Cognition,ADHD Symptoms,and Functional Impairment in Children and Adolescents With Neurofibromatosis Type 1

Abstract

Objective: We examined the contribution of attention and executive cognitive processes to ADHD symptomatology in NF1, as well as the relationships between cognition and ADHD symptoms with functional outcomes. Methods: The study sample consisted of 141 children and adolescents with NF1. Children were administered neuropsychological tests that assessed attention and executive function, from which latent cognitive variables were derived. ADHD symptomatology, adaptive skills, and quality of life (QoL) were assessed using parent-rated questionnaires. Path analyses were conducted to test relationships among cognitive functioning, ADHD symptomatology, and functional outcomes. Results: Significant deficits were observed on all outcome variables. Cognitive variables did not predict ADHD symptomatology. Neither did they predict functional outcomes. However, elevated ADHD symptomatology significantly predicted functional outcomes. Conclusion: Irrespective of cognitive deficits, elevated ADHD symptoms in children with NF1 negatively impact daily functioning and emphasize the importance of interventions aimed at minimizing ADHD symptoms in NF1.

Keywords

ADHD neurofibromatosis type 1 cognition executive function functional impairment

Introduction

Traditional causal models of neurodevelopmental disorders propose that genetic and environmental modifiers create structural and functional abnormalities in the brain which result in alterations in aspects of neurocognitive functioning (Morton & Frith, 1995). These neurocognitive deficits in turn hypothetically manifest as behavioral symptoms, which contribute to the diagnosis of neurodevelopmental disorders. These models have been particularly influential in ADHD, a neurodevelopmental condition characterized by impairing symptoms of inattention, hyperactivity, and impulsivity (Faraone et al., 2015).

Initial models of ADHD assumed a linear causal pathway in which abnormal frontostriatal functioning was theorized to result in executive disinhibition, causing secondary cognitive deficits in working memory, planning, or executive control, which manifest behaviorally as ADHD (Barkley, 1997). However, recent advances have challenged this linear model, with multiple lines of evidence suggesting that relationships among cognition, behavioral symptoms, and functional impairment are more complex than initially posited (Coghill et al., 2014). First, not all individuals with ADHD present with executive cognitive deficits (Willcutt et al., 2005). Second, cognitive and behavioral responses to stimulant treatment are dissociable. For example, although meta-analysis of 36 randomized, placebo-controlled trials of methylphenidate (MPH) suggest small, but significant, positive effects of MPH on cognitive abilities at the group level, cognitive and behavioral treatment effects are generally not associated with each other (Coghill et al., 2007). Third, there is little correspondence between longitudinal cognitive trajectories and behavioral symptoms over time (Coghill et al., 2014). Taken together, the decoupling of cognitive deficits and ADHD symptoms across these lines of evidence provides a convincing argument that primary cognitive deficits are neither sufficient nor necessary for all cases of ADHD. Instead, these data suggest an alternative causal model for ADHD in which cognitive deficits sit parallel to behavioral symptoms, and these may independently result in functional impairments (Coghill et al., 2014). Such models appear to better reflect the considerable neurobiological (Castellanos & Tannock, 2002), cognitive (Castellanos et al., 2006), and behavioral (Jensen et al., 2001) heterogeneity of ADHD.

Although such models may be required to account for the variability inherent to ADHD, it is unclear whether the same variable patterns are observed in genetic conditions with an ADHD-like phenotype, such as neurofibromatosis type 1 (NF1), a complex autosomal dominant genetic condition caused by loss-of-function mutations in the NF1 gene (Gutmann et al., 2017). Although individuals with NF1 present with a wide variety of neoplastic manifestations, cognitive deficits and behavioral problems are the clinical abnormalities most commonly encountered during childhood. Compared with other single-gene neurodevelopmental disorders, such as fragile X, intelligence is relatively spared in NF1 (Lehtonen et al., 2012). However, up to 80% of children demonstrate deficits in one or more cognitive domains, such as executive functioning (Payne et al., 2012), attention (Payne et al., 2011), visuoperception (Lehtonen et al., 2012), and social cognition (Payne, Porter, et al., 2016). Furthermore, an estimated 40% to 50% meet diagnostic criteria for ADHD (Lehtonen et al., 2012), which is significantly greater than the rates observed in the general population, estimated at 5.29% worldwide (Polanczyk et al., 2007).

Studies of NF1 from molecular and cellular perspectives have provided mechanistic insights into the etiology of behavioral features associated with mutation in the NF1 gene. Nf1^+/- mice with bi-allelic Nf1 inactivation in neuroglial cells exhibit attention deficits (Brown et al., 2010), as well as a presynaptic dopamine defect, assayed using raclopride positron emission tomography in vivo (Brown et al., 2011). Treatment with indirect dopamine agonists, such as MPH, restores striatal dopamine and rescues the behavioral phenotype (Brown et al., 2010, 2011). Furthermore, integrative studies using mouse models and human neural progenitor cells suggest a NF1 gene dose dependence for dopamine dysregulation, linking NF1 gene expression, dopamine dysregulation, and attention deficits (Anastasaki et al., 2015). Collectively, these observations establish NF1 as a genetic model of dopamine dysregulation and ADHD in which the relationships among cognition, behavioral symptoms, and functional impairments can be modeled in a more homogeneous population.

Guided by an alternate neurodevelopmental model, we investigated the association between cognitive deficits in attention and executive functioning, ADHD symptoms, and functional impairments in adaptive functioning and quality of life (QoL; a subjective perception of general well-being in day-to-day functioning across physical, psychological, and social domains) in children with NF1. We tested the hypotheses that (a) cognitive deficits are positively associated with elevated ADHD symptoms, (b) cognitive deficits directly predict poorer adaptive functioning and reduced QoL, and (c) elevated ADHD symptoms predict adaptive impairments and poorer QoL.

Method

Participants

Data analyzed in this study were taken from a sample of 144 children, aged 8 to 15 years collected as part of an international, double-blind, randomized, placebo-controlled trial of lovastatin (STAtin randomized study; STARS; Payne, Barton, et al., 2016). Only relevant baseline data were analyzed in this study. All participants were clinically diagnosed with NF1 (Neurofibromatosis Conference Statement, 1988). Recruitment included participants from one Australian and 10 U.S. academic clinics affiliated with the Neurofibromatosis Clinical Trials Consortium. After screening for missing data, 141 children with NF1 were included in the final analyses (M age = 11.7 years, SD = 2.1; 61% male; M full scale IQ = 90.5, SD = 13.6). To be considered eligible for the clinical trial, children had to demonstrate cognitive deficit on at least one primary outcome (defined by ≥1 SD below the population mean): Paired Associate Learning (PAL) from the Cambridge Neuropsychological Automated Battery (CANTAB) assessing visuospatial learning (Payne et al., 2013), or Score from the Test of Everyday Attention for Children (TEA-Ch), measuring sustained attention (Manly et al., 2001). Exclusion criteria included a full scale IQ < 70, symptomatic central nervous system pathology (asymptomatic optic pathway gliomas were allowed), significantly impaired vision/hearing, insufficient comprehension of English, and medications contraindicated on lovastatin. Given lovastatin is a HMG-CoA reductase inhibitor known to lower cholesterol (Tobert, 2003), children with low baseline total cholesterol (<90 mg/dL) were excluded for safety reasons. Children on a stable dose of stimulant medication were considered eligible (n = 22; 16.3%). Institutional review boards at each site approved the protocol, and the study was undertaken in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Parent/guardians provided written informed consent and as appropriate, children provided assent to participate in the study.

Outcome Measures

Cognitive outcomes

Participants were tested on cognitive measures administered by trained psychologists at baseline, as previously described (Payne, Barton, et al., 2016). In this study, we only included attention and executive measures for these cognitive latent variables, because they are particularly associated with ADHD (Willcutt et al., 2005). These were defined a priori. Attention measures consisted of Sky Search (attention score), Score, Creature Counting (total score), and Sky Search DT from the TEA-Ch (Manly et al., 2001); and omission errors from the Conners Continuous Performance Test second edition (CPT-II; Conners, 2000). Executive measures consisted of Stockings of Cambridge (SOC; mean number of moves for hardest problem attempted), Stop Signal Task (SST; stop signal reaction time), and Spatial Working Memory (SWM; total between search errors and strategy score) from the CANTAB, as well as commission errors from the CPT-II.

Behavioral outcomes

ADHD symptoms were assessed using the parent version of the Conners-3 questionnaire (Conners, 2008). We report Inattention and Hyperactivity/Impulsivity Symptom Scales.

Functional outcomes

Two parent-rated questionnaires provided information on functional outcomes. We used the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales, which is a widely used instrument designed to measure health-related QoL across physical, emotional, social, and school domains (Varni et al., 2001). For this study, we report the Total Scale Score. We also used the Adaptive Skills scale from the Behavior Assessment System for Children, second edition (BASC-II), which is a composite score measuring the participants’ ability to complete simple activities of daily living and engage in social skills, leadership skills, and functional communication (Reynolds & Kamphaus, 2004).

Procedure

Participants initially underwent screening consisting of a medical assessment, laboratory tests, and a brief cognitive examination. Eligible participants returned to complete the baseline cognitive and behavioral measures.

Data Analysis

Following a visual check of all data, and screening for normality, we completed statistical analyses using Stata 15.1 (StataCorp, Texas). All assumptions for the confirmatory factor analysis (CFA) and path analysis were met (i.e., outliers, multivariate normality, multicollinearity, linearity). To determine whether cognitive measures, ADHD symptom ratings, adaptive functioning, and QoL for NF1 participants differed significantly from the general population, we applied one-sample t-tests and generated effect sizes (Cohen’s d; Cohen, 1988). Lack of a typically developing comparison group precluded us from comparing cognitive latent variables. Comparison of predictors and outcomes were also carried out between those who were taking ADHD medication and those not. To accommodate smaller medicated group size, Mann–Whitney U tests were used.

CFA

To overcome the psychometric limitations of single observed cognitive test scores in NF1 (Payne et al., 2019), we created two theoretically based latent variables using CFA, which allows for modeling of the random and systematic measurement error in the observed variables such that the latent constructs can be interpreted with the measurement error taken into account. CFA was chosen over exploratory factor analysis because it is a hypothesis-driven variant of structural equation modeling that relies on accepted theoretical constructs to examine expected connections between variables. We transformed observed test scores prior to CFA such that positive scores reflected superior performance. We utilized the maximum likelihood method. Latent variables were allowed to inter-correlate. The best model was determined by the best overall fit indices including the chi-square value, comparative fit index (CFI), Bollen’s incremental fit index (IFI), and the root mean square error of approximation (RMSEA). Smaller chi-square and RMSEA values and larger CFI and IFI values indicate a better fit. Scores of each latent factor represent cognitive performance for the domains they represent.

Path analysis

Next, we used path analysis, a form of causal modeling, to describe the directed dependencies among our outcome variables. In essence, path analysis is a hypothesis-testing technique used to analyze a structural theory, indicated by a number of regression equations. We conceptualized two models, allowing us to test directional relationships among the cognitive latent variables, behavioral ADHD symptoms, and functional impairment. In the first model, the functional outcome consisted of adaptive functioning (BASC-II). In the second, it was QoL (PedsQL). The fit of each model was evaluated on the basis of multiple criteria: relative chi-square, chi-square p value, RMSEA, and the CFI. Model fit was considered acceptable if the ratio of chi-square value to the degree of freedom was between 2 and 5; the chi-square p value was not significant, the RMSEA was <0.07; the Standardized Root Mean Square Residual was <0.08, and the CFI was >0.90. RMSEA values above 0.10 indicated poor fit. The level of statistical significance was set to 0.05.

Results

Table 1 contains cognitive, behavioral, and functional results for study participants. All mean scores were significantly poorer than population reference data (all, p < .001), demonstrating the presence of attention and executive cognitive deficits, elevated ADHD symptoms, and functional impairments in adaptive skills and QoL. Comparison of participants taking ADHD medication and those not showed little subgroup difference on cognitive, behavioral, and functional outcomes (Supplemental Table S1). Only the ADHD Hyperactive/Impulsive scale showed a potential difference (p = .024); however, following multiple comparison adjustment, statistical significance did not hold up. Given these results, we decided to continue with the pooled non-medicated and medicated population to maximize power for the CFA and path models.

Table 1.

Observed Cognitive Test Scores, Behavioral ADHD Symptoms, and Functional Outcomes at Baseline.

Neuropsychological outcome	n	Mean	[95% CI]	Z-score	[95% CI]
Cognitive test
Executive Function
SWM Errors^a	139	47.1	[44.39, 49.82]	−0.83	[–0.97, –0.68]
SWM Strategy^a	139	36.67	[35.93, 37.41]	−0.67	[–0.84, –0.51]
SOC^a	136	7.93	[7.70, 8.15]	−0.51	[–0.69, –0.32]
CPT Commissions^b	135	55.62	[54.08, 57.17]	0.56	[0.41, 0.72]
SST	135	252.15	[236.87, 267.43]	N/A	N/A
Attention
Score^a	136	5.86	[5.49, 6.23]	−1.69	[–1.81, –1.57]
Sky Search^a	137	4.66	[4.33, 4.99]	−0.73	[–0.90, –0.55]
Sky Search DT^a	136	9.97	[7.05, 12.89]	−1.7	[–1.91, –1.48]
Creature Counting^a	137	3.93	[3.55, 4.30]	−0.82	[–1.02, –0.61]
CPT Omissions^b	135	59.72	[57.14, 62.30]	0.97	[0.71, 1.23]
ADHD symptoms
ADHD Inattentive^b	137	64.66	[62.40, 66.93]	1.47	[1.24, 1.69]
ADHD Hyperactive/Impulsive^b	137	63.81	[61.36, 66.26]	1.38	[1.14, 1.63]
Functional outcome
BASC-II Adaptive Skills^b	133	43.73	[41.89, 45.57]	−0.63	[–0.81, –0.44]
PedsQL Total Score^c	133	67.63	[64.85, 70.41]	−0.98	[–1.16, –0.80]

Note. CI = confidence interval; SWM = spatial working memory; SOC = Stockings of Cambridge; CPT = Continuous Performance Test; SST = stop signal task; DT = Divided Attention; BASC-II = Behavior Assessment System for Children, second edition.

Raw score. ^bT score. ^cSummary score.

CFA

To reduce the number of variables and account for measurement error associated with observed cognitive variables, we performed a series of CFAs to create (a) executive functioning and (b) attention latent factors. The best-fitting models are shown in Figure 1. The fit of the two-factor CFA was acceptable, χ²(34) = 41.73, p = .170; CFI = 0.94; RMSEA = 0.04, p = .630. Two factor loadings were not significant within the executive domain (SST stop signal reaction time and CPT commission errors) and were removed from the final model to improve the fit. We explored the possibility of creating a third response inhibition EF factor, but the model did not hold together so preceded with an executive functioning latent factor that included SWM and strategic planning. We also established a composite variable representing ADHD symptoms by averaging inattention and hyperactivity/impulsivity scales from the Conners 3.

Figure 1.

Data reduction for executive and attention cognitive measures.

Path Analyses

Having established cognitive latent variables and the composite ADHD score, we conducted two path analyses to better understand the contribution of cognitive performance and ADHD symptoms on functional impairment in children with NF1. In the first model, we examined associations between cognitive and ADHD outcomes on Adaptive Skills from the BASC-II (Figure 2A). Neither attention nor our executive functioning factor were significant predictors of ADHD symptoms, nor were they significantly associated with adaptive skills (all, p > .1). In contrast, ADHD symptoms significantly predicted adaptive skills (p < .001).

Figure 2.

Model fit between attention, executive function, ADHD symptoms, and (A) adaptive functioning or (B) QoL.

In the second model, we examined associations between cognitive outcomes and ADHD symptoms on QoL (total score, PedsQL; Figure 2B). Similarly, cognitive variables of attention and executive functioning did not predict ADHD symptoms, nor were they related to QoL (all, p > .1). Again, however, ADHD symptoms significantly predicted QoL (p < .001).

Discussion

Understanding the common “causal” dysfunctions of complex neurodevelopmental conditions such as autism spectrum disorder and ADHD is somewhat of a holy grail for the field of developmental neuroscience. Although a number of causal models have been proposed for ADHD (e.g., Castellanos et al., 2006; Coghill et al., 2014; Sonuga-Barke et al., 2010), there is currently no universally accepted framework, which likely reflects the heterogeneous nature of the disorder with multifactorial pathways to causation. One approach to reducing the heterogeneity in ADHD is to study relationships between the different levels of analysis in children with NF1, a genetic model of ADHD. Herein, we analyzed baseline data from STARS to define the contribution of attention and select executive cognitive deficits to ADHD symptomatology in children with NF1, as well as the relationships between cognitive and symptom outcomes with functional impairment.

Children with NF1 demonstrated deficits in attention and executive functioning at baseline, with moderate-to-large effect sizes on observed test data (Cohen’s d, ranging from 0.5 to 1.7). Large effect sizes were also evident for ADHD symptoms, with dimensional assessment of both inattentive and hyperactive/impulsive behaviors showing symptoms well above those seen in the general population (Cohen’s d, 1.5 and 1.4, respectively). Consistent with previous studies in children with NF1 (Eby et al., 2019; Graf et al., 2006) and those with ADHD (Stein et al., 1995; Wehmeier et al., 2010), this sample of children with NF1 further demonstrated impaired adaptive skills and QoL.

Path analyses examining the interrelationships between the three levels of analysis demonstrated several important findings. First, neither attention nor the working memory-strategic planning executive factor significantly predicted ADHD symptomatology in this population. These findings imply that the degree of cognitive deficit in these domains is independent of the severity of ADHD symptoms in NF1. Although seemingly contrary to our first hypothesis, these results are in agreement with previous research in children with NF1, where executive deficits on CANTAB tasks of working memory and response inhibition were equivalent in children with NF1, whether or not the child was comorbid for ADHD (Payne et al., 2012). There are several possible explanations for this finding. While attentional and executive working memory/planning deficits are common in children with NF1, it is possible they do not directly underlie ADHD behaviors in this group. That is, despite NF1 resulting from a common cause, the cognitive and behavioral phenotype remains quite heterogeneous, such that NF1 mutation can result in abnormal neurobiological processes that significantly but independently affect cognitive and behavioral outcomes. Akin to ADHD in the general population, it is also possible that there are different subgroups of children with NF1, in which some have a cognitive mechanism contributing to ADHD symptoms while others do not (Nigg et al., 2005). Supporting this notion is evidence from mouse models investigating neurobehavioral deficits in NF1, which suggest that different types of single and bi-allelic mutations at NF1 can result in distinct molecular consequences such as loss of synaptic plasticity due to aberrant RAS signaling and GABA neurotransmission (Li et al., 2005), or conversely, decreased striatal dopamine (Brown et al., 2010). A further explanation may be that the tools we used to assess cognitive outcomes in our cohort did not validly or reliably assess attentional or executive processes. Indeed, we have previously demonstrated questionable re-test reliability of the observed cognitive endpoints in this study; however, we have also shown that these psychometric properties can be substantially improved by creating latent cognitive variables (Payne et al., 2019), which we have done here. Regardless, these results suggest that like ADHD in the general population, there is a dissociation between cognitive deficits and ADHD symptoms in children with NF1, and the presence of attentional or executive working memory/planning deficits is not a requirement for ADHD symptoms in this population (Willcutt et al., 2005).

Second, attention and executive deficits did not predict impaired functional outcomes, namely poorer adaptive skills and QoL. Although we hypothesized a significant direct association between cognitive deficits and functional impairments based on models of ADHD (Coghill et al., 2014), the nature of these associations have not been widely researched in ADHD and even less so within NF1. In terms of QoL, one previous study has also failed to demonstrate relationships between cognitive outcomes and QoL in NF1 (Garwood et al., 2012). For adaptive functioning, strong correlations between adaptive abilities and intellectual functioning have been reported in young children with NF1 (Klein-Tasman et al., 2013); however, a recent retrospective study reported only weak associations between cognitive ability and adaptive outcomes (Eby et al., 2019). Different adaptive and cognitive measures used in the studies may partly explain discrepancies in results. The inclusion of different age groups in these study samples may also be a contributing factor as relationships among cognition, ADHD symptoms, and functional outcomes may vary across development.

Third, as expected, we found the presence of elevated ADHD symptoms to be a strong predictor of poorer adaptive functioning and QoL. These data indicate that children with NF1 who are experiencing significant ADHD symptoms demonstrate more difficulty developing age-appropriate adaptive skills across domains of activities of daily living, functional communication, leadership, social skills, and situational adaptability. Furthermore, they experience poorer QoL than children with NF1 with fewer ADHD symptoms. These results support evidence that adaptive functioning is significantly reduced in children with ADHD (Stein et al., 1995), and aligns with research indicating parent-reported QoL in ADHD to consistently fall 1.5 to 2 SD below population norms, and that QoL decreases as ADHD severity increases (Danckaerts et al., 2010). Interestingly, the links between ADHD and reduced QoL persist, even against a background of ASD (Sikora et al., 2012), another common neurodevelopmental phenotype in NF1 (Chisholm et al., 2018). Considering this study within a broader context, the results support an alternate approach to clinical trials research and clinical care in NF1—one that primarily targets behavioral symptoms, rather than cognitive processes. Such an approach may be most effective in alleviating functional impairments and improving the QoL of children with NF1.

This study is not without limitations. We did not categorize children into subtypes that met diagnostic criteria for ADHD. NF1 is also associated with a number of medical complications, including tumor burden and pain, which may additionally affect adaptive skills and QoL. We did not account for these complications in this study. Furthermore, examination of cognitive predictors was limited to attention, and for statistical reasons, executive functions that assessed working memory and strategic planning. Although attention, working memory, and planning are consistently associated with ADHD, this study did not include executive measures of response inhibition or other cognitive processes such as impaired signaling of delayed rewards and motivational problems, which are also theorized to contribute to ADHD (Sonuga-Barke, 2005). It may, for example, be that motivational reward circuit processes are more predictive of ADHD in NF1 than the executive disturbances measured within this study. Future psychopharmacological studies using drug probes of executive and reward circuit processing combined with functional neuroimaging may represent fruitful strategies for dissecting these pathways. Finally, it is possible that cognitive inclusion criteria may have affected the overall representativeness of the NF1 sample. However, we believe the risk of this is minimal, given the mean full scale IQ of the cohort is consistent with those reported across many previous studies (e.g., Lehtonen et al., 2012).

In conclusion, the relationships among cognitive deficits, ADHD symptoms, and functional impairment are complex in children with NF1, suggesting that ADHD symptoms, not cognitive deficits, are strong predictors of poor adaptive functioning and reduced QoL. A more comprehensive understanding of the factors contributing to ADHD symptoms and functional impairment will enable progress toward more accurate subtyping and an individualized approach to treatment.

Supplemental Material

Supplementary_Material – Supplemental material for Cognition, ADHD Symptoms, and Functional Impairment in Children and Adolescents With Neurofibromatosis Type 1

Supplemental material, Supplementary_Material for Cognition, ADHD Symptoms, and Functional Impairment in Children and Adolescents With Neurofibromatosis Type 1 by Jonathan M. Payne, Kristina M. Haebich, Rachel MacKenzie, Karin S. Walsh, Stephen J. C. Hearps, David Coghill, Belinda Barton, Natalie A. Pride, Nicole J. Ullrich, James H. Tonsgard, David Viskochil, Elizabeth K. Schorry, Laura Klesse, Michael J. Fisher, David H. Gutmann, Tena Rosser, Roger J. Packer, Bruce Korf, Maria T. Acosta, Mark A. Bellgrove and Kathryn N. North in Journal of Attention Disorders

Footnotes

Author’s Note

Kristina M. Haebich is also affiliated with University of Melbourne, Parkville, Victoria, Australia.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: D.C. reports grants and personal fees from Shire, and personal fees from Eli Lilly, Medice, Novartis, Oxford University Press, Servier, and grants from Vifor. D.H.G. reports a patent Neurofibromatosis Gene with royalties paid, a patent Neurofibromin pathway modulators issued, and a patent Neurofibromin/dopamine signaling biomarker for cognitive and behavioral problems in children with neurofibromatosis type 1 pending.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the United States Army Medical Research and Materiel Command, Office of the Congressionally Directed Medical Research Programs, Department of Defense Neurofibromatosis Research Program (Grant Number W81XWH-05-1-0615); J.M.P. is supported by a Murdoch Children’s Research Institute Clinician-Scientist Fellowship; M.A.B. is supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia.

ORCID iD

Jonathan M. Payne

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Jonathan M. Payne, DPsych, is a neuropsychologist with research interests in developmental neuroscience, the psychology of genetic disorders, and targeted interventions. He is the Co-Group leader of Brain and Mind Research at the Murdoch Children’s Research Institute, where he leads the Genetics and Neurodevelopment team. He holds a joint appointment as a Senior Clinical Neuropsychologist in the Psychology Department, Royal Children’s Hospital Melbourne, and is an Honorary Senior Fellow within the Department of Pediatrics, University of Melbourne. He is currently Co-Chair of the Neurocognitive Committee, NF Clinical Trials Consortium.

Kristina M. Haebich, DPsych, is a neuropsychologist and post-doctoral research fellow within the Genetics and Neurodevelopment team, Brain and Mind Research Group at the Murdoch Children’s Research Institute. She is also a fellow within the Department of Pediatrics, University of Melbourne. She completed her doctoral thesis at Monash University, examining executive functioning in adolescents who were born very preterm children using cognitive outcomes as well as resting state functional imaging.

Rachel MacKenzie, BSc(Hons), is a post-graduate student currently completing her Masters of Neuropsychology at La Trobe University, Melbourne. She competed her undergraduate training in psychology at Monash University and her Honors thesis at the Murdoch Children’s Research Institute, which used some of the data reported on in this study.

Karin S. Walsh, PsyD, is a pediatric neuropsychologist in the Division of Pediatric Neuropsychology, Children’s National Health System, where she oversees a program of clinical research in children with brain tumors and neurofibromatosis. She is an associate professor of Pediatrics and Psychiatry, and Behavioral Medicine with the George Washington University School of Medicine. She is currently chair of the Neurocognitive Committee, NF Clinical Trials Consortium.

Stephen J. C. Hearps, MBiostat, is a biostatistician and senior research officer within the Brain and Mind Research Group, Clinical Sciences Theme, at the Murdoch Children’s Research Institute. He consults across a range of studies including cohort and longitudinal studies as well as randomized controlled trials.

David Coghill, MD, has been chair of Developmental Mental Health at the Department of Pediatrics, University of Melbourne and a professor of Child and Adolescent Psychiatry at the Royal Children’s Hospital since 2016. He is also an honorary research fellow at the Murdoch Children’s Research Institute. Prior to this, he worked for 17 years at the University of Dundee in the United Kingdom where he was professor of Child and Adolescent Psychiatry. He is currently chair of the EUNETHYDIS International Conferences and vice president of the Australian ADHD Professional Association.

Belinda Barton, PhD, is Department Head of the Children’s Hospital Education Research Institute, The Children’s Hospital at Westmead, which aims to produce high-quality research that promotes children’s health and well-being. She is the team leader of NF Research at the Kids Neuroscience Center and holds joint appointments at a Conjoint Senior Lecturer, Discipline of Pediatrics & Child Health, Faculty of Medicine, The University of Sydney.

Natalie A. Pride, MPsych, PhD, is a clinical neuropsychologist with research interests in neurogenetic disorders, neuroscience, and neuropsychology. She is currently the neuropsychology lead within the Neurocognitive Team, Clinical genetics, and Kids Neuroscience Research Center within The Children’s Hospital at Westmead. She holds a joint appointment as a Clinical Lecturer, Sydney Medical School, The University of Sydney.

Nicole J. Ullrich, MD, is an associate professor of Neurology at the Harvard Medical School. She is a neurologist at the Boston Children’s Hospital, she is the director of Neurologic Neuro-Oncology, and associate director of Clinical Trials within the Neurofibromatosis Program. She is currently co-chair of the Neurocognitive Committee, NF Clinical Trials Consortium.

James H. Tonsgard, MD, is an associate professor of Pediatrics and Neurology at the University of Chicago Medicine. He is also director of The University of Chicago Ambulatory Program for Neurofibromatosis. He completed his medical education at the Yale University School of Medicine and his Fellowship at the University of Chicago Medicine.

David Viskochil, PhD, is professor of pediatrics and a clinical geneticist within the Division of Medical Genetics, University of Utah Health. He serves as co-director of the NF Clinic at the University of Utah and is the director of Clinical Genetics Services in the Division of Medical Genetics. He received his bachelor’s degree in biology from the University of Arizona, and his doctorate in biochemistry and medical degree from the University of North Carolina 1985. He completed his pediatric residency and clinical genetics fellowship at the University of Utah Affiliated Hospitals.

Elizabeth K. Schorry, MD, is a clinical geneticist and professor at Cincinnati Children’s Hospital Medical Center. She received her undergraduate degree at the University of Cincinnati and her MD at the University of Michigan. She completed a residency in pediatrics and a fellowship in medical genetics at Cincinnati Children’s Hospital Medical Center. She has been a faculty member in the Division of Human Genetics at Cincinnati Children’s since 1988. Her research interests include bone as well as learning and behavioral problems in NF1.

Laura Klesse, MD, PhD, is an associate professor in the Department of Pediatrics at University of Texas Southwestern Medical Center. She is a pediatric hematologist-oncologist specializing in neurofibromatosis, and co-directs the center’s NF2 program. She is a Dedman Family Scholar in Clinical Care and director of the comprehensive neurofibromatosis program at Children’s Health.

Michael J. Fisher, MD, is chief of the Section of Neuo-Oncology, director of the Neuro-Oncology Fellowship Program, and director of the neurofibromatosis Program at the Children’s Hospital of Philadelphia. He is a professor of pediatrics, Perelman School of Medicine at the University of Pennsylvania and is currently serving as chair of the Steering Committee, NF Clinical Trials Consortium.

David H. Gutmann, MD, PhD, is a full professor of Neurology, Neurosurgery, Pediatrics, and Genetics at the Washington University School of Medicine, St. Louis, where he is also vice chair for Research. He received his degrees at the University of Michigan Medical School and received residency training in Neurology at the University of Pennsylvania, where he was mentored by Dr. Kenneth Fischbeck in neurogenetics, prior to joining the laboratory of Dr. Francis Collins to identify the function of the NF1 gene.

Tena Rosser, MD, is pediatric neurologist at the Children’s Hospital, Los Angeles and an associate professor of Clinical Pediatrics, Keck School of Medicine of USC. She completed her pediatric internship at the Children’s National Health System, where she also completed her fellowship in pediatric neurology.

Roger J. Packer, MD, is senior vice president, Center for Neuroscience and Behavioral Medicine, Gilbert Distinguished Professor of Neurofibromatosis, and is director of both the Gilbert Neurofibromatosis Institute and the Brain Tumor Institute of Children’s National Health System. His present academic titles include Professor of Neurology and Pediatrics at George Washington University and Clinical Professor of Neurosurgery at the University of Virginia in Charlottesville, Virginia.

Bruce Korf, PhD, is Wayne H and Sara Crews Finley Chair in Medical Genetics, professor and chair of the Department of Genetics, and director of the Heflin Center for Genomic Sciences at the University of Alabama. He is a medical geneticist, pediatrician, and pediatric neurologist. He is past president of the Association of Professors of Human and Medical Genetics, past president of the American College of Medical Genetics and Genomics, and current president of the American College of Medical Genetics.

Maria T. Acosta, MD, is a Pediatric Neurologist working with the Undiagnosed Disease Program in the National Human Genome Research Institute, National Institutes of Health, where she is the medical director of the ADHD Genetic Study. She was formally clinical director of The Gilbert Neurofibromatosis Institute, Children’s National Health System.

Mark A. Bellgrove, PhD, is director of Research at the Monash Institute of Cognitive and Clinical Neurosciences, and a professor in Cognitive Neuroscience in the School of Psychological Sciences, at Monash University, where he leads a multidisciplinary team studying the biological basis of attention and cognitive control.

Kathryn N. North, MD, is a translational scientist trained as a pediatric physician, neurologist, and clinical geneticist. She is the director of the Murdoch Children’s Research Institute and the David Danks Professor of Child Health Research at the University of Melbourne. She leads Australian Genomics, a National Health and Medical Research Council-funded national network of over 80 institutions around Australia. She is on the Executive and Steering Committee of the Global Alliance for Genomics and Health, a collaborative network of more than 500 organizations across 71 countries.

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