Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

Abstract

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.

Data Descriptor: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

OPEN

Received: 21 September 2016 Accepted: 05 January 2017

Published: 14 March 2017

Adriana Di Martino1,#, David OConnor2,3, Bosi Chen1, Kaat Alaerts4, Jeffrey S. Anderson5,6,7,8, Michal Assaf9,10, Joshua H. Balsters11, Leslie Baxter12, Anita Beggiato13,14,Sylvie Bernaerts4, Laura M.E. Blanken15, Susan Y. Bookheimer16,17,18,19, B. Blair Braden12,20, Lisa Byrge21, F. Xavier Castellanos,1,2, Mirella Dapretto17,18,19,22, Richard Delorme13,14, Damien A. Fair23,24,25, Inna Fishman26, Jacqueline Fitzgerald27,28, Louise Gallagher27,28,R. Joanne Jao Keehn26, Daniel P. Kennedy21, Janet E. Lainhart6,7,29,30, Beatriz Luna31, Stewart H. Mostofsky32,33,34, Ralph-Axel Mller26, Mary Beth Nebel32,33, Joel T. Nigg23,24, Kirsten OHearn31, Marjorie Solomon35,36, Roberto Toro14, Chandan J. Vaidya37,38,Nicole Wenderoth11, Tonya White15, R. Cameron Craddock2,3, Catherine Lord39,Bennett Leventhal40 & Michael P. Milham2,3

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregateof resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identication of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.

Design Type(s) data integration objective observation design

Measurement Type(s) nuclear magnetic resonance assay

Technology Type(s) digital curation

Factor Type(s) research institute study design Autism

Sample Characteristic(s) Homo sapiens brain

Correspondence and requests for materials should be addressed to A.D.M. (email: mailto:[email protected]

Web End [email protected] ) or to M.P.M. (email: mailto:[email protected]

Web End [email protected] ).#A full list of afliations appears at the end of the paper.

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Background & Summary

Multiple sources of evidence have substantiated models of abnormal neural connectivity in autism spectrum disorder (ASD)15. At the macroscale, abnormal connections among brain regions have been revealed by functional and structural neuroimaging in children, adolescents and, adults with ASD1,69. Yet, both the complexity of the brain connectome10,11 and the striking heterogeneity of ASD1216 have hampered efforts to specify the nature of putative dysconnections. In response, open-data sharing is increasingly being encouraged to rapidly amass the large-scale datasets needed to confront heterogeneity, engage a broader range of scientic disciplines, and facilitate independent replications1721. To bring the open data sharing model to autism neuroimaging, the Autism Brain Imaging Data Exchange (ABIDE)22 was launched in 2012. The initial ABIDE initiativenow termed ABIDE Iwas the rst open-access brain imaging repository of resting state functional magnetic resonance imaging (R-fMRI) and corresponding structural data of individuals with ASD and typical controls (N = 539 and 573, respectively) aggregated from multiple international institutions. Here, we introduce ABIDE II (Data Citation 1), a new multi-site open data resource containing 1,044 independent datasets (ASD N = 487; Controls N = 557) created to enhance the signicance of the questions that can be addressed regarding the neural correlates of ASD and accelerate the pace of discovery.

The initial ABIDE I effort established the feasibility of aggregating multisite data without prior harmonization, leading to more than 55 peer-reviewed studies in the 48 months since inception. Despite its success, ABIDE I is limited in regard to sample characterization and sample size. Specically, despite containing more than 1,000 datasets, ABIDE I was not sufciently large to furnish optimally sized discovery and replication subsamples. By combining the ABIDE I and ABIDE II data resources, investigators can select larger samples for discovery and replication, depending on their investigative endeavors. Replication samples are needed to minimize false positives and avoid settling for approximate replications19a practice that has plagued biological psychiatry19 and neuroscience more broadly17. Additionally, as recently demonstrated, the utility of datasets for prediction increases with sample sizeeven if heterogeneous data sources are used to amass large samples23.

Along with increased sample size, ABIDE II provides greater phenotypic characterization than was available across the ABIDE I data collections to better address two key sources of heterogeneity. The rst is psychopathology co-occurring with ASD, which has been largely overlooked in the imaging literature15,16,24. Accordingly, ABIDE II actively encouraged investigators to provide phenotypic information regarding co-occurring illness, if assessed. The second source of heterogeneity is driven by sex-related differences. These have been generally ignored in the ASD imaging literature due to the markedly higher prevalence of males with ASD and the tendency of single sites to exclude or minimally represent females. The ABIDE II sample has increased the number of available datasets from females with ASD from 65 in ABIDE I to 138 when ABIDE I+II are combined. We believe these enhancements will allow investigators to more directly investigate pathophysiology specic to ASD, to potentially identify neurobiological subgroups and facilitate the identication of protective and risk factors.

Finally, beyond its focus on intrinsic functional connectivity and other indices of intrinsic brain function, ABIDE II now includes a subset of datasets (N = 284) with diffusion-weighted images. In order to facilitate immediate access and use of ABIDE II, the methods utilized to generate this resource, the resulting currently available data and their technical validation are described below.

Methods

Criteria for data contributions

We solicited investigators willing and able to openly share their previously collected awake R-fMRI data of individuals with ASD and controls, along with corresponding high-resolution anatomical images and phenotypic information. Contributions have been sought from all charter ABIDE I members and invitations are extended to any other investigators involved in ASD neuroimaging. The present work includes information regarding all contributions received prior to June 24, 2016. Contributions will continue to be accepted up to December 2016.

Contributors are encouraged to share at least 20 unique datasets per diagnostic group (i.e., ASD and controls). Data collections of only individuals with ASD are also accepted as they can be utilized for data-driven explorations addressing heterogeneity e.g., refs 25,26. Consistent with prior FCP/INDI efforts27, investigators are also encouraged to contribute nearly all MRI datasets, without a priori quality criteria (see Technical Validation for quality assessment (QA) measures incorporated into ABIDE II).

The availability of minimal phenotypic information essential for data analyses and sample characterization (i.e., diagnostic classication, age, sex) is required for contribution. To enhance phenotypic characterization, sharing of additional measures commonly used in ASD research, information on psychiatric comorbidity, medication status, cognition and/or language are highly encouraged. Similarly, to enhance the breath of investigations about the ASD connectome, whenever available, contributions of corresponding diffusion images for each individual are welcome for aggregation.

Finally, prior to data contribution, sites are required to conrm that their local Institutional Review Board (IRB) or ethics committee have approved both the initial data collection and the retrospective sharing of a fully de-identied version of the datasets (i.e., after removal of the 18 protected health

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information identiers including facial information from structural images as identied by the Health Insurance Portable and Accountability Act [HIPAA]).

Of note, two institutions provided longitudinal MRI scans from subsets of individuals datasets (n = 23 ASD and n = 15 controls) previously contributed to ABIDE I. Given the relevance of developmental changes2832, these datasets are also included in the ABIDE II. To distinguish them from the cross-sectional aggregates, these datasets are organized into a separate set of collections focused on longitudinal data using the original ABIDE I IDs.

Data preparation and aggregation

Prior to contribution, each institution is asked to rename all data by replacing local subject identication numbers with FCP/INDI identiers. They are also asked to remove personally identifying information (PHI) including those from images (e.g., NIFTI headers and face information from any high-resolution images) using the FCP/INDI anonymization script available in http://fcon_1000.projects.nitrc.org/

Web End =http://fcon_1000.projects.nitrc.org/ . Once data are fully anonymized at each site, they are submitted to the coordinating centers (Nathan Kline Institute and New York University) for review and harmonization within and across sites. Specically, MRI data are visually inspected and edited as needed to ensure complete removal of facial information. Additionally, to further protect personal privacy, images of ears are removed from high-resolution images. Regarding phenotypic datasets, each entry is also reviewed to identify and correct missing data, any impossible entry values (e.g., beyond published maxima and minima), and extreme outliers (relative to each sample). To ensure uniformity across sites, all entries are recorded as needed and organized in a common template along with a legend of code keys. As a nal step in preparation for release, both donating and coordinating sites jointly prepare a narrative for each data collection, documenting information on the methods utilized, funding sources, the investigators involved, whether any link with other databases (e.g., National Database for Autism ResearchNDAR33) exists, along with publications related to the contributed datasets. Before open release, each donating site reviews their reorganized phenotypic records, ve random images per imaging modality and their collection-specic narrative for nal approval.

Data Records

Overview

The current ABIDE II dataset encompasses 17 collections of unique independent datasets (i.e., from individuals whose data were not previously shared in ABIDE I) yielding 487 datasets classied as ASD and 557 as controls (Fig. 1a, Table 1). These represent previously collected datasets across 16 sites, including nine charter ABIDE I institutions and seven new members (See Supplementary Table 1 for information on each institution). A simple naming convention is used to label each data collection: oABIDEII> -oinstitution acronym name>_ocollection number>(e.g., ABIDEII-NYU_1). When a collection in ABIDE II is a continuation of one initiated in ABIDE I, we employ the same collection number used in ABIDE I (or 1 if none was used, e.g., SDSU_1, KKI_1). For new collections, a unique consecutive number is assigned (e.g., BNI_1, KUL_3). Accompanying the primary cross-sectional aggregate, two longitudinal collections are also aggregated in ABIDE II. These include MRI datasets collected as follow-ups to the MRI and phenotypic data released in ABIDE I (N total = 38 unique IDs). These pilot longitudinal collections are identied as oABIDEII> -oinstitution acronym name>_o-

Long> (Table 1).

All ABIDE II datasets can be accessed, after establishing a login and user password, through FCP/INDI at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =http://fcon_1000.projects. http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =nitrc.org/indi/abide/ ). The datasets are organized by data collection and stored in.tar les, each containing imaging and phenotypic data.

Phenotypic information

All phenotypic data are stored in comma separated value (.csv) les. A legend describing each phenotypic variable source is available at the website http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html

Web End =http://fcon_1000.projects.nitrc.org/indi/abide/abide_II.html . Phenotypic les are organized by data collection; a phenotypic composite le including all variables across all collections is also available. Counts of phenotypic variables available for each collection and distributions of selected key variables for each diagnostic group are provided in Supplementary Tables 2 and 3. Below, we briey describe the main demographics and key phenotypic variables provided in the 17 cross-sectional ABIDE II data collections (Figs 1 and 2).

Diagnostic classication. A dummy variable indicates diagnostic group (1 and 2 for ASD and controls, respectively). Given the retrospective nature of this data aggregate, assessment protocols used to identify ASD and controls varied across institutions. They are documented in each data collection narrative. Briey, ASD classication was determined by either 1) combining clinical judgment with gold standard diagnostic instrumentsAutism Diagnostic Observation Scale34,35 and/or Autism Diagnostic Interview-Revised36 [ADOS, ADI-R]; (n = 12 data collections; 368 ASD datasets) or 2) by using these gold standard diagnostic instruments only (n = 4 collections; 92 ASD datasets), with one exception. Specically, in EMC_1 (n = 27 datasets), which was selected from the longitudinal Generation R sample37, the ASD classication was based on prior medical records documenting ASD among those individuals

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Sample Size

Sex

Age Intelligence

ADOS

Figure 1. Key phenotypic characteristics. (a) Total number of datasets per group (gray = controls;blue = autism spectrum disorder (ASD)) for the 17 cross-sectional ABIDE II data collections (i.e., collections from individuals not included in ABIDE I). Data are ordered as a function of sample size. (b) Number of males (light blue) and females (red) for each data collection, irrespective of diagnostic group. Data are ordered as a function of sample size. (c) Age at time of scan in years per collection (ordered by mean age per collection), irrespective of diagnostic group. The median age across collections (11.7 years) is depicted with a thick red dashed line; 25th, 75th, and 90th percentiles (9.3, 18.6, and 25.5 years, respectively) are represented by thin red dashed lines. (d) Distribution of full scale IQ (FIQ) standard scores per collection (ordered by lowest FIQ included per collection) for all datasets, irrespective of diagnostic group. The median FIQ across collections (112) is depicted with a thick red dashed line; 25th, 75th, and 90th percentiles (101, 122, and 130, respectively) are represented by thin red dashed lines. (e) Tukeys box-whiskers plots depict the distribution of Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) total calibrated severity scores (CSS) for ASD datasets in the nine collections sharing them (ordered by mean CSS per collection). The black plus sign depicts the mean CSS for each collection. (f) Distribution of Social Responsiveness Scale (SRS) total T scores(gray = controls; blue = ASD) in the 12 collections sharing them. For each collection, red dashed and solid lines indicate mean SRS total T scores of ASD and controls, respectively.

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ASD

ASD

Controls

SRS

Data collection

MRI scanner Head coil R-fMRI High res MRI DWI*

Manufacturer Model T # channels ASD Contr ASD Contr ASD Contr Primary Cross Sectional AggregateABIDEII-BNI_1 Philips Ingenia 3 15 29 29 29 29 29 29 ABIDEII-EMC_1 GE MR750 3 8 27 27 27 27 0 0 ABIDEII-ETH_1 Philips Achieva 3 32 13 24 13 24 0 0 ABIDEII-GU_1 Siemens TriTim 3 12 51 55 51 55 0 0 ABIDEII-IU_1 Siemens TriTim 3 32 20 20 20 20 0 0 ABIDEII-IP_1 Philips Achieva 1.5 8 22 33 22 34 21 32 ABIDEII-KKI_1 Philips Achieva 3 8 56 155 56 155 0 0 ABIDEII-KUL_3 Philips Achieva Ds 3 32 28 0 28 0 0 0 ABIDEII-NYU_1 Siemens Allegra 3 8 48 30 48 30 33 24 ABIDEII-NYU_2 Siemens Allegra 3 8 27 0 27 0 19 0 ABIDEII-ONRC_2 Siemens Skyra 3 32 24 35 24 35 0 0 ABIDEII-OHSU_1 Siemens TriTim 3 12 37 56 37 56 0 0 ABIDEII-SDSU_1 GE MR750 3 8 33 25 33 25 33 24 ABIDEII-TCD_1 Philips Achieva 3 8 21 21 21 21 20 20 ABIDEII-UCD_1 Siemens TriTim 3 32 18 14 18 14 0 0 ABIDEII-UCLA_1 Siemens TriTim 3 12 16 16 16 16 0 0 ABIDEII-USM_1 Siemens TriTim 3 12 17 16 17 16 0 0

Total N 487 523 487 557 155 129

Pilot Longitudinal Aggregate

UCLA_Long Siemens TriTim 3 12 9 8 14 7 NA NA UPSM_Long Siemens Allegra 3 8 14 7 9 8 NA NA

Total N 23 15 23 15

Table 1. Information on scanners, head coils (same across MRI modalities) and MRI individual's datasets counts for each collection included in the primary ABIDE II collections (i.e., datasets collected in a given institution from individuals not included in ABIDE I) and in the longitudinal pilot collections (i.e., data from the same individuals scanned twice: Time 1 data originally released in ABIDE I and Time 2 data released in ABIDE II). *For longitudinal collections Diffusion Weighted

Imaging (DWI) data were not included ABIDE I, thus are not applicable (NA) here. For IP one individual ID only has sMRI available. For KKI_1 an 8-channel head coil was used for n = 149 datasets and a 32-channel head coil was used for n = 62 datasetsa list of the IDs with the corresponding head coil is provided in the ABIDE II website page for this collection under Additional scan Information. SDSU also includes eld maps corresponding to the R-fMRI and all DTI datasets but two. ASD, Autism Spectrum Disorder; Contr, Controls; GE, General Electrics; High res MRI, High Resolution Magnetic Resonance Imaging; Res, Resolution; R-fMRI, Resting-state functional MRI; T, Tesla; #, number. Also See Supplementary Table 1 for a list of the institutions/investigators.

meeting screening cutoffs in at least one of two distinct ASD questionnaires or for whom the mother reported a diagnosis of ASD. Regarding controls (N = 557, available for 15 collections), all datasets are characterized by absence of ASD diagnosis and absence of history of any other major neurodevelopmental disorders for the vast majority of the datasets (N = 546; 98%). This was determined using semi-structured/unstructured in-person interviews (N = 7 data collections; 353 datasets), or parent/self- (if adults) reports/questionnaires (N = 8; 193 datasets). The remaining 11 control datasets (OHSU_1 data collection) are from individuals assigned a rule out psychiatric disorder, but without ASD or Attention-Decit/Hyperactivity Disorder (ADHD) diagnoses.

Other specic inclusion/exclusion criteria used for selecting controls (e.g., IQ range, rst degree relative with ASD) or ASD (e.g., absence of reported seizure and genetic syndromes) varied across collections. Each collection narrative on the ABIDE II website provides details regarding these criteria.

Demographics. Across collections, age at time of scanning ranges from 5 to 64 years; four of the collections focused specically on adultswith one of these collections specically enrolling on older adults (BNI_1)and eight enrolling only children and/or adolescents. The remaining ve data collections include children, teens and young adults, which allows for cross-sectional age-related explorations

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Figure 2. Data on psychiatric comorbidity in Autism Spectrum Disorder (ASD). (a) Number of ASD datasets with and without psychiatric diagnoses for each of the nine data collections sharing information on categorical psychiatric diagnoses other than ASD. (b) Percentage of ASD datasets with one (black), two (dark gray) or more (light gray) comorbid diagnoses and those without any comorbidity (white) across the nine collections sharing comorbidity information. (c) Distribution of ASD datasets with comorbidity divided into those with comorbid Attention-Decit/Hyperactivity Disorder (ADHD, green), anxiety (blue/white pattern) or others (cyan-white pattern) for each collection. Other comorbidities include enuresis, and/or mood, speech and language and/or disruptive behavior disorders. Here, given that multiple comorbid disorders can co-occur, the number of comorbid ASD datasets across categories exceeds the absolute number of comorbid ASD datasets.

(Fig. 1c; Supplementary Table 3). All but four collections include data from both sexes (Fig. 1b). Reecting the higher prevalence of males in ASD38, 15% of the ASD datasets consist of females versus 31% of the control datasets (Supplementary Table 3).

Intelligence. Full scale intelligence quotient (FIQ) and/or verbal and/or performance IQ standard scores are provided. Across collections, although variation exists with respect to the minimum FIQ, 97% of the datasets have FIQ above 80 (Fig. 1d). For both groups, mean FIQ is above average, albeit signicantly higher in controls versus ASD (Mann Whitney U = 86.5; Po0.0001; Supplementary

Table 3).

Handedness. Categorical handedness codes for right, left or mixed handedness are available across all collections. Additionally, handedness strength scores are available for eight collections, enabling dimensional characterization of handedness (n = 244 ASD and n = 327 controls). Across collections, right-handedness is more frequent in both diagnostic groups (84 and 90% for ASD and controls, respectively), though a signicantly higher prevalence of non-right-handedness (either left or mixed handedness) occurs in ASD relative to controls (12 = 10.6, P = 0.01; Supplementary Table 3).

ASD core measures. Scores from the ADOS and ADI-R are available (Supplementary Table 3). Only nine collections share ADOS-2 calibrated severity scores34 (CSS; N = 9 sites; 228 ASD datasets) recently designed to adjust for differences in age, intellectual abilities and language skills across ADOS modules39,40. As illustrated in Fig. 1f, CSS distribution are similar across most sites. ADOS-G41 scaled total scores are available for 15 collections (n = 280 ASD datasets). Additionally, data from parent or self-report questionnaires commonly used in the eld to quantify severity on multiple ASD domains collected across both diagnostic groups are also available. The Social Responsiveness Scale42 is the most common (n = 378 ASD, n = 407 controls; Fig. 1f) followed by the Repetitive Behavior Scale Revised43,44 (n = 217 ASD, n = 208 controls; Supplementary Table 3).

Comorbid psychopathology in ASD. Information on psychopathology accompanying ASD is provided either as 1) categorical diagnostic labels (or its absence, if assessed) with corresponding

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diagnostic code based on the International Classication of Diseases-9th edition45 (N = 9 data collections; 281 ASD datasets) and/or as severity scores in one or multiple psychopathology dimensions across available for 11 collections (see Supplementary Table 3 for a list of measures used) (Fig. 2). Categorical comorbid psychiatric diagnoses were determined based on clinicians assessments in seven data collections, parent-questionnaires in one data collection (UCD_1) and self-report in another (KUL_3). Consistent with the clinical literature4648, approximately 60% of the ASD data correspond to individuals with one or more co-occurring psychiatric diagnoses (Fig. 2b); the most frequent are ADHD and anxiety disorders.

MRI data

For each of the 17 ABIDE II cross-sectional collections, for each unique ID#, at least one structural MRI (sMRI), one corresponding R-fMRI dataset are available (except for one individual in the IP collection for which only MRI is available); corresponding diffusion MRI (dMRI) datasets are available for six

PF

RO PE SL RO PE SL min:sec

ABIDEII-BNI_1 9 900 3.10 6.7 240.5 2,500 SS1.8 S AP 1.06 1.06 1.06 256 256 193 5:34 ABIDEII-EMC_1 16 350 4.24 10.26 81.4 1,664 AP2 S AP 0.90 0.90 0.90 256 256 186 5:40 ABIDEII-ETH_1 8 1,150 3.90 7.9 188.3 3,000 SP2.3 T RL 0.90 0.90 0.90 256 256 180 5:46 ABIDEII-GU_1 7 1,100 3.50 8.2 190 2,530 GP2 S AP 1.00 1.00 1.00 256 256 276 8:05 ABIDEII-IU_1 8 1,000 2.30 7 210 2,400 GP2 P 7/8 S AP 0.70 0.70 0.70 320 320 256 7:02 ABIDEII-IP_1 30 5.60 25 141.7 2,500 SP2+SS2 S AP 1.00 1.00 1.00 240 240 170 4:37 ABIDEII-KKI_1* 8 1,000 3.70 8 191.5 3,500 SS2 C RL 1.00 1.00 1.00 256 200 200 8:08 ABIDEII-KKI_1* 8 900 3.70 8.2 192.9 3,000 SP1.2+SS2 T RL 0.95 0.95 1.00 224 224 150 4:24 ABIDEII-KUL_3 8 900 4.60 9.4 130.6 2,000 SP1.5+SS2.5 C RL 0.98 0.98 1.20 256 256 182 1:43 ABIDEII-NYU_1 7 1,100 3.25 7.4 200 2,530 S AP 1.30 1.00 1.33 256 256 128 8:07 ABIDEII-NYU_2 7 1,100 3.25 7.2 200 2,530 S AP 1.30 1.00 1.33 256 256 128 8:07 ABIDEII-ONRC_2 13 794 2.88 7.1 200 2,200 GP3 T RL/AP 0.80 0.80 0.80 220 320 208 3:25 ABIDEII-OHSU_1 10 900 3.58 8.2 180 2,300 S AP 1.00 1.00 1.10 256 240 160 9:14 ABIDEII-SDSU_1 8 600 3.17 8.136 244.1 2,683 S AP 1.00 1.00 1.00 256 256 172 4:54 ABIDEII-TCD_1 8 1,150 3.90 7.9 188.3 3,000 SP2.3 T RL 0.90 0.90 0.90 256 256 180 5:43 ABIDEII-UCD_1 8 1,050 3.16 7.5 220 2,000 GP2 S AP 1.00 1.00 1.00 256 224 192 4:06 ABIDEII-UCLA_1 9 853 2.86 6.7 240 2,300 S AP 1.00 1.00 1.20 256 240 160 9:14 ABIDEII-USM_1 9 900 2.91 6.8 240 2,300 S AP 1.00 1.00 1.20 256 240 160 9:14 ABIDEII-UCLA_Long 9 853 2.86 6.7 240 2,300 S AP 1.00 1.00 1.20 256 240 160 9:14 ABIDEII-UPSM_Long 7 1,000 3.93 9.4 130 2,100 S AP 1.05 1.05 1.05 256 256 176 8:59

Table 2. Sequence parameters of structural MRI datasets at each data collection. The 3D MPRAGE (three dimensional magnetization prepared rapid acquisition gradient echo) sequence, or a vendor specic variant, was used to acquire all data. BW, bandwidth per pixel; Dims, dimensions; ES, echo spacing; FA, ip angle (indexed in degrees); PA, parallel acquisition; PE, phase encoding; PF, partial Fourier (halfscan); SO, Slice orientation; TA, Acquisition Time; TE, echo time; TI, inversion time; TR, repetition time; For parallel acquisitions; SS, SENSE acceleration in the slice direction; SP, SENSE acceleration in the phase encoding direction; GP, GRAPPA acceleration in the phase encoding direction; AP, ASSET acceleration in the phase encoding direction. For partial Fourier, the under-sampled dimension is listed with the under sampling factor; P, phase encoding. For slice orientation; S, sagittal; T, transverse (axial); C, coronal. For phase encoding direction; RL, right-to-left; AP, Anterior to posterior. Reconstructed resolution and image dimensions refer to the images after they have been reconstructed from the k-space data, the matrix size and resolution used for the acquisition may differ. For these categories, RO, read out direction; PE, phase encoding direction, and SL, slice direction. *62 datasets of the KKI-1 collection were acquired with an 8-channel head coil, 149 datasets were acquired with a 32 channel coil and different scanning parameters. A list of the IDs with the corresponding head coil is provided in the ABIDE II website page for this collection as Additional scan Information.

ONRC_2 has variable phase encoding direction, specic information for each dataset is provided in the release/website (http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =http://fcon_1000.projects.nitrc.org/indi/abide/). SDSU has used a GE scanner that refers to TR as the amount of time between the RF pulses that are used to read out the lines of k-space (i.e., echo space). Here to be consistent across all collections we report TR using the Siemens convention that refers to TR as the time between sequential inversion recovery pulses. Based on the simple equation TR = TI+n*ES, where n is the number of actual (corrected for parallel imaging) phase encoding lines, we calculated the TR of SDSU to be 2,683 ms.

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Collection FA TI TE ES BW TR PA

SO

PE

Reconstructed Resolution (mm) Recontructed Image Dims TA

Label ms ms ms Hz/Px ms

collections. One data collection (SDSU) provided eld map-corrected version of its R-fMRI and DTI data. The two pilot longitudinal collections include sMRI and R-fMRI datasets collected at two time points (12 years apart) for 23 individuals with ASD and 15 controls.

Consistent with its popularity in the imaging community and prior usage in FCP/INDI efforts, the NIFTI le format was selected for storage of the ABIDE II MRI datasets. With the exception of a single collection (IP_1, 1.5 Tesla), all MRI data were acquired using 3 Tesla scanners. Table 1 lists the specic MRI scanners and head coils utilized for each collection, along with the number of individuals available for each MRI modality within diagnostic groups (i.e., ASD and controls). Specic MRI sequence parameters for the various data collections are summarized in Table 2 and detailed on the ABIDE II website. Across collections, R-fMRI acquisition durations varied from ve to eight minutes (6:21 0.04 min) per individual; in all but four collections, individuals were verbally asked to keep their eyes open. For 12 collections, exposure to scan simulators prior to scanning was also used for habituation, as documented in the narratives.

Technical Validation

Consistent with the established FCP/INDI policy, all data contributed to ABIDE II was made available to

Collection FA TE TR BW PA

Label ms ms Hz/Px

ABIDEII-BNI_1 80 25 3,000 3,280 SP2 AP N T A 0 3.75 3.75 4.00 64 64 50 120 0 6:09 ABIDEII-EMC_1 85 30 2,000 7,812 AP T ID 0 3.59 3.59 4.00 64 64 37 160 5:20 ABIDEII-ETH_1 90 25 2,000 1,590 SP2.5 AP Y T D 10 3.00 3.00 3.30 80 80 40 210 0 7:06 ABIDEII-GU_1 90 30 2,000 2,442 GP2 AP N T IA 20 3.00 3.00 3.00 64 64 43 154* 2 5:14 ABIDEII-IU_1 60 28 813 2,604 MB3 AP Y TO IA 0 3.44 3.44 3.40 64 64 42 433 2 6:00 ABIDEII-IP_1 90 45 2,700 2,213 AP Y T A 0 3.60 3.70 4.00 64 64 32 85 2 7:55 ABIDEII-KKI_1 75 30 2,500 2,697 SP3 AP Y T A 0 3.00 3.00 3.00 96 96 47 156 2 6:40 ABIDEII-KUL_3 90 30 2,500 2,188 SP2 AP Y T A 14.8 1.56 1.56 3.10 128 128 45 162 4 7:00 ABIDEII-NYU_1 90 15 2,000 3,906 RL Y TO IA 0 3.00 3.00 4.00 80 64 33 180 2 6:00 ABIDEII-NYU_2 82 30 2,000 3,906 RL Y T IA 0 3.00 3.00 3.00 80 64 34 180 2 6:00 ABIDEII-OHSU_1 90 30 2,500 2,298 AP Y TO IA 0 3.75 3.75 3.80 64 64 36 120 2 5:07 ABIDEII-ONRC_2 60 30 475 2,604 MB8 AP Y T IA 0 3.00 3.00 3.00 80 80 48 947 2 7:37 ABIDEII-SDSU_1 90 30 2,000 7,813 A AP N T IA 0 3.44 3.44 3.40 64 64 42 180 5 6:10 ABIDEII-TCD_1 90 27 2,000 2,420 AP Y T A 10.94 3.00 3.00 3.20 80 80 37 210 0 7:06 ABIDEII-UCD_1 90 24 2,000 2,232 AP Y TO IA 0 3.50 3.50 3.50 64 64 36 151 2 5:06 ABIDEII-UCLA_1 90 28 3,000 2,442 AP Y TO IA 0 3.00 3.00 4.00 64 64 34 120 2 6:06 ABIDEII-USM_1 90 28 2,000 2,894 GP2 AP Y TO IA 10 3.40 3.40 3.00 64 64 40 240 2 8:06 ABIDEII-UCLA_Long 90 28 3,000 2,442 AP Y TO IA 0 3.00 3.00 4.00 64 64 34 120 2 6:06 ABIDEII-UPSM_Long 70 25 1,500 3,126 AP Y TO IA 0 3.13 3.13 4.00 64 64 29 200 2 5:06

Table 3. Sequence parameters of resting state fMRI datasets at each data collection included in the primary ABIDE II. All data were collected with echo planar imaging (EPI) sequences. BW, bandwidth per pixel; Dims, dimensions; FA, ip angle; FS, fat suppression; Gap, gap between slices; Nacq, number of volumes collected; Ndisc, number of initial volumes discarded by the scanner; PA, parallel acquisition; PE, Phase encoding; PF, Partial Fourier (half scan); SA, slice acquisition order; SO, slice orientation; TA, acquisition time; TE, echo time; TR, repetition time. For parallel acquisition the acceleration technology and dimension are listed followed by the acceleration factor, AP, ASSET acceleration in the phase encoding direction; GP, GRAPPA acceleration in the phase encoding direction; MB, multi-band imaging; SP, SENSE acceleration in the phase encoding direction. For partial Fourier, the under-sampled dimension is listed with the under sampling factor, P, phase encoding. For slice orientation; T, transverse (axial), and TO, transverse oblique. For phase encoding direction; RL, right-to-left; AP, Anterior to posterior. For slice acquisition order; A, ascending; D, descending; IA, interleaved ascending and ID, interleaved descending. Reconstructed resolution and image dimensions refer to the images after they have been reconstructed from the k-space data, the matrix size and resolution used for the acquisition may differ. For these categories, RO, read out direction; PE, phase encoding direction, and SL, slice direction. *GU discarded the rst 2 scans in addition to the 2 discarded by the sequence resulting in 152 volumes. For the KKI_1 collection, an 8-channel head coil was used for n = 149 datasets and a 32-channel head coil was used for n = 62 datasetssee Table 1. One R-fMRI datasets was collected with different EPI sequence with voxel size 3.5 3.5 4specics are provided in the ABIDE II website (http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =http://fcon_1000. http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =projects.nitrc.org/indi/abide/ ).

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 8

PF

PE

FS

SO

SA

Gap Recon resolution

(mm)

Recon image

Matrix (px)

Nacq

Ndisc

TA

% RO PE SL RO PH SL

min:sec

users regardless of data quality27. The rationale of this decision includes the lack of consensus on optimal quality criteria in regards to specic measures or their combinations and cutoffs. Additionally, depending on the study goal, the availability of scans with a range of quality can facilitate the development of artifact correction techniques18. For initiatives focusing on clinical populations like ABIDE II, the inclusion of datasets with artifacts such as motion are valuable, as they enable investigators to determine impact of such real-world confounds on reliability and reproducibility.

To facilitate quality assessment of the ABIDE II collections and selection of datasets for analyses by individual users, we used the Preprocessed Connectome Project quality assurance protocol49 (http://preprocessed-connectomes-project.github.io

Web End =http://preprocessed-connectomes-project.github.io). These encompass quantitative metrics commonly used in the imaging literature for assessing data quality, particularly for multisite projects, e.g., ref. 50. They include spatial metrics of scanner performance such as contrast to noise ratio50 artifactual voxel detection51 as well as temporal metrics including those quantifying head motion52; all metrics are summarized in Table 5 and all are available in the data release. As expected by design, within- and between-site variation exists across quality metrics (see Figs 3 and 4 and Supplementary Fig. 1 for examples of spatial and temporal metrics in sMRI, R-fMRI and DTI). It is important to note that the eld remains without consensus standards for the usage of QA measures. Additionally, differences in some measures across collections may reect purposeful tradeoffs in the design of an imaging protocol, which may not be readily obvious at times. As such, caution should be taken in over-interpretation of between-collection differences in QA measures. At a minimum, the various QA measures provided can be used to nd outlier datasets for a given site; though, potentially they may be used to provide insights into the impact of differences in acquisition protocols on quality measures as well.

Usage Notes

As data aggregation followed independent data collections across multiple sites, various sources of heterogeneity exist between collections. They can range from inclusion/exclusion criteria, recruitment/ sampling strategies, MRI scanner types, data acquisition parameters and instructions (e.g., eyes open versus closed). Users must be aware of such factors when designing their research questions and selecting data for analyses accordingly. Care should be taken when attempting to draw comparisons across ABIDE I and ABIDE II, as they are independently created aggregate datasets, bringing with them both commonalities and differences. Nine institutions participated in both initiatives with either related collections in regard to both phenotypic and imaging protocols (e.g., NYU_1 in ABIDE II is a continuation of NYU in ABIDE I) or collections acquired through independent protocols (e.g., KUL_3 in ABIDE II). We suggest consideration of the commonalities and differences among contributions when attempting to combine datasets from the two ABIDE initiatives. The narratives included in the ABIDE II website should facilitate this processsee Supplementary Fig. 2 for the collections distributed among the ABIDE initiatives. As a general rule, for aggregate data analyses datasets should be selected to ensure that the number ASD and TDC data are balanced at each collection, unbalanced designs (e.g., all typical

PA

ABIDEII-BNI_1 101 7,850 2,621.1 Y SP2 None AP T 0 1.41 1.41 3 192 192 48 1 32 2,500 1 4:34 ABIDEII-IP_1 86 5,407 1,972.5 Y SP2 P 0.683 AP T 0 2.5 2.5 2.5 96 96 45 1 32 1,000 1 3:09 ABIDEII-NYU_1 78

5,200

3,720

Y

None

None

RL

T

0

3

3

3

64

64

50

1

64

1,000

1

5:43

ABIDEII-NYU_2

ABIDEII-SDSU_1*

81.8 8,500 3,906.25 Y None None RL T 0 0.94 0.94 2 256 256 68

1

61

1,000

1

8:56

7.5 1,097 250 Y None None RL T 0 1.88 1.88 2 128 128

5:34

ABIDEII-TCD_1 79 20,244 2,590.6 Y SP2 None AP T 0 1.94 1.94 2 128 128 65 1 61 1,500 4 24:21

Table 4. Sequence parameters of diffusion MRI datasets for each of the six collections sharing these data along with corresponding high resolution anatomical and resting state functional MRI data. All diffusion data were collected with spin echo planar imaging (SE-EPI) sequences. *Data were collected with two slightly different sequences; Bvals, the gradient strength used for diffusion weighting; BW, bandwidth per pixel; Dims, dimensions; FS, fat suppression; Gap, gap between slices; Navg, number of volumes collected for each direction and subsequently averaged; Nb0, number of volumes acquired with b = 0; Ndir, number of directions acquired with diffusion weighting; PA, parallel acquisition; PE, Phase encoding; PF, Partial Fourier (half scan), SO, Slice orientation; TA, Acqusition Time; TE, echo time; TR, repetition time. For parallel acquisition the acceleration technology and dimension are listed followed by the acceleration factor, SP, SENSE acceleration in the phase encoding direction. For partial Fourier, the under-sampled dimension is listed with the undersampling factor, P, phase encoding. For slice orientation; T, transverse (axial). For phase encoding direction; RL, right-to-left; AP, Anterior to posterior. Reconstructed resolution and image dimensions refer to the images after they have been reconstructed from the k-space data, the matrix size and resolution used for the acquisition may differ. For these categories, RO, read out direction; PE, phase encoding direction, and SL, slice direction.

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 9

Collection TE TR BW FS

PF

PE

SO

TA

Gap Recon Resolution (mm) Recon Image Matrix (px) Nb0

% RO PE SL RO PH SL

Ndir

Navg

Bvals

Label ms ms Hz/Px

min:sec

Spatial Metrics Description

Contrast-to-noise ratio (CNR) (sMRI

only)

* voxels with intensity corrupted by artifacts/ *voxels in the background. Larger values reect more artifacts which likley

due to motion or image instability.

Entropy Focus Criteria (EFC) Shannons entropy of each voxel's intensity used to measure ghosting and blurring due to head motion. Larger values

reect more blurring likley to motion or techincal differences.

Smoothness of Voxels (FWHM) Full-width half maximum of the spatial distribution of the image intensity values. Larger values reect more spatial

smoothing maybe due to motion or technical differences.

Foreground to Background Energy Ratio

(FBER)

Sum absolute displacement changes in the x, y and z directions and rotational changes around them. Rotational

changes are given distance values based on changes across the surface of a 50 mm radius sphere. Larger values reect

more movement.% and * volumes with FD>0.2 mm % and *volume to volume motion >0.2 mm FD. Larger values reect more movement.

Standardized DVARS Spatial SD of the data temporal derivative normalized by the temporal SD and autocorrelation. Larger values reect

larger frame-to-frame differences in signal intensity due to head motion or scanner instability.

Outlier Detection M fraction of outliers in each volume per 3dToutcount AFNI command. Higher values reect more outlying voxels,

which may be due to scanner instability or RF artifacts.

Global Correlation (GCORR) M correlation of all combinations of voxels in a time series. Illustrates differences between data due to motion/

physiological noise. Larger values reect a greater degree of spatial correlation between slices, which may be due to head

motion or signal leakage in simultaneous multi-slice acquisitions.

Median Distance Index M distance (1spearmans rho) between each time-point's volume and the median volume using AFNIs 3dTqual

command. Higher values reect greater differences between subsequent frames, which may be due to head motion or

technical issues.

Table 5. Spatial and temporal indices of MRI data quality selected from the Preprocessed Connectome Project. They have been computed for all structural MRI (sMRI), Diffusion Tensor Imaging (DTI), and Resting State functional MRI (R-fMRI) datasets unless indicated otherwise. All are released for each dataset in ABIDE II (le and link of release pending). See Figs 3 and 4 and Supplementary Fig. 1 for illustrations of the distribution of a selection of spatial and temporal metrics within and across collection. *For all R-fMRI data temporal metrics have been computed after discarding the rst 5 time points of the time series which were eld map corrected if eld maps were provided (only in the SDSU_1 data collection). Computation of all spatial metrics excluded absolute zero background values. For R-fMRI data these metrics are computed on mean functional data. For R-fMRI these metrics are computed on time series data. M, Mean; GM, Gray Matter; WM, White Matter; s.d., Standard Deviation.

participants selected from one collection, all ASD selected from another) should be avoided.

The impact of known and unknown sources of heterogeneity between collections should also be taken in account at the analytical level. First, we encourage the use of standardization at individual-and group-level analyses e.g., refs 5355. Second, we recommend to model data collection as a covariate at the group level when possible, to account for the variance related to the specic site protocol e.g., refs 53,56,57. Users can also employ meta-analytic approaches that have been shown to be fruitful for examination of cortical thickness or structural volumes e.g., ref. 58. Awareness of site-related variability should also be reected in the presentation of ndings. For example, effects within each data collection should be reported along with those obtained across collections e.g., refs 29,56,59. Inconsistencies that arise may be informative and provide insights into known or unknown differences in samples including and beyond data acquisition protocols. Finally, we note that along with the challenges related to its multisite post-hoc data aggregation, ABIDE II also offers a unique opportunity to develop analytical approaches to address these challenges. For example, a recent effort based on ABIDE I demonstrated the ability to optimize classiers for the prediction of data from previously unseen imaging sites23.

The need for careful consideration of variation in acquisition parameters also applies to the use of the quality assurance (QA) metrics available for the ABIDE-II sample. Some QA measures may be more or less comparable across data collections. Mean FD is an example of a measure commonly used for QA in resting state fMRI studies, albeit without signicant considerations on the impact of the specic acquisition protocol employed. Motion-induced uctuations in the BOLD signal are primarily due to spin history effects and partial voluming, which are proportional to the amount of tissue displacement between subsequent excitations. From this perspective, one might expect that factors capable of impacting spin history effects or partial voluming, would in turn impact meanFD. Importantly, these relationships

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 10

M intensityM intensity/SD intensity. Larger values reect a better WM GM distinction.

Signal-to-noise ratio (SNR) M intensity/SD intensity. Larger values reect less noise

Artifactual voxel detection (Qi1) (sMRI

only)

M energy of image intensity (i.e., mean of squares) within the head relative to that of outside the head. Larger values

reect higher signal in relation to noise.

Ghost to Signal Ratio (GSR) M signal in the ghost image divided by the M signal within the brain. Larger values reect more ghosting likley due to

physiological noise, motion, or technical issues.

Temporal Metrics (R-fMRI* and DTI only)

Mean framewise displacement-

Jenkinson (mFD)

Structural MRI Spatial QA

Qi1 FWHM

Figure 3. Selection of spatial quality assurance (QA) metrics for high resolution MRI datasets.(a) Contrast-to-noise ratio (CNR)50, (b) smoothness of voxels indexed as full half-width maximum (FHWM)62, (c) signal-to-noise ratio (SNR)50, (d) artifactual voxel detection (Qi1)51- See Table 5 for details on this and the other quality metrics released. The colored scatterplots illustrate the quality metrics distribution for spatial MRI dataset within a given ADBIE II collection (17 cross-sectional and 2 longitudinal collections). The black and white violin plots represent a kernel density estimation of the distribution across all datasets for each quality metrics. The midline thick gray line represents the value that occurs most commonly in the distribution. For each plot the horizontal gray lines mark the 1st, 5th, 25th, 50th (solid gray line), 75th, 95th and 99th percentiles starting from the bottom.

may not necessarily be linear or additive. As such, some caution is suggested when interpreting systematic differences in meanFD, or related motion metrics (e.g., DVARS), across collections. Users may also employ this and other shared multisite datasets e.g., refs 60,61 to explore the impact of possible differences related to acquisition parameters, such as TR and other, on motion metrics. MeanFD computed in DTI data should not be used for comparisons between different collections with different MRI protocols. Mean FD in DTI is the result of the combination of both eddy current effects and head motion. As a result, meanFD can be used to compare and select data within collections obtained with the same scanning protocols and equipment.

Finally, to facilitate replications among studies using ABIDE data, we encourage users to provide the ID list utilized for their published manuscripts in the manuscript section of the ABIDE website (http://fcon_1000.projects.nitrc.org/indi/abide/manuscripts.html

Web End =http://fcon_1000.projects.nitrc.org/indi/abide/manuscripts.html). Users are also requested to

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 11

CNR

SNR

R-fMRI QA Metrics

Spatial QA

Temporal QA GSR

Mean FD

FWHM

SNR

GCORR

Figure 4. Selection of spatial and temporal quality metrics for resting state functional MRI (R-fMRI). Spatial metrics include: (a) Ghost to single ratio (GSR)50; (b) smoothness of voxels indexed as full-width half maximum (FWHM)62, (c) signal to noise ratio (SNR)50. Temporal metrics are: (d) mean framewise displacement52; (e) standardized DVARS63, and (f) global correlation (GCORR)64See Table 5 for details on this and the other quality metrics released. The colored scatterplots illustrate the quality metrics distribution for spatial MRI dataset within a given ADBIE II collection (17 cross-sectional and 2 longitudinal collections). The black and white violin plots represent a kernel density estimation of the distribution across all datasets for each quality metrics with its midline thick gray line representing the value that occurs most commonly in the distribution. For each plot, the horizontal gray lines mark the 1st, 5th, 25th, 50th (solid gray line), 75th, 95th and 99th percentiles starting from the bottom.

acknowledge the primary funding source for ABIDE II (NIMH 5R21MH107045) in any manuscripts using the ABIDE II data.

References

1. Minshew, N. J. & Williams, D. L. The new neurobiology of autism: cortex, connectivity, and neuronal organization. Archives of Neurology 64, 945950 (2007).

2. Geschwind, D. H. & Levitt, P. Autism spectrum disorders: developmental disconnection syndromes. Current opinion in neurobiology 17, 103111 (2007).

3. Frith, C. Is autism a disconnection disorder? Lancet Neurology 3, 577 (2004).4. Hutsler, J. J. & Casanova, M. F. Review: Cortical construction in autism spectrum disorder: columns, connectivity and the subplate. Neuropathology and applied neurobiology 42, 115134 (2016).

5. Bourgeron, T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nature reviews Neuroscience 16, 551563 (2015).

6. Minshew, N. J. & Keller, T. A. The nature of brain dysfunction in autism: functional brain imaging studies. Current Opinion in Neurology 23, 124130 (2010).

7. Vissers, M. E., Cohen, M. X. & Geurts, H. M. Brain connectivity and high functioning autism: a promising path of research that needs rened models, methodological convergence, and stronger behavioral links. Neuroscience and biobehavioral reviews 36, 604625 (2012).

8. Just, M. A., Cherkassky, V. L., Keller, T. A. & Minshew, N. J. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain: a journal of neurology 127, 18111821 (2004).

9. Picci, G., Gotts, S. J. & Scherf, K. S. A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism. Developmental science 19, 524549 (2016).

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 12

Standardized DVARS

10. Kelly, C., Biswal, B. B., Craddock, R. C., Castellanos, F. X. & Milham, M. P. Characterizing variation in the functional connectome: promise and pitfalls. Trends in cognitive sciences 16, 181188 (2012).

11. Craddock, R. C. et al. Imaging human connectomes at the macroscale. Nat Med 10, 524539 (2013).12. Hughes, V. Epidemiology: Complex disorder. Nature 491, S2S3 (2012).13. Geschwind, D. H. Advances in autism. Annu Rev Med 60, 367380 (2009).14. Lenroot, R. K. & Yeung, P. K. Heterogeneity within Autism Spectrum Disorders: What have We Learned from Neuroimaging Studies? Front Hum Neurosci 7, 733 (2013).

15. Lai, M. C., Lombardo, M. V., Chakrabarti, B. & Baron-Cohen, S. Subgrouping the autism spectrum: reections on DSM-5. PLoS Biol. 11, e1001544 (2013).

16. Grzadzinski, R., Huerta, M. & Lord, C. DSM-5 and autism spectrum disorders (ASDs): an opportunity for identifying ASD subtypes. Mol. Autism 4, 12 (2013).

17. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews Neuroscience 14, 365376 (2013).

18. Milham, M. P. Open neuroscience solutions for the connectome-wide association era. Neuron 73, 214218 (2012).19. Kapur, S., Phillips, A. G. & Insel, T. R. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular psychiatry 17, 11741179 (2012).

20. Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D. & Milham, M. P. Clinical applications of the functional connectome. NeuroImage 80, 527540 (2013).

21. Gorgolewski, K. J., Margulies, D. S. & Milham, M. P. Making data sharing count: a publication-based solution. Frontiers in neuroscience 7, 9 (2013).

22. Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry 19, 659667 (2014).

23. Abraham, A. et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage 147, 736745 (2016).

24. Solomon, M., Miller, M., Taylor, S. L., Hinshaw, S. P. & Carter, C. S. Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders. J Autism Dev Disord. 42, 4859 (2012).

25. Karalunas, S. L. et al. Subtyping attention-decit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. JAMA psychiatry 71, 10151024 (2014).

26. Yang, Z. et al. Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. NeuroImage. 63, 403414 (2012).

27. Mennes, M., Biswal, B., Castellanos, F. X. & Milham, M. P. Making data sharing work: The FCP/INDI experience. NeuroImage 82, 683691 (2012).

28. Padmanabhan, A., Lynn, A., Foran, W., Luna, B. & O'Hearn, K. Age related changes in striatal resting state functional connectivity in autism. Frontiers in human neuroscience 7, 814 (2013).

29. Alaerts, K. et al. Age-related changes in intrinsic function of the superior temporal sulcus in autism spectrum disorders. Social cognitive and affective neuroscience 10, 14131423 (2015).

30. Di Martino, A. et al. Unraveling the miswired connectome: a developmental perspective. Neuron 83, 13351353 (2014).31. Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L. & Martin, A. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism. Proceedings of the National Academy of Sciences of the United States of America. 112, E6699E6706 (2015).

32. Lynn, A. C. et al. Functional connectivity differences in autism during face and car recognition: underconnectivity and atypical age-related changes. Developmental science (2016).

33. Hall, D., Huerta, M. F., McAuliffe, M. J. & Farber, G. K. Sharing heterogeneous data: the national database for autism research. Neuroinformatics 10, 331339 (2012).

34. Gotham, K., Risi, S., Pickles, A. & Lord, C. The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity. Journal of autism and developmental disorders 37, 613627 (2007).

35. Lord, C., Rutter, M., DiLavore, P. C. & Risi, S. Autism Diagnostic Observation Schedule (Western Psychological Service, 1999).36. Rutter, M., LeCoutuer, A. & Lord, C.. Autism Diagnostic Interview-Revised (ADI-R) manual (Western Psychological Services, 2003).

37. Jaddoe, V. W. et al. The Generation R Study: design and cohort update 2012. European journal of epidemiology 27, 739756 (2012).

38. Centers for Disease Control and Prevention. Prevalence of Autism Spectrum DisordersAutism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. MMWR Surveill Summ 61, 119 (2012).

39. Gotham, K., Pickles, A. & Lord, C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. Journal of autism and developmental disorders 39, 693705 (2009).

40. Jones, R. M. & Lord, C. Diagnosing autism in neurobiological research studies. Behavioural brain research 251, 113124 (2012).

41. Lord, C. et al. The Autism Diagnostic Observation Schedule-Generic: A standard measure of social and communication decits associated with the spectrum of autism. Journal of autism and developmental disorders 30, 205223 (2000).

42. Constantino, J. N. & Gruber, C. P.. Social Responsiveness Scale (SRS): Manual (Western Psychological Services, 2005).43. Bodsh, J. W., Symons, F. J., Parker, D. E. & Lewis, M. H. Varieties of repetitive behavior in autism: comparisons to mental retardation. Journal of autism and developmental disorders 30, 237243 (2000).

44. Lam, K. S. & Aman, M. G. The Repetitive Behavior Scale-Revised: independent validation in individuals with autism spectrum disorders. Journal of autism and developmental disorders 37, 855866 (2007).

45. World Health Organization. ICD-9-CM: International classication of diseases, 9th revision, clinical modication (Medicode, 1996).

46. Simonoff, E. et al. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. Journal of the American Academy of Child and Adolescent Psychiatry 47, 921929 (2008).

47. Simonoff, E. et al. The persistence and stability of psychiatric problems in adolescents with autism spectrum disorders. Journal of child psychology and psychiatry, and allied disciplines 54, 186194 (2013).

48. Lai, M. C., Lombardo, M. V. & Baron-Cohen, S. Autism. Lancet 383, 896910 (2014).49. Shehzad, Z. et al. The Preprocessed Connectomes Project Quality Assessment Protocola resource for measuring the quality of MRI data. Frontiers in neuroscience (2015).

50. Magnotta, V. A., Friedman, L. & First, B. Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN Multicenter Imaging Study. Journal of digital imaging 19, 140147 (2006).

51. Mortamet, B. et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med. 62, 365372 (2009).

52. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825841 (2002).

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 13

53. Yan, C. G., Craddock, R. C., Zuo, X. N., Zang, Y. F. & Milham, M. P. Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage 80, 246262 (2013).

54. Panta, S. R. et al. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Frontiers in neuroinformatics 10, 9 (2016).

55. Chen, S., Kang, J. & Wang, G. An empirical Bayes normalization method for connectivity metrics in resting state fMRI. Frontiers in neuroscience 9, 316 (2015).

56. Valk, S. L., Di Martino, A., Milham, M. P. & Bernhardt, B. C. Multicenter mapping of structural network alterations in autism. Human brain mapping 36, 23642373 (2015).

57. Fard, P. K., Matthis, C., Balsters, J. H., Maathuis, M. & Wenderoth, N. Promises, pitfalls, and basic guidelines for applying machine learning classiers to psychiatric imaging data, with autism as an example. Front. Psychiatry 7, 177 (2015).

58. Schmaal, L., Hibar, D., Thompson, P., Veltman, D. & Grp, E.-M. W. Cortical brain alterations in major depressive disorder (MDD) from adolescence to adulthood: ndings from the enigma-MDD working group. Bipolar disorders 18, 1919 (2016).

59. Nielsen, J. A. et al. Multisite functional connectivity MRI classication of autism: ABIDE results. Frontiers in human neuroscience 7 (2013).

60. Zuo, X. N. et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1, 140049 (2014).

61. Fair, D. A. et al. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Frontiers in systems neuroscience 6, 80 (2012).

62. Friedman, L., Glover, G. H., Krenz, D. & Magnotta, V. Reducing inter-scanner variability of activation in a multicenter fMRI study: Role of smoothness equalization. NeuroImage. 32, 16561668 (2006).

63. Nichols, T. E. Standardizing DVARS. http://blogs.warwick.ac.uk/nichols/entry/standardizing_dvars

Web End =http://blogs.warwick.ac.uk/nichols/entry/standardizing_dvars (2012).64. Saad, Z. S. et al. Correcting brain-wide correlation differences in resting-state FMRI. Brain connectivity 3, 339352 (2013).65. Atkinson, D., Hill, D. L. G., Stoyle, P. N. R., Summers, P. E. & Keevil, S. F. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. Ieee T Med Imaging 16, 903910 (1997).

66. Giannelli, M., Diciotti, S., Tessa, C. & Mascalchi, M. Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5T MR scanner systems: effect of readout bandwidth and echo spacing. J Appl Clin Med Phys. 11, 170180 (2010).

67. Cox, R. W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29, 162173 (1996).

Data Citation

1. Di Martino, A. et al. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative http://dx.doi.org/10.15387/FCP_INDI.ABIDE2

Web End =http://dx.doi.org/ http://dx.doi.org/10.15387/FCP_INDI.ABIDE2

Web End =10.15387/FCP_INDI.ABIDE2 (2016).

Acknowledgements

We would like to thank the numerous contributors at each donating institution (see Supplementary Table 1 and http://fcon_1000.projects.nitrc.org/indi/abide/

Web End =http://fcon_1000.projects.nitrc.org/indi/abide/ ) as well as the http://www.nitrc.org

Web End =www.nitrc.org team for providing the data-sharing platform. We are particularly thankful to Tanmay Nath for support in aspects of website organization and assistance in some aspects of MRI data review along with Dorothea Floris, Yuta Aoki, Lindsay Alexander and Erica Ho. Support for ABIDE II coordination and data aggregation was provided by NIMH (521MH107045) to A.D.M. and gifts from Joseph P. Healey, Phyllis Green and Randolph Cowen to M.P.M. (M.P.M. is a Randolph Cowen and Phyllis Green Scholar). Support for data collection at each site was provided by NIH (MH084961-GU_1; MH094409-IU_1; NS048527, MH085328, MH078160-KKI_1; MH102660, MH087770, MH084126, MH081218, HD065282-NYU_1; MH102660-NYU_2; MH095888-ONRC_2; MH096773, MH091238, MH096773-03S1, MH086654-OHSU_1; MH081023, MH097972-SDSU_1; MH099250-01-UCD_1; HD055784-UCLA_1; MH092697, MH080826, MH60450, DC008553, NS34783-USM_1; HD065280-01-UCLA_Long; MH081191, MH67924, HD55748-UPSM_Long), Autism Speaks (KKI_1, 04593; UPSM_Long), the Simons Foundation (307280; EMC_1, OHSU_1), IDDRC (HD040677-07; GU_1), State of Arizona Alzheimers Consortium, BNI and Department of Defense (AR140105; BNI_1), Dutch ZonMw TOP grant (91211021; EMC_1), European Communitys 7th Framwork Programme (FP7/2008-2013, 212652; EMC_1), Physical Sciences Division, SURFsara, the Municipal Health Service Rotterdam area, the Rotterdam Homecare Foundation, and the Stichting Trombosedienst & Artsenlaboratorium Rijnmond STAR-MDC (EMC_1), Institut Pasteur, CNRS, INSERM, AP-HP, University Paris 7 Diderot, the BioPsy Labex, the DHU PROTECT, the Bettencourt-Schueller Foundation, the FondaMental Foundation, and the ANR SynDiv (IP_1), Branco Weiss fellowship of the Society in Science ETH Zrich, Marguerite-Marie Delacroix Foundation, Flanders Fund for Scientic Research (FWO project 1521313N, G.0401.12;1206013N; KUL_3), the Stavros Niarchos Foundation, The Leon Levy Foundation, an endowment provided by Phyllis Green and Randolph Cowen, and Goldman Sachs Gives on behalf of Ram Sundaram (NYU_1), DeStefano Family Foundation, Oregon Clinical and Translational Institute, and Medical Research Foundation (OHSU_1), National Childrens Research Centre Our Ladys Childrens Hospital (TCD_1), University of Utah Multidisciplinary Research Seed Grant, NRSA Predoctoral Fellowship (F32 DC010143; USM_1), Ben B. and Iris M. Margolis Foundation (USM_1), and UCLA Autism Center for Excellence (UCLA_Long).

Author Contributions

Conception, design and implementation of the ABIDE II initiative, data analyses and preparation of the manuscript: A.D.M. & M.P.M. Data aggregation and analyses: D.OC. & B.C. Development of the Quality Control Pipeline for MRI data analyses: R.C.C. Conceptualization on some aspects of study design: B.L., C.L. Data contribution: K.A., J.S.A., M.A., J.H.B., L.B.a., A.B., S.B., L.B.l., S.Y.B., B.B.B., L.B.y., F.X.C.,

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 14

M.D., R.D., A.D.M., D.A.F., I.F., J.F., L.G., R.J.J.K., D.P.K., J.E.L., B.L., S.H.M., R.A.M., M.B.N., J.T.N.,K.OH., M.S., R.T., C.J.V., N.W., & T.W. Editorial input in preparation of manuscript: F.X.C. All authors have provided critical review and nal approval of the manuscript version submitted.

Additional Information

Supplementary Information accompanies this paper at http://www.nature.com/sdata

Web End =http://www.nature.com/sdata

Competing nancial interests: C.L. receives royalties from the publication of the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule. The remaining authors declare no competing nancial interests.

How to cite this article: Di Martino, A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4:170010 doi: 10.1038/sdata.2017.10 (2017).

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Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/

Web End =http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse.

The Author(s) 2017

1The Child Study Center at NYU Langone Medical Center, New York, New York 10016, USA. 2Child Mind Institute, New York, New York 10022, USA. 3Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, New York 10962, USA. 4KU Leuven, Department of Rehabilitation Sciences, Neuromotor Rehabilitation Research Group 3000, Leuven, Belgium. 5Division of Neuroradiology, University of Utah, Salt Lake City,Utah 84112, USA. 6Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, Utah 84116, USA. 7The Brain Institute at the University of Utah, Salt Lake City, Utah 84116, USA. 8Department of Bioengineering, University of Utah, Salt Lake City, Utah 84116, USA. 9Olin Neuropsychiatry Research Center, The Institute of Living, Hartford Hospital, Hartford, Connecticut 06102, USA. 10Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06510, USA. 11Neural Control of Movement Lab, ETH Zrich, Zrich 8092, Switzerland. 12Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona 85013, USA. 13Department of Child and Adolescent Psychiatry, Robert Debr Hospital, APHP, 75019 Paris, France.

14Human Genetics and Cognitive Functions, Institut Pasteur, 75015 Paris, France. 15Department of Child and

Adolescent Psychiatry, Erasmus University Medical Centre, 3015 Rotterdam, Netherlands. 16Center for Cognitive Neuroscience, UCLA, Los Angeles, California 90095, USA. 17Department of Psychiatry & Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, California 90095, USA.

18Interdepartmental Neuroscience Program, UCLA, Los Angeles, California 90095, USA. 19David Geffen School of Medicine, UCLA, Los Angeles, California 90095, USA. 20Department of Speech and Hearing Science, Arizona State University, Tempe, Arizona 85004, USA. 21Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405, USA. 22Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, California 90095, USA. 23Behavioral Neuroscience Department, Oregon Health & Science University, Portland, Oregon 97239, USA. 24Psychiatry Department, Oregon Health & Science University, Portland, Oregon 97239, USA. 25Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon 97239, USA. 26Department of Psychology, San Diego State University, San Diego, California 92182, USA. 27Department of Psychiatry, School of Medicine, Trinity Centre for Health Science, St Jamess Hospital, Dublin 8, Ireland. 28Trinity Institute of

Neuroscience, Trinity College Dublin, Dublin 2, Ireland. 29Department of Psychiatry, University of Utah, Salt Lake City, Utah 84108, USA. 30Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA.

31University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15261, USA. 32Center for

Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland 21205, USA.

33Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA. 34Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA. 35UC Davis Department of Psychiatry and Behavioral Science, Sacramento, California 95817, USA. 36UC Davis MIND Institute, Sacramento, California 95817, USA. 37Childrens Research Institute, Childrens National Medical Center, Washington, District Of

Columbia 20010, USA. 38Department of Psychology, Georgetown University, Washington, District Of Columbia 20010, USA. 39Center for Autism and the Developing Brain, Weill Cornell Medical College, White Plains, New York 10605, USA. 40Department of Psychiatry, University of California San Francisco, San Francisco, California 94103, USA.

SCIENTIFIC DATA | 4:170010 | DOI: 10.1038/sdata.2017.10 15