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Figure 1.
Results of Categorical Analyses
Results of Categorical Analyses

The x, y, and z are Montreal Neurologic Institute coordinates. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) showed significant influences of diagnosis. Statistically significant clusters converged in the corpus callosum, especially the anterior portions. FWE indicates familywise error; R, right; and TFCE, threshold-free cluster enhancement. The numbers in the Venn diagram denote the number of voxels in the clusters that were significant for each diffusion tensor imaging metric alone or combined with any of the others. Secondary post hoc group comparisons showed that these differences were driven by autism spectrum diagnosis (eFigure 6 in the Supplement).

Figure 2.
Results of Dimensional Analyses for the Social Responsiveness Scale by Parents (SRS-P)
Results of Dimensional Analyses for the Social Responsiveness Scale by Parents (SRS-P)

The x, y, and z are Montreal Neurologic Institute coordinates. The dimensional approach identified clusters in which fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) were associated with SRS-P total T scores. Clusters identified by these 3 diffusion tensor imaging metrics converged in the corpus callosum from its anterior to posterior regions. The numbers in the Venn diagram denote the number of voxels in the clusters that were significant for each diffusion tensor imaging metric alone or combined with any of the others. In the right column, scatterplots show the relationship across all participants between each diffusion tensor imaging metric and SRS-P total T scores (residuals accounting for the nuisance covariates included in the model are plotted). ADHD indicates attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; FWE, familywise error; R, right; TDC, typically developing children; and TFCE, threshold-free cluster enhancement.

Table.  
Participant Characteristics
Participant Characteristics
Supplement.

eAppendix. Supplemental Appendix

eTable 1. Characteristics of the Study Clinical Sample

eTable 2. Comparisons Between Data Passing DTI QA and Those Who Had Not

eTable 3. Comparisons Between ADHD and TDC Kids Included in Final Sample and Those Excluded

eTable 4. Clusters Results of Categorical TBSS Analyses

eTable 5. Clusters Results of Dimensional TBSS Analyses With SRS-P Covarying for CPRS-R:LV DSM-IV Total T Score

eTable 6. Clusters Results of Dimensional TBSS With CPRS-R:LV DSM-IV Inattentive T Score Covarying for CPRS-R:LV Hyperactive/Impulsive

eTable 7. Clusters Results of Dimensional TBSS With CPRS-R:LV DSM-IV Inattentive T Score Covarying for SRS-P and CPRS-R:LV Hyperactive/Impulsive

eFigure 1. Scatterplots of the Social Responsiveness Scale by Parents (SRS-P) and Conners’ Parent Rating Scales-Revised: Long Version (CPRS-R:LV) DSM-IV Scores by Diagnostic Group

eFigure 2. TBSS Fractional Anisotropy (FA) Analyses Based on Skeletons Generated With Different FA Thresholds

eFigure 3. Raw Images of Categorical Analyses of Each DTI Metric

eFigure 4. Raw Images of the Dimensional Analyses of the Social Responsiveness Scale by Parents (SRS-P)

eFigure 5. Raw Images of Dimensional Analyses of Conners’ Parent Rating Scales-Revised: Long Version (CPRS-R:LV) DSM-IV Inattentive T Scores

eFigure 6. Post Hoc Analyses of Categorical Approaches in Data With and Without DTIPrep Preprocessing

eFigure 7. Results of Voxelwise Dimensional Model Conducted in the Primary Analyses Adding Autism Spectrum Disorder (ASD) Membership as a Covariate

eFigure 8. Scatterplots of the Children’s Communication Checklist-2 (CCC-2) Subscores Against Extracted DTI Metrics

eFigure 9. Results of Dimensional Analyses of the Conners’ Parent Rating Scales-Revised: Long Version (CPRS-R:LV) DSM-IV Inattentive T Scores

eFigure 10. Results of Cluster Level Dimensional Analyses Using Data With DTIPrep

eFigure 11. Results of Cluster Level Dimensional Analyses of Subdomain of Attention-Deficit/Hyperactivity Disorder Symptoms Using Data With DTIPrep

1.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis [published correction appears in Lancet. 2013;381(9875):1360].  Lancet. 2013;381(9875):1371-1379.PubMedGoogle ScholarCrossref
2.
Insel  T, Cuthbert  B, Garvey  M,  et al.  Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.  Am J Psychiatry. 2010;167(7):748-751.PubMedGoogle ScholarCrossref
3.
Rommelse  NN, Franke  B, Geurts  HM, Hartman  CA, Buitelaar  JK.  Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder.  Eur Child Adolesc Psychiatry. 2010;19(3):281-295.PubMedGoogle ScholarCrossref
4.
Goldstein  S, Schwebach  AJ.  The comorbidity of pervasive developmental disorder and attention deficit hyperactivity disorder: results of a retrospective chart review.  J Autism Dev Disord. 2004;34(3):329-339.PubMedGoogle ScholarCrossref
5.
Gadow  KD, DeVincent  CJ, Pomeroy  J.  ADHD symptom subtypes in children with pervasive developmental disorder.  J Autism Dev Disord. 2006;36(2):271-283.PubMedGoogle ScholarCrossref
6.
Grzadzinski  R, Dick  C, Lord  C, Bishop  S.  Parent-reported and clinician-observed autism spectrum disorder (ASD) symptoms in children with attention deficit/hyperactivity disorder (ADHD): implications for practice under DSM-5 Mol Autism. 2016;7:7.PubMedGoogle ScholarCrossref
7.
Hernandez  LM, Rudie  JD, Green  SA, Bookheimer  S, Dapretto  M.  Neural signatures of autism spectrum disorders: insights into brain network dynamics.  Neuropsychopharmacology. 2015;40(1):171-189.PubMedGoogle ScholarCrossref
8.
Minshew  NJ, Williams  DL.  The new neurobiology of autism: cortex, connectivity, and neuronal organization.  Arch Neurol. 2007;64(7):945-950.PubMedGoogle ScholarCrossref
9.
Vissers  ME, Cohen  MX, Geurts  HM.  Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links.  Neurosci Biobehav Rev. 2012;36(1):604-625.PubMedGoogle ScholarCrossref
10.
Just  MA, Keller  TA, Malave  VL, Kana  RK, Varma  S.  Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity.  Neurosci Biobehav Rev. 2012;36(4):1292-1313.PubMedGoogle ScholarCrossref
11.
Castellanos  FX, Aoki  Y.  Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: a science in development.  Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(3):253-261.PubMedGoogle ScholarCrossref
12.
Anagnostou  E, Taylor  MJ.  Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here.  Mol Autism. 2011;2(1):4.PubMedGoogle ScholarCrossref
13.
Konrad  K, Eickhoff  SB.  Is the ADHD brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder.  Hum Brain Mapp. 2010;31(6):904-916.PubMedGoogle ScholarCrossref
14.
Jelescu  IO, Zurek  M, Winters  KV,  et al.  In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy.  Neuroimage. 2016;132:104-114.PubMedGoogle ScholarCrossref
15.
Travers  BG, Adluru  N, Ennis  C,  et al.  Diffusion tensor imaging in autism spectrum disorder: a review.  Autism Res. 2012;5(5):289-313.PubMedGoogle ScholarCrossref
16.
Aoki  Y, Abe  O, Nippashi  Y, Yamasue  H.  Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: a meta-analysis of diffusion tensor imaging tractography studies.  Mol Autism. 2013;4(1):25.PubMedGoogle ScholarCrossref
17.
Ameis  SH, Catani  M.  Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder.  Cortex. 2015;62:158-181.PubMedGoogle ScholarCrossref
18.
Liston  C, Malter Cohen  M, Teslovich  T, Levenson  D, Casey  BJ.  Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point?  Biol Psychiatry. 2011;69(12):1168-1177.PubMedGoogle ScholarCrossref
19.
van Ewijk  H, Heslenfeld  DJ, Zwiers  MP, Buitelaar  JK, Oosterlaan  J.  Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis.  Neurosci Biobehav Rev. 2012;36(4):1093-1106.PubMedGoogle ScholarCrossref
20.
Aoki  Y, Cortese  S, Castellanos  FX.  Diffusion tensor imaging studies of attention-deficit/hyperactivity disorder: meta-analyses and reflections on head motion [published online July 3, 2017].  J Child Psychol Psychiatry. doi:0.1111/jcpp.12778PubMedGoogle Scholar
21.
Ray  S, Miller  M, Karalunas  S,  et al.  Structural and functional connectivity of the human brain in autism spectrum disorders and attention-deficit/hyperactivity disorder: a rich-club organization study.  Hum Brain Mapp. 2014;35(12):6032-6048.PubMedGoogle ScholarCrossref
22.
Ameis  SH, Lerch  JP, Taylor  MJ,  et al.  A diffusion tensor imaging study in children with ADHD, autism spectrum disorder, OCD, and matched controls: distinct and non-distinct white matter disruption and dimensional brain-behavior relationships.  Am J Psychiatry. 2016;173(12):1213-1222.PubMedGoogle ScholarCrossref
23.
Di Martino  A, Zuo  XN, Kelly  C,  et al.  Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2013;74(8):623-632.PubMedGoogle ScholarCrossref
24.
Chantiluke  K, Christakou  A, Murphy  CM,  et al; MRC AIMS Consortium.  Disorder-specific functional abnormalities during temporal discounting in youth with attention deficit hyperactivity disorder (ADHD), autism and comorbid ADHD and autism.  Psychiatry Res. 2014;223(2):113-120.PubMedGoogle ScholarCrossref
25.
Constantino  JN, Davis  SA, Todd  RD,  et al.  Validation of a brief quantitative measure of autistic traits: comparison of the Social Responsiveness Scale with the Autism Diagnostic Interview–Revised.  J Autism Dev Disord. 2003;33(4):427-433.PubMedGoogle ScholarCrossref
26.
Conners  CK. Conners’ Rating Scales–Revised: User’s Manual. North Tonawanda, NY: Multi-Health Systems Inc; 1997.
27.
Feldman  HM, Yeatman  JD, Lee  ES, Barde  LH, Gaman-Bean  S.  Diffusion tensor imaging: a review for pediatric researchers and clinicians.  J Dev Behav Pediatr. 2010;31(4):346-356.PubMedGoogle ScholarCrossref
28.
Gotham  K, Risi  S, Pickles  A, Lord  C.  The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity.  J Autism Dev Disord. 2007;37(4):613-627.PubMedGoogle ScholarCrossref
29.
Gotham  K, Pickles  A, Lord  C.  Standardizing ADOS scores for a measure of severity in autism spectrum disorders.  J Autism Dev Disord. 2009;39(5):693-705.PubMedGoogle ScholarCrossref
30.
Lord  C, Rutter  M, Le Couteur  A.  Autism Diagnostic Interview–Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders.  J Autism Dev Disord. 1994;24(5):659-685.PubMedGoogle ScholarCrossref
31.
Lord  C, Pickles  A, McLennan  J,  et al.  Diagnosing autism: analyses of data from the Autism Diagnostic Interview.  J Autism Dev Disord. 1997;27(5):501-517.PubMedGoogle ScholarCrossref
32.
Kaufman  J, Birmaher  B, Brent  D,  et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL): initial reliability and validity data.  J Am Acad Child Adolesc Psychiatry. 1997;36(7):980-988.PubMedGoogle ScholarCrossref
33.
Wechsler  D.  Manual for the Wechsler Abbreviated Intelligence Scale (WASI). San Antonio, TX: Psychological Corp; 1999.
34.
Elliott  CD, Murray  GJ, Pearson  LS.  Differential Ability Scales. San Antonio, TX: Psychological Corp; 1990.
35.
Bishop  DVM.  The Children’s Communication Checklist: CCC-2. London, England: American Speech-Language-Hearing Association; 2003.
36.
Achenbach  TM, Rescorla  L.  Manual for the Child Behavior Checklist and Revised Child Behavior Profile. Burlington: University of Vermont Dept of Psychiatry; 1983.
37.
Hollingshead  AB.  Four-Factor Index of Socioeconomic Status. New Haven, CT: Yale University; 1975.
38.
Yoncheva  YN, Somandepalli  K, Reiss  PT,  et al.  Mode of anisotropy reveals global diffusion alterations in attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2016;55(2):137-145.PubMedGoogle ScholarCrossref
39.
Jenkinson  M, Beckmann  CF, Behrens  TE, Woolrich  MW, Smith  SM.  FSL.  Neuroimage. 2012;62(2):782-790.PubMedGoogle ScholarCrossref
40.
Yendiki  A, Koldewyn  K, Kakunoori  S, Kanwisher  N, Fischl  B.  Spurious group differences due to head motion in a diffusion MRI study.  Neuroimage. 2014;88:79-90.PubMedGoogle ScholarCrossref
41.
Winkler  AM, Ridgway  GR, Webster  MA, Smith  SM, Nichols  TE.  Permutation inference for the general linear model.  Neuroimage. 2014;92:381-397.PubMedGoogle ScholarCrossref
42.
Smith  SM, Nichols  TE.  Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.  Neuroimage. 2009;44(1):83-98.PubMedGoogle ScholarCrossref
43.
O’brien  RM.  A caution regarding rules of thumb for variance inflation factors.  Qual Quant. 2007;41(5):673-690.Google ScholarCrossref
44.
Mori  S, Oishi  K, Jiang  H,  et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template.  Neuroimage. 2008;40(2):570-582.PubMedGoogle ScholarCrossref
45.
Hus  V, Bishop  S, Gotham  K, Huerta  M, Lord  C.  Factors influencing scores on the Social Responsiveness Scale.  J Child Psychol Psychiatry. 2013;54(2):216-224.PubMedGoogle ScholarCrossref
46.
Oguz  I, Farzinfar  M, Matsui  J,  et al.  DTIPrep: quality control of diffusion-weighted images.  Front Neuroinform. 2014;8:4.PubMedGoogle ScholarCrossref
47.
Anderson  JS, Druzgal  TJ, Froehlich  A,  et al.  Decreased interhemispheric functional connectivity in autism.  Cereb Cortex. 2011;21(5):1134-1146.PubMedGoogle ScholarCrossref
48.
Hahamy  A, Behrmann  M, Malach  R.  The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder.  Nat Neurosci. 2015;18(2):302-309.PubMedGoogle ScholarCrossref
49.
van Ewijk  H, Heslenfeld  DJ, Zwiers  MP,  et al.  Different mechanisms of white matter abnormalities in attention-deficit/hyperactivity disorder: a diffusion tensor imaging study.  J Am Acad Child Adolesc Psychiatry. 2014;53(7):790-9.e3.PubMedGoogle ScholarCrossref
50.
Paul  LK, Brown  WS, Adolphs  R,  et al.  Agenesis of the corpus callosum: genetic, developmental and functional aspects of connectivity.  Nat Rev Neurosci. 2007;8(4):287-299.PubMedGoogle ScholarCrossref
51.
Wegiel  J, Flory  M, Kaczmarski  W,  et al.  Partial agenesis and hypoplasia of the corpus callosum in idiopathic autism.  J Neuropathol Exp Neurol. 2017;76(3):225-237.PubMedGoogle Scholar
52.
Pryweller  JR, Schauder  KB, Anderson  AW,  et al.  White matter correlates of sensory processing in autism spectrum disorders.  Neuroimage Clin. 2014;6:379-387.PubMedGoogle ScholarCrossref
53.
Alexander  AL, Lee  JE, Lazar  M,  et al.  Diffusion tensor imaging of the corpus callosum in autism.  Neuroimage. 2007;34(1):61-73.PubMedGoogle ScholarCrossref
54.
Aboitiz  F, Scheibel  AB, Fisher  RS, Zaidel  E.  Fiber composition of the human corpus callosum.  Brain Res. 1992;598(1-2):143-153.PubMedGoogle ScholarCrossref
55.
Hofer  S, Frahm  J.  Topography of the human corpus callosum revisited: comprehensive fiber tractography using diffusion tensor magnetic resonance imaging.  Neuroimage. 2006;32(3):989-994.PubMedGoogle ScholarCrossref
56.
Uekermann  J, Kraemer  M, Abdel-Hamid  M,  et al.  Social cognition in attention-deficit hyperactivity disorder (ADHD).  Neurosci Biobehav Rev. 2010;34(5):734-743.PubMedGoogle ScholarCrossref
57.
Korrel  H, Mueller  KL, Silk  T, Anderson  V, Sciberras  E.  Research review: language problems in children with attention-deficit hyperactivity disorder: a systematic meta-analytic review.  J Child Psychol Psychiatry. 2017;58(6):640-654.PubMedGoogle ScholarCrossref
58.
Kaiser  ML, Schoemaker  MM, Albaret  JM, Geuze  RH.  What is the evidence of impaired motor skills and motor control among children with attention deficit hyperactivity disorder (ADHD)? systematic review of the literature.  Res Dev Disabil. 2014;36C:338-357.PubMedGoogle Scholar
59.
Alexander  AL, Lee  JE, Lazar  M, Field  AS.  Diffusion tensor imaging of the brain.  Neurotherapeutics. 2007;4(3):316-329.PubMedGoogle ScholarCrossref
60.
Dean  DC  III, Lange  N, Travers  BG,  et al.  Multivariate characterization of white matter heterogeneity in autism spectrum disorder.  Neuroimage Clin. 2017;14:54-66.PubMedGoogle ScholarCrossref
61.
Chen  L, Hu  X, Ouyang  L,  et al.  A systematic review and meta-analysis of tract-based spatial statistics studies regarding attention-deficit/hyperactivity disorder.  Neurosci Biobehav Rev. 2016;68:838-847.PubMedGoogle ScholarCrossref
62.
Adisetiyo  V, Tabesh  A, Di Martino  A,  et al.  Attention-deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development.  Hum Brain Mapp. 2014;35(5):2148-2162.PubMedGoogle ScholarCrossref
63.
Cooper  M, Thapar  A, Jones  DK.  White matter microstructure predicts autistic traits in attention-deficit/hyperactivity disorder.  J Autism Dev Disord. 2014;44(11):2742-2754.PubMedGoogle ScholarCrossref
64.
de Luis-García  R, Cabús-Piñol  G, Imaz-Roncero  C,  et al.  Attention deficit/hyperactivity disorder and medication with stimulants in young children: a DTI study.  Prog Neuropsychopharmacol Biol Psychiatry. 2015;57:176-184.PubMedGoogle ScholarCrossref
65.
Milham  MP.  Open neuroscience solutions for the connectome-wide association era.  Neuron. 2012;73(2):214-218.PubMedGoogle ScholarCrossref
66.
Kapur  S, Phillips  AG, Insel  TR.  Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?  Mol Psychiatry. 2012;17(12):1174-1179.PubMedGoogle ScholarCrossref
67.
Chabernaud  C, Mennes  M, Kelly  C,  et al.  Dimensional brain-behavior relationships in children with attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2012;71(5):434-442.PubMedGoogle ScholarCrossref
68.
Elton  A, Di Martino  A, Hazlett  HC, Gao  W.  Neural connectivity evidence for a categorical-dimensional hybrid model of autism spectrum disorder.  Biol Psychiatry. 2016;80(2):120-128.PubMedGoogle ScholarCrossref
69.
Bishop  SL, Huerta  M, Gotham  K,  et al.  The Autism Symptom Interview, School-Age: a brief telephone interview to identify autism spectrum disorders in 5-to-12-year-old children.  Autism Res. 2017;10(1):78-88.PubMedGoogle ScholarCrossref
70.
Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC).  Prevalence of autism spectrum disorder among children aged 8 years: Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010.  MMWR Surveill Summ. 2014;63(2):1-21.PubMedGoogle Scholar
71.
Insel  TR.  The NIMH Research Domain Criteria (RDoC) project: precision medicine for psychiatry.  Am J Psychiatry. 2014;171(4):395-397.PubMedGoogle ScholarCrossref
72.
Castellanos  FX, Di Martino  A, Craddock  RC, Mehta  AD, Milham  MP.  Clinical applications of the functional connectome.  Neuroimage. 2013;80:527-540.PubMedGoogle ScholarCrossref
Original Investigation
November 2017

Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Author Affiliations
  • 1Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, New York
  • 2Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York
  • 3Center for Brain Imaging, New York University, New York
  • 4The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
  • 5Child Mind Institute, New York, New York
JAMA Psychiatry. 2017;74(11):1120-1128. doi:10.1001/jamapsychiatry.2017.2573
Key Points

Question  Do the neural correlates of autistic traits extend across diagnostic boundaries among children with autism spectrum disorder and children with attention-deficit/hyperactivity disorder?

Findings  This cross-sectional diffusion tensor imaging study analyzed data from 174 children, including 50 typically developing children and children with a primary diagnosis of autism spectrum disorder (n = 69) or attention-deficit/hyperactivity disorder (n = 55). While categorical comparisons detected a significant influence of autism spectrum disorder on multiple white matter metrics in the corpus callosum, dimensional analyses yielded an association with autism spectrum disorder symptoms and white matter metrics in a set of both callosal and other tracts, regardless of diagnosis.

Meaning  The frequent co-occurrence of autism spectrum disorder and attention-deficit/hyperactivity disorder symptoms may reflect underlying neural mechanisms that transcend diagnostic boundaries.

Abstract

Importance  Clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date.

Objective  To examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches.

Design, Setting, and Participants  This investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center. Participants were children with ASD, children with ADHD, or typically developing children. Data collection was ongoing from December 2008 to October 2015.

Main Outcomes and Measures  The primary measure was voxelwise fractional anisotropy (FA) analyzed via tract-based spatial statistics. Additional voxelwise DTI metrics included radial diffusivity (RD), mean diffusivity (MD), axial diffusivity (AD), and mode of anisotropy (MA).

Results  This cross-sectional DTI study analyzed data from 174 children (age range, 6.0-12.9 years), selected from a larger sample after quality assurance to be group matched on age and sex. After quality control, the study analyzed data from 69 children with ASD (mean [SD] age, 8.9 [1.7] years; 62 male), 55 children with ADHD (mean [SD] age, 9.5 [1.5] years; 41 male), and 50 typically developing children (mean [SD] age, 9.4 [1.5] years; 38 male). Categorical analyses revealed a significant influence of ASD diagnosis on several DTI metrics (FA, MD, RD, and AD), primarily in the corpus callosum. For example, FA analyses identified a cluster of 4179 voxels (TFCE FEW corrected P < .05) in posterior portions of the corpus callosum. Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (eg, corona radiata and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis. For example, FA analyses revealed clusters overall encompassing 12121 voxels (TFCE FWE corrected P < .05) with a significant association with parent ratings in the social responsiveness scale. Similar results were evident using an independent measure of ASD traits (ie, children communication checklist, second edition). Total severity of ADHD-traits was not significantly related to DTI metrics but inattention scores were related to AD in corpus callosum in a cluster sized 716 voxels. All these findings were robust to algorithmic correction of motion artifacts with the DTIPrep software.

Conclusions and Relevance  Dimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.

Introduction

Shared clinical and biological traits across psychiatric diagnoses have challenged the usefulness of a categorical nosology of psychiatric disorders for biological research.1-3 The challenges associated with categorical perspectives of illness are exemplified by the frequent clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD).4-6 Whether such shared clinical presentations reflect common underlying neural mechanisms remains unknown to date.

In both ASD and ADHD, abnormal large-scale networks have been consistently reported using a range of neuroimaging methods.3,7-13 Among these modalities, diffusion tensor imaging (DTI) can provide insight into the pathology of white matter organization.14 Diffusion tensor imaging studies have found atypical structural connectivity in individuals with ASD15-17 or ADHD18,19 compared with typical controls, but the findings were based on independent comparisons. These studies varied in regard to DTI metrics examined, specific spatial locations, and the nature of the DTI abnormalities found in ASD or ADHD. Nevertheless, in most cases, lower fractional anisotropy (FA) in different areas of the corpus callosum (CC) has been reported in ADHD19,20 or ASD15 relative to typical controls.

The only 2 studies21,22 that have directly contrasted white matter structure in individuals with ADHD, those having ASD, and typically developing children (TDC) yielded mixed results. One tractography study21 of 8 children with ASD, 20 children with ADHD, and 20 TDC revealed disorder-specific patterns of densely interconnected hubs (ie, rich clubs). Beyond concerns about sample size, that preliminary study did not assess the influence on white matter organization of co-occurring ASD or ADHD symptoms across diagnoses, thus missing a potential source of commonalities in DTI associations. A second DTI study22 applied tract-based spatial statistics (TBSS) to a larger sample (n = 200) of school-aged children with ASD, ADHD, or obsessive-compulsive disorder compared with TDC. Decreased FA within the splenium of the CC was common among the 3 groups.22 Brain-behavior relationships with symptom domains characteristic of each disorder, examined separately, did not yield significant findings.22 Therefore, the implications of shared CC abnormalities remain unclear. While functional MRI studies23,24 have shown the utility of stratifying ASD subgroups based on ADHD comorbidity, no imaging study to date has simultaneously examined both ASD and ADHD dimensionally in the same sample.

Accordingly, to identify specific or shared patterns of white matter organization, we analyzed DTI data from 174 school-aged children with ASD, those having ADHD, or TDC. We adopted both a categorical diagnostic approach (ie, comparisons of diagnostic groups) and dimensional analyses of ASD-related and ADHD-related traits across diagnostic groups. The Social Responsiveness Scale by Parents (SRS-P)25 and Conners’ Parent Rating Scales–Revised: Long Version (CPRS-R:LV)26 were used for dimensional analyses. To examine their unique contributions, these measures were included in the same model, hence removing shared variance. Fractional anisotropy was our primary measure of interest. Because other DTI metrics may provide distinct complementary information about white matter structure,27 although rarely investigated together in ASD or ADHD,20 secondary analyses also explored voxelwise mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and mode of anisotropy (MA). Data collection was ongoing from December 2008 to October 2015.

Methods
Participants

We analyzed data from 174 children (age range, 6.0-12.9 years), including 50 TDC and children with a primary diagnosis of either ASD (n = 69) or ADHD (n = 55), selected from a larger sample after quality assurance (see “Preprocessing” and the eAppendix in the Supplement) to be group matched on age and sex (Table and eTable 1 in the Supplement). Clinicians’ diagnoses of ASD and ADHD were based on DSM-IV-TR codes supported by parent interview, direct observation, available teacher forms, and prior records (eAppendix in the Supplement). Autism spectrum disorder diagnosis was supported by the Autism Diagnostic Observation Schedule (research reliable n = 68)28,29 and the Autism Diagnostic Interview–Revised (research reliable n = 65).30,31 Absence of Axis I diagnosis per the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version32 and absence of a history of psychotropic medication use were required for inclusion as TDC. Exclusion criteria for all participants were current use of antipsychotics, known genetic diseases, or below 80 on the estimated full-scale IQ.33,34

To discern brain-behavior relationships, we used parent ratings of ASD traits and ADHD traits indexed by SRS-P total T scores25 and CPRS-R:LV DSM-IV total T scores,26 respectively (eFigure 1 in the Supplement). Parent ratings of the Children’s Communication Checklist 2 (CCC-2)35 and the Child Behavior Checklist36 further characterized the sample. Parents also reported on race and socioeconomic status indexed by the Four-Factor Index of Socioeconomic Status by Hollingshead.37 Data from 29 TDC and 29 children with ADHD in our sample were included in a previous DTI study.38 The institutional review boards of the New York University and the New York University School of Medicine granted ethical approval of the study. Written parental consent and verbal assent were obtained for all participants; children older than 7 years also provided written informed assent.

Data Acquisition

Two DTI scans were acquired using a twice-refocused diffusion-weighted echoplanar imaging sequence (repetition time, 5200 milliseconds; echo time, 78 milliseconds; 50 sections; 64 × 64–pixel acquisition matrix; field of view, 192 mm; voxel size, 3 × 3 × 3 mm; 64 noncollinear diffusion directions, uniformly distributed around a unit sphere with B value of 1000 s/mm2; 1 image with no diffusion weighting) at the New York University Center for Brain Imaging using a 3.0-T imaging system (Allegra; Siemens). We obtained T1-weighted images using 3-dimensional magnetization-prepared rapid acquisition gradient echo for anatomical registration (eAppendix in the Supplement).

Preprocessing

To enhance signal to noise, analyses were conducted only in children who completed 2 DTI scans. Analyses were conducted with Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library version 5 (http://www.fmrib.ox.ac.uk). Quality assurance involved eddy current and motion corrections, as well as removal of nonbrain tissue. Our head motion index was the mean absolute intervolume displacement with respect to the first image of each run; root-mean-square (RMS) deviation was calculated using FSL (the FMRIB Software Library) rmsdiff.39 Given that motion introduces artifacts in DTI metrics,40 we only included data with a mean absolute RMS less than 5 mm and image quality passing visual inspection. As a result, 69 of 83 children with ASD, 66 of 82 children with ADHD, and 68 of 79 TDC passed quality assurance; they did not differ from those excluded in demographic or primary clinical measures (eTable 2 and eTable 3 in the Supplement). As detailed in the eAppendix in the Supplement, to match diagnostic groups by age and sex, 11 children with ADHD and 18 TDC were further excluded, yielding a final sample of 174 children.

TBSS Proprocessing

First, nonlinear registration to a common space was conducted by aligning each participant’s FA image to the Montreal Neurologic Institute 152 space template. Then, a mean FA image and a mean FA skeleton of the aligned images were created. Voxelwise analyses were subsequently conducted for skeleton areas with an FA of at least 0.2. Analyses with more stringent FA thresholds (ie, ≥0.25 and ≥0.3) yielded similar results (eFigure 2 in the Supplement). Voxelwise analyses for secondary DTI metrics were conducted using values projected onto the mean FA skeleton.

Group Analyses
Categorical Approach

To examine the main influence of diagnosis on DTI metrics, we performed an F test using FSL Randomize.41 Age, sex, and motion were included as nuisance covariates. Statistical significance was set at threshold-free cluster enhancement (TFCE) P < .05 to control for familywise error (FWE) rate (α = .05), thus accounting for multiple comparisons.42 Post hoc pairwise group comparisons were conducted for clusters showing significant main group associations for each metric separately.

Dimensional Approach

We assessed the association between DTI metrics and ASD traits or ADHD traits (indexed by SRS-P or CPRS-R:LV DSM-IV total T scores, respectively) using FSL Randomize across all participants, regardless of diagnosis (ie, ASD, ADHD, and TDC). Nuisance covariates were age, sex, and motion. In addition, to identify their unique contributions to brain-behavior relationships and given their significant relationship (r = 0.59, df = 169, P < .001), we included both SRS-P and CPRS-R:LV DSM-IV total T scores in the same model. There was no multicollinearity across covariates (variation inflation factor <4).43 Analogous to the categorical approach, statistical significance was set at TFCE FWE corrected 1-sided P < .05. For interpretation, only TBSS results within the International Consortium for Brain Mapping DTI-81 atlas44 are reported and labeled accordingly (raw images are shown in eFigures 3, 4, and 5 in the Supplement).

Results
Categorical Approach
Fractional Anisotropy

Values in clusters located in the genu, body, and splenium of the CC differed across diagnostic groups (overall 4179 voxels; Figure 1 and eTable 4 in the Supplement). Post hoc analyses revealed significantly lower FA among children with ASD compared with both children with ADHD and TDC in these clusters. Children with ADHD and TDC did not differ significantly in regard to the mean FA for these clusters (eFigure 6 in the Supplement).

Other DTI Metrics

A significant influence of ASD diagnosis was also evident in several portions of the CC for all DTI metrics examined except MA (Figure 1 and eTable 4 in the Supplement). Similar to the FA results, children with ADHD did not differ significantly from TDC in any of the diffusivity metrics or MA. In these clusters, children with ASD had significantly higher mean MD, RD, and AD compared with those having ADHD and TDC (eFigure 6 in the Supplement). The number of voxels identified by these categorical TBSS analyses of MD, RD, and AD was 1280, 2721, 523, respectively. Across these DTI metrics, ASD-related abnormalities converged on the midbody and anterior portions of the CC (Figure 1).

Dimensional Approach
Fractional Anisotropy

Dimensional analyses revealed a significant relationship between FA and SRS-P total T scores but not for CPRS-R:LV DSM-IV total T scores. Specifically, across participants, FA was significantly and negatively related to SRS-P scores in clusters overall encompassing 12121 voxels extending from anterior to posterior regions of the CC and other tracts not identified by the categorical approach (Figure 2 and eTable 5 in the Supplement). These tracts extending within and outside the CC encompassed the anterior limb of the internal capsule, the inferior longitudinal fasciculus, and the corona radiata.

Other DTI Metrics

Among our 4 complementary DTI metrics, MD and RD were significantly positively related to SRS-P total T scores in clusters of 18058 and 15441 voxels, respectively. These relationships converged in the CC, corona radiata, and inferior longitudinal fasciculus areas that were also identified in FA analyses (Figure 2 and eTable 5 in the Supplement).

Follow-up Analyses
Validating DTI-ASD Dimensional Relationship

To ensure that the above associations in the Dimensional Approach subsection did not simply depend on ASD diagnosis, we assessed the relationship between SRS-P total T scores and FA measures at the clusters identified in TBSS analyses across all children after regressing out the nuisance covariates and ASD diagnostic status (ie, ASD is 1, and non-ASD is 0). The pattern of results was similar to that observed in primary analyses (β6,164 = −0.27, P = .01). Similar findings were obtained for MD (β6,164 = 0.32) and RD (β6,164 = 0.31) (P = .005 for both). To verify this pattern’s robustness to TFCE FWE correction, we repeated a TBSS dimensional DTI-SRS analysis adding ASD membership as a nuisance covariate. The pattern of DTI-SRS relationships was similar to the patterns identified in primary analyses, particularly at corrected P < .10 (eFigure 7 in the Supplement).

Furthermore, given concerns that SRS-P total T scores may be confounded by non-ASD behavioral symptoms,45 secondary analyses examined the reproducibility of these relationships using an alternative, independent measure of ASD traits. We focused on the 5 subscales of the CCC-2 capturing pragmatic aspects of language and behaviors commonly impaired in ASD. After regressing the influences of age, sex, motion, and CPRS-R:LV DSM-IV total T scores, we tested the relationships between these CCC-2 scaled scores and the mean FA, MD, and RD values in the clusters identified in primary voxelwise dimensional analyses. Results were in line with the pattern observed with SRS-P total T scores. Use of context, stereotyped language, and nonverbal communication subscale scores were significantly and positively related to FA in these clusters(β5162 = 0.22, 0.22, 0.18, and P = .01, 0.01, 0.05, respectively); use of context and stereotyped language were negatively associated with RD (β5162 = −0.25 and −0.20, P = .004 and 0.02, respectively) and MD (β5162 = −0.27 and −0.18, P = .002 and 0.04, respectively) (eFigure 8 in the Supplement).

Exploring the ADHD Subdomains

Because no significant DTI relationships were identified in regard to CPRS-R:LV DSM-IV total T scores for any DTI metrics examined, we secondarily explored whether subscores of the ADHD subdomains (ie, inattention and hyperactivity/impulsivity) may provide additional information. We first performed dimensional TBSS analyses of CPRS-R:LV DSM-IV inattention and hyperactivity/impulsivity T scores separately (controlling for each other in the same model while also including age, sex, and motion). Results indicated a significant relationship of CPRS-R:LV DSM-IV inattention T scores with AD in a cluster of 716 voxels localized the anterior and middle CC (eTable 6 and eFigure 9A in the Supplement). Given that SRS-P total scores were positively associated with both CPRS-R:LV DSM-IV subdomains, to determine the specificity of the AD-inattention relationship, we repeated analyses, including SRS-P in the model. Results indicated significant effects of CPRS-R:LV DSM-IV inattention T scores for AD in clusters of overall 6142 voxels centered in CC, as well as for MD in a small cluster with 37 voxels in the posterior CC (eTable 7 and eFigure 9B in the Supplement). No significant relationships were detected with CPRS-R:LV DSM-IV hyperactive/impulsivity T scores.

DTIPrep-Based Analyses

To further address concerns about head motion, we repeated group TBSS analyses after automatic artifact correction using DTIPrep (eAppendix in the Supplement).46 The pattern of results was similar to the patterns reported above in the Categorical Approach and Dimensional Approach subsections and in Exploring the ADHD Subdomains in the Follow-up Analyses subsection (eFigures 5, 10, and 11 in the Supplement).

Discussion

We assessed white matter organization within and beyond the diagnostic boundaries of ASD and ADHD using TBSS analyses in a moderately large, well-characterized, and low-motion sample of 174 school-aged children with ASD, those having ADHD, or TDC. Taken together, our results indicate that white matter organization was affected by both ASD diagnosis and ASD traits across diagnoses. While categorical analyses revealed white matter abnormalities only in children with ASD, dimensional analyses provided a more complete picture of the influence of ASD or ADHD symptoms on white matter. By indexing individuals as a function of ASD severity, our approach revealed brain-behavior relationships, regardless of diagnostic status. Notably, these relationships were observed across distinct measures of ASD traits (SRS-P and CCC-2) while controlling for ADHD severity, thereby underscoring the specific role of autistic traits. Although ADHD total T scores were not significantly related to any DTI metrics, ADHD subdomain analysis revealed a significant dimensional association, primarily between AD and inattention, mostly localized in the CC.

Consistent with models emphasizing the role of abnormal interhemispheric interactions in neurodevelopmental disorders,47-51 results from both our categorical and dimensional approaches converged on the CC. Lower FA, along with greater MD and RD, in multiple CC regions both characterized ASD diagnosis, as well as ASD traits across children. While atypical DTI findings in CC regions have been consistently reported in prior studies15,19,20 comparing ASD or ADHD vs TDC separately, our assessment of both ASD and ADHD dimensions across groups provided a novel perspective on the influence of ASD traits on white matter organization in both disorders. Indeed, emerging clinical evidence highlights the significance of autistic traits in a substantial group of children with ADHD and vice versa.5,6 Still, their underlying neural mechanisms have been virtually ignored to date. Findings of brain-behavior relationships that are specific to the ASD domain and yet shared across disorders suggest that these are potentially shared biomarkers. Future longitudinal studies may elucidate whether common developmental pathways exist.

The wide spatial extent of significant findings emerging from dimensional analyses of ASD symptoms likely reflects the multifaceted nature of ASD-related impairments. The posterior CC has been shown to be involved in sensory and visuospatial processing.52,53 In contrast, consistent with its role of connecting the bilateral frontal cortex,54 the anterior CC has been associated with social functioning impairment in ASD.17 The midbody of the CC connects the bilateral premotor, primary motor, and primary sensory cortex,55 suggesting that atypicalities in this CC region are related to sensory and motor processing abnormalities. Social, sensorimotor, and language impairments have also been previously reported in children with ADHD, but their nature remains unclear.56-58 Different dimensional impairments common to children with ASD or ADHD may account for our findings. Future studies that include fine-grained measures of ASD subdomains and are obtained from multiple sources and in large samples are needed to differentiate the specific functional roles of these tracts in children with ASD and ADHD.

Although dimensional analyses with combined ADHD (ie, inattention and hyperactivity/impulsivity combined) revealed no significant relationships, those exploring inattention did. These findings converged on the CC and affected AD, which did not relate to ASD symptoms. A tantalizing hypothesis is that abnormalities in different aspects of white matter structure might correspond to distinct psychopathological profiles. This hypothesis can only be tested with larger samples and more advanced diffusion methods able to capture finer aspects of white matter organization.59,60 Overall, the results herein suggest that brain-behavior relationships vary depending on the ADHD subdomains; therefore, inclusion of ADHD heterogeneous samples without accounting for their core subdomains may have contributed to prior conflicting findings.

Lack of regional differences associated with ADHD diagnosis is not consistent with results of some prior TBSS studies, including meta-analyses.20,61 Several scenarios can explain this inconsistency. First, the effect size of white matter abnormalities in ADHD may be small and thus missed in multiple-group comparisons. Second, as discussed in the previous paragraph, more homogeneous ADHD presentations may be necessary to detect group differences. Third, negative findings for ADHD diagnosis may reflect our emphasis on groups not differing in head motion. Indeed, our negative findings are consistent with recent reports38,62,63 verifying lack of group differences in head motion.20 Fourth, other additional factors may contribute to discrepancies related to ADHD diagnosis. Similar to the present work, a recent study22 compared multiple disorders using a broader range of diagnoses (ie, ASD, ADHD, and obsessive-compulsive disorder). While the authors reported low FA in the posterior CC for each of the 3 clinical groups relative to controls, our analyses only revealed low FA with respect to ASD diagnosis and traits. In comparing that study and the present study, we note that our ADHD sample had a lower rate of medication use and lower prevalence of psychiatric comorbidities, both of which have been reported to affect diffusion findings in ADHD.62,64 Our results underscore the need for future comprehensive investigations of factors contributing to clinical heterogeneity in substantially large samples, by using multimodal objective phenotypic assessments and approaches that facilitate replication, such as those providing for open data sharing.65,66

Limitations

Our findings should be interpreted in light of some limitations. First, although we assessed both categorical and dimensional models, we did not test their interactions (hybrid analyses) as done in prior studies67,68 that only examined either ASD or ADHD. Measuring interactions between diagnostic status and 2 psychopathological dimensions requires larger samples than have been seen to date to capture optimal distributions of both traits within each group. Ideally, these studies should use gold standard diagnostic measures of ASD (ie, the Autism Diagnostic Observation Schedule28,29 or the Autism Diagnostic Interview–Revised30,31) across all groups to confirm exclusion of ASD in ADHD and TDC, as well as providing independent metrics of ASD severity. Unfortunately, the extensive training requirements and lengthy administrations limit their use in non-ASD populations. Abbreviated assessments, such as the recently validated Autism Symptom Interview, School-Age,69 may bypass this concern. Second, although the diagnostic groups did not differ significantly in sex distribution, most participants were male, reflecting the higher prevalence of boys in ASD and ADHD.70 Therefore, results herein may not generalize to girls.

Conclusions

In summary, our dimensional approach was more sensitive in detecting brain-behavior relationships with ASD traits and ADHD traits than the categorical approach. The clinical observation that these traits extend across diagnostic categories4,6 likely reflects shared underlying neural mechanisms. Therefore, this study emphasizes investigations of constructs and domains transcending traditional categorical boundaries, with the ultimate goal of identifying biomarkers on the path toward precision medicine.71,72

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Article Information

Accepted for Publication: June 29, 2017.

Corresponding Author: Adriana Di Martino, MD, Department of Child and Adolescent Psychiatry at NYU Langone Medical Center, One Park Avenue, Seventh Floor, New York, NY 10016 (adriana.dimartino@nyumc.org).

Published Online: September 6, 2017. doi:10.1001/jamapsychiatry.2017.2573

Author Contributions: Drs Aoki and Di Martino had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Aoki, Yoncheva, Milham, Di Martino.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Aoki, Di Martino.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Aoki, Yoncheva, Nath, Milham, Di Martino.

Obtained funding: Aoki, Di Martino.

Administrative, technical, or material support: Aoki, Chen, Nath, Sharp, Velasco, Di Martino.

Study supervision: Milham, Di Martino.

Conflict of Interest Disclosures: Dr Di Martino reported receiving royalties from the publication of the Italian version of the Social Responsiveness Scale–Child Version. No other disclosures were reported.

Funding/Support: This work was supported in part by the Japan Society for the Promotion of Science, the Kanae Foundation for the Promotion of Medical Science, and the Uehara Memorial Foundation (Dr Aoki). Funding was also provided by grants K23MH087770 and R01MH105506 (Dr Di Martino) and grants R01MH081218 and R01HD065282 from the Leon Levy Foundation (Dr Di Martino).

Role of Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: F. Xavier Castellanos, MD (Child Study Center at NYU Langone Medical Center and The Nathan S. Kline Institute for Psychiatric Research) contributed helpful discussions and editorial suggestions (no compensation was received). We are grateful to the children and parents who generously contributed their time to this research. We thank the supporting staff at the New York University Center for Brain Imaging for their technical support. We also thank the research staff at the Autism Research and Clinical Program and the Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience of the Child Study Center at New York University Langone Medical Center for their help in aspects of participant recruitment, assessment, data collection, and data entry.

Additional Information: Some data included in this article were deposited as fully anonymized data in the Autism Brain Imaging Data Exchange (http://fcon_1000.projects.nitrc.org/indi/abide/) or the National Database for Autism Research (http://ndar.nih.gov/).

References
1.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis [published correction appears in Lancet. 2013;381(9875):1360].  Lancet. 2013;381(9875):1371-1379.PubMedGoogle ScholarCrossref
2.
Insel  T, Cuthbert  B, Garvey  M,  et al.  Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.  Am J Psychiatry. 2010;167(7):748-751.PubMedGoogle ScholarCrossref
3.
Rommelse  NN, Franke  B, Geurts  HM, Hartman  CA, Buitelaar  JK.  Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder.  Eur Child Adolesc Psychiatry. 2010;19(3):281-295.PubMedGoogle ScholarCrossref
4.
Goldstein  S, Schwebach  AJ.  The comorbidity of pervasive developmental disorder and attention deficit hyperactivity disorder: results of a retrospective chart review.  J Autism Dev Disord. 2004;34(3):329-339.PubMedGoogle ScholarCrossref
5.
Gadow  KD, DeVincent  CJ, Pomeroy  J.  ADHD symptom subtypes in children with pervasive developmental disorder.  J Autism Dev Disord. 2006;36(2):271-283.PubMedGoogle ScholarCrossref
6.
Grzadzinski  R, Dick  C, Lord  C, Bishop  S.  Parent-reported and clinician-observed autism spectrum disorder (ASD) symptoms in children with attention deficit/hyperactivity disorder (ADHD): implications for practice under DSM-5 Mol Autism. 2016;7:7.PubMedGoogle ScholarCrossref
7.
Hernandez  LM, Rudie  JD, Green  SA, Bookheimer  S, Dapretto  M.  Neural signatures of autism spectrum disorders: insights into brain network dynamics.  Neuropsychopharmacology. 2015;40(1):171-189.PubMedGoogle ScholarCrossref
8.
Minshew  NJ, Williams  DL.  The new neurobiology of autism: cortex, connectivity, and neuronal organization.  Arch Neurol. 2007;64(7):945-950.PubMedGoogle ScholarCrossref
9.
Vissers  ME, Cohen  MX, Geurts  HM.  Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links.  Neurosci Biobehav Rev. 2012;36(1):604-625.PubMedGoogle ScholarCrossref
10.
Just  MA, Keller  TA, Malave  VL, Kana  RK, Varma  S.  Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity.  Neurosci Biobehav Rev. 2012;36(4):1292-1313.PubMedGoogle ScholarCrossref
11.
Castellanos  FX, Aoki  Y.  Intrinsic functional connectivity in attention-deficit/hyperactivity disorder: a science in development.  Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1(3):253-261.PubMedGoogle ScholarCrossref
12.
Anagnostou  E, Taylor  MJ.  Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here.  Mol Autism. 2011;2(1):4.PubMedGoogle ScholarCrossref
13.
Konrad  K, Eickhoff  SB.  Is the ADHD brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder.  Hum Brain Mapp. 2010;31(6):904-916.PubMedGoogle ScholarCrossref
14.
Jelescu  IO, Zurek  M, Winters  KV,  et al.  In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy.  Neuroimage. 2016;132:104-114.PubMedGoogle ScholarCrossref
15.
Travers  BG, Adluru  N, Ennis  C,  et al.  Diffusion tensor imaging in autism spectrum disorder: a review.  Autism Res. 2012;5(5):289-313.PubMedGoogle ScholarCrossref
16.
Aoki  Y, Abe  O, Nippashi  Y, Yamasue  H.  Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: a meta-analysis of diffusion tensor imaging tractography studies.  Mol Autism. 2013;4(1):25.PubMedGoogle ScholarCrossref
17.
Ameis  SH, Catani  M.  Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder.  Cortex. 2015;62:158-181.PubMedGoogle ScholarCrossref
18.
Liston  C, Malter Cohen  M, Teslovich  T, Levenson  D, Casey  BJ.  Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point?  Biol Psychiatry. 2011;69(12):1168-1177.PubMedGoogle ScholarCrossref
19.
van Ewijk  H, Heslenfeld  DJ, Zwiers  MP, Buitelaar  JK, Oosterlaan  J.  Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis.  Neurosci Biobehav Rev. 2012;36(4):1093-1106.PubMedGoogle ScholarCrossref
20.
Aoki  Y, Cortese  S, Castellanos  FX.  Diffusion tensor imaging studies of attention-deficit/hyperactivity disorder: meta-analyses and reflections on head motion [published online July 3, 2017].  J Child Psychol Psychiatry. doi:0.1111/jcpp.12778PubMedGoogle Scholar
21.
Ray  S, Miller  M, Karalunas  S,  et al.  Structural and functional connectivity of the human brain in autism spectrum disorders and attention-deficit/hyperactivity disorder: a rich-club organization study.  Hum Brain Mapp. 2014;35(12):6032-6048.PubMedGoogle ScholarCrossref
22.
Ameis  SH, Lerch  JP, Taylor  MJ,  et al.  A diffusion tensor imaging study in children with ADHD, autism spectrum disorder, OCD, and matched controls: distinct and non-distinct white matter disruption and dimensional brain-behavior relationships.  Am J Psychiatry. 2016;173(12):1213-1222.PubMedGoogle ScholarCrossref
23.
Di Martino  A, Zuo  XN, Kelly  C,  et al.  Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2013;74(8):623-632.PubMedGoogle ScholarCrossref
24.
Chantiluke  K, Christakou  A, Murphy  CM,  et al; MRC AIMS Consortium.  Disorder-specific functional abnormalities during temporal discounting in youth with attention deficit hyperactivity disorder (ADHD), autism and comorbid ADHD and autism.  Psychiatry Res. 2014;223(2):113-120.PubMedGoogle ScholarCrossref
25.
Constantino  JN, Davis  SA, Todd  RD,  et al.  Validation of a brief quantitative measure of autistic traits: comparison of the Social Responsiveness Scale with the Autism Diagnostic Interview–Revised.  J Autism Dev Disord. 2003;33(4):427-433.PubMedGoogle ScholarCrossref
26.
Conners  CK. Conners’ Rating Scales–Revised: User’s Manual. North Tonawanda, NY: Multi-Health Systems Inc; 1997.
27.
Feldman  HM, Yeatman  JD, Lee  ES, Barde  LH, Gaman-Bean  S.  Diffusion tensor imaging: a review for pediatric researchers and clinicians.  J Dev Behav Pediatr. 2010;31(4):346-356.PubMedGoogle ScholarCrossref
28.
Gotham  K, Risi  S, Pickles  A, Lord  C.  The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity.  J Autism Dev Disord. 2007;37(4):613-627.PubMedGoogle ScholarCrossref
29.
Gotham  K, Pickles  A, Lord  C.  Standardizing ADOS scores for a measure of severity in autism spectrum disorders.  J Autism Dev Disord. 2009;39(5):693-705.PubMedGoogle ScholarCrossref
30.
Lord  C, Rutter  M, Le Couteur  A.  Autism Diagnostic Interview–Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders.  J Autism Dev Disord. 1994;24(5):659-685.PubMedGoogle ScholarCrossref
31.
Lord  C, Pickles  A, McLennan  J,  et al.  Diagnosing autism: analyses of data from the Autism Diagnostic Interview.  J Autism Dev Disord. 1997;27(5):501-517.PubMedGoogle ScholarCrossref
32.
Kaufman  J, Birmaher  B, Brent  D,  et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL): initial reliability and validity data.  J Am Acad Child Adolesc Psychiatry. 1997;36(7):980-988.PubMedGoogle ScholarCrossref
33.
Wechsler  D.  Manual for the Wechsler Abbreviated Intelligence Scale (WASI). San Antonio, TX: Psychological Corp; 1999.
34.
Elliott  CD, Murray  GJ, Pearson  LS.  Differential Ability Scales. San Antonio, TX: Psychological Corp; 1990.
35.
Bishop  DVM.  The Children’s Communication Checklist: CCC-2. London, England: American Speech-Language-Hearing Association; 2003.
36.
Achenbach  TM, Rescorla  L.  Manual for the Child Behavior Checklist and Revised Child Behavior Profile. Burlington: University of Vermont Dept of Psychiatry; 1983.
37.
Hollingshead  AB.  Four-Factor Index of Socioeconomic Status. New Haven, CT: Yale University; 1975.
38.
Yoncheva  YN, Somandepalli  K, Reiss  PT,  et al.  Mode of anisotropy reveals global diffusion alterations in attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2016;55(2):137-145.PubMedGoogle ScholarCrossref
39.
Jenkinson  M, Beckmann  CF, Behrens  TE, Woolrich  MW, Smith  SM.  FSL.  Neuroimage. 2012;62(2):782-790.PubMedGoogle ScholarCrossref
40.
Yendiki  A, Koldewyn  K, Kakunoori  S, Kanwisher  N, Fischl  B.  Spurious group differences due to head motion in a diffusion MRI study.  Neuroimage. 2014;88:79-90.PubMedGoogle ScholarCrossref
41.
Winkler  AM, Ridgway  GR, Webster  MA, Smith  SM, Nichols  TE.  Permutation inference for the general linear model.  Neuroimage. 2014;92:381-397.PubMedGoogle ScholarCrossref
42.
Smith  SM, Nichols  TE.  Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.  Neuroimage. 2009;44(1):83-98.PubMedGoogle ScholarCrossref
43.
O’brien  RM.  A caution regarding rules of thumb for variance inflation factors.  Qual Quant. 2007;41(5):673-690.Google ScholarCrossref
44.
Mori  S, Oishi  K, Jiang  H,  et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template.  Neuroimage. 2008;40(2):570-582.PubMedGoogle ScholarCrossref
45.
Hus  V, Bishop  S, Gotham  K, Huerta  M, Lord  C.  Factors influencing scores on the Social Responsiveness Scale.  J Child Psychol Psychiatry. 2013;54(2):216-224.PubMedGoogle ScholarCrossref
46.
Oguz  I, Farzinfar  M, Matsui  J,  et al.  DTIPrep: quality control of diffusion-weighted images.  Front Neuroinform. 2014;8:4.PubMedGoogle ScholarCrossref
47.
Anderson  JS, Druzgal  TJ, Froehlich  A,  et al.  Decreased interhemispheric functional connectivity in autism.  Cereb Cortex. 2011;21(5):1134-1146.PubMedGoogle ScholarCrossref
48.
Hahamy  A, Behrmann  M, Malach  R.  The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder.  Nat Neurosci. 2015;18(2):302-309.PubMedGoogle ScholarCrossref
49.
van Ewijk  H, Heslenfeld  DJ, Zwiers  MP,  et al.  Different mechanisms of white matter abnormalities in attention-deficit/hyperactivity disorder: a diffusion tensor imaging study.  J Am Acad Child Adolesc Psychiatry. 2014;53(7):790-9.e3.PubMedGoogle ScholarCrossref
50.
Paul  LK, Brown  WS, Adolphs  R,  et al.  Agenesis of the corpus callosum: genetic, developmental and functional aspects of connectivity.  Nat Rev Neurosci. 2007;8(4):287-299.PubMedGoogle ScholarCrossref
51.
Wegiel  J, Flory  M, Kaczmarski  W,  et al.  Partial agenesis and hypoplasia of the corpus callosum in idiopathic autism.  J Neuropathol Exp Neurol. 2017;76(3):225-237.PubMedGoogle Scholar
52.
Pryweller  JR, Schauder  KB, Anderson  AW,  et al.  White matter correlates of sensory processing in autism spectrum disorders.  Neuroimage Clin. 2014;6:379-387.PubMedGoogle ScholarCrossref
53.
Alexander  AL, Lee  JE, Lazar  M,  et al.  Diffusion tensor imaging of the corpus callosum in autism.  Neuroimage. 2007;34(1):61-73.PubMedGoogle ScholarCrossref
54.
Aboitiz  F, Scheibel  AB, Fisher  RS, Zaidel  E.  Fiber composition of the human corpus callosum.  Brain Res. 1992;598(1-2):143-153.PubMedGoogle ScholarCrossref
55.
Hofer  S, Frahm  J.  Topography of the human corpus callosum revisited: comprehensive fiber tractography using diffusion tensor magnetic resonance imaging.  Neuroimage. 2006;32(3):989-994.PubMedGoogle ScholarCrossref
56.
Uekermann  J, Kraemer  M, Abdel-Hamid  M,  et al.  Social cognition in attention-deficit hyperactivity disorder (ADHD).  Neurosci Biobehav Rev. 2010;34(5):734-743.PubMedGoogle ScholarCrossref
57.
Korrel  H, Mueller  KL, Silk  T, Anderson  V, Sciberras  E.  Research review: language problems in children with attention-deficit hyperactivity disorder: a systematic meta-analytic review.  J Child Psychol Psychiatry. 2017;58(6):640-654.PubMedGoogle ScholarCrossref
58.
Kaiser  ML, Schoemaker  MM, Albaret  JM, Geuze  RH.  What is the evidence of impaired motor skills and motor control among children with attention deficit hyperactivity disorder (ADHD)? systematic review of the literature.  Res Dev Disabil. 2014;36C:338-357.PubMedGoogle Scholar
59.
Alexander  AL, Lee  JE, Lazar  M, Field  AS.  Diffusion tensor imaging of the brain.  Neurotherapeutics. 2007;4(3):316-329.PubMedGoogle ScholarCrossref
60.
Dean  DC  III, Lange  N, Travers  BG,  et al.  Multivariate characterization of white matter heterogeneity in autism spectrum disorder.  Neuroimage Clin. 2017;14:54-66.PubMedGoogle ScholarCrossref
61.
Chen  L, Hu  X, Ouyang  L,  et al.  A systematic review and meta-analysis of tract-based spatial statistics studies regarding attention-deficit/hyperactivity disorder.  Neurosci Biobehav Rev. 2016;68:838-847.PubMedGoogle ScholarCrossref
62.
Adisetiyo  V, Tabesh  A, Di Martino  A,  et al.  Attention-deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development.  Hum Brain Mapp. 2014;35(5):2148-2162.PubMedGoogle ScholarCrossref
63.
Cooper  M, Thapar  A, Jones  DK.  White matter microstructure predicts autistic traits in attention-deficit/hyperactivity disorder.  J Autism Dev Disord. 2014;44(11):2742-2754.PubMedGoogle ScholarCrossref
64.
de Luis-García  R, Cabús-Piñol  G, Imaz-Roncero  C,  et al.  Attention deficit/hyperactivity disorder and medication with stimulants in young children: a DTI study.  Prog Neuropsychopharmacol Biol Psychiatry. 2015;57:176-184.PubMedGoogle ScholarCrossref
65.
Milham  MP.  Open neuroscience solutions for the connectome-wide association era.  Neuron. 2012;73(2):214-218.PubMedGoogle ScholarCrossref
66.
Kapur  S, Phillips  AG, Insel  TR.  Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?  Mol Psychiatry. 2012;17(12):1174-1179.PubMedGoogle ScholarCrossref
67.
Chabernaud  C, Mennes  M, Kelly  C,  et al.  Dimensional brain-behavior relationships in children with attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2012;71(5):434-442.PubMedGoogle ScholarCrossref
68.
Elton  A, Di Martino  A, Hazlett  HC, Gao  W.  Neural connectivity evidence for a categorical-dimensional hybrid model of autism spectrum disorder.  Biol Psychiatry. 2016;80(2):120-128.PubMedGoogle ScholarCrossref
69.
Bishop  SL, Huerta  M, Gotham  K,  et al.  The Autism Symptom Interview, School-Age: a brief telephone interview to identify autism spectrum disorders in 5-to-12-year-old children.  Autism Res. 2017;10(1):78-88.PubMedGoogle ScholarCrossref
70.
Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators; Centers for Disease Control and Prevention (CDC).  Prevalence of autism spectrum disorder among children aged 8 years: Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010.  MMWR Surveill Summ. 2014;63(2):1-21.PubMedGoogle Scholar
71.
Insel  TR.  The NIMH Research Domain Criteria (RDoC) project: precision medicine for psychiatry.  Am J Psychiatry. 2014;171(4):395-397.PubMedGoogle ScholarCrossref
72.
Castellanos  FX, Di Martino  A, Craddock  RC, Mehta  AD, Milham  MP.  Clinical applications of the functional connectome.  Neuroimage. 2013;80:527-540.PubMedGoogle ScholarCrossref
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