supplementary online content - jama · children in the fxs, iaut and dd groups were sedated during...

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Supplementary Online Content Hoeft F, Walter E, Lightbody AA, Hazlett HC, Chang C, Piven J, Reiss AL. Neuroanatomical differences in toddler boys with fragile X syndrome and idiopathic autism. Arch Gen Psychiatry. Published online November 1, 2010. doi:10.1001/archgenpsychiatry.2010.153 eAppendix. Methods. eTable 1. Tissue volumes. eTable 2. Demographic information for FXS children who meet criteria for autism (FXS+A) compared to FXS children who do not meet criteria for autism (FXS-A). eTable 3. Demographic information from participants who were misclassified in the SVM analyses. eFigure 1. Adjusted tissue volumes for each group. eFigure 2. GM and WM differences between iAUT and controls. eFigure 3. GM and WM differences between FXS and controls. eFigure 4. GM and WM differences between iAUT and FXS. This supplementary material has been provided by the authors to give readers additional information about their work.

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eAppendix Genotype All children in the fragile X syndrome (FXS), idiopathic autism (iAUT) and idiopathic developmental delay (DD) groups underwent DNA testing for the typical fragile X mental retardation 1 (FMR1) expansion mutation. This testing confirmed the presence of the mutation in children in the FXS group, and ruled out fragile X as a cause of delay in both the iAUT and DD groups. To assess the presence of the fragile X mutation, standard Southern blot analysis was performed, followed by FMR1-specific probe hybridization.1 To quantify FMRP expression, the percentage of peripheral lymphocytes containing fragile X mental retardation protein (FMRP) was calculated using immunostaining techniques.2 Clinical diagnoses for the iAUT group were confirmed using the Autism Diagnostic Interview-Revised (ADI-R)3 and the Autism Diagnostic Observation Schedule-G (ADOS-G).4 Children were included in this group if they met all criteria on both the ADI-R and ADOS-G. Children in the DD group comprised developmental delays of unknown etiology (with a composite standard score below 85 on the Mullen Scales of early Learning), did not exhibit symptoms indicating an autism spectrum disorder, nor any symptoms indicative of another developmental disorder (e.g. Down syndrome, Williams syndrome). Cognitive Measures and Neuropsychiatric Assessments All participants were given a standard battery of cognitive, adaptive, and behavioral measures which included the Mullen Scales of Early Learning5 and the Repetitive Behavior Scale-Revised (RBS).6 In addition, the ADI-R3 and ADOS-G4 were administered to participants in the FXS and iAUT groups. The ADI-R is a semi-structured parent interview that assesses autistic symptoms in three categories: social deficits, communication and language impairments, and repetitive, ritualistic and stereotyped behaviors. The ADOS is a structured behavioral observation that assesses social, communication, play, and ritualistic/repetitive behaviors of children with autism from 18 months to adulthood. Both of these assessments use diagnostic algorithms based on the three domains of impairment observed in autism as delineated in the DSM-IV.7 Individual item scores on the ADI-R and ADOS quantify severity of impairment as a function of symptom frequency and degree of interference in everyday life. ADI communication sub-scale scores and ADI sum scores and were adjusted based on the number of items (verbal: 13 vs. non-verbal: 7 items). ADOS scores were not adjusted as all participants received Modules 1 or 2 (which have the same number of items). Standardized ADOS scores as a measure of the severity of autistic symptoms were calculated based on 8. See Table 1 for further details regarding between group statistics for our participants. Magnetic Resonance Imaging Scanning Procedures Imaging data was acquired between April 2000 and October 2007 at Stanford University (SU, Lucile Packard Children’s Hospital) and University of North Carolina (UNC, Brain Imaging and Analysis Center) using identical pulse sequences and scanners (General Electric [GE] 1.5 Tesla Signa scanner; GE Imagine Systems, Milwaukee, WI) at both sites. The pulse sequences used were designed to maximize contrast between grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) for the participants’ age



range. Images acquired included a coronal T1-weighted sequence (inversion recovery preparation pulse = 300 ms; repetition time (TR) = 12 ms; echo-time (TE) = 5 ms; flip angle = 20°; slice thickness = 1.5 mm; number of excitations = 1; field-of-view (FOV) = 20 cm; matrix = 256 x 192). A magnetic resonance (MR) quality control (QC) phantom was scanned after each participant at both sites in order to standardize assessment over sites, individuals and time. Children in the FXS, iAUT and DD groups were sedated during the MRI. Sedation was administered by a pediatric anesthesiologist, and children were monitored continually during the magnetic resonance imaging (MRI) procedure. Typically developing (TD) children were scanned while sleeping, at a time that was later than the participant’s normal bedtime. In addition, for the sleep scans, parents were asked to wake their child up slightly earlier on the morning of the scan, and to shorten nap time that day, in order to increase the likelihood of the child sleeping through the entire scan. To prepare for the scan, families received a packet of materials, including a CD of scanner noises designed to sensitize him/her to the scanner sounds. TD children also participated in a simulated MRI session at the lab, in which they were asked to practice holding still, in order to mitigate fear should the child awaken during the MRI. Cross-Site Analysis For details regarding cross-site analytic methods, please see 9. In brief, in order to maximize scan compatibility across sites, identical pulse sequences and scanners were used at the two collection sites. In addition, in order to characterize scanner quality and signal-to-noise ratio (SNR) during our study, we collected phantom scans after each participant. During the course of the study, 24 random phantom scans (12 per scanner, selected across the entire period of the study by a research assistant blind to scans) were analyzed using the method described in 10. Two SNR measurements were performed and were not significantly different across sites (p = 0.39 and p = 0.22, respectively). We also assessed whether brain volumes within each group differed as a function of scan-site. No tissue type (GM volume [GMV], WMV, or total tissue volume [GMV + WMV, TTV]) was significantly different across sites (all p’s > 0.05). Finally, we included scan-site as a nuisance variable in all reported analyses in order to mitigate small (non-significant) differences found between sites. Voxel-Based Morphomatry (VBM) Processing VBM analyses of MR images were carried out using the Statistical Parametric Mapping 5 (SPM5) statistical package (http://www.fil.ion.ucl.ac.uk/spm) and VBM5.1 (http://dbm.neuro.uni-jena.de/vbm). Images were bias-field corrected and segmented to GM, WM and CSF. A Hidden Markov Random Field (HMRF; prior probability weight 0.3) was applied in order to use the spatial constraints of neighboring voxels to encode spatial information. The images were normalized with a 12-parameter affine transformation with a spatial frequency cut-off of 25 in all three (x,y,z) directions and resampled to 1x1x1 mm voxels. Linear and non-linear Jacobian modulation was applied and was followed by smoothing with an isotropic Gaussian kernel with full-width at half-maximum (FWHM) of 8 mm. Customized GM, WM and CSF templates created using all participants were used for VBM preprocessing. For each participant, segmentation and normalization accuracy were manually inspected.



Analyses of TGMV, TWMV and TTV We examined TGMV, TWMV and TTV (GMV + WMV) differences between TD, DD, FXS and iAUT groups, using values obtained from VBM processing using analysis of covariance (ANCOVA), covarying out age and site. Additional Notes Related to Univariate VBM Analyses Statistical images were overlaid on a representative brain from the TD group, except where noted, using MRIcron (http://www.sph.sc.edu/comd/rorden/mricron/). Talairach coordinates of peaks of significant clusters were converted from MNI space using the mni2tal function (http://www.mrc-cbu.cam.ac.uk/Imaging/Common/mnispace.shtml). Talairach Daemon (Research Imaging Center, University of Texas Health Science Center in San Antonio; RIC UTHSCSA, TX, USA) and the atlas by Talairach and Tournoux11 were initially used to identify Brodmann Areas. The final anatomic locations are reported according to their anatomic location overlaid on the custom template. References 1. Oberle I, Rousseau F, Heitz D, et al. Instability of a 550-base pair DNA segment and abnormal methylation in fragile X syndrome. Science. May 24 1991;252(5010):1097-1102. 2. Willemsen R, Mohkamsing S, de Vries B, et al. Rapid antibody test for fragile X syndrome. Lancet. May 6 1995;345(8958):1147-1148. 3. 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. Oct 1994;24(5):659-685. 4. Lord C, Risi S, Lambrecht L, et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. Jun 2000;30(3):205-223. 5. Mullen EM. Mullen Scales of Early Learning AGS Edition. Circle Pines, MN: American Guidance Service, Inc.; 1995. 6. Lam KS, Aman MG. The Repetitive Behavior Scale-Revised: independent validation in individuals with autism spectrum disorders. J Autism Dev Disord. May 2007;37(5):855-866. 7. Association AP. Diagnostic and Statistical Manual of Mental Disorders DSM-IV-TR. Fourth Edition (Text Revision) ed. Washington DC: Author; 2000. 8. Gotham K, Pickles A, Lord C. Standardizing ADOS Scores for a Measure of Severity in Autism Spectrum Disorders. J Autism Dev Disord. Dec 12 2008. 9. Hoeft F, Lightbody AA, Hazlett HC, Patnaik S, Piven J, Reiss AL. Morphometric spatial patterns differentiating boys with fragile X syndrome, typically developing boys, and developmentally delayed boys aged 1 to 3 years. Arch Gen Psychiatry. Sep 2008;65(9):1087-1097. 10. Henkelman RM. Measurement of signal intensities in the presence of noise in MR images. Med Phys. Mar-Apr 1985;12(2):232-233. 11. Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. New York: Thieme; 1988.



eTable 1. Tissue volumes.



eTable 2. Demographic information for FXS children who meet criteria for autism (FXS+A) compared to FXS children who do not meet criteria for autism (FXS-A).

ANOVA Post-Hoc FXS+A FXS-A iAUT F P P

N 14 38 63 Value Mean 3.14 2.82 2.77 3.06 n.s. Age SD 0.47 0.66 0.41

N 14 38 63 Value Mean 50.93 56.42 54.10 1.95 n.s. Mullen SD 4.08 10.05 9.41

N 10 27 16 Value Mean 26.10 15.96 26.25 5.12 <0.01 RBS SD 11.00 14.36

FXS+A > FXS-A*

N 13 37 63 Value Mean 4.31 2.70 4.84 21.33 <0.001 ADI Rep

Total SD 1.38 1.31 1.76

FXS+A> FXS-A**

N 13 37 63 Value Mean 14.69 7.24 18.62 99.83 <0.001 ADI Social

Total SD 3.35 3.79 4.04

FXS+A> FXS-A***; <iAUT**

N 5 5 Value Mean 6.80 12.80 5.56 <0.05 ADI Comm.

Verbal SD 5.07 2.59

N 13 32 58 Value Mean 10.85 8.38 11.59 13.09 <0.001 ADI Comm.

Nonverbal SD 2.15 4.03 2.12

FXS+A> FXS-A*

N 5 5 Value Mean 18.20 35.60 11.09 0.01 ADI Sum

[Verbal] SD 10.92 4.16

N 13 31 58 Value Mean 34.46 23.06 39.52 65.71 <0.001 ADI Sum

[Nonverbal] SD 5.33 7.90 5.78

FXS+A> FXS-A***; <iAUT*

N 13 36 63 Value Mean 1.28 0.81 1.43 68.22 <0.001 Adjusted

ADI Sum SD 0.20 0.32 0.23

FXS+A> FXS-A***

N 14 38 54 Value Mean 15.79 8.13 18.00 76.09 <0.001

ADOS Soc/Com Total SD 2.39 5.16 2.87

FXS+A> FXS-A***

N 14 38 53 Value Mean 6.14 3.34 7.62 65.90 <0.001

ADOS Severity Measure SD 1.75 2.13 1.43

FXS+A> FXS-A***; <iAUT*

N 13 37 Value Mean 5.50 5.95 0.12 n.s. FMRP SD 4.82 3.64

See main text for acronyms.



eTable 3. Demographic information from participants who were misclassified in the SVM analyses.

See main text for acronyms.



eFigure 1. Adjusted tissue volumes for each group. Total grey matter volume (GMV), total white matter volume (WMV), total tissue volume (TTV: GMV+WMV) are adjusted for scan-site and age. Error bars represent standard deviation. TD: typically developing group, DD: developmentally delayed group, FXS: fragile X syndrome group, iAUT: idiopathic autism group. n.s. p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001.



eFigure 2. GM and WM differences between iAUT and controls. a-c. Regions that show significant differences in regional GMV and WMV between iAUT and TD/DD. Left side shows right hemisphere. Statistical threshold is set at p = 0.01 family-wise error (FWE) cluster-level corrected. d-e. Adjusted GMVs from each region in a-c are plotted for each group. Letters on x-axis correspond to ROI labels in Tables 2 and 3. n.s.: p > 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001. Error bars represent standard deviation. Left side shows right hemisphere.



eFigure 3. GM and WM differences between FXS and controls. a-c. Regions that show significant differences in regional GMV and WMV between FXS and TD/DD. Left side shows right hemisphere. Statistical threshold is set at p = 0.01 family-wise error (FWE) cluster-level corrected. d-e. Adjusted GMVs from each region in a-c are plotted for each group. Letters on x-axis correspond to ROI labels in Tables 2 and 3. n.s.: p > 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001. Error bars represent standard deviation.



eFigure 4. GM and WM differences between iAUT and FXS. a-d. Regions that show significant differences in regional GMV and WMV between iAUT and FXS. Left side shows right hemisphere. Statistical threshold is set at p = 0.01 family-wise error (FWE) cluster-level corrected. e-f. Adjusted GMVs from each region in a-d are plotted for each group. Letters on x-axis correspond to ROI labels in Tables 2 and 3. n.s.: p > 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001. Error bars represent standard deviation. Left side shows right hemisphere.


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