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

Scientific Data

Volumn 4, Issue , 2017, Pages

  Di Martino, Adriana ^a   O'Connor, David ^b,c   Chen, Bosi ^a   Alaerts, Kaat ^d   Anderson, Jeffrey S ^e,f,g   Assaf, Michal ^h,i   Balsters, Joshua H ^j   Baxter, Leslie ^k   Beggiato, Anita ^l,m   Bernaerts, Sylvie ^d   Blanken, Laura M E ⁿ   Bookheimer, Susan Y ^l,o,p,q   Braden, B Blair ^k,r   Byrge, Lisa ^s   Castellanos, F Xavier ^a,b   Dapretto, Mirella ^o,p,q   Delorme, Richard ^l,m   Fair, Damien A ^t   Fishman, Inna ^u   Fitzgerald, Jacqueline ^v,w   Gallagher, Louise ^v,w   Keehn, R Joanne Jao ^u   Kennedy, Daniel P ^s   Lainhart, Janet E ^e,f,x   Luna, Beatriz ^y   Mostofsky, Stewart H ^af   Müller, Ralph Axel ^z,aa   Nebel, Mary Beth ^u   Nigg, Joel T ^z,aa   O'Hearn, Kirsten ^t   Solomon, Marjorie ^y   Toro, Roberto ^ab,ac   Vaidya, Chandan J ^m   Wenderoth, Nicole ^ad,ae   White, Tonya ^j   Craddock, R Cameron ⁿ   Lord, Catherine ^af   Leventhal, Bennett ^ag   Milham, Michael P ^b,c   more..

^a NEW YORK UNIVERSITY CHILD STUDY CENTER   (United States)

^b CHILD MIND INSTITUTE   (United States)

^c NATHAN S KLINE INSTITUTE FOR PSYCHIATRIC RESEARCH   (United States)

^d KU LEUVEN DEPARTMENT OF REHABILITATION SCIENCES   (Belgium)

^e UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^f The Brain Institute at the University of Utah   (United States)

^g Department of Electrical and Computer Engineering   (United States)

^h Department of Cardiology   (United States)

ⁱ YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^j ETH ZURICH   (Switzerland)

^k BARROW NEUROLOGICAL INSTITUTE   (United States)

^l HÔPITAL ROBERT DEBRÉ   (France)

^m INSTITUT PASTEUR   (France)

ⁿ ERASMUS MEDICAL CENTER   (Netherlands)

^o UNIVERSITY OF CALIFORNIA   (United States)

^p UNIVERSITY OF CALIFORNIA   (United States)

^q UNIVERSITY OF CALIFORNIA   (United States)

^r ARIZONA STATE UNIVERSITY   (United States)

^s INDIANA UNIVERSITY   (United States)

^t OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^u SAN DIEGO STATE UNIVERSITY   (United States)

^v ST JAMES S HOSPITAL   (Ireland)

^w TRINITY COLLEGE DUBLIN   (Ireland)

^x UNIVERSITY OF WISCONSIN MADISON   (United States)

^y UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^z KENNEDY KRIEGER INSTITUTE   (United States)

^aa JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^ab UNIVERSITY OF CALIFORNIA   (United States)

^ac UNIVERSITY OF CALIFORNIA   (United States)

^ad CHILDREN S RESEARCH INSTITUTE   (United States)

^ae GEORGETOWN UNIVERSITY   (United States)

^af WEILL CORNELL MEDICAL COLLEGE   (United States)

^ag UNIVERSITY OF CALIFORNIA   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

AUTISM; CONNECTOME; HUMAN; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING;

AUTISM SPECTRUM DISORDER; CONNECTOME; HUMANS; MAGNETIC RESONANCE IMAGING; NEUROIMAGING;

EID: 85015451155     PISSN: None     EISSN: 20524463     Source Type: Journal    
DOI: 10.1038/sdata.2017.10     Document Type: Article

References (67)

1. Minshew, N. J. & Williams, D. L. The new neurobiology of autism: cortex, connectivity, and neuronal organization. Archives of Neurology 64, 945-950 (2007)
2. Geschwind, D. H. & Levitt, P. Autism spectrum disorders: developmental disconnection syndromes. Current opinion in neurobiology 17, 103-111 (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, 115-134 (2016)
5. Bourgeron, T. From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nature reviews Neuroscience 16, 551-563 (2015)
6. Minshew, N. J. & Keller, T. A. The nature of brain dysfunction in autism: functional brain imaging studies. Current Opinion in Neurology 23, 124-130 (2010)
7. Vissers, M. E., Cohen, M. X. & Geurts, H. M. Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neuroscience and biobehavioral reviews 36, 604-625 (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, 1811-1821 (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, 524-549 (2016)
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, 181-188 (2012)
11. Craddock, R. C. et al. Imaging human connectomes at the macroscale. Nat Med 10, 524-539 (2013)
12. Hughes, V. Epidemiology: Complex disorder. Nature 491, S2-S3 (2012)
13. Geschwind, D. H. Advances in autism. Annu Rev Med 60, 367-380 (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: reflections 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, 365-376 (2013)
18. Milham, M. P. Open neuroscience solutions for the connectome-wide association era. Neuron 73, 214-218 (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, 1174-1179 (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, 527-540 (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, 659-667 (2014)
23. Abraham, A. et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. NeuroImage 147, 736-745 (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, 48-59 (2012)
25. Karalunas, S. L. et al. Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. JAMA psychiatry 71, 1015-1024 (2014)
26. Yang, Z. et al. Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks. NeuroImage. 63, 403-414 (2012)
27. Mennes, M., Biswal, B., Castellanos, F. X. & Milham, M. P. Making data sharing work: The FCP/INDI experience. NeuroImage 82, 683-691 (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, 1413-1423 (2015)
30. Di Martino, A. et al. Unraveling the miswired connectome: a developmental perspective. Neuron 83, 1335-1353 (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, E6699-E6706 (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, 331-339 (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, 613-627 (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, 739-756 (2012)
38. 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 61, 1-19 (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, 693-705 (2009)
40. Jones, R. M. & Lord, C. Diagnosing autism in neurobiological research studies. Behavioural brain research 251, 113-124 (2012)
41. Lord, C. 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 30, 205-223 (2000)
42. Constantino, J. N. & Gruber, C. P.. Social Responsiveness Scale (SRS): Manual (Western Psychological Services, 2005)
43. Bodfish, 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, 237-243 (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, 855-866 (2007)
45. World Health Organization. ICD-9-CM: International classification of diseases, 9th revision, clinical modification (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, 921-929 (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, 186-194 (2013)
48. Lai, M. C., Lombardo, M. V. & Baron-Cohen, S. Autism. Lancet 383, 896-910 (2014)
49. Shehzad, Z. et al. The Preprocessed Connectomes Project Quality Assessment Protocol-a 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, 140-147 (2006)
51. Mortamet, B. et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med. 62, 365-372 (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, 825-841 (2002)
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, 246-262 (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, 2364-2373 (2015)
57. Fard, P. K., Matthis, C., Balsters, J. H., Maathuis, M. & Wenderoth, N. Promises, pitfalls, and basic guidelines for applying machine learning classifiers 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: findings from the enigma-MDD working group. Bipolar disorders 18, 19-19 (2016)
59. Nielsen, J. A. et al. Multisite functional connectivity MRI classification of autism: ABIDE results. Frontiers in human neuroscience 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, 1656-1668 (2006)
63. Nichols, T. E. Standardizing DVARS. 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, 339-352 (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, 903-910 (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, 170-180 (2010)
67. Cox, R. W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29, 162-173 (1996)