.2014 Jun;19(6):659-67.

doi: 10.1038/mp.2013.78. Epub 2013 Jun 18.

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

A Di Martino¹, C-G Yan², Q Li³, E Denio¹, F X Castellanos⁴, K Alaerts⁵, J S Anderson⁶, M Assaf⁷, S Y Bookheimer⁸, M Dapretto⁹, B Deen¹⁰, S Delmonte¹¹, I Dinstein¹², B Ertl-Wagner¹³, D A Fair¹⁴, L Gallagher¹¹, D P Kennedy¹⁵, C L Keown¹⁶, C Keysers¹⁷, J E Lainhart¹⁸, C Lord¹⁹, B Luna²⁰, V Menon²¹, N J Minshew²², C S Monk²³, S Mueller¹³, R-A Müller¹⁶, M B Nebel²⁴, J T Nigg²⁵, K O'Hearn²⁰, K A Pelphrey²⁶, S J Peltier²³, J D Rudie²⁷, S Sunaert²⁸, M Thioux¹⁷, J M Tyszka²⁹, L Q Uddin²¹, J S Verhoeven²⁸, N Wenderoth³⁰, J L Wiggins²³, S H Mostofsky³¹, M P Milham³²

Affiliations

¹ Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA.
² Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
³ Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
⁴ 1] Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA [2] Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
⁵ 1] Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA [2] KU Leuven, Leuven, Belgium.
⁶ 1] Division of Neuroradiology, University of Utah, Salt Lake City, UT, USA [2] Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA [3] The Brain Institute at the University of Utah, Salt Lake City, UT, USA [4] Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.
⁷ 1] Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA [2] Yale School of Medicine, New Haven, CT, USA.
⁸ 1] Center for Cognitive Neuroscience, UCLA, Los Angeles, CA, USA [2] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [3] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [4] David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
⁹ 1] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [2] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [3] Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA.
¹⁰ 1] Yale School of Medicine, New Haven, CT, USA [2] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹¹ Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, UK.
¹² 1] Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA [2] Psychology, Ben Gurion University of the Negev, Beersheba, Israel.
¹³ Institute for Clinical Radiology, Ludwig Maximilians University Munich, Munich, Germany.
¹⁴ Behavioral Neuroscience and Psychiatry Departments, and Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA.
¹⁵ 1] Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA [2] Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
¹⁶ Department of Psychology, San Diego State University, San Diego, CA, USA.
¹⁷ 1] Netherlands Institute for Neuroscience, Royal Dutch Academy of Science (KNAW), Amsterdam, The Netherlands [2] BCN NeuroImaging Center, University Medical Center Groningen, Rijksuniversiteit Groningen, Groningen, The Netherlands.
¹⁸ 1] Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI, USA [2] Department of Psychiatry, Division of Child and Adolescent Psychiatry, University of Wisconsin, Madison, WI, USA.
¹⁹ Weill-Cornell Medical College, New York, NY, USA.
²⁰ University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
²¹ Stanford University, Stanford, CA, USA.
²² Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
²³ University of Michigan, Ann Arbor, MI, USA.
²⁴ Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA.
²⁵ Oregon Health and Science University, Portland, OR, USA.
²⁶ Yale School of Medicine, New Haven, CT, USA.
²⁷ 1] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [2] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [3] David Geffen School of Medicine, UCLA, Los Angeles, CA, USA [4] Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA.
²⁸ Translational MRI, University of Leuven (KU Leuven), Leuven, Belgium.
²⁹ Division of Biology, Caltech, Pasadena, CA, USA.
³⁰ KU Leuven, Leuven, Belgium.
³¹ 1] Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA [2] Departments of Neurology and Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA.
³² 1] Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA [2] Center for the Developing Brain, Child Mind Institute, New York, NY, USA.

PMID:23774715
PMCID: PMC4162310
DOI: 10.1038/mp.2013.78

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

A Di Martino et al. Mol Psychiatry.2014 Jun.

.2014 Jun;19(6):659-67.

doi: 10.1038/mp.2013.78. Epub 2013 Jun 18.

Authors

Affiliations

¹ Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA.
² Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
³ Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
⁴ 1] Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA [2] Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
⁵ 1] Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA [2] KU Leuven, Leuven, Belgium.
⁶ 1] Division of Neuroradiology, University of Utah, Salt Lake City, UT, USA [2] Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA [3] The Brain Institute at the University of Utah, Salt Lake City, UT, USA [4] Department of Bioengineering, University of Utah, Salt Lake City, UT, USA.
⁷ 1] Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT, USA [2] Yale School of Medicine, New Haven, CT, USA.
⁸ 1] Center for Cognitive Neuroscience, UCLA, Los Angeles, CA, USA [2] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [3] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [4] David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
⁹ 1] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [2] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [3] Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA.
¹⁰ 1] Yale School of Medicine, New Haven, CT, USA [2] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹¹ Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, UK.
¹² 1] Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA [2] Psychology, Ben Gurion University of the Negev, Beersheba, Israel.
¹³ Institute for Clinical Radiology, Ludwig Maximilians University Munich, Munich, Germany.
¹⁴ Behavioral Neuroscience and Psychiatry Departments, and Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA.
¹⁵ 1] Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA [2] Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
¹⁶ Department of Psychology, San Diego State University, San Diego, CA, USA.
¹⁷ 1] Netherlands Institute for Neuroscience, Royal Dutch Academy of Science (KNAW), Amsterdam, The Netherlands [2] BCN NeuroImaging Center, University Medical Center Groningen, Rijksuniversiteit Groningen, Groningen, The Netherlands.
¹⁸ 1] Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI, USA [2] Department of Psychiatry, Division of Child and Adolescent Psychiatry, University of Wisconsin, Madison, WI, USA.
¹⁹ Weill-Cornell Medical College, New York, NY, USA.
²⁰ University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
²¹ Stanford University, Stanford, CA, USA.
²² Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
²³ University of Michigan, Ann Arbor, MI, USA.
²⁴ Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA.
²⁵ Oregon Health and Science University, Portland, OR, USA.
²⁶ Yale School of Medicine, New Haven, CT, USA.
²⁷ 1] Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA [2] Interdepartmental Neuroscience Program, UCLA, Los Angeles, CA, USA [3] David Geffen School of Medicine, UCLA, Los Angeles, CA, USA [4] Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, CA, USA.
²⁸ Translational MRI, University of Leuven (KU Leuven), Leuven, Belgium.
²⁹ Division of Biology, Caltech, Pasadena, CA, USA.
³⁰ KU Leuven, Leuven, Belgium.
³¹ 1] Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA [2] Departments of Neurology and Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA.
³² 1] Nathan S Kline Institute for Psychiatric Research, Orangeburg, NY, USA [2] Center for the Developing Brain, Child Mind Institute, New York, NY, USA.

PMID:23774715
PMCID: PMC4162310
DOI: 10.1038/mp.2013.78

Abstract

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest

Catherine Lord receives royalties from the publication of the Autism Diagnostic Interview–Revised and the Autism Diagnostic Observation Schedule. All other authors declare no conflict of interest.

Figures

**Figure 1. ABIDE Sample Characteristics**
**(A)** Total number of participants per group (green=TypicalControls [TC], purple=Autism Spectrum Disorders[ASD]) for each contributing site ordered as a function ofsample size (labeled alphabetically, see Supplementary Table 2 for labelkey). The same site labels are used for Figures 1 B-F. (B) Numberof males (blue-white) and females (red) for each site irrespective of diagnosticgroup (groups were matched for sex). (C) Age (in years) for allindividuals per site (ordered by youngest age included per site) irrespective ofdiagnostic group (groups were age matched). Each site’s mean isrepresented as a solid red line; the median age across sites (14.7 years) isdepicted with a thick red dashed line; 25^th, 75^th, and90^th percentiles (11.7, 20.1, and 28.3 years, respectively) arerepresented by thin red dashed lines.(D) Distribution of full IQ(FIQ) standard scores per site (ordered by lowest FIQ included per site) forindividuals with ASD (purple, left plot) and TC (green, right plot),respectively. Solid black lines indicate mean FIQ per site.(E) TheTukey box-whiskers plots depict the distribution of Total Autism DiagnosticObservation Scale (ADOS) scores (i.e., sum of scaled Communication andReciprocal Social interaction subtotals) for individuals with ASD in the 13sites using the ADOS.(F) Number of probands assigned to specificASD diagnostic categories per site. Categories were DSM-IV-TR Autistic Disorder(red), Asperger Syndrome (aqua green), and Pervasive DevelopmentalDisorder—Not Otherwise Specified (PDD-NOS) (white-gray pattern), andindividuals identified as ASD but not further differentiated into specificDSM-IV-TR subtypes (gray). Data displayed in D and E were imputed as describedin main text.

**Figure 2. Whole-Brain Intrinsic Functional Connectivity (iFC) Analyses**
**(A)** Significant group differences (i.e., Autism Spectrum Disorders[ASD] vs. Typical Controls [TC]) for iFC betweeneach of the 112 parcellation units (56 per hemisphere) included in thestructural Harvard-Oxford Atlas (HOA). Parcellations are represented with theircenter of mass overlaid as spheres on glass brains. The upper panel shows theintrinsic functional connections (blue lines) that were significantly weaker inASD vs. TC. The lower panel shows the intrinsic functional connections that weresignificantly stronger in ASD relative to TC (red lines). Each HOA unit iscolored based on its membership in the six functional divisions per Mesulam etal.[yellow=primary sensorimotor (SM); green=unimodalassociation; blue=heteromodal association; orange=paralimbic;red=limbic; pink=subcortical]. Interhemispheric iFC isnoted on dorsal and coronal views. Glass brains (left lateral, dorsal, andcoronal views, shown from left to right) are generated using BrainNet Viewer(http://www.nitrc.org/projects/bnv/). Displayed results arecorrected for multiple comparisons using false discovery rate (FDR) atp<0.05.(B) The table summarizes the absolute number andpercentage of node-to-node iFC surviving statistical threshold for groupcomparisons within and between functional divisions. Gray cells representabsence of significant iFC, blue cells represent ASD-related hypoconnectivity(*Hypo*: ASD<TC), while red cells representhyperconnectivity (*Hyper*: ASD>TC). Blue and red shadingsdecrease proportionally from the highest percentage (37%) to the lowest(~0%). See Supplementary Tables 4, 5 and 6 for results based on lobar andhemispheric divisions as well as for those based on the Crad-200 functionalparcellation and SupplementaryInformation for further discussion on the approach.

**Figure 3. Regional Measures of Intrinsic Functional Architecture**
**(A)** Z maps of the grand means (i.e., across all 763 individuals) and(B) significant group differences between individuals withAutism Spectrum Disorders (ASD) and Typical Controls (TC) for each of the fourregional measures examined. These were fractional amplitude of low frequencyfluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopicconnectivity (VMHC), and degree centrality (DC). We employed Gaussian randomfield theory to carry out cluster-level corrections for multiple comparisons(voxel-level Z>2.3; cluster significance: p<0.05, corrected). Significantclusters are overlaid on inflated surface maps generated using BrainNet Viewer(http://www.nitrc.org/projects/bnv/), as well as on axial imagesgenerated with REST Slice Viewer (http://www.restfmri.net).L= Left hemisphere; R= Right hemisphere.

**Figure 4. Overlap Between Regional Measures of Intrinsic Brain Function**
**(A)** Surface and axial maps depict the extent of overlap forsignificant group differences (i.e., Autism Spectrum Disorders[ASD] vs. Typical Controls [TC]) among any ofthe four regional measures of intrinsic brain function shown in Figure 3. Purple clusters represent areas ofsignificant group differences emerging for only one measure; orange and yellowclusters indicate measures with overlap among 2 and 3 measures, respectively.(B) For each of the yellow and orange clusters in panel A, thetable lists the cluster’s anatomical area label, cluster size in numberof voxels, and stereotaxic coordinates for the center of mass in MontrealNeurological Institute (MNI) coordinates, the specific measures involved in theoverlap, and the group difference direction (ASD>TC in red, ASD<TC inblue). L= Left hemisphere; R= Right hemisphere.

**Figure 5. Seed Based Correlation Analyses: Default Network**
**(A)** Z maps of the grand means (i.e., across all 763 individuals) and(B) of the group differences between individuals with AutismSpectrum Disorders (ASD) and Typical Controls (TC) for the two midline core seedregions located in Posterior Cingulate Cortex (PCC) and Anterior MedialPrefrontal Cortex (aMPFC). Seeds were centered at Montreal NeurologicalInstitute stereotaxic coordinates x=−8, y=−56,z=26 for PCC and x=−6, y=52,z=−2 for aMPFC, and are depicted as white dots on the surfacemaps. Gaussian random field theory was used to carry out cluster-levelcorrections for multiple comparisons (voxel-level Z>2.3; clustersignificance: p<0.05, corrected). Significant clusters are overlaid oninflated surface maps generated using BrainNet Viewer (http://www.nitrc.org/projects/bnv/), as well as on axial imagesgenerated with REST Slice Viewer (http://www.restfmri.net).L= Left hemisphere; R= Right hemisphere.

See this image and copyright information in PMC

Cited by

Association between spectral electroencephalography power and autism risk and diagnosis in early development.
Huberty S, Carter Leno V, van Noordt SJR, Bedford R, Pickles A, Desjardins JA, Webb SJ; BASIS Team; Elsabbagh M.Huberty S, et al.Autism Res. 2021 Jul;14(7):1390-1403. doi: 10.1002/aur.2518. Epub 2021 May 6.Autism Res. 2021.PMID:33955195Free PMC article.
Effect of data harmonization of multicentric dataset in ASD/TD classification.
Serra G, Mainas F, Golosio B, Retico A, Oliva P.Serra G, et al.Brain Inform. 2023 Nov 25;10(1):32. doi: 10.1186/s40708-023-00210-x.Brain Inform. 2023.PMID:38006422Free PMC article.
Categorical Dimensions of Social Impairment and Disrupted Functional Connectivity in Autism Spectrum Disorders: When Does Continuous Become Discrete?
Ragland JD, Solomon M.Ragland JD, et al.Biol Psychiatry. 2016 Jul 15;80(2):90-91. doi: 10.1016/j.biopsych.2016.05.009.Biol Psychiatry. 2016.PMID:27346081Free PMC article.No abstract available.
Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.
He J, Kang J.He J, et al.Stat Sin. 2022 Oct;32(4):2095-2117. doi: 10.5705/ss.202020.0427.Stat Sin. 2022.PMID:36052338Free PMC article.
Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions.
Moreau CA, Kumar K, Harvey A, Huguet G, Urchs SGW, Schultz LM, Sharmarke H, Jizi K, Martin CO, Younis N, Tamer P, Martineau JL, Orban P, Silva AI, Hall J, van den Bree MBM, Owen MJ, Linden DEJ, Lippé S, Bearden CE, Almasy L, Glahn DC, Thompson PM, Bourgeron T, Bellec P, Jacquemont S.Moreau CA, et al.Brain. 2023 Apr 19;146(4):1686-1696. doi: 10.1093/brain/awac315.Brain. 2023.PMID:36059063Free PMC article.

See all "Cited by" articles

References

1. Centers for Disease Control and Prevention. Prevalence of Autism Spectrum Disorders — Autism and Developmental Disabilities Monitoring Network, 14 Sites, United States, 2008. MMWR Surveill Summ. 2012;61:1–19. - PubMed
1. Kim YS, Leventhal BL, Koh YJ, Fombonne E, Laska E, Lim EC, et al. Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry. 2011;168(9):904–912. - PubMed
1. Lord C, Petkova E, Hus V, Gan W, Lu F, Martin DM, et al. A multisite study of the clinical diagnosis of different autism spectrum disorders. Arch Gen Psychiatry. 2012;69(3):306–313. - PMC - PubMed
1. Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, et al. The Autism Diagnostic Observation Schedule-Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders. 2000;30(3):205–223. - PubMed
1. Geschwind DH, Sowinski J, Lord C, Iversen P, Shestack J, Jones P, et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am J Hum Genet. 2001;69(2):463–466. - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Related information

Cited in Books

Grants and funding

LinkOut - more resources

Full Text Sources
Other Literature Sources
- The Lens - Patent Citations Database
- scite Smart Citations

Movatterモバイル変換

Account

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Full text links

Actions

Share

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Affiliations

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources