial to inform both theory and intervention, and underline the importance of syndrome-specific treatment schedules. In general, comparisons across syndromes yield subtle impairments that a focus on a single genetic syndrome or population defined only by behavioural symptoms would not necessarily reveal.

Within-syndrome group cognitive variability and ADHD risk. Key data to address these issues are found in studies of variability in cognitive processing and ADHD symptoms within disorder groups. However, such studies are remarkably rare: the majority of studies focus on group-level averages, rather than variable outcomes. This is although even in a monogenic disorder such as FXS, the resulting cognitive and behavioural phenotype displays significant variability, as for instance demonstrated by studies of individual differences in saccadic eye-movement control and selective attention in young children with FXS (Scerif et al., 2004, 2005). For example, when assessed with a very simple infant-friendly modification of the antisaccade task, young children with FXS differed from typically developing control children: unlike neurotypical children, at the group level children with FXS did not decrease looking to a suddenly appearing peripheral cue, a marker of inhibitory difficulties and a possible endophenotype of ADHD later in life. Crucially, performance was highly variable and not predicted by developmental level for young children with FXS, unlike neurotypical controls (Scerif et al., 2005). Inhibitory control deficits and relationship with ADHD-like behavioural disturbance have been the focus of several studies of FMR1 knock-out mice (e.g Krueger et al. PNAS 2011), but not yet related to within-syndrome variability in the human patient population. In a large scale prospective longitudinal study, a cognitive marker of attentional variation for boys with FXS,

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their visual attention abilities at ages four to 10 years, predicted individual differences in ADHD symptoms when followed up 1 year later, even after controlling for individual differences in IQ (Scerif et al., 2012). This study indicated that prospective cognitive markers of ADHD risk can be identified early in FXS, and need to be tested in future in other disorders at high ADHD risk. For instance, in 22q11DS, ADHD symptoms are associated with executive function deficits in late childhood and adolescence, but the trajectory and variability in cognitive and behavioural disturbance within this syndrome is not fully understood (Hooper et al., 2013).

Developmental progression. From a cognitive neuroscience perspective, a key element of all genetically identified disorders reviewed here is that they are developmental in nature, and therefore affect neurocognitive development through the life span. This important conclusion is drawn both when examining neurocognitive correlates of inattention and hyperactivity at the level of individual disorders and when comparing trajectories of attention and cognitive control across disorders. For example, in the case of FXS subtle cognitive deficits are often evident in infancy, with parents becoming concerned about their children on average as early as 9-13 months of age, even if a full diagnosis of FXS is often not attained until as late as 32-35 months of age (Bailey, Skinner, Hatton, & Roberts, 2000). Although we know that significant delays in attentional control are present in older boys with FXS, this turns out not to be a case of developmental arrest. Instead, dynamic trajectories of delayed development have now been identified. For example, early profiles of attention and working memory impairment in FXS were studied both cross-sectionally and longitudinally (Cornish, Cole, Longhi, Karmiloff-Smith, & Scerif, 2013). When investigated cross-sectionally, an apparent plateau of difficulties emerged for boys with FXS, with no substantial improvement for older compared to younger children. In contrast, despite poorer overall performance, when followed longitudinally boys with FXS improved at the same rate as TD children, suggesting that cognitive attention and working memory, although delayed in FXS, improve over time (see also Cornish, Cole et al., 2012; for similar small but significant longitudinal improvements in global intellectual ability, whereas cross-sectional data suggests a decline). In turn, these findings suggest that a cross-sectional picture of a genetic disorder can be grossly flawed: ascertainment bias and differences in when a diagnosis of FXS was obtained for younger versus older cases could account for the discrepancy in this and other populations. From a cross-syndrome perspective, it is also becoming clear that genotype groups differ not only in their average profile of cognitive deficits that may link to behavioural inattention and hyperactivity,

but also in their progression over time. Using a now popular developmental trajectories approach designed to encompass atypical development for domains of cognition beyond attention and control (Thomas et al., 2009). Cornish et al. (2007) reported that developmental trajectories of attentional functions differed in infants, toddlers and older children with FXS, WS or Trisomy 21 with, for example, chronological age and developmental level predicting inhibitory control functions in Trisomy 21 but not in FXS, and the reverse held for sustained attention, suggesting that the developmental progression of distinct attentional difficulties also differentiates genotype groups. More recently, Quintero et al. (2014) found that age-related attention trajectories, rather than simple group differences, differentiated girls with 22q11.2DS, Turner and fragile X syndrome, with 22q11.2DS uniquely demonstrating cognitive decline alongside emergent symptoms in late childhood. As a whole, these studies therefore pinpoint the need to investigate similarities and differences in developmental trajectories to ADHD risk across distinct genotypes. To summarize, this section paints a complex picture of ADHD risk at the cognitive level, with converging and diverging endophenotypes across disorders. Many ADHD-relevant comparisons are marred by the use of different measures and techniques, and by limited explicit cross-syndrome studies (Scerif & Steele, 2011). Greater efforts to conduct multisite cross-syndrome prospective longitudinal studies going below the symptom domain are necessary, if we are to understand the strikingly variable patterns reported here across and within groups at high risk for ADHD. However, a number of promising conclusions already emerge. First, distinguishable cognitive underpinnings and developmental trajectories can be identified for seemingly similar behavioural symptoms. Second, early diagnosis opens the way to finding early prospective cognitive markers of later ADHD risk.

The neural systems level Can neuroimaging studies of rare genotypes associated with high ADHD risk shed light on the neural systems involved in distinct pathways to risk? In addition to cognitive performance variables, Table 2 highlights diverse structural and functional imaging differences identified within each genetic disorder where such studies have been carried out to date. Crucially, brain imaging findings in these groups need to be evaluated carefully because they could reveal differences related to or unrelated to ADHD risk. Indeed, only in limited cases have direct correlations been analysed between neuroanatomical variation, functional abnormalities and ADHD symptoms. In the following section, we summarize the current, though limited evidence, for divergent and convergent neural systems underlying ADHD risk in © 2014 Association for Child and Adolescent Mental Health.


the context of our target genetic syndromes. Intersections with literature on the correlation between neural systems abnormalities and cognitive endophenotypes implicated in idiopathic ADHD will also be alluded to.

Diverse neural correlates of ADHD risk. As outlined in the introduction, idiopathic ADHD is associated with structural, functional and electrophysiological abnormalities of a distributed lateralized corticostriatal network implicated in inhibitory control deficits (Batty et al., 2010; Durston et al., 2003; Groom et al., 2010), as well as with global neuroanatomical atypicalities, such as for example changes in cortical thickness across the cerebrum (Shaw et al., 2007). Fragile X syndrome, again, provides our initial test case for whether genetic disorders can highlight either known or novel neural correlates of ADHD risk. Abnormal structural development of the caudate nucleus (increased volume, regional topography, Peng et al., 2014) has been reported in a number of MRI studies of FXS, probably with greater consistency than observed in the idiopathic ADHD population. Recent neuroanatomical studies of caudate nucleus structure in FXS, as a marker of atypical frontostriatal circuitry, have begun to reveal specific associations between neuroanatomical differences and within-syndrome behavioural variation including hyperactivity and stereotypies (Peng et al., 2014). Specifically, Peng et al. (2014) identified positive correlations between dorsolateral and ventral caudate shape (structurally implicated in specific cortical-subcortical networks and functionally implicated in executive functions) and total scores on an abberant behaviour checklist, although further analysis did not highlight specificity of this relationship with hyperactivity scores. In WS, reduced frontal and temporal volumes, but increased caudate volumes correlate with inattention symptoms (Campbell et al., 2009). Of note, global differences in gyral morphology, cortical thickness, and white matter connectivity (Marenco et al., 2007; Reiss et al., 2004), and opposing volumetric differences in the basal ganglia (Meda et al., 2012) have been reported in WS compared to those reported for FXS, potentially indicating a different developmental origin to atypical frontostriatal network maturation, perhaps intersecting with some but not all individuals with idiopathic ADHD. Studies of the neural correlates of idiopathic ADHD have begun to implicate abnormalities in neural networks beyond the classical frontostriatal system (Castellanos & Proal, 2012; Cortese et al., 2012). Structural MRI studies of FXS and WS have also highlighted that other pathways may confer attentional control risk or provide compensatory routes that are not immediately obvious when studying idiopathic ADHD (e.g. Hoeft et al., 2007; see below). For example, shared neuroanatomical substrates for attentional control difficulties other © 2014 Association for Child and Adolescent Mental Health.

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than the frontostriatal circuitry -fronto-parietal dorsal-stream vulnerability- have been proposed for both FXS and WS, (Fung, Quintin, Haas, & Reiss, 2012; Marenco et al., 2007) which might depend on converging molecular pathways regulating postsynaptic actin cytoskeleton and dendritic morphology that are at risk in both of these disorders (Walter, Mazaika, & Reiss, 2009). Recent neuroimaging efforts in FXS have been devoted to investigation of large-scale networks differences, rather than localized abnormalities, compared to controls (Hall, Jiang, Reiss, & Greicius, 2013), so it will soon be possible to ask which of these differences relate to risk of ADHD symptoms or to ADHD cognitive endophenotypes. Beyond the examples of FXS and WS, other rare genotypes offer another key observation: disruption to structural and functional neural systems distinct from the fronto-striatal circuitry can be associated with ADHD symptoms. For example, Peters et al. (2011) investigated both global and specific white matter abnormalities in Angelman syndrome. Estimates of fronto-temporal connectivity and connections between frontal and visual cortices were associated with irritability and agitation scores. White matter abnormalities and frontoparietal dysfunction have also been emphasized in Monosomy X. In 22q11.2 deletion syndromes, longitudinal imaging studies increasingly highlight dynamic differences in cortical maturation and connectivity rather than focal, static abnormalities (see Table 2). These data suggest that there may be contrasting neuroanatomical pathways and neurodevelopmental mechanisms underpinning symptoms of inattention and hyperactivity in ID-associated syndromes and more broadly. Functional MRI abnormalities identified in FXS include frontostriatal hypo-activation, intersecting precisely with the functional abnormalities reported in idiopathic ADHD. For instance, adolescent boys with FXS showed reduced activation in the right ventrolateral prefrontal cortex and right caudate head during an inhibitory control task, compared to both neurotypical controls and IQ matched controls (Hoeft et al., 2007). Intriguingly, boys with FXS, but not controls, also showed a significant positive correlation between task performance and activation in the left ventrolateral prefrontal cortex, raising the possibility of a potential compensatory mechanism that may in future be investigated in individuals with idiopathic ADHD and variable inhibitory control difficulties. FXS is by no means the only genetic disorder with functional frontal and striatal abnormalities and high ADHD risk: in an inhibitory control task akin to that used by Hoeft et al. (2007), individuals with WS showed bilateral hypo-activation of the striatum, dorsolateral prefrontal, and dorsal anterior cingulate cortices compared to controls (Mobbs et al., 2007). Although a direct statistical comparison across the two studies has not been carried out, WS and FXS seem to be

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characterized by common frontostriatal circuit function vulnerabilities. Moreover, methylphenidate (MPH), a dopamine reuptake inhibitor that alleviates symptoms of the disorder in the majority of affected idiopathic ADHD cases (Volkow, Wang, Fowler, & Ding, 2005), is the most efficient pharmacological treatment for resolving ADHD-symptoms in both FXS (Roberts et al., 2011) and WS (Rhodes, Riby, Matthews et al., 2011).

Developmental progression. Much of the early neuroimaging research on genetic disorders was carried out on older children and adults, but recently imaging studies have instead investigated very young children to identify very early correlates of neurodevelopmental risk. For example, Hoeft et al. (2010) examined grey and white matter volumes over a 2-year period in 1- to 3-year-old boys with FXS. Multiple atypicalities in grey and white matter volumes changed dynamically in this sample. There were regions for which initial grey matter volume was similar to controls (orbital gyri, basal forebrain, and thalamus), but then increased in size in FXS. White matter volume of fronto-striatal regions was greater in FXS compared with controls from the first time-point, and differences increased over time. As a whole, these studies pinpoint how FXS differentially affects brain regions, and how structural (and perhaps functional) abnormalities of different brain regions in FXS develop differently over time. These studies emphasize time-dependent effects of genetic abnormalities, which have been explored in some disorders (e.g. 22q11.2DS) but not all, and not at such an early age. In part because of their focus on infancy and very early childhood, it is as yet unknown whether any of these early structural abnormalities predict later ADHD symptomatology. Again, relationship with symptom evolution has rarely been explored with regard to ADHD, but the longitudinal and prospective nature of recent neuroimaging studies on genetic disorders like FXS will open the way in this direction. This approach has already been fruitful in the context of understanding structural similarities, differences and their trajectories between very young children with FXS and idiopathic autism (Hazlett et al., 2009, 2012; Hoeft et al., 2011), for which an earlier diagnosis can be attained than for ADHD. In their ensemble, the literature to date suggests that known neural correlates of idiopathic ADHD also emerge as structural, functional and developmental abnormalities across a number of genetic disorders at high ADHD risk. However, contrasting neuroanatomical correlates also emerge, pinpointing diverse neural pathways to ADHD symptoms. Too few of the published studies have directly correlated neuroanatomical or functional parameters and ADHD severity, although this is an emerging approach for research on some genotypes. A larger number of studies on rare genotypes have investi-

gated diverse pathways leading to known neural endophenotypes of idiopathic ADHD. In the next few years, it is hoped that neuroimaging studies targeted to address these questions in newly-identified recurrent CNVs and rare Mendelian disorders associated with ADHD risk, and via additional imaging-acquisition and analysis methodologies, will be informative of novel pathways and mechanisms. The current findings converge with earlier conclusions on cognitive markers: when contrasting distinct rare genotypes, distinguishable attentional profiles and neuroanatomical abnormalities associate with ADHD risk.

Molecular neuroscience A focus on the specific molecular pathways involved in each disorder may clarify why inattention, hyperactivity and their neural correlates present sometimes so differently and sometimes so similarly across these groups. Table 3 summarizes identified molecular and cellular mechanisms for our target genetic disorders, and evidence from animal models of structural and functional neurodevelopmental impairments.

Cross-syndrome mechanistic comparison. A number of general observations emerge from comparing across rare genotypes at high risk for ADHD. Despite common behavioural risk, the genetic disorders under examination affect a broad range of mechanisms, ranging from neurochemical regulation, to neural proliferation, to dendritic or synaptic morphology and postsynaptic receptor dynamics. Across syndromes, neuropathology potentially relevant to ADHD risk seems to converge on core functional systems regulating glutamatergic transmission, modulation via ascending dopaminergic, serotonergic and cholinergic pathways, and in turn modification of synaptic plasticity (observed physiologically or via dendritic morphology). However, critically, contrasting molecular mechanisms can converge on these cellular processes, as for example regulation of protein translation (e.g. through FMR1), post-translational modification (e.g. through kinases), or degradation (e.g. UBE3A). With relevance to the expression of ADHD symptoms, these core functional abnormalities are disproportionally important for specific cell-types (inhibitory interneurons, nigrostriatal pathways), brain regions (striatum, hippocampus, prefrontal cortex) and networks (thalamocortical, frontostriatal) that have all been implicated in ADHD, as discussed above. Within-syndrome moderator genes. Variability in behavioural symptoms and neurocognitive correlates may depend on other genes interacting with the primary causative agent for each disorder. For example, within groups of men with FXS, aggressive behaviours seem moderated by variants © 2014 Association for Child and Adolescent Mental Health.

Causative gene(s)

DYRK1A identified as a critical dosage-sensitive gene within T21; mutations can cause ID with microcephaly and epilepsy Courcet et al. (2012)

Reduced dosage of genes in the pseudoautosomal region (PAR) which escape X-inactivation. Main candidate gene for ADHD risk = STS; Brookes et al. (2008); Kent et al. (2008)

FMR1 expansion (>200 CGG repeats) leads to transcriptional silencing of FMR1 production

Typical deletion involves 27 genes

At-risk population

Trisomy 21

Monosomy X

Fragile X syndrome

William syndrome

© 2014 Association for Child and Adolescent Mental Health. LIMK1: organization of the postsynaptic actin cytoskeleton

FMRP binds selectively to mRNA in the postsynaptic spaces of dendritic spines, repressing activity-dependent synaptic protein translation with downstream effects on mGluR regulation and plasticity Iliff et al. (2013)

RNA-binding protein – regulates intracellular RNA transport and translation of target mRNAs.

Candidate genes: LIMK1, a serine protein kinase, and GTF2I transcription factors Merla, Brunetti-Pierri, Micale, and Fusco (2010)

Impact of neurosteroid regulation on ADHD risk remains unknown

DYRK1A overexpression mimics cellular phenotype of T21 – reduced proliferation and premature neuronal differentiation Yabut, Domogauer, and D’Arcangelo (2010)

Kinase – posttranslational modification of many downstream targets

STS encodes steroid sulfatase – a regulator of steroid production with many CNS effects

Cellular functions

Molecular functions

Table 3 Molecular and neurobiological mechanisms – comparison across at-risk genetic disorders

Mouse model for typical deletion – small brain with immature neuronal and dendritic morphology Segura-Puimedon, Borralleras, Perez-Jurado, and Campuzano (2013) Limk1/ mice exhibit significant abnormalities in dendritic spine development and synaptic structures Meng et al. (2002)

Fmr1 knockout mice develop dense, immature dendritic spines Grossman, Aldridge, Weiler, & Greenough (2006). Fmr1-sensitive synaptic phenotypes are developmentally transient Meredith et al. (2012)

STS inhibition leads to increased hippocampal cholinergic and serotonin release, and modulation of GABAergic and glutamateric functions Trent and Davies (2012)

DYRK1A influences inhibitory neuron development and excitation/inhibition balance Souchet et al. (2014) DYRK1A regulates striatal dopamine system and influences dopaminergic neuron survival Barallobre et al. (2014)

Animal studies of ADHD-relevant neurobiology Mice overexpressing DRK1A show motoric and learning abnormalities including hyperactivity persistent into adulthood Altafaj et al. (2001). Intrastriatal injection of an RNA inhibitor of DRK1A reversed motoric abnormalities including hyperactivity Ortiz-Abalia et al. (2008) 39,XO mouse model shows specific deficits of discriminative response accuracy and reaction time, sensitive to attentional load Davies, Humby, Isles, Burgoyne, and Wilkinson (2007) mGluR-dependent LTD/LTP altered in fmr1 knockout mice Bear, Huber, and Warren (2004) mGluR antagonists can enhance cognitive and behavioural outcomes, as well as rescuing immature dendritic spine morphology Jacquemont et al. (2014) Many single gene mouse models but phenotypes relevant to inattention not assayed.

Animal studies of ADHD-relevant behaviour


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CHRNA7 is likely dosage sensitive gene based on deletion modelling

Deletions and duplications of PARK2 at 6q26

15q13.3 duplication

PARK2 CNVs

Cholinergic neuromodulation

Ubiquitination targets include synaptic proteins and regulators of neuronal survival Also implicated in mitochondrial biology

a7 subunit of neuronal nicotinic acetylcholine receptor/ion channel

E3 ubiquitin ligase – selective protein degradation.

Diverse including neurodevelopmental, synaptic and neurochemical regulation

Candidates include COMT, PRODH, DGCR8 and several regulatory miRNAs

>50 genes No critical region and no correlations between deletion size and phenotypes

22q11.2 deletion syndrome

One key substrate may be ARC (Activity-Regulated Cytoskeleton-associated protein) Greer et al. (2010)

E3 ubiquitin-protein ligase – selective protein degradation

UBE3A (imprinted locus)

Angelman syndrome

Cellular functions

Molecular functions

Causative gene(s)

At-risk population

Table 3 (continued)

UBE3A is required for activity-dependent maturation of excitatory cortical circuits Yashiro et al. (2009). Ube3am/p+ mice showed reduced number of dopaminergic neurons in the substantia nigra Mulherkar and Jana (2010). Increased mesolimbic DA release Riday et al. (2012) Decreased density of dendritic spines and glutamatergic synapses Mukai et al. (2008) Structural and functional abnormalities of cortical neurogenesis, plasticity and network development Meechan, Tucker, Maynard, and LaMantia (2012) Fenelon et al. (2013). Age-dependent impact on synaptic plasticity Earls et al. (2012) Receptor activation modulates dopamine release in prefrontal cortex and influences NMDA-dependent functions Livingstone et al. (2009) May increase dopamine uptake by enhancing the degradation of misfolded dopamine transporter Jiang, Ren, Zhao, and Feng (2004) excess dopamine might lead to compensatory dendritic sprouting Arkadir, Bergman, and Fahn (2014)

Animal studies of ADHD-relevant neurobiology

Inactivation of the parkin gene in mice results in motor and cognitive deficits, inhibition of amphetamine-induced dopamine release, and inhibition of glutamate neurotransmission Itier et al. (2003); Zhu et al. (2007) In combination with other mutations, may lead to enhanced behavioural performance Hennis, Marvin, Taylor, and Goldberg (2014)

Strongly implicated in regulation of attention circuits and working memory Yang et al. (2013)

Multiple deletion and overexpression mouse models, which partially recapitulate aspects of the phenotype Hiroi et al. (2013)

Deficits in striatal-dependent behavioural paradigms including instrumental conditioning and reward processing Hayrapetyan et al. (2014); Riday et al. (2012) amplified mGlu5-dependent long term depression at hippocampal synapses Pignatelli et al. (2014)

Animal studies of ADHD-relevant behaviour



Learning and memory deficits but ADHD-relevant assays not applied Meng, Meng, Hanna, Janus, and Jia (2005) Behavioural analysis of other MAGUK models highlights increased spontaneous activity levels and novelty-driven hyperactivity Nithianantharajah et al. (2013) PAK3/PAK1 double knockout mice have reduced brain size with simplified dendritic arbors/axons, reduced synapse density and severely impaired synaptic plasticity Huang et al. (2011) Loss-of-function mutations in the X-linked gene PAK3 PAK3 mutations

Membrane-associated guanylate kinase (MAGUK)

Regulates postsynaptic actin polymerization via RhoGTPase signalling, influencing dendritic spine stabilization and receptor localisation Ba, van der Raadt, and Kasri (2013) Role in activity-dependent growth and stabilization of new dendritic spines mediated via AMPA receptors Boda et al. (2004); Dubos et al. (2012)

Animal studies of ADHD-relevant behaviour Causative gene(s) At-risk population

Table 3 (continued)

Molecular functions

Cellular functions

Animal studies of ADHD-relevant neurobiology



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of serotonergic-related genes (Hessl et al., 2008). Moderating factors have been reported for other disorders: it has long been debated whether polymorphisms of the COMT gene result in poorer neurocognitive outcomes in children with 22q11.2DS, and this debate has not been concluded with regard to ADHD risk (Gothelf et al., 2005; Simon et al., 2005). Identification of moderating risk factors becomes even more essential when considering newly-identified risk-associated CNVs such as 15q13.3 duplication – whilst statistically associated with risk and potentially informative of biological mechanisms, these anomalies can be identified in individuals without ADHD or any other neurodevelopmental disorder, either within the general population or in first degree relatives of individuals with ADHD plus the specific genetic variant. Moreover, another recently identified risk factor, PARK2 deletion or duplication, has previously been associated with an entirely different disorder, early-onset Parkinson Disease. Whilst these findings raise exciting new opportunities to define neurobiological mechanisms at the level of cell biology, neurochemical regulation and neural control of motor activity/cognition, explaining reduced penetrance and variable expression is an increasing challenge.

Limitations – a poor understanding of developmental dynamics. There are limited current parallel investigations in animal models and humans, especially developmental studies. And yet these temporal dynamics are critical to understanding functional consequences. For example, while atypical synaptic function and anatomy are characteristic of most animal models of FXS, these synaptic phenotypes are in fact transient and appear developmentally only within short time windows. This means that these effects depend on the temporal expression of fmr1 (Meredith, Dawitz, & Kramvis, 2012). The wide variation in phenotypic outcome in this population may depend on a combination of individual temporal expression dynamics together with differing timing of environmental influences (Glaser et al., 2003). Of note, evidence of variability also highlights that, despite the high risk, these diverse genetic disorders are not associated with certainty of impairment. For example, a number of children with FXS function rather well, despite carrying the full mutation. These good outcomes are not fully accounted for by mosaicism or X inactivation. What protects these children from risk? Positive environmental influences, such as a rich home environment or well-co-ordinated intervention may act as protective factors. Indeed, there is evidence that a rich home environment, but not overall FMRP level, predicts overall IQ and adaptive behaviour in boys with FXS (Dyer-Friedman et al., 2002; Glaser et al., 2003). Young children with a diagnosis of FXS therefore point to multiple

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early predictors of risk and resilience, and early predictors of declining or improving neurocognitive trajectories. The crucial need to understand mechanisms of within-syndrome variability has also been highlighted in other genetic disorders (Simon et al., 2005). As a whole, the finding that some children with a given primary compromized gene pathway exhibit hyperactivity and/or inattention, while others with the same aetiological differences do not, strongly pinpoints moderating factors at the genetic, environmental, and epigenetic levels. The need to understand these within-syndrome differences has become increasingly apparent in the context of clinical trials of targeted pharmacological agents: in the case of FXS, emerging treatments (e.g. mGluR antagonists and GABAergic agonists) have thus far only been successful for particular subgroups of the FXS population, pointing to the need for within-syndrome stratification (see Jacquemont et al., 2014 for a recent review).

Conclusions: Towards a developmental cognitive neuroscience of ADHD risk We have surveyed, nonexhaustively, the origins and characteristics of ADHD associated with genetic variants demonstrating high-risk for behavioural psychopathology. At each level of analysis, we have uncovered genotype-specific features, and convergences toward numerous, discrete pathways resulting in common behavioural outcomes. Our view is that this is a burgeoning field of research opportunities – we are at the outset of exploring a complex landscape with the potential for many new, clinically useful discoveries. An increasingly large number of genetic disorders are diagnosed, many early in childhood, as well as peri- or prenatally. Increasing rates of genetic diagnosis raises a crucial clinical imperative: providing much needed information from early diagnosis. However, work in this area also has, we believe, huge neuroscientific potential: on the one hand, tracing developmental trajectories to a childhood diagnosis of ADHD, well before this would be normally possible in the typical population; on the other, identifying protective factors which provide resilience in the context of a high risk factor. A number of key observations emerge. First, for many genetic disorders, the focus of investigation has been to define neurocognitive features of the disorder in totality, rather than in relation of specific behavioural psychopathology per se, such as for example specific ADHD symptoms. Given the many uncertainties regarding basic epidemiology of clinical risk across disorders and across ages, there have been few attempts to investigate variation in within-syndrome outcomes, with the potential for identification of moderating risk and resilience factors. Developmental approaches to symptom

expression, either short term or long term, have been lacking – cross-sectional trajectories ultimately may not recapitulate longitudinal change, and change needs to be examined empirically (Cornish et al., 2013). Second, it is crucial for researchers to recall that early genetic modifications – whether CNVs, sequence variants or epigenetic dysregulations – are likely to affect neurocognitive functioning from the outset of development and to have widespread cascading effects and complex interactions over time on the resulting phenotype. Research on infants has highlighted the importance of investigating empirically the very early developmental profile across genetic disorders during infancy, rather than assuming a priori that the cognitive profile in adults is representative of earlier deficits and abilities (Karmiloff-Smith, 1998, 2013; Paterson, Brown, Gsodl, Johnson, & Karmiloff-Smith, 1999; Scerif & Karmiloff-Smith, 2005). Thus, while genetic disorders can provide unique insights into how relatively well understood genetic modifications, molecular pathways and systems neuroscience influence cognition, these complex interactions cannot be fully understood outside their developmental context. There are of course additional remaining limitations. Our discussion of shared or distinct clinical presentations and neurocognitive correlates is hampered by inadequately-powered datasets, inconsistent and biased ascertainment (for example exclusion of low IQ individuals) and suboptimal assessment methods (for example, developmentally inappropriate screening questionnaires and insensitive neuropsychological tests). Critical in this endeavour is the development of assessment methods that do not exclude the most severely impaired individuals in each group, and allow researchers and clinicians to follow the progression of symptoms from genetic diagnosis onwards. Such methods must include both detailed behavioural and cognitive assessment for all ages and levels of ability, and the development of noninvasive systems cognitive neuroscience techniques (EEG, MEG, eye-tracking technologies). These are necessary if we are to bridge knowledge about genetic and molecular pathways on the one hand, and risk for/protection from poor neurocognitive/behavioural outcomes on the other. Moreover, the utility of animal models is limited by assays that are not informed by or informative of human developmental cognitive mechanisms. These multiple limitations and caveats are gradually being overcome, for some disorders at least, and there are increasing opportunities for truly innovative research with clinical applicability. Rates of genetic diagnosis for individuals with neurodevelopmental disorder are increasing exponentially, and international consortia are facilitating the collation of large, consistently phenotyped and longitudinally investigated cohorts (Schneider et al., 2014). Such © 2014 Association for Child and Adolescent Mental Health.


efforts become even more important for the increasing number of ultrarare but recurrent genomic anomalies found to be associated with neurodevelopmental disorders (e.g. from our review these include PARK2 CNVs and mutations in MAGUK network genes). Strides are being made for autism-associated loci, which may be informative of ADHD risk mechanisms for some coassociated loci. It will soon be possible to systematically explore complex genomic, environmental and developmental interactions that mediate variable outcomes within and between specific genetic and nongenetic aetiologies. For Fragile X Syndrome, the prospect of aetiology-specific, biologically informed therapy for ASD or ADHD symptoms has been a major catalyst for international collaboration (Berry-Kravis et al., 2013). The psychiatric research community increasingly recognizes the necessity of exploring heterogeneity of pathways toward psychopathology, at multiple-levels of analysis, including from a genotype first perspective (Insel, 2014). These principles require a progressive approach amongst researchers to engage in collaborative, highly interdisciplinary work, and infrastructure and funding capable of supporting prospective, longitudinal, multimodal

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research with rare populations for the long-term benefit of all.

Acknowledgements We thank participants contributing to our studies for the clinical motivations they provide and our collaborators for their critical intellectual input. K.B. is funded by a National Institute for Health Research Academic Clinical Lectureship. G.S. is funded by a James S. McDonnell Foundation Understanding Human Cognition Scholar Award; she has received travel costs and an honorarium for a consultancy engagement for Novartis Pharma UK within the last 36 months. This review article was invited by the journal, for which the first author has been offered a small honorarium towards expenses; the article has undergone full, external peer review. The authors have declared that they have no competing or potential conflicts of interest in relation to this article.

Correspondence Gaia Scerif, Attention, Brain and Cognitive Development Group, Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK; Email: [email protected]

Key points

• • • • •

Genetic diagnoses are increasingly available for children with neurodevelopmental disorders and high risk of ADHD. Rare genotypes reveal diverse multi-level causal mechanisms converging on ADHD risk. Variable outcomes for each given genetic aetiology points to additional identifiable risk and protective factors. Developmental progression of symptoms and neurocognitive phenotypes varies across genetic disorders at risk. Disorder-specific developmental risk mechanisms need to be identified to improve prognosis and better tailor intervention.

References Ahuja, A., Martin, J., Langley, K., & Thapar, A. (2013). Intellectual disability in children with attention deficit hyperactivity disorder. Journal of Pediatrics, 163, 890. Allen, T.M., Hersh, J., Schoch, K., Curtiss, K., Hooper, S.R., & Shashi, V. (2014). Association of the family environment with behavioural and cognitive outcomes in children with chromosome 22q11.2 deletion syndrome. Journal of Intellectual Disability Research, 58, 31–47. Altafaj, X., Dierssen, M., Baamonde, C., Marti, E., Visa, J., Guimera, J., . . . & Estivill, X. (2001). Neurodevelopmental delay, motor abnormalities and cognitive deficits in transgenic mice overexpressing Dyrk1A (minibrain), a murine model of Down’s syndrome. Human Molecular Genetics, 10, 1915–1923. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th edn). Washington, DC: American Psychiatric Association. Amso, D., & Scerif, G. (in press). The attentive brain: Unique insights from developmental science. (Submitted). Antshel, K.M., Hendricks, K., Shprintzen, R., Fremont, W., Higgins, A.M., Faraone, S.V., & Kates, W.R. (2013). The longitudinal course of attention deficit/hyperactivity disorder in velo-cardio-facial syndrome. Journal of Pediatrics, 163, 187-U594. © 2014 Association for Child and Adolescent Mental Health.

Antshel, K.M., Phillips, M.H., Gordon, M., Barkley, R., & Faraone, S.V. (2006). Is ADHD a valid disorder in children with intellectual delays? Clinical Psychology Review, 26, 555–572. Arkadir, D., Bergman, H., & Fahn, S. (2014). Redundant dopaminergic activity may enable compensatory axonal sprouting in Parkinson disease. Neurology, 82, 1093–1098. Ba, W., van der Raadt, J., & Kasri, N.N. (2013). Rho GTPase signaling at the synapse: Implications for intellectual disability. Experimental Cell Research, 319, 2368–2374. Bailey, D.B., Skinner, D., Hatton, D., & Roberts, J. (2000). Family experiences and factors associated with the diagnosis of fragile X syndrome. Journal of Developmental and Behavioral Pediatrics, 21, 315–321. Barallobre, M.J., Perier, C., Bove, J., Laguna, A., Delabar, J.M., Vila, M., & Arbones, M.L. (2014). DYRK1A promotes dopaminergic neuron survival in the developing brain and in a mouse model of Parkinson’s disease. Cell Death & Disease, 5. Batty, M.J., Liddle, E.B., Pitiot, A., Toro, R., Groom, M.J., Scerif, G., . . . & Hollis, C. (2010). Cortical gray matter in attention-deficit/hyperactivity disorder: A structural magnetic resonance imaging study. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 229–238.

18


Bear, M.F., Huber, K.M., & Warren, S.T. (2004). The mGIuR theory of fragile X mental retardation. Trends in Neurosciences, 27, 370–377. Berry, R.J., Leitner, R.P., Clarke, A.R., & Einfeld, S.L. (2005). Behavioral aspects of Angelman syndrome: A case control study. American Journal of Medical Genetics Part A, 132A, 8– 12. Berry-Kravis, E., Hessl, D., Abbeduto, L., Reiss, A.L., Beckel-Mitchener, A., Urv, T.K., & Outcome Measures Working, G. (2013). Outcome measures for clinical trials in fragile X syndrome. Journal of Developmental and Behavioral Pediatrics, 34, 508–522. Bird, L.M. (2014). Angelman syndrome: Review of clinical and molecular aspects. The application of clinical genetics, 7, 93– 104. Bishop, D.V.M., Jacobs, P.A., Lachlan, K., Wellesley, D., Barnicoat, A., Boyd, P.A., . . . & Scerif, G. (2011). Autism, language and communication in children with sex chromosome trisomies. Archives of Disease in Childhood, 96, 954–959. Bishop, D.V.M., & Scerif, G. (2011). Klinefelter syndrome as a window on the aetiology of language and communication impairments in children: The neuroligin-neurexin hypothesis. Acta Paediatrica, 100, 903–907. Boda, B., Alberi, S., Nikonenko, I., Node-Langlois, R., Jourdain, P., Moosmayer, M., . . . & Muller, D. (2004). The mental retardation protein PAK3 contributes to synapse formation and plasticity in hippocampus. Journal of Neuroscience, 24, 10816–10825. van Bon, B.W.M., Mefford, H.C., Menten, B., Koolen, D.A., Sharp, A.J., Nillesen, W.M., . . . & de Vries, B.B.A. (2009). Further delineation of the 15q13 microdeletion and duplication syndromes: A clinical spectrum varying from non-pathogenic to a severe outcome. Journal of Medical Genetics, 46, 511–523. Borella, E., Carretti, B., & Lanfranchi, S. (2013). Inhibitory mechanisms in down syndrome: Is there a specific or general deficit? Research in Developmental Disabilities, 34, 65–71. Bray, S., Hoeft, F., Hong, D.S., & Reiss, A.L. (2013). Aberrant functional network recruitment of posterior parietal cortex in turner syndrome. Human Brain Mapping, 34, 3117–3128. Breckenridge, K., Braddick, O., Anker, S., Woodhouse, M., & Atkinson, J. (2013). Attention in Williams syndrome and down’s syndrome: Performance on the new early childhood attention battery. British Journal of Developmental Psychology, 31, 257–269. Brookes, K.J., Hawi, Z., Kirley, A., Barry, E., Gill, M., & Kent, L. (2008). Association of the steroid sulfatase (STS) gene with attention deficit hyperactivity disorder. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 147B, 1531–1535. Campbell, L.E., Daly, E., Toal, F., Stevens, A., Azuma, R., Karmiloff-Smith, A., . . . & Murphy, K.C. (2009). Brain structural differences associated with the behavioural phenotype in children with Williams syndrome. Brain Research, 1258, 96–107. Castellanos, F.X., & Proal, E. (2012). Large-scale brain systems in ADHD: Beyond the prefrontal-striatal model. Trends in Cognitive Sciences, 16, 17–26. Castellanos, F.X., Sonuga-Barke, E.J.S., Milham, M.P., & Tannock, R. (2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10, 117–123. Clayton-Smith, J. (2001). Angelman syndrome: Evolution of the phenotype in adolescents and adults. Developmental Medicine and Child Neurology, 43, 476–480. Cornish, K., Cole, V., Longhi, E., Karmiloff-Smith, A., & Scerif, G. (2012). Does attention constrain developmental trajectories in fragile X syndrome? A 3-year prospective longitudinal study Ajidd-American Journal on Intellectual and Developmental Disabilities, 117, 103.

Cornish, K., Cole, V., Longhi, E., Karmiloff-Smith, A., & Scerif, G. (2013). Mapping developmental trajectories of attention and working memory in fragile X syndrome: Developmental freeze or developmental change? Development and Psychopathology, 25, 365–376. Cornish, K., Scerif, G., & Karmiloff-Smith, A. (2007). Tracing syndrome-specific trajectories of attention across the lifespan. Cortex, 43, 672–685. Cornish, K., Steele, A., Rondinelli Cobra Monteiro, C., Karmiloff-Smith, A., & Scerif, G. (2012). Attention deficits predict phenotypic outcomes in syndrome-specific and domain-specific ways. Frontiers in Psychology, 3. Cortese, S., Kelly, C., Chabernaud, C., Proal, E., Di Martino, A., Milham, M.P., & Castellanos, F.X. (2012). Toward systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies. American Journal of Psychiatry, 169, 1038– 1055. Courcet, J.-B., Faivre, L., Malzac, P., Masurel-Paulet, A., Lopez, E., Callier, P., . . . & Thauvin-Robinet, C. (2012). The DYRK1A gene is a cause of syndromic intellectual disability with severe microcephaly and epilepsy. Journal of Medical Genetics, 49, 731–736. Davies, W., Humby, T., Isles, A.R., Burgoyne, P.S., & Wilkinson, L.S. (2007). X-monosomy effects on visuospatial attention in mice: A candidate gene and implications for turner syndrome and attention deficit hyperactivity disorder. Biological Psychiatry, 61, 1351–1360. Dubos, A., Combeau, G., Bernardinelli, Y., Barnier, J.-V., Hartley, O., Gaertner, H., . . . & Muller, D. (2012). Alteration of synaptic network dynamics by the intellectual disability protein PAK3. Journal of Neuroscience, 32, 519–527. Durston, S., Tottenham, N.T., Thomas, K.M., Davidson, M.C., Eigsti, I.M., Yang, Y.H., . . . & Casey, B.J. (2003). Differential patterns of striatal activation in young children with and without ADHD. Biological Psychiatry, 53, 871–878. Dyer-Friedman, J., Glaser, B., Hessl, D., Johnston, C., Huffman, L.C., Taylor, A., . . . & Reiss, A.L. (2002). Genetic and environmental influences on the cognitive outcomes of children with fragile X syndrome. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 237–244. Earls, L.R., Fricke, R.G., Yu, J., Berry, R.B., Baldwin, L.T., & Zakharenko, S.S. (2012). Age-dependent microRNA control of synaptic plasticity in 22q11 deletion syndrome and schizophrenia. Journal of Neuroscience, 32, 14132–14144. Edgin, J.O., Pennington, B.F., & Mervis, C.B. (2010). Neuropsychological components of intellectual disability: The contributions of immediate, working, and associative memory. Journal of Intellectual Disability Research, 54, 406– 417. Einfeld, S.L., & Tonge, B.J. (1996). Population prevalence of psychopathology in children and adolescents with intellectual disability.2. Epidemiological findings. Journal of Intellectual Disability Research, 40, 99–109. Ekstein, S., Glick, B., Weill, M., Kay, B., & Berger, I. (2011). Down syndrome and attention-deficit/hyperactivity disorder (ADHD). Journal of Child Neurology, 26, 1290–1295. Elia, J., Glessner, J.T., Wang, K., Takahashi, N., Shtir, C.J., Hadley, D., . . . & Hakonarson, H. (2012). Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder. Nature Genetics, 44, 78-U113. Elison, S., Stinton, C., & Howlin, P. (2010). Health and social outcomes in adults with Williams syndrome: Findings from cross-sectional and longitudinal cohorts. Research in Developmental Disabilities, 31, 587–599. Emerson, E., & Hatton, C. (2007). Mental health of children and adolescents with intellectual disabilities in Britain. British Journal of Psychiatry, 191, 493–499. Fair, D.A., Bathula, D., Nikolas, M.A., & Nigg, J.T. (2012). Distinct neuropsychological subgroups in typically


Rare genotypes and developmental mechanisms developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences of the United States of America, 109, 6769–6774. Farzin, F., Rivera, S.M., & Whitney, D. (2011). Resolution of spatial and temporal visual attention in infants with fragile X syndrome. Brain, 134, 3355–3368. Fenelon, K., Xu, B., Lai, C.S., Mukai, J., Markx, S., Stark, K.L., . . . & Gogos, J.A. (2013). The pattern of cortical dysfunction in a mouse model of a schizophrenia-related microdeletion. Journal of Neuroscience, 33, 14825–14839. Ford, T., Goodman, R., & Meltzer, H. (2003). The British child and adolescent mental health survey 1999: The prevalence of DSM-IV disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1203–1211. Fung, L.K., Quintin, E.-M., Haas, B.W., & Reiss, A.L. (2012). Conceptualizing neurodevelopmental disorders through a mechanistic understanding of fragile X syndrome and Williams syndrome. Current Opinion in Neurology, 25, 112– 124. Gath, A., & Gumley, D. (1986). Behavior problems in retarded-children with special reference to downssyndrome. British Journal of Psychiatry, 149, 156–161. Gizer, I.R., Ficks, C., & Waldman, I.D. (2009). Candidate gene studies of ADHD: A meta-analytic review. Human Genetics, 126, 51–90. Glaser, B., Hessl, D., Dyer-Friedman, J., Johnston, C., Wisbeck, J., Taylor, A., & Reiss, A. (2003). Biological and environmental contributions to adaptive behavior in fragile X syndrome. American Journal of Medical Genetics Part A, 117A, 21–29. Gothelf, D., Eliez, S., Thompson, T., Hinard, C., Penniman, L., Feinstein, C., . . . & Reiss, A.L. (2005). COMT genotype predicts longitudinal cognitive decline and psychosis in 22q11.2 deletion syndrome. Nature Neuroscience, 8, 1500–1502. Gothelf, D., Furfaro, J.A., Hoeft, F., Eckert, M.A., Hall, S.S., O’Hara, R., . . . & Reiss, A.L. (2008). Neuroanatomy of fragile X syndrome is associated with aberrant behavior and the fragile X mental retardation protein (FMRP). Annals of Neurology, 63, 40–51. Green, T., Weinberger, R., Diamond, A., Berant, M., Hirschfeld, L., Frisch, A., . . . & Gothelf, D. (2011). The effect of methylphenidate on prefrontal cognitive functioning, inattention, and hyperactivity in velocardiofacial syndrome. Journal of Child and Adolescent Psychopharmacology, 21, 589–595. Greer, P.L., Hanayama, R., Bloodgood, B.L., Mardinly, A.R., Lipton, D.M., Flavell, S.W., . . . & Greenberg, M.E. (2010). The Angelman syndrome protein Ube3A regulates synapse development by ubiquitinating arc. Cell, 140, 704–716. Groom, M.J., Scerif, G., Liddle, P.F., Batty, M.J., Liddle, E.B., Roberts, K.L., . . . & Hollis, C. (2010). Effects of motivation and medication on electrophysiological markers of response inhibition in children with attention-deficit/hyperactivity disorder. Biological Psychiatry, 67, 624–631. Grossman, A.W., Aldridge, G.M., Weiler, I.J., & Greenough, W.T. (2006). Local protein synthesis and spine morphogenesis: Fragile X syndrome and beyond. Journal of Neuroscience, 26, 7151–7155. Hall, S.S., Jiang, H., Reiss, A.L., & Greicius, M.D. (2013). Identifying large-scale brain networks in fragile X syndrome. JAMA Psychiatry, 70, 1215–1223. Hall, S.S., Lightbody, A.A., Hirt, M., Rezvani, A., & Reiss, A.L. (2010). Autism in fragile X syndrome: A category mistake? Journal of the American Academy of Child and Adolescent Psychiatry, 49, 921–933. Hayrapetyan, V., Castro, S., Sukharnikova, T., Yu, C., Cao, X., Jiang, Y.-H., & Yin, H.H. (2014). Region-specific impairments in striatal synaptic transmission and impaired instrumental learning in a mouse model of Angelman syndrome. European Journal of Neuroscience, 39, 1018–1025.


19

Hazlett, H.C., Poe, M.D., Lightbody, A.A., Gerig, G., MacFall, J.R., Ross, A.K., . . . & Piven, J. (2009). Teasing apart the heterogeneity of autism: Same behavior, different brains in toddlers with fragile X syndrome and autism. Journal of Neurodevelopmental Disorders, 1, 81–90. Hazlett, H.C., Poe, M.D., Lightbody, A.A., Styner, M., MacFall, J.R., Reiss, A.L., & Piven, J. (2012). Trajectories of early brain volume development in fragile X syndrome and autism. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 921–933. Hennis, M.R., Marvin, M.A., Taylor, C.M., II, & Goldberg, M.S. (2014). Surprising behavioral and neurochemical enhancements in mice with combined mutations linked to Parkinson’s disease. Neurobiology of disease, 62, 113–123. Hessl, D., Tassone, F., Cordeiro, L., Koldewyn, K., McCormick, C., Green, C., . . . & Hagerman, R.J. (2008). Brief report: Aggression and stereotypic behavior in males with fragile X syndrome - Moderating secondary genes in a “Single Gene” disorder. Journal of Autism and Developmental Disorders, 38, 184–189. Hiroi, N., Takahashi, T., Hishimoto, A., Izumi, T., Boku, S., & Hiramoto, T. (2013). Copy number variation at 22q11.2: From rare variants to common mechanisms of developmental neuropsychiatric disorders. Molecular Psychiatry, 18, 1153–1165. Hoeft, F., Carter, J.C., Lightbody, A.A., Hazlett, H.C., Piven, J., & Reiss, A.L. (2010). Region-specific alterations in brain development in one- to three-year-old boys with fragile X syndrome. Proceedings of the National Academy of Sciences of the United States of America, 107, 9335–9339. Hoeft, F., Hernandez, A., Parthasarathy, S., Watson, C.L., Hall, S.S., & Reiss, A.L. (2007). Fronto-striatal dysfunction and potential compensatory mechanisms in male adolescents with fragile X syndrome. Human Brain Mapping, 28, 543– 554. Hoeft, F., Walter, E., Lightbody, A.A., Hazlett, H.C., Chang, C., Piven, J., & Reiss, A.L. (2011). Neuroanatomical differences in Toddler boys with fragile X syndrome and idiopathic autism. Archives of General Psychiatry, 68, 295–305. Holzapfel, M., Barnea-Goraly, N., Eckert, M.A., Kesler, S.R., & Reiss, A.L. (2006). Selective alterations of white matter associated with visuospatial and sensorimotor dysfunction in Turner syndrome. Journal of Neuroscience, 26, 7007–7013. Hong, D.S., Hoeft, F., Marzelli, M.J., Lepage, J.-F., Roeltgen, D., Ross, J., & Reiss, A.L. (2014). Influence of the X-chromosome on neuroanatomy: Evidence from Turner and Klinefelter syndromes. Journal of Neuroscience, 34, 3509–3516. Hong, D.S., & Reiss, A.L. (2014). Cognitive and neurological aspects of sex chromosome aneuploidies. Lancet Neurology, 13, 306–318. Hooper, S.R., Curtiss, K., Schoch, K., Keshavan, M.S., Allen, A., & Shashi, V. (2013). A longitudinal examination of the psychoeducational, neurocognitive, and psychiatric functioning in children with 22q11.2 deletion syndrome. Research in Developmental Disabilities, 34, 1758–1769. Hooper, S.R., Hatton, D., Sideris, J., Sullivan, K., Hammer, J., Schaaf, J., . . . & Bailey, D.B., Jr (2008). Executive functions in young males fragile X syndrome in comparison to mental age-matched with controls: Baseline findings from a longitudinal study. Neuropsychology, 22, 36–47. Huang, W., Zhou, Z., Asrar, S., Henkelman, M., Xie, W., & Jia, Z. (2011). p21-activated kinases 1 and 3 control brain size through coordinating neuronal complexity and synaptic properties. Molecular and Cellular Biology, 31, 388–403. Iliff, A.J., Renoux, A.J., Krans, A., Usdin, K., Sutton, M.A., & Todd, P.K. (2013). Impaired activity-dependent FMRP translation and enhanced mGluR-dependent LTD in Fragile X premutation mice. Human Molecular Genetics, 22, 1180–1192.

20


Insel, T. (2014). The NIMH Research Domain Criteria (RDoC) Project: Precision medicine for psychiatry. American Journal of Psychiatry. Epub. Itier, J.M., Ibanez, P., Mena, M.A., Abbas, N., Cohen-Salmon, C., Bohme, G.A., . . . & de Yebenes, J.G. (2003). Parkin gene inactivation alters behaviour and dopamine neurotransmission in the mouse. Human Molecular Genetics, 12, 2277–2291. Itsara, A., Cooper, G.M., Baker, C., Girirajan, S., Li, J., Absher, D., . . . & Eichler, E.E. (2009). Population analysis of large copy number variants and hotspots of human genetic disease. American Journal of Human Genetics, 84, 148–161. Jacquemont, S., Berry-Kravis, E., Hagerman, R., von Raison, F., Gasparini, F., Apostol, G., . . . & Gomez-Mancilla, B. (2014). The challenges of clinical trials in fragile X syndrome. Psychopharmacology (Berl), 231, 1237–1250. Jarick, I., Volckmar, A.L., Puetter, C., Pechlivanis, S., Nguyen, T.T., Dauvermann, M.R., . . . & Hinney, A. (2014). Genome-wide analysis of rare copy number variations reveals PARK2 as a candidate gene for attention-deficit/ hyperactivity disorder. Molecular Psychiatry, 19, 115–121. Jensen, P.S., Hinshaw, S.P., Kraemer, H.C., Lenora, N., Newcorn, J.H., Abikoff, H.B., . . . & Vitiello, B. (2001). ADHD comorbidity findings from the MTA study: Comparing comorbid subgroups. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 147–158. Jiang, H.B., Ren, Y., Zhao, J.H., & Feng, J. (2004). Parkin protects human dopaminergic neuroblastoma cells against dopamine-induced apoptosis. Human Molecular Genetics, 13, 1745–1754. Karmiloff-Smith, A. (1998). Development itself is the key to understanding developmental disorders. Trends in Cognitive Sciences, 2, 389–398. Karmiloff-Smith, A. (2013). Challenging the use of adult neuropsychological models for explaining neurodevelopmental disorders: Developed versus developing brains. Quarterly Journal of Experimental Psychology, 66, 1–14. Kent, L., Emerton, J., Bhadravathi, V., Weisblatt, E., Pasco, G., Willatt, L.R., . . . & Yates, J.R.W. (2008). X-linked ichthyosis (steroid sulfatase deficiency) is associated with increased risk of attention deficit hyperactivity disorder, autism and social communication deficits. Journal of Medical Genetics, 45, 519–524. Kilarski, L.L., Pearson, J.P., Newsway, V., Majounie, E., Knipe, M.D.W., Misbahuddin, A., . . . & Morris, H.R. (2012). Systematic review and UK-based study of PARK2 (parkin), PINK1, PARK7 (DJ-1) and LRRK2 in early-onset Parkinson’s disease. Movement Disorders, 27, 1522–1529. Lahey, B.B., Pelham, W.E., Loney, J., Lee, S.S., & Willcutt, E. (2005). Instability of the DSM-IV subtypes of ADHD from preschool through elementary school. Archives of General Psychiatry, 62, 896–902. Lanfranchi, S., Cornoldi, C., Drigo, S., & Vianello, R. (2009). Working memory in individuals with fragile X syndrome. Child Neuropsychology, 15, 105–119. Lara, C., Fayyad, J., de Graaf, R., Kessler, R.C., Aguilar-Gaxiola, S., Angermeyer, M., . . . & Sampson, N. (2009). Childhood predictors of adult attention-deficit/ hyperactivity disorder: Results from the World Health Organization World Mental Health Survey Initiative. Biological Psychiatry, 65, 46–54. Leggett, V., Jacobs, P., Nation, K., Scerif, G., & Bishop, D.V.M. (2010). Neurocognitive outcomes of individuals with a sex chromosome trisomy: XXX, XYY, or XXY: A systematic review. Developmental Medicine and Child Neurology, 52, 119–129. Leyfer, O.T., Woodruff-Borden, J., Klein-Tasman, B.P., Fricke, J.S., & Mervis, C.B. (2006). Prevalence of psychiatric disorders in 4 to 16-year-olds with Williams syndrome. American

Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 141B, 615–622. Livingstone, P.D., Srinivasan, J., Kew, J.N.C., Dawson, L.A., Gotti, C., Moretti, M., . . . & Wonnacott, S. (2009). alpha 7 and non-alpha 7 nicotinic acetylcholine receptors modulate dopamine release in vitro and in vivo in the rat prefrontal cortex. European Journal of Neuroscience, 29, 539–550. Loesch, D.Z., Huggins, R.M., Bui, Q.M., Epstein, J.L., Taylor, A.K., & Hagerman, R.J. (2002). Effect of the deficits of fragile X mental retardation protein on cognitive status of fragile x males and females assessed by robust pedigree analysis. Journal of Developmental and Behavioral Pediatrics, 23, 416–423. Maatta, T., Tervo-Maatta, T., Taanila, A., Kaski, M., & Iivanainen, M. (2006). Mental health, behaviour and intellectual abilities of people with Down syndrome. Down’s Syndrome, Research and Practice: The Journal of the Sarah Duffen Centre/University of Portsmouth, 11, 37–43. Marenco, S., Siuta, M.A., Kippenhan, J.S., Grodofsky, S., Chang, W.-L., Kohn, P., . . . & Berman, K.F. (2007). Genetic contributions to white matter architecture revealed by diffusion tensor imaging in Williams syndrome. Proceedings of the National Academy of Sciences of the United States of America, 104, 15117–15122. Martens, M.A., Seyfer, D.L., Andridge, R.R., Foster, J.E.A., McClure, K.E., & Coury, D.L. (2013). Caregiver survey of pharmacotherapy to treat attention deficit/hyperactivity disorder in individuals with Williams syndrome. Research in Developmental Disabilities, 34, 1700–1709. Martens, M.A., Wilson, S.J., & Reutens, D.C. (2008). Research Review: Williams syndrome: A critical review of the cognitive, behavioral, and neuroanatomical phenotype. Journal of Child Psychology and Psychiatry, 49, 576–608. Meda, S.A., Pryweller, J.R., & Thornton-Wells, T.A. (2012). Regional brain differences in cortical thickness, surface area and subcortical volume in individuals with Williams syndrome. PLoS ONE, 7, e31913. doi: 10.1371/journal. pone.0031913. Meechan, D.W., Tucker, E.S., Maynard, T.M., & LaMantia, A.-S. (2012). Cxcr4 regulation of interneuron migration is disrupted in 22q11.2 deletion syndrome. Proceedings of the National Academy of Sciences of the United States of America, 109, 18601–18606. Meng, J.S., Meng, Y.H., Hanna, A., Janus, C., & Jia, Z.P. (2005). Abnormal long-lasting synaptic plasticity and cognition in mice lacking the mental retardation gene Pak3. Journal of Neuroscience, 25, 6641–6650. Meng, Y.H., Zhang, Y., Tregoubov, V., Janus, C., Cruz, L., Jackson, M., . . . & Jial, Z.P. (2002). Abnormal spine morphology and enhanced LTP in LIMK-1 knockout mice. Neuron, 35, 121–133. Meredith, R.M., Dawitz, J., & Kramvis, I. (2012). Sensitive time-windows for susceptibility in neurodevelopmental disorders. Trends in Neurosciences, 35, 335–344. Merenstein, S.A., Sobesky, W.E., Taylor, A.K., Riddle, J.E., Tran, H.X., & Hagerman, R.J. (1996). Molecular-clinical correlations in males with an expanded FMR1 mutation. American Journal of Medical Genetics, 64, 388–394. Merla, G., Brunetti-Pierri, N., Micale, L., & Fusco, C. (2010). Copy number variants at Williams-Beuren syndrome 7q11.23 region. Human Genetics, 128, 3–26. Mobbs, D., Eckert, M.A., Mills, D., Korenberg, J., Bellugi, U., Galaburda, A.M., & Reiss, A.L. (2007). Frontostriatal dysfunction in Williams syndrome. Biological Psychiatry, 62, 256–261. Mukai, J., Dhilla, A., Drew, L.J., Stark, K.L., Cao, L., MacDermott, A.B., ... & Gogos, J.A. (2008). Palmitoylation-dependent neurodevelopmental deficits in a mouse model of 22q11 microdeletion. Nature Neuroscience, 11, 1302–1310.


Rare genotypes and developmental mechanisms Mulherkar, S.A., & Jana, N.R. (2010). Loss of dopaminergic neurons and resulting behavioural deficits in mouse model of Angelman syndrome. Neurobiology of Disease, 40, 586– 592. Munir, F., Cornish, K.M., & Wilding, J. (2000). A neuropsychological profile of attention deficits in young males with fragile X syndrome. Neuropsychologia, 38, 1261–1270. Neale, B.M., Medland, S.E., Ripke, S., Asherson, P., Franke, B., Lesch, K.-P., . . . & Psychiat, G.C.A.S. (2010). Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 884–897. Neece, C.L., Baker, B.L., Blacher, J., & Crnic, K.A. (2011). Attention-deficit/hyperactivity disorder among children with and without intellectual disability: An examination across time. Journal of Intellectual Disability Research, 55, 623– 635. Nigg, J.T., Willcutt, E.G., Doyle, A.E., & Sonuga-Barke, E.J.S. (2005). Causal heterogeneity in attention-deficit/ hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biological Psychiatry, 57, 1224–1230. Nithianantharajah, J., Komiyama, N.H., McKechanie, A., Johnstone, M., Blackwood, D.H., St Clair, D., . . . & Grant, S.G.N. (2013). Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nature Neuroscience, 16, 16-U37. van Nuenen, B.F.L., Weiss, M.M., Bloem, B.R., Reetz, K., van Eimeren, T., Lohmann, K., . . . & Siebner, H.R. (2009). Heterozygous carriers of a Parkin or PINK1 mutation share a common functional endophenotype. Neurology, 72, 1041– 1047. Oliver, C., Berg, K., Moss, J., Arron, K., & Burbidge, C. (2011). Delineation of behavioral phenotypes in genetic syndromes: Characteristics of autism spectrum disorder, affect and hyperactivity. Journal of Autism and Developmental Disorders, 41, 1019–1032. Ortiz-Abalia, J., Sahun, I., Altafaj, X., Andreu, N., Estivill, X., Dierssen, M., & Fillat, C. (2008). Targeting Dyrk1A with AAVshRNA attenuates motor alterations in TgDyrk1A, a mouse model of down syndrome. American Journal of Human Genetics, 83, 479–488. Paterson, S.J., Brown, J.H., Gsodl, M.K., Johnson, M.H., & Karmiloff-Smith, A. (1999). Cognitive modularity and genetic disorders. Science, 286, 2355–2358. Peippo, M., Koivisto, A.M., Sarkamo, T., Sipponen, M., von Koskull, H., Ylisaukko-oja, T., . . . & Jarvela, I. (2007). PAK3 related mental disabillity: Further characterization of the phenotype. American Journal of Medical Genetics Part A, 143A, 2406–2416. Pelc, K., Cheron, G., & Dan, B. (2008). Behavior and neuropsychiatric manifestations in Angelman syndrome. Neuropsychiatric disease and treatment, 4, 577–584. Peng, D.X., Kelley, R.G., Quintin, E.-M., Raman, M., Thompson, P.M., & Reiss, A.L. (2014). Cognitive and behavioral correlates of caudate subregion shape variation in fragile X syndrome. Human Brain Mapping, 35, 2861– 2868. Peters, S.U., Kaufmann, W.E., Bacino, C.A., Anderson, A.W., Adapa, P., Chu, Z., . . . & Wilde, E.A. (2011). Alterations in white matter pathways in Angelman syndrome. Developmental Medicine and Child Neurology, 53, 361– 367. Pignatelli, M., Piccinin, S., Molinaro, G., Di Menna, L., Riozzi, B., Cannella, M., . . . & Bruno, V. (2014). Changes in mGlu5 receptor-dependent synaptic plasticity and coupling to homer proteins in the hippocampus of Ube3A hemizygous mice modeling Angelman syndrome. Journal of Neuroscience, 34, 4558–4566. Poelmans, G., Pauls, D.L., Buitelaar, J.K., & Franke, B. (2011). Integrated genome-wide association study findings:


21

Identification of a neurodevelopmental network for attention deficit hyperactivity disorder. American Journal of Psychiatry, 168, 365–377. Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., & Rohde, L.A. (2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry, 164, 942–948. Quintero, A.I., Beaton, E.A., Harvey, D.J., Ross, J.L., & Simon, T.J. (2014). Common and specific impairments in attention functioning in girls with chromosome 22q11.2 deletion, fragile X or Turner syndromes. Journal of Neurodevelopmental Disorders, 6, 5. Reiss, A.L., Eckert, M.A., Rose, F.E., Karchemskiy, A., Kesler, S., Chang, M., . . . & Galaburda, A. (2004). An experiment of nature: Brain anatomy parallels cognition and behavior in Williams syndrome. Journal of Neuroscience, 24, 5009–5015. Rhodes, S.M., Riby, D.M., Fraser, E., & Campbell, L.E. (2011). The extent of working memory deficits associated with Williams syndrome: Exploration of verbal and spatial domains and executively controlled processes. Brain and Cognition, 77, 208–214. Rhodes, S.M., Riby, D.M., Matthews, K., & Coghill, D.R. (2011). Attention-deficit/hyperactivity disorder and Williams syndrome: Shared behavioral and neuropsychological profiles. Journal of Clinical and Experimental Neuropsychology, 33, 147–156. Rhodes, S.M., Riby, D.M., Park, J., Fraser, E., & Campbell, L.E. (2010). Executive neuropsychological functioning in individuals with Williams syndrome. Neuropsychologia, 48, 1216–1226. Riday, T.T., Dankoski, E.C., Krouse, M.C., Fish, E.W., Walsh, P.L., Han, J.E., . . . & Malanga, C.J. (2012). Pathway-specific dopaminergic deficits in a mouse model of Angelman syndrome. Journal of Clinical Investigation, 122, 4544–4554. Roberts, J.E., Hatton, D.D., Long, A.C.J., Anello, V., & Colombo, J. (2012). Visual attention and autistic behavior in infants with fragile X syndrome. Journal of Autism and Developmental Disorders, 42, 937–946. Roberts, J.E., Miranda, M., Boccia, M., Janes, H., Tonnsen, B.L., & Hatton, D.D. (2011). Treatment effects of stimulant medication in young boys with fragile X syndrome. Journal of Neurodevelopmental Disorders, 3, 175–184. Ross, J.L., Zeger, M.P.D., Kushner, H., Zinn, A.R., & Roeltgen, D.P. (2009). An extra X OR Y chromosome: Contrasting the cognitive and motor phenotypes in childhood in boys with 47, XYY syndrome OR 47, XXY Klinefelter syndrome. Developmental Disabilities Research Reviews, 15, 309–317. Russell, H.F., Wallis, D., Mazzocco, M.M.M., Moshang, T., Zackai, E., Zinn, A.R., . . . & Muenke, M. (2006). Increased prevalence of ADHD in Turner syndrome with no evidence of imprinting effects. Journal of Pediatric Psychology, 31, 945– 955. Scerif, G. (2010). Attention trajectories, mechanisms and outcomes: At the interface between developing cognition and environment. Developmental Science, 13, 805–812. Scerif, G., Cornish, K., Wilding, J., Driver, J., & Karmiloff-Smith, A. (2004). Visual search in typically developing toddlers and toddlers with Fragile X or Williams syndrome. Developmental Science, 7, 116–130. Scerif, G., & Karmiloff-Smith, A. (2005). The dawn of cognitive genetics? Crucial developmental caveats. Trends in Cognitive Sciences, 9, 126–135. Scerif, G., Karmiloff-Smith, A., Campos, R., Elsabbagh, M., Driver, J., & Cornish, K. (2005). To look or not to look? Typical and atypical development of oculomotor control. Journal of Cognitive Neuroscience, 17, 591–604. Scerif, G., Longhi, E., Cole, V., Karmiloff-Smith, A., & Cornish, K. (2012). Attention across modalities as a longitudinal predictor of early outcomes: The case of fragile X syndrome. Journal of Child Psychology and Psychiatry, 53, 641–650.

22


Scerif, G., & Steele, A. (2011). Neurocognitive development of attention across genetic syndromes: Inspecting a disorder’s dynamics through the lens of another. In O. Braddick, J. Atkinson, & G.M. Innocenti (Eds.), Gene expression to neurobiology and behavior: Human brain development and developmental disorders. Progress in Brain Research, 189, 285–301. Schneider, M., Debbane, M., Bassett, A.S., Chow, E.W.C., Fung, W.L.A., van den Bree, M.B.M., . . . & Int Consortium Brain Behav q. (2014). Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: Results from the international consortium on brain and behavior in 22q11.2 deletion syndrome. American Journal of Psychiatry, 171, 627–639. Segura-Puimedon, M., Borralleras, C., Perez-Jurado, L.A., & Campuzano, V. (2013). TFII-I regulates target genes in the PI-3K and TGF-beta signaling pathways through a novel DNA binding motif. Gene, 527, 529–536. Shashi, V., Kwapil, T.R., Kaczorowski, J., Berry, M.N., Santos, C.S., Howard, T.D., . . . & Keshavan, M.S. (2010). Evidence of gray matter reduction and dysfunction in chromosome 22q11.2 deletion syndrome. Psychiatry Research-Neuroimaging, 181, 1–8. Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J.P., Greenstein, D., . . . & Rapoport, J.L. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences of the United States of America, 104, 19649–19654. Simon, T.J., Bish, J.P., Bearden, C.E., Ding, L.J., Ferrante, S., Nguyen, V., . . . & Emanuel, B.S. (2005). A multilevel analysis of cognitive dysfunction and psychopathology associated with chromosome 22q 11.2 deletion syndrome in children. Development and Psychopathology, 17, 753–784. Simonoff, E., Pickles, A., Wood, N., Gringras, P., & Chadwick, O. (2007). ADHD symptoms in children with mild intellectual disability. Journal of the American Academy of Child and Adolescent Psychiatry, 46, 591–600. Simonoff, E., Taylor, E., Baird, G., Bernard, S., Chadwick, O., Liang, H., . . . & Jichi, F. (2013). Randomized controlled double-blind trial of optimal dose methylphenidate in children and adolescents with severe attention deficit hyperactivity disorder and intellectual disability. Journal of Child Psychology and Psychiatry, 54, 527–535. Souchet, B., Guedj, F., Sahun, I., Duchon, A., Daubigney, F., Badel, A., . . . & Delabar, J.M. (2014). Excitation/inhibition balance and learning are modified by Dyrk1a gene dosage. Neurobiology of Disease, 69, 65–75. Stergiakouli, E., Hamshere, M., Holmans, P., Langley, K., Zaharieva, I., Hawi, Z., . . . & Subgrp, A. (2012). Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. American Journal of Psychiatry, 169, 186–194. Sullivan, K., Hatton, D., Hammer, J., Sideris, J., Hooper, S., Ornstein, P., & Bailey, D., Jr (2006). ADHD symptoms in children with FXS. American Journal of Medical Genetics Part A, 140A, 2275–2288. Sullivan, K., Hatton, D.D., Hammer, J., Sideris, J., Hooper, S., Ornstein, P.A., & Bailey, D.B., Jr (2007). Sustained attention and response inhibition in boys with fragile X syndrome: Measures of continuous performance. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 144B, 517–532. Swillen, A., Fryns, J.P., Kleczkowska, A., Massa, G., Vanderschuerenlodeweyckx, M., & Vandenberghe, H. (1993). Intelligence, behavior and psychosocial development in Turner syndrome - A cross-sectional study of 50 Preadolescent and adolescent girls (4–20 years). Genetic Counseling, 4, 7–18. Tartaglia, N.R., Ayari, N., Hutaff-Lee, C., & Boada, R. (2012). Attention-deficit hyperactivity disorder symptoms in

children and adolescents with sex chromosome aneuploidy: XXY, XXX, XYY, and XXYY. Journal of Developmental and Behavioral Pediatrics, 33, 309–318. Thapar, A., Cooper, M., Eyre, O., & Langley, K. (2013). Practitioner Review: What have we learnt about the causes of ADHD? Journal of Child Psychology and Psychiatry, 54, 3–16. Thapar, A., & Harold, G. (2014). Editorial perspective: Why is there such a mismatch between traditional heritability estimates and molecular genetic findings for behavioural traits? Journal of child psychology and psychiatry, and allied disciplines, 55, 1088–1091. Thomas, M.S.C., Annaz, D., Ansari, D., Scerif, G., Jarrold, C., & Karmiloff-Smith, A. (2009). Using developmental trajectories to understand developmental disorders. Journal of Speech Language and Hearing Research, 52, 336–358. Torrioli, M.G., Vernacotola, S., Peruzzi, L., Tabolacci, E., Mila, M., Militerni, R., . . . & Neri, G. (2008). A double-blind, parallel, multicenter comparison of L-acetylcarnitine with placebo on the attention deficit hyperactivity disorder in fragile X syndrome boys. American Journal of Medical Genetics Part A, 146A, 803–812. Torrioli, M.G., Vernacotola, S., Setini, C., Bevilacqua, F., Martinelli, D., Snape, M., . . . & Neri, G. (2010). Treatment with valproic acid ameliorates ADHD symptoms in fragile X syndrome boys. American Journal of Medical Genetics Part A, 152A, 1420–1427. Trent, S., & Davies, W. (2012). The influence of sex-linked genetic mechanisms on attention and impulsivity. Biological Psychology, 89, 1–13. Turk, J. (1998). Fragile X syndrome and attentional deficits. Journal of Applied Research in Intellectual Disabilities, 11, 175–191. Van der Molen, M.J.W., Van der Molen, M.W., Ridderinkhof, K.R., Hamel, B.C.J., Curfs, L.M.G., & Ramakers, G.J.A. (2012). Auditory and visual cortical activity during selective attention in fragile X syndrome: A cascade of processing deficiencies. Clinical Neurophysiology, 123, 720–729. Volkow, N.D., Wang, G.J., Fowler, J.S., & Ding, Y.S. (2005). Imaging the effect of methylphenidate on brain dopamine: New model on its therapeutic actions for attention-deficit/ hyperactivity disorder. Biological Psychiatry, 57, 1410– 1415. Walter, E., Mazaika, P.K., & Reiss, A.L. (2009). Insights into brain development from neurogenetic syndromes: Evidence from fragile X syndrome, Williams syndrome, Turner syndrome and velocardiofacial syndrome. Neuroscience, 164, 257–271. Wheeler, A., Raspa, M., Bann, C., Bishop, E., Hessl, D., Sacco, P., & Bailey, D.B., Jr (2014). Anxiety, attention problems, hyperactivity, and the aberrant behavior checklist in fragile X syndrome. American Journal of Medical Genetics Part A, 164, 141–155. White, N.S., Alkire, M.T., & Haier, R.J. (2003). A voxel-based morphometric study of nondemented adults with Down Syndrome. NeuroImage, 20, 393–403. Willcutt, E.G., Doyle, A.E., Nigg, J.T., Faraone, S.V., & Pennington, B.F. (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57, 1336–1346. Williams, C.A., Driscoll, D.J., & Dagli, A.I. (2010). Clinical and genetic aspects of Angelman syndrome. Genetics in Medicine, 12, 385–395. Williams, N.M., Franke, B., Mick, E., Anney, R.J.L., Freitag, C.M., Gill, M., . . . & Faraone, S.V. (2012). Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: The role of rare variants and duplications at 15q13.3. American Journal of Psychiatry, 169, 195–204. Williams, N.M., Zaharieva, I., Martin, A., Langley, K., Mantripragada, K., Fossdal, R., . . . & Thapar, A. (2010). © 2014 Association for Child and Adolescent Mental Health.

Rare genotypes and developmental mechanisms Rare chromosomal deletions and duplications in attention-deficit hyperactivity disorder: A genome-wide analysis. Lancet, 376, 1401–1408. Yabut, O., Domogauer, J., & D’Arcangelo, G. (2010). Dyrk1A overexpression inhibits proliferation and induces premature neuronal differentiation of neural progenitor cells. Journal of Neuroscience, 30, 4004–4014. Yang, L., Neale, B.M., Liu, L., Lee, S.H., Wray, N.R., Ji, N., . . . & Psychiat, G.C.A. (2013). Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: Genome-wide association study of both common and rare variants. American Journal of


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Medical Genetics Part B-Neuropsychiatric Genetics, 162B, 419–430. Yashiro, K., Riday, T.T., Condon, K.H., Roberts, A.C., Bernardo, D.R., Prakash, R., . . . & Philpot, B.D. (2009). Ube3a is required for experience-dependent maturation of the neocortex. Nature Neuroscience, 12, 777-U132. Zhu, X.-R., Maskri, L., Herold, C., Bader, V., Stichel, C.C., Guentuerkuen, O., & Luebbert, H. (2007). Non-motor behavioural impairments in parkin-deficient mice. European Journal of Neuroscience, 26, 1902–1911.

Accepted for publication: 10 November 2014

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