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. Author manuscript; available in PMC: 2025 Dec 12.
Published in final edited form as: Appl Neuropsychol Child. 2024 Jun 12;15(1):33–41. doi: 10.1080/21622965.2024.2365383

Social Challenges, Autism Spectrum Disorder, and Attention Deficit/Hyperactivity Disorder in Youth with Neurofibromatosis Type I

Matthew C Hocking 1,2, May V Albee 1, Mina Kim 1, Jeffrey I Berman 1,2, Michael J Fisher 1,2, Timothy PL Roberts 1,2, Lisa Blaskey 1,2
PMCID: PMC11635006  NIHMSID: NIHMS2002191  PMID: 38864448

Abstract

Objective:

Youth with neurofibromatosis type I (NF1) demonstrate high rates of Autism Spectrum Disorder (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD), which often have overlapping behaviors. Diagnostic clarity is important to guide services. This study evaluated ASD classification in NF1 using various methods and whether those with ADHD suspicion have more social challenges associated with ASD.

Method:

34 youth with NF1 (Mage=10.5±1.6 years), completed ASD assessments that combined direct observation and informant ratings to yield a Clinician Best Estimate (CBE) classification. Caregivers rated ASD-related social challenges using the Social Responsiveness Scale- 2nd Edition (SRS-2).

Results:

ASD classification varied depending on method, ranging from 32% using low-threshold SRS-2 cut-scores (T≥60) to under 6% when combining cut scores for diagnostic observational tools and stringent SRS-2 cut-scores (T≥70). 14.7% had a CBE ASD classification. 44% were judged to have autism traits associated with a non-ASD diagnosis. The 52.9% with a suspicion of ADHD had higher SRS-2 scores than those without ADHD, F (7, 26) = 3.45, p < .05, Wilk’s lambda = 0.518, partial eta squared = 0.482.

Conclusions:

Findings highlight the importance of rigorous diagnostic methodology when evaluating ASD in NF1 to inform the selection of targeted interventions for socialization challenges in NF1.

Keywords: neurofibromatosis type 1, autism, social challenges, ADHD

Neurofibromatosis type I (NF1) is an autosomal dominant, multisystem genetic disorder that affects 1 in 3,000 people. The NF1 gene codes for the neurofibromin protein, which plays a critical role in tumor suppression and is expressed in the developing brain in both neurons and glial cells (Daston & Ratner, 1992; Zhu et al., 2005). Pathogenic variants in NF1 increase the risk for various clinical symptoms and abnormal tissue growth in many organs, including the peripheral and central nervous system (CNS). The abnormalities that occur in NF1 disrupt development in brain regions and networks, thus increasing the risk for comorbid neurodevelopmental difficulties, including cognitive impairments, speech and language difficulties, autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD) (Klein-Tasman et al., 2014; Torres Nupan et al., 2017).

Youth with NF1 are at increased risk for poor social functioning, including difficulty forming and maintaining friendships and frequent teasing and rejection by peers (Barton & North, 2004; Dilts et al., 1996; Johnson et al., 1999; North et al., 1995). Furthermore, children with NF1 are rated by teachers and peers as being more socially isolated, less well-liked by peers, and having fewer reciprocal friendships compared to classmates (Noll et al., 2007). The etiology of these difficulties is worthy of investigation given that social difficulties in typically developing youth are associated with higher rates of psychological distress, risky behavior, and suicidality (Ladd & Troop-Gordon, 2003; Lansford et al., 2014; Prinstein & Aikins, 2004; Prinstein et al., 2000). Whether the pathophysiology of NF1 increases risk for certain neurodevelopmental disorders (e.g., ASD, ADHD) versus whether the neurocognitive vulnerabilities in NF1 (e.g., executive dysfunction) drive outcomes related to social difficulties (without being a discrete neurodevelopmental disorder) is not yet clear.

Given the overlap in behavioral phenotypes and possible genetic contributors of NF1 and ASD, including the Rat sarcoma (RAS)/ Mitogen Activated Protein Kinase (MAPK) pathway (Adviento et al., 2014; Levitt & Campbell, 2009), the comorbidity of ASD in NF1 is of interest. ASD is a developmental condition characterized by impairments in social communication and interactions as well as by restricted and repetitive behaviors. Prevalence rates of high levels of ASD symptoms in NF1 range from 43–60% (Chisholm et al., 2018; Morris et al., 2016; Plasschaert et al., 2016; Walsh et al., 2013). These data are largely derived only from commonly used behavior rating scales of social challenges within ASD, including the Social Responsiveness Scale, Second Edition (SRS-2; Constantino & Gruber, 2012). However, studies using well-established ASD diagnostic tools suggest much lower prevalence of a formal ASD diagnosis, with rates ranging from 11 – 34% (Chisholm et al., 2022; Eijk et al., 2018; Garg et al., 2013; Lubbers et al., 2022; Plasschaert et al., 2016), nonetheless much higher than the population ~2% rate of ASD prevalence. Differences in sampling methods likely account for the varying rates across these studies using diagnostic tools, with some relying heavily on clinically referred samples or those showing high levels of symptoms on screening (Chisholm et al., 2022; Garg et al., 2013; Plasschaert et al., 2016). Such a sampling approach may bias findings to elevated levels of ASD diagnosis in youth with NF1 and lead to inappropriate selection of interventions or services for youth mischaracterized as having ASD.

Additionally, upwards of 60% of youth with NF1 demonstrate high levels of ADHD symptoms, while rates of those meeting diagnostic criteria for ADHD range from 30–50% (Hyman et al., 2005; Mautner et al., 2002). ADHD symptoms, such as inattention and impulsivity, are known contributors to social impairments in non-NF1 samples (Humphreys et al., 2016; Kofler et al., 2018; Kofler et al., 2011). In youth with NF1, co-occurring ADHD is related to increased social challenges, or difficulties with social interactions, and ASD traits (Barton & North, 2004; Chisholm et al., 2018; Walsh et al., 2013). In a longitudinal study of young children with NF1, ADHD symptoms in early childhood significantly predicted social skills later in the school-age range (Glad et al., 2021). Similarly, elevated ADHD symptoms have been associated with poorer social skills and social challenges typically related to ASD in youth with NF1 (Chisholm et al., 2022; Haebich et al., 2022). The extent to which ADHD may mask a co-occurring ASD diagnosis or symptoms in children with NF1 is relatively unexplored.

The variability in prevalence rates of ASD in youth with NF1 and the relatively few studies that account for the presence of ADHD while evaluating social impairments warrants further research. Specifically, studies are needed in non-referred samples of youth with NF1 that evaluate rates of ASD with a variety of assessment instruments and multiple sources of data. Additionally, clarity in determining factors related to social impairments in NF1 is important to guide clinical services. The primary goals of this study were to: (1) investigate rates of ASD classification in a non-referred sample of youth with NF1 based on various diagnostic tools and cutoffs; and (2) evaluate whether suspicion of an ADHD diagnosis is associated with higher levels of social impairments that could be attributed to ASD.

Methods

Participants:

Participants were youth ages 8–12 years with a confirmed diagnosis of NF1 both with and without a concurrent diagnosis of ASD. Exclusion criteria included 1) premature birth (<34 weeks) or serious birth complications, 2) prior treatment for a brain tumor or other NF-related tumor, 3) metallic implanted devices or other non-removable metal in the body, 4) known neurological disorders, genetic conditions (apart from NF1), or severe head trauma that can affect brain functioning, and 5) intellectual functioning below full-scale IQ of 70. 151 families were approached about the study and 34 completed study procedures. Top reasons for declining included passive refusal, worries about COVID-19, and time commitment. See Figure 1 for CONSORT diagram. Recruitment occurred between July 2019-February 2020, July-November 2020, and February-August 2021. Pauses in recruitment were due to the COVID-19 pandemic.

Figure 1.

Figure 1.

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CONSORT Diagram.

Procedures:

The present data represent a secondary data analysis for a study on the neurobiological factors associated with ASD in NF1 that was conducted at a large, urban pediatric medical center. All procedures were approved by the Institutional Review Board and written informed consent and child assent were obtained. Potentially eligible patients were identified through medical teams and NF1 registry records and contacted irrespective of any potential ASD diagnoses or symptoms. Families were invited to participate in a study on the brain-related processes in children with NF1 with and without ASD. Participants completed a neuropsychological evaluation and an observation-based ASD assessment. Caregivers completed questionnaires regarding social impairments and previous behavioral and mental health diagnoses. Participants were asked to complete a neuroimaging protocol that occurred on a separate day as part of the larger study. Families received a feedback letter with the results of the evaluation and were compensated $100 for the study visit. Relevant medical information was abstracted from the medical chart.

ASD assessments were performed by two licensed psychologists (MK, LB) who have 10- and 20-years ASD diagnostic research expertise, respectively. Diagnostic evaluations employed direct observation with the Autism Diagnostic Observation Schedule-2nd Edition (ADOS-2) prior to March 2020 and the Brief Observation of Symptoms of Autism (BOSA) after March 2020 due to COVID-19 pandemic mask mandates. Both psychologists had ADOS-2 research reliability. Autism symptoms were further evaluated by parent report on the Social Communication Questionnaire (SCQ) and Social Responsiveness Scale-2nd Edition (SRS-2) and supplemented with a structured, clinician-administered Autism Symptom Interview, augmented with unstructured clinical interview, when further information was deemed necessary to inform diagnostic impression. Following the evaluation, the psychologist aggregated all data and scores to assign a DSM-5 Clinician Best Estimate (CBE) classification, guided by use of a modified DSM-5 Ohio State Autism Rating Scale (OARS). This diagnostic approach followed gold standard methodology for ASD diagnosis modeled after the Collaborative Programs for Excellence in Autism (CPEA) and Simons Simplex Collection methodology, integrating direct observation and parent report on standardized tools with medical history and expert clinician judgment to assign a diagnostic classification (Harstad et al., 2023; Lainhart et al., 2006; Lord et al., 2012). The psychologist also assigned a 5-point ASD diagnostic confidence score (rated from ‘not sure’ to ‘very sure’) as well as a Clinical Global Impression (CGI) score for ASD severity (1=no autism, 2–7=level of autism severity, 99=autism symptoms better accounted for by another disorder).

A CBE approach also was used to classify participants as having a strong suspicion of an ADHD diagnosis based on all data available, including behavioral rating forms (e.g., Child Behavior Checklist), and past history (e.g., prior diagnosis, concerns about attention/hyperactivity documented in medical records, IEP/educational records when available). As per DSM-5 criteria, this determination was made with evidence of impairment in at least two settings (e.g., school and home) per parent report, though study constraints precluded conducting clinical interviews or obtaining teacher ratings.

Measures:

Cognitive Function.

IQ was measured using the Wechsler Intelligence Scale for Children, 5th edition (WISC-V) (Wechsler, 2014). The WISC-V is a widely-used, well-validated measure of intellectual functioning. The Full-Scale IQ (FSIQ), which has a mean of 100 and a standard deviation of 15, was used in relevant analyses.

Autism Measures.

The ADOS-2 (Lord et al., 2000) is a 60-minute semi-structured, interaction-based assessment and is considered the “gold standard” for ASD diagnosis. The measure examines communication, restricted and repetitive behaviors, and reciprocal social interactions. 21 participants completed the ADOS-2. Module 3 was used in all evaluations and the autism-spectrum cut-score was used.

The BOSA (Dow et al., 2022; Lord et al., 2020) is a 15–20 minute play-based assessment completed by a parent and child with a licensed psychologist observing from a different room. Similar to the ADOS-2, the BOSA integrates standardized materials and activities and assesses the dyadic interaction between the child and the parent. Scoring uses ADOS-2 coding, with ADOS-2 scores evaluated against the DSM-5 criteria of ASD symptom categories to determine if such symptoms were present. Convergent validity with the ADOS-2 is strong and reliability is high (Dow et al., 2022). 13 participants completed the BOSA.

The Social Communication Questionnaire (SCQ; Rutter et al., 2003) is a parent-completed measure of a child’s lifetime and current ASD-related characteristics. The SCQ is strongly correlated with other validated measures of ASD characteristics (Berument et al., 1999) and was used to inform the CBE diagnostic decision for ASD.

The Ohio State Autism Rating Scale (OARS; Psychopharmacology, 2005) also informed CBE judgment of DSM-5 ASD diagnosis. The OARS documents and quantifies severity of impairment for each DSM-5 symptom of ASD. For each of the DSM-5 symptom criteria, clinician judgment is assessed by a Likert scale severity rating from 0–3. Clinician judgment also quantifies overall severity of functional impairment from ASD traits (CGI) on a 1–7 Likert scale (1=no autism, 7=”classic” autism with severe impairment); a score of “99” can be used to summarize clinician judgment that another DSM disorder better accounts for the symptoms of ASD noted.

The Social Responsiveness Scale 2nd Edition (SRS-2 (Constantino & Gruber, 2012)) is a parent-report measure which gauges the presence and severity of behaviors typically associated with the social impairments of ASD. It yields a gender-stratified Total T-score of social impairments (m = 50, SD = 10), with higher scores indicating more impairments. Subscales include Social Awareness, Social Cognition, Social Communication, and Social Motivation, as well as a Social Cognition Index and a Restricted and Repetitive Behaviors Index. The SRS-2 has good internal consistency with a Cronbach’s alpha of 0.95 in clinical samples and is related to other measures of social impairments (Constantino & Gruber, 2012).

In clinical settings with presumed high ASD base rates, a raw score cut-point of 85 (T-score of 74 in females and 70 in males) is suggested as very strong evidence for the presence of ASD (96% specificity) (Constantino & Gruber, 2012). A study with a German sample using a slightly lower raw score cut-point of 75 yielded sensitivity estimates of ~ 0.80, with lower specificities of ~ 0.69 – 0.78 when comparing an ASD sample to a mixed non-ASD clinical group, an ADHD group, and an anxiety group (Bolte et al., 2011). Lower cut-score values (total raw score=70) are recommended for unselected general-population samples (positive predictive value of 0.93). Given the higher prevalence of ASD in NF1 compared to the general population, we used the more rigorous cut-score in informing diagnostic decisions (i.e., raw score of 85 or T-score of 70 or above). The higher cut-point also was chosen given literature indicating that children with ADHD (without ASD diagnosis) obtain elevated scores on the SRS-2 (Reiersen et al., 2007). We also present prevalence data using the lower cut-point for comparison purposes.

The Autism Symptom Interview (ASI; Bishop et al., 2017) is a brief, structured parent interview that assesses autism traits in children 5 to 12 years of age (school-age version). It consists of multiple-choice questions read by the interviewer to the parent. Designed as a brief tool for identifying children with ASD for research studies, the ASI is based on questions from the Autism Diagnostic Interview-Revised (ADI-R). The ASI was administered by the study psychologist when additional information about ASD traits was needed to make a diagnostic judgment and was supplemented by additional interviewer questions to obtain examples of endorsed behaviors, so that the clinician could make a confident determination regarding whether an ASD symptom was endorsed.

The Child Behavior Checklist (CBCL) (Achenbach & Rescorla, 2001) is a parent-report measure of overall behavioral functioning. Scores on the Attention Problems and ADHD Problems subscales informed CBE ADHD classification, as they have been shown to be effective in identifying ADHD (Chen et al., 1994).

Statistical Analyses

Descriptive statistics summarized demographic and relevant variables, including classification of those with and without ASD. Chi-square analyses (e.g., sex, ethnicity) and ANOVAs (e.g., age, IQ) evaluated potential differences in demographics between those who completed the ADOS versus the BOSA, and differences between those with and without ADHD suspicion. T-tests compared ADHD-related scales on the CBCL between those with and without a suspicion of ADHD. MANOVA evaluated group differences on the SRS-2 scores between youth with and without ADHD suspicion followed by post-hoc testing of each specific SRS-2 score. Partial eta squared estimated effect sizes, with values of 0.01, 0.06, and 0.14 representing small, medium, and large effects, respectively. Pearson correlations evaluated associations between SRS-2 scores and ADHD-related scales on the CBCL.

Results

Participants

34 participants completed the cognitive and diagnostic portion of this study. Participants were 10.5 ± 1.6 years-old at their study visit. There were no group differences on race (Γ2 = 1.18, p = .28), ethnicity (Γ2 = 0.13, p = .72), sex (Γ2 = 0.04, p = .85), SRS-2 Total T Score (t = 0.87, p = .39), or IQ (t = −0.90, p = .38), for participants who completed the ADOS (n=21) versus those who completed the BOSA (n=13). The sample had an average IQ of 94.82 ± 14.51. See Table 1 for more information about the sample.

Table 1.

Participant Characteristics

Demographics
N ± SD or N (%)
Age 10.48 ± 1.59
Race
 Black
 White
5 (14.7)
29 (85.3)
Sex
 Female
 Male
18 (52.9)
16 (47.5)
Ethnicity
 Hispanic
 Non-Hispanic
2 (5.9)
32 (94.1)
NF Mutation
 Sporadic
 Inherited
22 (64.1)
12 (35.3)
IQ 94.82 ± 14.51
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ADHD Diagnostic Classifications

More than half of the sample (n = 18, 52.9%) were noted to have a strong suspicion of ADHD based on CBE classification, with 13 of 18 having a previous community ADHD diagnosis based on parent report. On the ADHD Problems and Attention Problems scales of the CBCL, participants with a CBE suspicion of ADHD (m = 65.39, SD = 9.27 and m = 66.28, SD = 8.84, respectively) had significantly higher scores compared to those without a CBE suspicion of ADHD (m = 54.50, SD = 4.73 and m = 55.45, SD = 5.14, respectively), t[32] = −4.23, p < .001 and t[32] = −4.43, p < .001.

ASD Diagnostic Classifications

Rates of ASD classification varied depending on the method used (see Table 2). Using the gold-standard CBE, 14.7% (n = 5) were deemed to meet criteria for an ASD classification. Of the 5 with a CBE ASD classification, 4 were female. Chi-square analyses revealed no differences for ASD classification based on sex (Γ2 = 1.38, p = .24). ASD classification ranged from 32% when only using the low-threshold cutoff on the SRS-2 (T score ≥ 60) to approximately 6% when combining the threshold for the ADOS-2/BOSA and a high-threshold cut-off score above 70 on the SRS-2. When solely using the thresholds on either the ADOS-2 or the BOSA, 29.4% (n = 10) could be classified as having ASD. Approximately 44% of the sample were judged using the CBE to have ASD characteristics that did not meet DSM-5 criteria for an ASD classification and judged better attributed to a non-ASD diagnosis, including ADHD (n = 10), anxiety disorder (n = 3), language impairment (n = 1), and developmental delay (n = 1). Notably, 3 participants had pre-existing community diagnoses of ASD, all of whom were classified as having ASD using CBE.

Table 2.

ASD Classification Rates by Method.

Measure CBE ASD Diagnosis “Low Threshold”
SRS-2 Total
T-Score ≥ 60		 Above ADOS/BOSA Threshold ADOS/BOSA Above Threshold and SRS ≥ 60 Ohio State University Autism Rating Scale (OARS)+ “High Threshold”
SRS-2 Total
T-score ≥ 70		 ADOS/BOSA Above Threshold and SRS-2 ≥70
N (%) Above Cutoff 5 (14.7%) 11 (32.4%) Total: 10 (29.4%)
ADOS: 6 (17.6%)
BOSA: 4 (11.8%)		 5 (14.7%) ASD: 5 (14.7%)
99*: 15 (44.1%)		 4 (11.8%) 2 (5.9%)
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*

99 on OARS indicates that impairments are explained by something other than ASD: ADHD (N=10), Language Impairment, processing speed, borderline intellectual functioning (N=1), Anxiety (N=3), developmental delay (N=1)

+

OARS captures clinician judgment of DSM-5 ASD symptoms and diagnosis versus impression that ASD symptoms better accounted for by another condition.

Note: CBE = Clinician Best Estimate; ASD = Autism Spectrum Disorder; SRS-2 = Social Responsiveness Scale, 2nd Edition; ADOS = Autism Diagnostic Observation Schedule; BOSA = Brief Observation of Symptoms of Autism.

ADHD and Social Impairments

Four of the five participants with ASD were noted to have ADHD suspicion based on CBE classification. There were no group differences in IQ, sex, or age between participants with NF1 only versus NF1 and ADHD suspicion. However, participants identifying as Black were more likely to have a CBE suspicion of ADHD compared to those identifying as White, χ2 [1, N = 34] = 5.21, p < .05. On a MANOVA, those with NF1 and ADHD suspicion had significantly more impairments on scales of the SRS-2 than those with NF1 without ADHD, F (7, 26) = 3.45, p < .05, Wilk’s lambda = 0.518, partial eta squared = 0.482. Post-hoc testing revealed group differences on many of the SRS-2 scales, including the Total Score (see Table 3). The largest difference between groups occurred on the Social Awareness scale, F (1, 32) = 8.03, p < .01, which assesses the ability to pick up on social cues. These differences on SRS-2 scores, between those with and without (suspected) ADHD, held even when excluding participants who met criteria for an ASD classification (four of whom had suspected ADHD). Half of the participants with NF1 and ADHD suspicion had T scores ≥ 60 on the SRS-2 Total Score compared to only 12.5% of those with NF1 only. Further, higher scores on the CBCL ADHD Problems and Attention Problems scales were related to higher SRS-2 Total T-scores, r’s = .57 and .56, respectively, p’s < .001. SRS-2 scores did not differ between males (m = 52.80, SD = 10.06) and females (m = 55.68, SD = 10.66), t[32] = −.80, p > .43.

Table 3.

Social Impairments on the SRS-2 by ADHD Suspicion Status

Measure NF1+ADHD
  
 NF1 Only
 F (1, 32) Partial eta squared
SRS-2 Scale T Score M SD % Above Cut Point M SD % Above Cut Point
Social Awareness 60.94 12.19 50.57 8.61 8.03** 0.20
Social Cognition 56.22 8.38 51.06 7.78 3.44 0.10
Social Communication 57.17 10.69 50.06 8.00 4.71* 0.13
Social Motivation 53.50 10.20 50.81 9.59 0.62 0.02
Social Cognition Index 57.50 10.58 50.44 7.83 4.79* 0.13
Restricted & Repetitive Behaviors Index 58.78 11.33 49.31 9.01 7.14* 0.18
SRS-2 Total Score 58.00 10.76 50% 50.38 7.83 12.5% 5.18* 0.14
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*

p < .05

**

p < .01

Note: SRS-2 = Social Responsiveness Scale, 2nd Edition; ADHD = Attention/Deficit Hyperactivity Disorder; NF1 = Neurofibromatosis Type 1

Discussion

Difficulties with social acceptance, including making and maintaining friendships and social rejection, are significant consequences of the behavioral phenotype associated with NF1 (Barton & North, 2004; Dilts et al., 1996; Johnson et al., 1999; Noll et al., 2007; North et al., 1995). Prior research has underscored the importance of ASD and ADHD to the social impairments in youth with NF1, however few studies have evaluated them concurrently. Given the variability in prevalence rates of ASD in NF1 in earlier studies, this study assessed the presence of ASD in a non-referred sample of youth with NF1 using rigorous, gold standard diagnostic methodology. This study also considered the extent to which the presence of suspected ADHD increases the level of social impairments in NF1 without being attributable to a co-occurring ASD classification. Findings from this study demonstrate the importance of rigorous diagnostic methodology when evaluating ASD prevalence in NF1 and highlight how behavioral symptoms associated with ADHD relate to socialization challenges in NF1. The prevalence of ASD in this study of 14.7% was within the range seen in prior studies in NF1, with about one-third of the sample having some social difficulties on the SRS-2. These findings underscore the importance of appropriate screening and targeted intervention for socialization challenges in youth with NF1.

The rates of an ASD classification in the current study varied depending on the tools used. When solely relying on the low threshold of a T score of 60 or above on the SRS-2 total score, 32.4% would have met criteria. This number dropped to 11.8% when using a threshold of a T score of 70 or above. Similarly, when relying on the given thresholds for the ADOS or BOSA, 29.4% of the sample could have been given an ASD classification. However, when integrating clinician judgments of DSM-5 ASD criteria and impressions that suggested the social impairments are better accounted for by another condition, such as anxiety or ADHD, the rate of ASD classification fell to 14.7% of the sample. Further, on the OARS, 44.1% were judged to have impairments due to something other than autism. Such findings underscore the heterogeneity of developmental issues seen in NF1 and the need to account for comorbid neurobehavioral conditions, particularly ADHD and ASD, during evaluations of youth with NF1. For example, the ADHD Rating Scale −5 (DuPaul et al., 2016), broadband rating scales measuring anxiety, depression, and attention challenges, including the Child Behavior Checklist (Achenbach & Rescorla, 2001) or the Behavior Assessment Scale for Children, Second Edition (Reynolds & Kamphaus, 2006), and narrow-band rating scales of anxiety or depression, such as the Screen for Child Anxiety Related Emotional Disorders (Behrens et al., 2019) and the Child Depression Inventory, Second Edition (Kovacs, 2003), could be administered in conjunction with ASD rating scales, such as the SRS.

The percentage of youth with NF1 with an ASD classification observed in this study (14.7%) is within the prevalence ranges of 11 – 34% seen in prior studies with NF1 samples (Chisholm et al., 2022; Eijk et al., 2018; Garg et al., 2013; Plasschaert et al., 2016). Methodological differences may account for differences in rates seen across these studies. The other study that employed clinician judgment in addition to the SRS and ADOS (Eijk et al., 2018) in a non-referred sample of youth with NF1 found a prevalence rate of 10.9% that is similar to the current study. The other studies that found rates of 24.9% (Garg et al., 2013), 26% (Plasschaert et al., 2016), and 34% (Chisholm et al., 2022) respectively, used referred samples or participants that screened into the study based on elevated SRS scores. These studies also did not employ clinician judgment and relied on either ADOS or Autism Diagnostic Interview-Revised cut-offs and other algorithms for their diagnostic classifications. Additionally, prior studies generally used DSM-IV criteria, while the current study used DSM-5 criteria. Research comparing diagnosis rates based on the different versions of DSM criteria tend to show lower rates of ASD using DSM-5 criteria (Gibbs et al., 2012; Wilson et al., 2013; Young & Rodi, 2014), which could contribute to the slightly lower rates of ASD diagnosis observed in this study than others (Garg et al., 2013; Plasschaert et al., 2016).

Over half of the sample in this study were deemed to have a strong suspicion of ADHD, which is consistent with previous findings of 30 – 50% (Hyman et al., 2005; Mautner et al., 2002). Also similar to prior research (Barton & North, 2004; Chisholm et al., 2018; Walsh et al., 2013), this study found that those youth with NF1 and ADHD suspicion demonstrated more social impairments on the SRS-2 compared to those without ADHD suspicion, with large effect sizes across the omnibus MANOVA and most of the post-hoc comparisons. Notably, the differences between the two groups were most pronounced on scales measuring the ability to notice (Social Awareness scale) and interpret social cues (Social Cognition Index). In their study, Walsh and colleagues (Walsh et al., 2013) reported an overlap between ASD and ADHD symptoms and also noted an association between attention difficulties and impairments in social awareness. It is likely that youth with NF1 and ADHD have more difficulties with social awareness, including focusing during social interactions and missing relevant social cues from peers, and social motivation (Walsh et al., 2013). Further, the presence of ADHD may impact the ability to understand nonliteral language (Haebich et al., 2023), implementation of social skills (Haebich et al., 2022), and adaptive skills (Payne et al., 2021). Additional research is needed that incorporates both measures of ASD and ADHD when evaluating the social functioning of youth with NF1.

Findings from this study have implications for clinical care and research with youth with NF1. First, this study highlights the need for comprehensive assessment of behavioral issues during psychoeducational, neuropsychological, or ASD evaluations in youth with NF1. In addition to validated tools for assessing ASD, evaluations should consider the role of other behavioral problems that are common in NF1 (e.g., ADHD) and how they contribute to social difficulties that are characteristic of ASD prior to determining if an ASD diagnosis is appropriate. Clinical interviews are likely an essential component to ascertain the developmental trajectory of symptoms and distinguish between ADHD and ASD symptoms. Such information will be critical for effective treatment plans to address the broad range of issues experienced in youth with NF1. Second, despite a relatively low rate of youth with NF1 and ASD, social difficulties were prevalent for a significant portion of the sample. It is important to screen for social difficulties in all youth with NF1, regardless of concern for ASD or ADHD, when they present for evaluation of psychoeducational or behavioral issues. Third, clinicians should consider integrated interventions for youth with NF1 with social challenges that address common areas of difficulty in both ASD and ADHD. For example, interventions targeting ASD/ADHD-related difficulties with executive function may be appropriate for youth with NF1 and affect social challenges (Kenworthy et al., 2014; Yerys et al., 2019). Similarly, the use of stimulant medications to address the ADHD symptoms of youth with NF1 may have downstream benefits on their social functioning (Mautner et al., 2002). Additional research with youth with NF1 is needed to document whether improvements in ADHD symptoms relate to improvements in social impairments.

This study has several methodological strengths. First, we enrolled a non-referred sample of youth with NF1 to evaluate rates of ASD and ADHD. Studies using referred samples may reflect elevated rates of these diagnoses due to the nature of the sample. Second, we integrated the judgment of an expert clinician with well-validated observation-based and informant-report measures of ASD to evaluate the presence of ASD rather than relying on cut-off scores or behavioral rating scales. Such an approach allows for a more comprehensive assessment of a child’s behavior and provides increased classification accuracy.

There are some limitations to consider when interpreting this study’s findings. First, this study’s sample size is relatively small and was potentially biased due to the time commitment required for both the ASD assessment (data presented here) and the neuroimaging component of the study. The sample is comprised mostly of White participants. While participants of color were contacted about the study, their recruitment generally resulted in passive refusals. Further, while most group comparisons revealed large effects, the current study may not have been adequately powered to detect small effects in group comparisons. Thus, the size and composition of the sample may affect the generalizability of the findings. Studies with larger, more diverse samples are needed to replicate the findings from this study. Additionally, the sample size and methodologies of the study were affected by the COVID-19 pandemic. Recruitment for the study began in July 2019 and the study was paused for multiple months due to COVID-related issues. The COVID-19 pandemic also likely made it more challenging and less appealing for families to attend study visits. Additionally, the requirement to wear masks during data collection after the onset of the pandemic necessitated a change from the ADOS-2 to the BOSA due to the inability to wear masks during the administration of the ADOS-2. While preliminary indications suggest high comparability between the ADOS-2 and the BOSA, it is possible that the change in measures affected the findings. The inclusion requirement of an IQ over 70 may have biased the sample to be higher-functioning. Finally, this study did not evaluate the impact of the attentional or behavioral (e.g., hyperactivity/impulsivity) aspects of ADHD separately on ASD-related social challenges. Future work could consider exploring these components of ADHD in relation to social function in youth with NF1.

This study offers additional evidence on the prevalence of ASD in NF1, how it may vary depending on the assessment tool used, and how the presence of ADHD may contribute to social impairments generally seen within the ASD umbrella. Findings underscore the need for rigorous diagnostic evaluations in youth with NF1 when assessing behavioral and social challenges. Longitudinal studies with larger samples that employ comprehensive assessments of ASD and ADHD could further elucidate the prevalence and development of these neurodevelopmental conditions in NF1 over time and identify factors contributing to social impairments. Such research could inform the development of screening approaches and identify targets for interventions to improve the social functioning of youth with NF1.

Funding Source:

This research was supported by the National Institutes of Neurological Disorders and Stroke of the National Institutes of Health.

Footnotes

Disclosures: Dr Roberts disclosures include consulting/equity relationships with Prism Clinical Imaging, Proteus Neurodynamics and Fieldline Inc.

Data Availability:

The data that support the findings of this study are available upon request from the corresponding author (MCH). The data are not publicly available due to their containing information that could compromise the privacy of the research participants.

REFERENCES

  1. Achenbach TM, & Rescorla LA (2001). Manual for the ASEBA School-Age Forms & Profiles. University of Vermont, Research Center for Children, Youth, & Families. [Google Scholar]
  2. Adviento B, Corbin IL, Widjaja F, Desachy G, Enrique N, Rosser T, Risi S, Marco EJ, Hendren RL, Bearden CE, Rauen KA, & Weiss LA (2014). Autism traits in the RASopathies. Journal of Medical Genetics, 51(1), 10–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barton B, & North K (2004). Social skills of children with neurofibromatosis type 1. Developmental Medicine & Child Neurology, 46(08), 553–563. [DOI] [PubMed] [Google Scholar]
  4. Behrens B, Swelitz C, Pine DS, & Pagliaccio D (2019). The Screen for Child Anxiety Related Emotional Disorders (SCARED): Informant Discrepancy, Measurement Invariance, and Test-Retest Reliability. Child Psychiatry and Human Development, 50(3), 473–482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berument SK, Rutter M, Lord C, Pickles A, & Bailey A (1999). Autism screening questionnaire: Diagnostic validity. British Journal of Psychiatry, 175, 444–451. [DOI] [PubMed] [Google Scholar]
  6. Bishop SL, Huerta M, Gotham K, Alexandra Havdahl K, Pickles A, Duncan A, Hus Bal V, Croen L, & Lord C (2017). The autism symptom interview, school age: A brief telephone interview to identify autism spectrum disorders in 5-to-12-year-old children. Autism Research, 10(1), 78–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bolte S, Westerwald E, Holtmann M, Freitag C, & Poustka F (2011). Autistic traits and autism spectrum disorders: The clinical validity of two measures presuming a continuum of social communication skills. Journal of Autism and Developmental Disorders, 41(1), 66–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen WJ, Faraone SV, Biederman J, & Tsuang MT (1994). Diagnostic accuracy of the Child Behavior Checklist scales for attention-deficit hyperactivity disorder: A receiver-operating characteristic analysis. Journal of Consulting and Clinical Psychology, 62(5), 1017–1025. [DOI] [PubMed] [Google Scholar]
  9. Chisholm AK, Anderson VA, Pride NA, Malarbi S, North KN, & Payne JM (2018). Social function and autism spectrum disorder in children and adults with neurofibromatosis type 1: a systematic review and meta-analysis. Neuropsychology Review, 28, 317–340. [DOI] [PubMed] [Google Scholar]
  10. Chisholm AK, Haebich KM, Pride NA, Walsh KS, Lami F, Ure A, Maloof T, Brignell A, Rouel M, Granader Y, Maier A, Barton B, Darke H, Dabscheck G, Anderson VA, Williams K, North KN, & Payne JM (2022). Delineating the autistic phenotype in children with neurofibromatosis type 1. Molecular Autism, 13(1), 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Constantino JN, & Gruber CP (2012). (SRSTM −2) Social Responsiveness ScaleTM, Second Edition. Western Psychological Services. [Google Scholar]
  12. Daston MM, & Ratner N (1992). Neurofibromin, a predominantly neuronal GTPase activating protein in the adult, is ubiquitously expressed during development. Dev Dyn, 195(3), 216–226. 10.1002/aja.1001950307 [DOI] [PubMed] [Google Scholar]
  13. Dilts CV, Carey JC, Kircher JC, Hoffman RO, Creel D, Ward K, Clark E, & Leonard CO (1996). Children and adolescents with neurofibromatosis type 1: A behavioral phenotype. Journal of Developmental and Behavioral Pediatrics, 17, 229–239. [PubMed] [Google Scholar]
  14. Dow D, Holbrook A, Toolan C, McDonald N, Sterrett K, Rosen N, Hyun Kim S, & Lord C (2022). The Brief Observation of Symptoms of Autism (BOSA): Development of a New Adapted Assessment Measure for Remote Telehealth Administration Through COVID-19 and Beyond. Journal of Autism and Developmental Disorders, 52(12), 5383–5394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. DuPaul GJ, Power TJ, Anastopoulos AD, & Reid R (2016). ADHD Rating Scale - 5 for Children and Adolescents: Checklists, Norms, and Clinical Intepretation. Guilford Press. [Google Scholar]
  16. Eijk S, Mous SE, Dieleman GC, Dierckx B, Rietman AB, de Nijs PFA, Ten Hoopen LW, van Minkelen R, Elgersma Y, Catsman-Berrevoets CE, Oostenbrink R, & Legerstee JS (2018). Autism Spectrum Disorder in an Unselected Cohort of Children with Neurofibromatosis Type 1 (NF1). Journal of Autism and Developmental Disorders, 48(7), 2278–2285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Garg S, Green J, Leadbitter K, Emsley R, Lehtonen A, Evans DG, & Huson SM (2013). Neurofibromatosis Type 1 and Autism Spectrum Disorder. Pediatrics, 132(6), e1642–1648. [DOI] [PubMed] [Google Scholar]
  18. Gibbs V, Aldridge F, Chandler F, Witzlsperger E, & Smith K (2012). Brief report: An exploratory study comparing diagnostic outcomes for autism spectrum disorders under DSM-IV-TR with the proposed DSM-5 revision. Journal of Autism and Developmental Disorders, 42(8), 1750–1756. [DOI] [PubMed] [Google Scholar]
  19. Glad DM, Casnar CL, Yund BD, Lee K, & Klein-Tasman BP (2021). Parent-reported social skills in children with neurofibromatosis type 1: Longitudinal patterns and relations with attention and cognitive functioning. Journal of Developmental and Behavioral Pediatrics, 42(8), 656–665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Haebich KM, Dao DP, Pride NA, Barton B, Walsh KS, Maier A, Chisholm AK, Darke H, Catroppa C, Malarbi S, Wilkinson JC, Anderson VA, North KN, & Payne JM (2022). The mediating role of ADHD symptoms between executive function and social skills in children with neurofibromatosis type 1. Child Neuropsychology, 28(3), 318–336. [DOI] [PubMed] [Google Scholar]
  21. Haebich KM, Pride NA, Collins A, Porter MA, Anderson V, Maier A, Darke H, North KN, & Payne JM (2023). Understanding nonliteral language abilities in children with neurofibromatosis type 1. Neuropsychology, 37(8), 872–882. [DOI] [PubMed] [Google Scholar]
  22. Harstad E, Hanson E, Brewster SJ, DePillis R, Milliken AL, Aberbach G, Sideridis G, & Barbaresi WJ (2023). Persistence of Autism Spectrum Disorder From Early Childhood Through School Age. JAMA Pediatrics e234003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Humphreys KL, Galan CA, Tottenham N, & Lee SS (2016). Impaired social decision-making mediates the association between ADHD and social problems. Journal of Abnormal Child Psychology, 44(5), 1023–1032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hyman SL, Shores A, & North KN (2005). The nature and frequency of cognitive deficits in children with neurofibromatosis type 1. Neurology, 65, 1037–1044. [DOI] [PubMed] [Google Scholar]
  25. Johnson NS, Saal HM, Lovell AM, & Schorry EK (1999). Social and emotional problems in children with neurofibromatosis type 1: evidence and proposed interventions. The Journal of Pediatrics, 134(6), 767–772. [DOI] [PubMed] [Google Scholar]
  26. Kenworthy L, Anthony LG, Naiman DQ, Cannon L, Wills MC, Luong-Tran C, Werner MA, Alexander KC, Strang J, Bal E, Sokoloff JL, & Wallace GL (2014). Randomized controlled effectiveness trial of executive function intervention for children on the autism spectrum. Journal of Child Psychology and Psychiatry, 55(4), 374–383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Klein-Tasman BP, Janke KM, Luo W, Casnar CL, Hunter SJ, Tonsgard J, Trapane P, van der Fluit F, & Kais LA (2014). Cognitive and psychosocial phenotype of young children with neurofibromatosis-1. Journal of International Neuropsychological Society, 20, 88–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kofler MJ, Harmon SL, Aduen PA, Day TN, Austin KE, Spiegel JA, Irwin L, & Sarver DE (2018). Neurocognitive and behavioral predictors of social problems in ADHD: A Bayesian framework. Neuropsychology, 32(3), 344–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kofler MJ, Rapport MD, Bolden J, Sarver DE, Raiker JS, & Alderson RM (2011). Working memory deficits and social problems in children with ADHD. Journal of Abnormal Child Psychology, 39(6), 805–817. [DOI] [PubMed] [Google Scholar]
  30. Kovacs M (2003). Children’s Depression Inventory (CDI). Multi-Health Systems Inc. [Google Scholar]
  31. Ladd GW, & Troop-Gordon W (2003). The role of chronic peer difficulties in the development of children’s psychological adjustment problems. Child Development, 74(5), 1344–1367. [DOI] [PubMed] [Google Scholar]
  32. Lainhart JE, Bigler ED, Bocian M, Coon H, Dinh E, Dawson G, Deutsch CK, Dunn M, Estes AM, Tager-Flusberg H, Folstein S, Hepburn S, Hyman S, McMahon WM, Minshew M, Munson J, Osann K, Ozonoff S, Rodier P, Rogers S, Sigman M, Spence MA, Stodgell CJ, & Volkmar F (2006). Head circumference and height in autism: a study by the Collaborative Program of Excellence in Autism. American Journal of Medical Genetics, Part A, 140(21), 2257–2274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lansford JE, Dodge KA, Fontaine RG, Bates JE, & Pettit GS (2014). Peer rejection, affiliation with deviant peers, delinquency, and risky sexual behavior. Journal of Youth and Adolescence, 43, 1742–1751. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4163526/pdf/nihms623291.pdf [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Levitt P, & Campbell DB (2009). The genetic and neurobiologic compass points toward common signaling dysfunctions in autism spectrum disorders. The Journal of Clinical Investigation, 119(4), 747–754. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lord C, Holbrook A, Dow D, Byrne K, Grzadzinksi R, Sterrett K, Toolan C, & Kim SH (2020). Brief observation of symptoms of autism (BOSA). Western Psychological Services. [Google Scholar]
  36. Lord C, Petkova E, Hus V, Gan W, Lu F, Martin DM, & Risi S (2012). A multisite study of the clinical diagnosis of different autism spectrum disorders. Archives of general psychiatry, 69(3), 306–313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, Pickles A, & Rutter M (2000). The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205–223. [PubMed] [Google Scholar]
  38. Lubbers K, Stijl EM, Dierckx B, Hagenaar DA, Ten Hoopen LW, Legerstee JS, de Nijs PFA, Rietman AB, Greaves-Lord K, Hillegers MJ, D’ieleman GC, & Mous SE (2022). Autism Symptoms in Children and Young Adults With Fragile X Syndrome, Angelman Syndrome, Tuberous Sclerosis Complex, and Neurofibromatosis Type 1: A Cross-Syndrome Comparison. Frontiers in Psychiatry, 13, 852208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Mautner VF, Kluwe L, Thakker SD, & Leark RA (2002). Treatment of ADHD in neurofibromatosis type 1. Developmental Medicine & Child Neurology, 44(3), 164–170. [DOI] [PubMed] [Google Scholar]
  40. Morris SM, Acosta MT, Garg S, Green J, Huson S, Lequis E, North KN, Payne JM, Plasschaert E, Frazier TW, Weiss LA, Zhang Y, Gutmann DH, & Constantino JN (2016). Disease Burden and Symptom Structure of Autism in Neurofibromatosis Type 1: A study of the International NF1-ASD Consortium Team (INFACT). JAMA Psychiatry, 73(12), 1276–1284. 10.1001/jamapsychiatry.2016.2600 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Noll RB, Reiter-Purtill J, Moore BD, Schorry EK, Lovell AM, Vannatta K, & Gerhardt CA (2007). Social, emotional, and behavioral functioning of children with NF1. American Journal of Medical Genetics Part A, 143A, 2261–2273. [DOI] [PubMed] [Google Scholar]
  42. North K, Joy P, Yuille D, Cocks N, & Hutchins P (1995). Cognitive function and academic performance in children with neurofibromatosis type 1. Dev Med Child Neurol, 37(5), 427–436. [DOI] [PubMed] [Google Scholar]
  43. Payne JM, Haebich KM, MacKenzie R, Walsh KS, Hearps SJC, Coghill D, Barton B, Pride NA, Ullrich NJ, Tonsgard J, Viskochil D, Schorry EK, Klesse L, Fisher MJ, Gutmann DH, Rosser T, Packer RJ, Korf B, Acosta MT, Bellgrove MA, & North KN (2021). Cognition, ADHD symptoms, and functional impairment in children and adolescents with Neurofibromatosis Type 1. Journal of Attention Disorders, 25(8), 1177–1186. [DOI] [PubMed] [Google Scholar]
  44. Plasschaert E, Descheemaeker M-J, Van Eylen L, Noens I, Steyaert J, & Legius E (2016). Prevalence of autism spectrum disorder symptoms in children with Neurofbromatosis Type 1. American Journal of Medical Genetics Part B, 168B, 72–80. [DOI] [PubMed] [Google Scholar]
  45. Prinstein MJ, & Aikins JW (2004). Cognitive mediators of the longitudinal association between peer rejection and adolescent depressive symptoms. Journal of Abnormal Child Psychology, 32(2), 147–158. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165605/pdf/nihms625981.pdf [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Prinstein MJ, Boergers J, Spirito A, Little TD, & Grapentine WL (2000). Peer functioning, family dysfunction, and psychological symptoms in a risk factor model for adolescent inpatients’ suicidal ideation severity. Journal of Clinical Child Psychology, 29(3), 392–405. [DOI] [PubMed] [Google Scholar]
  47. Psychopharmacology, O. R. U. o. P. (2005). The OSU Research Unit on Pediatric Psychopharmacology: OSU Autism Rating Scale - DSM-IV (OARS-4). [Google Scholar]
  48. Reiersen AM, Constantino JN, Volk HE, & Todd RD (2007). Autistic traits in a population-based ADHD twin sample. Journal of Child Psychology and Psychiatry, 48, 464–472. [DOI] [PubMed] [Google Scholar]
  49. Reynolds CR, & Kamphaus RW (2006). BASC-2: Behavior Assessment System for Children, Second Edition. Pearson Education, Inc. . [Google Scholar]
  50. Rutter M, Bailey A, & Lord C (2003). Social Communication Questionnaire (SCQ). Western Psychological Services. [Google Scholar]
  51. Torres Nupan MM, Velez Van Meerbeke A, Cabra CAL, & Herrera Gomez PM (2017). Cognitive and behavioral disorders in children with neurofibromatosis type 1. Frontiers in Pediatrics, 5, 227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Walsh KS, Velez JI, Kardel PG, Imas DM, Muenke M, Packer RJ, Castellanos FX, & Acosta MT (2013). Symptomatology of autism spectrum disorder in a population with neurofibromatosis type 1. Developmental Medicine & Child Neurology, 55(2), 131–138. [DOI] [PubMed] [Google Scholar]
  53. Wechsler D (2014). Wechsler Intelligence Scale for Children - Fifth Edition (WISC-V). Pearson. [Google Scholar]
  54. Wilson CE, Gillan N, Spain D, Robertson D, Roberts G, Murphy CM, Maltezos S, Zinkstok J, Johnston K, Dardani C, Ohlsen C, Deeley Q, Craig M, Mendez MA, Happe F, & Murphy DG (2013). Comparison of ICD-10R, DSM-IV-TR and DSM-5 in an adult autism spectrum disorder diagnostic clinic. Journal of Autism and Developmental Disorders, 43(11), 2515–2525. [DOI] [PubMed] [Google Scholar]
  55. Yerys BE, Bertollo JR, Pandey J, Guy L, & Schultz RT (2019). Attention-deficit/hyperactivity disorder symptoms are associated with lower adaptive behavior skills in children with autism. Journal of American Academy of Child and Adolescent Psychiatry, 58, 525–533. [DOI] [PubMed] [Google Scholar]
  56. Young RL, & Rodi ML (2014). Redefining autism spectrum disorder using DSM-5: The implications of the proposed DSM-5 criteria for autism spectrum disorders. Journal of Autism and Developmental Disorders, 44(4), 758–765. [DOI] [PubMed] [Google Scholar]
  57. Zhu Y, Harada T, Liu L, Lush ME, Guignard F, Harada C, Burns DK, Bajenaru ML, Gutmann DH, & Parada LF (2005). Inactivation of NF1 in CNS causes increased glial progenitor proliferation and optic glioma formation. Development, 132(24), 5577–5588. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available upon request from the corresponding author (MCH). The data are not publicly available due to their containing information that could compromise the privacy of the research participants.

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