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Research Article                                                                                                                                                           Open Access

## Are Autistic Traits in Youth Meaningful? A Replication study in Non-referred Siblings of Youth with and without Attention-Deficit/Hyperactivity Disorder

Joseph Biederman 1-3 , Maura Fitzgerald 1,2 , Stephen V. Faraone 4,5 , Ronna Fried 1-3 , K. Yvonne Woodworth 1,2 , Alexandra Saunders 1,2 , Kristina Conroy 1,2 , Gagan Joshi 1-3

1 Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD,

Massachusetts General Hospital, Boston, MA, USA

2 Alan and Lorraine Bressler Clinical and Research Program for Autism Spectrum Disorder, Massachusetts General Hospital, Boston, MA, USA

3 Department of Psychiatry, Harvard Medical School, Boston, MA, USA

4 Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University,

Syracuse, NY, USA

5 K.G.Jebsen Centre for Psychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen,Norway

*Corresponding author: jbiederman@partners.org

## Abstract

Background: We previously  described  the  high  prevalence  and  burden  of  significant  autistic  traits  (ATs)  in  youth  with attention-deficit/hyperactivity  disorder  (ADHD).  These  traits  are  associated  with  significantly  greater  impairment  in psychopathological, interpersonal, educational, and neuropsychological functioning. Because the sample consisted of referred ADHD youth, uncertainty remained regarding whether these findings are generalizable to non-referred populations of youths with and without ADHD.

Objective: The aim of the current study was to assess the prevalence and implications of ATs in a non-referred population of siblings of probands with and without ADHD.

Method: Participants were non-referred siblings of probands with ADHD (N = 257) and control probands (N = 234) of longitudinal, case-control family studies conducted at Massachusetts General Hospital. Assessments included measures of psychiatric, psychosocial, educational, and cognitive functioning. The presence of significant ATs was operationalized using the Child Behavior Checklist AT profile, which consists of combined aggregate T-scores of ≥ 195 on the Withdrawn, Social, and Thought Problems subscales.

Results : ATs were significantly more prevalent among the siblings of probands with ADHD as compared with siblings of control probands (6% vs. 1%; P = .02). Siblings of probands with ADHD with a positive AT profile (N = 15) were significantly more impaired than those without an AT profile (N = 242) with regard to psychopathological, interpersonal, educational, and neuropsychological functioning.

Conclusions : The current study reports a higher-than-expected prevalence of ATs in a non-referred sample of siblings of youth with ADHD, which is consistent with previous findings regarding ATs in a referred sample of youth with ADHD. The presence of ATs is associated with higher levels of morbidity and dysfunction.

Keywords : attention-deficit/hyperactivity disorder (ADHD); autistic trait (AT); youth

## Introduction

Both attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have strong heritable components. Although their underlying  etiologies  are  yet  to  be  fully  elucidated, evidence  from  twin  and  family  studies  indicate shared heritability (1,2). Individuals with ADHD may manifest  different  forms  of  ASD,  from  a  fully developed syndromic form to a milder form of ASD symptomatology  that  involves  the  exhibition  of autistic  traits  (ATs).  However,  in  contrast  with  the well-developed literature addressing ASD, much less is  known  about  the  morbidity  and  dysfunction associated with ATs for individuals with and without ADHD.

Recent  studies  have  shown  that  symptoms  of autism or ATs appear in 20% to 30% of children with ADHD (3-5). Children with ADHD and ATs appear to be more impaired and dysfunctional than children with  ADHD  and  no  ATs.  With  these  literature findings  in  mind,  we  assessed  the  prevalence  and correlates of ATs in youth with and without ADHD, with  a  diagnosis  of  autism  being  exclusionary  (6). ATs were operationalized with the use of a profile from the Child Behavior Checklist (CBCL) by using extreme  values  from  the  sums  of  the  Withdrawn, Social, and Thought Problems T-scores (7).

In  the  prior  study,  a  positive  AT  profile  was significantly  more  prevalent  among  children  with ADHD as compared with control children (18% vs. 0.87%; p < .01). Children with ADHD with positive AT profiles  were  significantly  more  impaired  than other children with ADHD and control children with regard to psychopathology and interpersonal, school, family, and cognitive functioning. However, because our  results and  the  results  of  previous  reports examined ATs in referred samples of children with ADHD  (3-5),  it  remains  unknown  whether  these findings can be generalized to non-referred samples of children with and without ADHD. Studies of such samples would allow clinicians to identify children at high risk for adverse outcomes in multiple domains of functioning. This information could further lead to  the  development  of  appropriate  intervention strategies aimed at mitigating the adverse outcomes associated with ATs.

The aim of the current study was to examine both the prevalence and correlates of ATs in non-referred youth with and without ADHD. To this end, we used data  from  an  existing  large-scale  sample  of  nonreferred  siblings  of  probands  with  and  without ADHD. On the basis of the findings in probands, we hypothesized that ATs would be identifiable in nonreferred siblings and that the presence of ATs would be  associated  with  higher  levels  of  morbidity  and dysfunction.

## Methods Subjects

Subjects  were  youth  of  both  sexes  who  had  been enrolled  in  longitudinal  case-control  family  studies conducted at Massachusetts General Hospital (8,9). These studies included probands between the ages of 6 and 18 years with (N = 280) and without (N = 242) ADHD according to Diagnostic and Statistical Manual of  Mental  Disorders,  Third  Edition,  Revised, criteria  as determined  by  pediatric  and  psychiatric  sources. Adoption, unavailable nuclear family, major sensorimotor handicaps, psychosis, autism, language barriers, estimated intelligence quotients of less than 80 were exclusionary for both probands with ADHD and  control  probands.  Parents  provided  written informed  consent,  and  children  and  adolescents provided  written  assent.  The  institutionnal  review board at Massachusetts General Hospital approved the  study.  The  present  analysis  relied  on  the  nonreferred siblings of these probands (N = 395 siblings of  probands  with  ADHD;  N  =  317  siblings  of control probands). Siblings with estimated intelligence quotients of less than 80 were excluded from the present analysis.

## Assessment Procedures

We  used  an  empirically  derived  profile  from  the CBCL to define ATs (CBCL-AT) by using a cutoff of 195 or more from the combined T-scores of the Withdrawn, Social Problems, and Thought Problems subscales.  We  previously  reported  that  this  profile correctly classified 78% of all subjects with ASD in a psychiatrically  referred  sample  of  youth  with  and without  ASD  (7).  CBCL  emotional  dysregulation profiles were created from the combined T-scores of the Anxiety/Depression, Aggression, and Attention subscales.  Deficient  emotional  self-regulation  was defined as a combined T-score of 180 or more but less  than  210;  severe  emotional  dysregulation  was defined as a combined T-score of 210 or more.

Psychiatric assessments relied on the Kiddie Schedule for Affective Disorders and Schizophrenia for  School-Age  Children,  Epidemiological  Version (K-SADS-E) (10,11). Indirect interviews were conducted  with  each  subject's  parent  or  guardian, usually the mother. Direct interviews were conducted with subjects who were at least 12 years old. After combining data from direct and indirect interviews, a diagnosis was considered positive if it was endorsed in either interview.

Interviews were administered by highly trained and closely supervised raters with bachelor's or master's degrees in psychology or related fields. Raters were blinded  to  the  referral  source  and  the  proband's diagnostic  status  (i.e.,  ADHD  vs.  control).  On  the basis of 500 assessments obtained via interviews of children  and  adults,  the  median κ coefficient  of agreement  between  a  rater  and  an  experienced clinician was 0.98.

The parent or guardian also provided information about pregnancy and delivery history and the child's infancy. Socioeconomic status (SES) was established using categories delineated by Hollingshead (12). The Global Assessment of Functioning (GAF) scale was used to measure overall adaptive functioning (13,14).

>

Siblings with CBCL

FIGURE 1. PRISMA diagram. Siblings were excluded if they were not 6 - 18 years old or if they were missing CBCL data. Our

Siblings of Probands

Siblings ages 6 - 18

Siblings excluded:

Siblings excluded:

Siblings of Control

Siblings of ADHD

Siblings without ATs

Siblings with ATs

Siblings without ATs

Siblings with ATs

All siblings of Control

N = 231

final sample was restricted to the siblings of ADHD probands because only 3 siblings of control probands had ATs

N = 712

N = 588

too young or too old

data

missing CBCL data

Probands

Probands

N = 3

N = 242

N = 15

probands excluded due to

small number with ATs All Probands N = 522

Interpersonal and psychosocial functioning was assessed  with  the  use  of  the  Social  Adjustment Inventory  for  Children  and  Adolescents  (SAICA) (15). To better gauge social difficulties, we computed an  index  of  social  dysfunction  that  we  previously called Social Disability . This index was computed with the use of methodology recommended by Reynolds (16); it was previously implemented by our team to assess learning disabilities on the basis of the discrepancy  between  the  expected  SAICA  scaled score (derived from the estimated full-scale intelligence  quotient)  and  the  actual  SAICA  scaled score (17).

To  evaluate  school  functioning,  three  indices  of school  difficulties  were  used:  placement  in  special classes, extra tutoring, and repeated grades as reported by the parent or guardian.

Intellectual functioning was assessed through the Vocabulary, Block Design, Digit Span, Digit Symbol, Digit Coding, and Arithmetic subtests of the Wechsler  Intelligence  Scale  for  Children-Revised (WISC-R)  (18).  By  using  procedures  suggested  by Sattler  (19),  we  estimated  the  full-scale  intelligence quotient  from  the  Block  Design  and  Vocabulary subtests  of  the  WISC-R  using  age-corrected  scaled scores. We computed the Freedom from Distractibility  intelligence  quotient  using  the  Digit Span, Digit Coding, and oral Arithmetic subscales of the  WISC-R.  Reading  and  arithmetic  achievement were  assessed  using  subtests  of  the  Wide  Range Achievement Test-Revised (WRAT-R) (20).

FIGURE 1. PRISMA diagram. Siblings were excluded if they were not 6 - 18 years old or if they were missing CBCL data. Our final sample was restricted to the siblings of ADHD probands because only 3 siblings of control probands had ATs

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## Statistical Analyses

All analyses were performed using regression models with robust standard errors to account for the nonindependence of siblings. Differences in sociodemographic characteristics were assessed with the use of linear regression for continuous outcomes and  logistic  regression  for  binary  outcomes.  We controlled  for  any  demographic  confounder  that reached significance at the 0.05 alpha level. Rates of ATs in siblings of probands with ADHD and control probands  were  analyzed  using  logistic  regression. Analyses for psychiatric disorders, emotional dysregulation,  school  functioning,  social  disability, and  perinatal  complications  were  performed  using exact logistic regression with permutation testing to deal with the issues of small expected numbers and non-independence.  Analyses  of  the  CBCL,  GAF, SAICA, WISC-R, and WRAT-R items were performed with the use of linear regression. All tests were  two-tailed,  with  the  alpha  set  at  0.05.  We calculated  all  statistics  using  Stata  software  version 14.0 (StataCorp, College Station, TX).

## Results

Although there were 712 siblings in total, 221 siblings were  excluded  because  they  did  not  meet  the  age criteria of 6 to 18 years or because they were missing CBCL information (Figure 1). Thus, our final sample included 257 siblings of probands with ADHD and 234 siblings of control probands. Siblings had mean ± standard  deviation  ages  of  10.7  ±  3.2  years  and SES scores of 1.8 ± 0.9. Of the 230 subjects who reported their race, 94% were Caucasian.

FIGURE 2. Rate of ATs in siblings of Control probands (n = 234), siblings of ADHD probands without ADHD (n = 201), and siblings of ADHD probands with ADHD (n = 56). a Compared to siblings of Control

b

probands.

Compared to siblings of ADHD probands without ADHD.

*p<

0.05, **<0.005,

***p<

0.001

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As  shown  in  Figure  2,  rates  of  ATs  were  lowest among the siblings of control probands, intermediate among  siblings  of  probands  with  ADHD  without ADHD,  and  highest  among  siblings  of  probands with ADHD with ADHD. Siblings of probands with ADHD  had  significantly  higher  rates  of  ATs  as compared with siblings of control probands (6% vs. 1%; χ 2  = 5.92; p = .02). Because only three siblings of control probands had positive AT  profiles, analyses  were  limited  to  siblings  of  probands  with ADHD with ATs (n = 15) and without ATs (n = 242).

TABLE 1. Sociodemographic Characteristics

| Characteristic   | Sibling without ATs N = 242   | Siblings with ATs N = 15   | Test Statistic   |   p- Value |
|------------------|-------------------------------|----------------------------|------------------|------------|
| Age              | 10.6 ± 3.2                    | 9.9 ± 3.4                  | t (174) = -0.83  |       0.41 |
| Sex (Male)       | 123 (51)                      | 11 (73)                    | X 2 = 3.03       |       0.08 |
| SES              | 1.9 ± 0.9                     | 2.3 ± 0.9                  | X 2 = 4.86       |       0.03 |

Data are presented as mean ± SD or n (%)

TABLE 2. Pregnancy and Infancy Characteristics

|                               | Sibling without ATs N = 222   | Siblings with ATs N = 15   | Test Statistic   | p- Value   |
|-------------------------------|-------------------------------|----------------------------|------------------|------------|
| Pregnancy Characteristics     |                               |                            |                  |            |
| Excessive nausea              | 48 (22)                       | 3 (20)                     | Exact            | 0.30       |
| Infection                     | 26 (12)                       | 5 (33)                     | Exact            | <0.001     |
| High blood pressure           | 37 (17)                       | 4 (27)                     | Exact            | 0.002      |
| Accidents                     | 6 (3)                         | 0 (0)                      | Exact            | 0.04       |
| Family Problems               | 31 (14)                       | 5 (33)                     | Exact            | <0.001     |
| Medications                   | 55 (25)                       | 7 (47)                     | Exact            | <0.001     |
| Smoking ( 3 mo. at gestation) | 32 (14)                       | 5 (33)                     | Exact            | 0.002      |
| Infancy Characteristics       |                               |                            |                  |            |
| Switch formulas               | 18 (8)                        | 7 (47)                     | Exact            | <0.001     |
| Crying infant                 | 28 (13)                       | 5 (33)                     | Exact            | 0.003      |
| Stiffened infant              | 5 (2)                         | 2 (13)                     | Exact            | <0.001     |

Data are presented as mean ± SD or n (%)

## Sociodemographic Characteristics

The siblings with ATs were of similar age and sex as the  siblings  without  ATs  but  were  of  a  more disadvantaged SES status. Siblings with ATs scored an  average  of  2.3  on  the  Hollingshead  measure  of SES  as  compared  with  a  score  of  1.9  for  siblings without  ATs  (Table  1).  Therefore,  all  subsequent analyses corrected for SES.

## Patterns of Psychiatric Comorbidity and Psychopathology

The  average  number  of  psychiatric  disorders  was significantly higher for siblings with ATs as compared with siblings without ATs (t (174) = 3.68; p <  .001;  Figure  3, A ).  As  compared  with  siblings without  ATs,  siblings  with  ATs  had  a  significantly higher prevalence of ADHD,  other disruptive behavior disorders, mood disorders, multiple (two or more)  anxiety  disorders,  language  disorders,  and elimination disorders ( p < .005 for all; see Figure 3, B ). Likewise, individual and composite scores on all CBCL  clinical  scales  as  well  as  the  two  CBCL emotional  dysregulation  profiles  were  significantly more impaired among siblings with ATs as compared with those without ATs ( p ≤ .001 for all; see Figure 3, C and D ).

## Psychosocial Functioning

As compared with siblings without ATs, siblings with ATs had significantly more impaired GAF scores (t (174) = -4.75; p < .001; Figure 4, A ), significantly more impaired  scores  on  the  CBCL  social  and  school competence scales ( p < .001 for both; see Figure 4, B ), significantly more impaired SAICA scaled scores on 7 of the 10 subscales ( p < .05 for all; see Figure 4, C ),  and  a  significantly  higher  prevalence  of  social disability (Exact; p = .001; see Figure 4, D ).

## School Functioning

Siblings  with  ATs  were  significantly  more  likely  to receive  tutoring  (χ 2  =  10.69; p =  .001)  and  to  be placed in special classes (Exact; p <  .001) as compared with siblings without ATs (Figure 5, A ).

## Neuropsychological Functioning

Siblings with ATs had lower composite cognitive and achievement  WISC-R  and  WRAT-R  scores  than siblings without ATs (see Figure 5, B ) as well as lower scores on the five WISC-R subscales (see Figure 5, C ). However, these differences failed to reach our a priori threshold for statistical significance.

Ave rage #

of Payckiatric Diorden

80

75

70

65

60

55

50

45

40

35

T-Score FIGURE 3. Rates of psychopathology in siblings with and without ATs. A. Average number of psychiatric disorders. B. Comorbid disorders. C. Clinical CBCL

D. CBCL emotional dysregulation profiles. · Compared to siblings withoutATs. *p<0.05, **p<0.005, ***p<0.001

A. Average Number of Psychiatric Disorders

· Without ATs · With AT

3

2

C. Clinical CBCL

Wande B. Comorbid Disorders

· Without ATs · With ATs

ADHD

Disruptive Disorders

Mood Disorders

Multiple Anxiety Disorders

Language Disorders

Elimination Disorders

Substance Use Disorders

Clinical Scales

0 10 20 30 40 50

% Endorsed

* Age restricted to 2 12 years old. Without A Ts n = 88; With ATsn = 5

<!-- image -->

<!-- image -->

Composite Scales

<!-- image -->

2***

60

70

80

Average GAF Score

SAICA Scaled Score FIGURE 4. Social functioning in siblings with and without ATs. A. GAF score. B. CBCL social functioning scale. C. SAICA individual item scores. D. Social disability. ·Comparedto siblings without ATs. *ps0.05,

**p<0.005, ***p<0.001

A. Average GAF Score

· Without ATs· With ATs

70

60

50

40

30

20

10

0

C. SAICA

3,5

2***

Pree lema

C. SAICA-Relationship with Father: WithoutATs n = 203, With ATs n = 9

D. Social Disability: Without ATs n = 204, With ATs n = 9

B. CBCL Social Functioning Scale

→ Without ATs

55

2*** 50 FIGURE 5. School functioning in siblings with and withoutATs. ·Compared to siblings without ATs. *p«0.05, **p‹0.005, ***p<0.001

<!-- image -->

<!-- image -->

2,5

,5

0,5

0

- With ATs FIGURE 6. Neuropsychological functioning in siblings. A. Composite cognitive and achievement scores for WISC-R and WRAT. B. WISC-R subscales A. Composite Cognitive and Achierement Scores for WISC-R and WRAT

→ Without ATs

Scaled Score

110

105

100

95

90

85

B. WISC-R Subscales

14

13

12

= °

9

Without ATs n = 107, With ATs n = 10

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## Perinatal Complications

Rates of all but two pregnancy and infancy characteristics were significantly higher among siblings with ATs as compared with siblings without ATs ( p < .005 for all), and the rate of accidents during pregnancy  was  significantly  lower  among  siblings with ATs (Exact; p = .04; Table 2).

## Discussion

Our findings reveal that ATs can be identified in a sizeable number of non-referred children, particularly  in  those  with  ADHD.  The  results  also show that the presence of ATs heralds a significantly more  compromised  clinical presentation that is characterized by higher rates of psychopathological, neuropsychological, and  interpersonal deficits as compared with children without these traits. These results  in  non-referred  youth  are  highly  consistent with the findings of previous reports (3-5) and with our findings from probands with ADHD (21). Thus, these results provide further support for the clinical relevance of ATs, irrespective of referral status.

Non-referred youth with ATs exhibited significantly higher rates of comorbid psychopathology as compared with those without ATs as expressed by the significantly higher mean number of comorbid  psychiatric disorders; the significantly higher  rates  of  disruptive  behavior,  mood,  anxiety, language,  and  elimination  disorders;  the  marked impairments  noted  in  all  individual  and  composite CBCL clinical scales; and higher rates of emotional dysregulation.  These  findings  are  highly  consistent with those previously reported for referred probands with ADHD with ATs and therefore stress the heavy burden of psychopathology associated with ATs in both referred and non-referred youth. These findings are also in line with the prevailing literature, which highlights higher-than-expected rates of major psychiatric disorders among youth with ASD (22-24).

Also consistent with previously reported findings in  probands  with  ADHD  (6)  is  that  non-referred siblings  with  ATs  experienced  greater  social  and interpersonal  deficits  as  manifested  by  significantly worse  GAF  scores,  significantly  more  impaired CBCL social and school competence scores, significantly  more  impaired SAICA  scores,  and

- With ATs higher  rates  of  social  disability  as  compared  with siblings without ATs. Taken together, these findings provide further evidence that ATs severely affect the social  functioning  of  afflicted  children,  irrespective of referral status.

Although  the  results  did  not  reach  our  a  priori threshold  for  statistical  significance,  we  found  that non-referred siblings  with  ATs  had  similarly  lower full-scale intelligence quotients, Freedom from Distractibility intelligence quotients, and Block Design and Digit Symbol WISC-R scores as well as lower WRAT-R reading and arithmetic scores. These findings, which mirror what we reported for probands with ADHD, suggest that neurocognitive functioning is compromised in the presence of ATs.

Also in line with the findings reported for probands with ADHD is that non-referred children with ATs had higher rates of most pregnancy and infancy complications as compared with those of other children without ATs. These findings further support the hypothesis that perinatal complications alone-or in combination with genetic risk factors-could account for the development of ATs in some children.

Extending findings related to ATs to non-referred children has important clinical and scientific implications.  Documenting  the  broad  and  severe spectrum  of  morbidity  and  dysfunction  associated with ATs in non-referred youth supports the clinical relevance  of  ATs  independent  of  referral  status. Considering how easy it is to screen for ATs with the use of the CBCL, clinicians in the community have access to a rapid, inexpensive, and informative tool to  screen  children  in  clinical  practice.  Significant morbidity and dysfunction are associated with ATs, so  the  ability  to  identify  children  with  ATs  may facilitate the development of appropriate interventions that target afflicted children. This knowledge will help to inform future research aimed at identifying distinct genetic, neural, and therapeutic targets related to ATs.

The present study contains several strengths. Most notably, it relies on a large number of non-referred youth and an operationalized definition of ATs that is based on a unique profile of the empirically derived CBCL for the identification of ATs, which can be easily used in the clinical setting.

However, our findings need to be viewed in light of  certain  limitations.  Despite  the  strength  of  the findings,  our  sample  of  non-referred  siblings  with ATs was relatively small. Although ATs were more strongly associated with ADHD, the sample size was too small to allow for a separate analysis limited to siblings with ADHD. Because we lacked an adequate sample of siblings of control probands with ATs, the analysis  was  limited  to  siblings  of  probands  with ADHD.  Although  autism  was  excluded  in  the probands, it was not systematically ruled out in the siblings,  thereby  allowing  for  the  possibility  that some  of  the  children  may  have  had  undiagnosed ASD.  Although  this  analysis  was  limited  to  the siblings of probands with ADHD with and without ATs,  we  have  previously  documented  the  high discriminant validity of the CBCL-AT profile for the identification  of  ASD  in  a  psychiatrically  referred population of youth. (5) It is  also  notable  that  the presence  of  ATs  in  the  siblings  of  children  with ADHD was associated with lower SES, even though the  analysis  was  controlled  for  social  class.  The present findings are in line with our previous report of  poorer  SES  being  more  highly  associated  with children  with  ADHD  with ATs  than  without  ATs (6).  Our  findings  are  consistent  with  those  of  the prevailing literature, which suggest a higher association of ASD with low SES (25). Furthermore, we found a lower rate of accidents during pregnancy among siblings with ATs. This was an unexpected finding, so additional studies are needed to verify this result. Finally, our sample was largely Caucasian, but our  findings  may  not  generalize  to  other  ethnic groups.

Despite these considerations, our work shows that ATs can be identified in a sizeable minority of nonreferred children and that these children are at high risk  for  significant  morbidity  and  disability.  More work  is  needed  to  replicate  these  findings  and  to further examine their prognostic usefulness.

## Clinical Significance

The current findings suggest that elevated scores on the CBCL-AT subscale may indicate a need to clinically assess a child for ASD and ADHD; mood, anxiety, and disruptive behavior disorders; emotional dysregulation; and impaired social and school functioning.

## Authors' Disclosures:

Dr.  Joseph  Biederman  is  currently  receiving  research support from the following sources: the US Department of  Defense,  the  US  Food  and  Drug  Administration, Ironshore,  Lundbeck,  Magceutics  Inc.,  Merck,  Pamlab, Pfizer,  Shire  Pharmaceuticals  Inc.,  SPRITES,  Sunovion, Vaya Pharma/Enzymotec, and the National Institutes of Health. In 2015, Dr. Biederman received honoraria from the  Massachusetts  General  Hospital  (MGH)  Psychiatry Academy for tuition-funded continuing medical education (CME) courses. He has a US patent application pending (Provisional Number #61/233,686) through  MGH corporate  licensing  for  a  method  to  prevent  stimulant abuse. In 2014, Dr. Biederman received honoraria from the  MGH Psychiatry Academy for tuition-funded CME courses. He received research support from the American Academy  of  Child  and  Adolescent  Psychiatry,  Alcobra, Forest Research Institute, and Shire Pharmaceuticals Inc. Dr.  Biederman  received  departmental  royalties  from  a copyrighted rating scale used for ADHD diagnoses paid by  Ingenix,  Prophase,  Shire,  Bracket  Global,  Sunovion, and Theravance; these royalties were paid to the Department of Psychiatry at MGH.  In 2013, Dr. Biederman  received  an  honorarium  from  the  MGH Psychiatry Academy for a tuition-funded CME course. He received research support from the American Professional Society  of  ADHD  and  Related  Disorders,  ElMindA, McNeil, and Shire. Dr. Biederman received departmental royalties from a copyrighted rating scale used for ADHD diagnoses paid by Shire and Sunovion; these royalties were paid to the Department of Psychiatry at MGH. In 2012, Dr. Biederman received an honorarium from the MGH Psychiatry  Academy  and  The  Children's  Hospital  of Southwest Florida/Lee Memorial  Health System for tuition-funded CME courses.

Dr.  Stephen  Faraone  has  received  income,  potential income,  travel  expenses,  and/or  research  support  from Arbor,  Pfizer,  Ironshore,  Shire,  Akili  Interactive  Labs, CogCubed, Alcobra, VAYA Pharma, Neurovance, Impax, and  NeuroLifeSciences  during  the  past  year.  With  his institution, he has US patent no. US20130217707 A1 for the  use  of  sodium-hydrogen  exchange  inhibitors  in  the treatment  of  ADHD.  In  previous  years,  he  received income or research support from Shire, Alcobra, Otsuka, McNeil,  Janssen,  Novartis,  Pfizer,  and  Eli  Lilly.  Dr. Faraone  receives royalties  from  books  published  by Guilford  Press (Straight  Talk  about  Your  Child's  Mental Health) , Oxford University Press (Schizophrenia: The Facts) , and Elsevier (ADHD: Non-Pharmacologic Interventions) .

Dr. Ronna Fried is currently receiving research support from  Lundbeck.  In  2015,  Dr.  Fried  received  honoraria from  the  MGH  Psychiatry  Academy  for  tuition-funded CME courses. During previous years, Dr. Fried received research support from the National Institutes of Health and Shire.

Dr. Gagan Joshi is supported by the National Institute of  Mental  Health  of  the  National  Institutes  of  Health under  award  no.  K23MH100450.  He  receives  research support from Pfizer and the Simons Center for the Social Brain as a principal investigator for investigator-initiated studies.  In  addition,  he  receives  research  support  from Duke  University, Forest Research Laboratories, and Sunovion Pharmaceuticals as a site principal investigator for multi-site trials. He is a co-investigator for clinical trials sponsored  by  the  US  Department  of  Defense,  Pamlab, and Schering-Plough Corporation. He received an honorarium  from  the  Governor's  Council  for  Medical Research  and  Treatment  of  Autism  in  New  Jersey  for grant review activities, and he received speaker's honorariums from the American Academy of Child and Adolescent  Psychiatry,  MGH  Psychiatry  Academy,  and the Medical Society of Delaware.

Ms.  Maura  Fitzgerald,  Ms.  Yvonne  Woodworth,  Ms. Alexandra Saunders, and Ms. Kristina Conroy do not have any disclosures to declare.

## Acknowledgments:

The data acquisition from which this analysis was derived was funded by National Institute of Mental Health grants MH-41314,  HD036317,  and  MH050657  to  Dr.  Joseph Biederman. The manuscript and the analysis of the data were indirectly supported by the Pediatric Psychopharmacology  Research  Council  Fund  and  the Saylor  family  fund  supporting  research  in  autism.  Dr. Gagan  Joshi  is  supported  by  the  National  Institute  of Mental Health of the National Institutes of Health under award no. K23MH100450. Professor Faraone is supported  by  the  K.G.  Jebsen  Centre  for  Research  for Neuropsychiatric Disorders, University of Bergen, Bergen, Norway; the European Union's Seventh Framework Programme for Research, Technological Development, and Demonstration under grant agreement no. 602805, and National Institute of Mental Health grant nos. R13MH059126 and R01MH094469.

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