Interacting with the National Database for Autism Research (NDAR) via the LONI Pipeline workflow environment

Brain Imaging and Behavior

Volumn 9, Issue 1, 2015, Pages 89-103

  Torgerson, Carinna M ^a   Quinn, Catherine ^a   Dinov, Ivo ^b   Liu, Zhizhong ^a   Petrosyan, Petros ^a   Pelphrey, Kevin ^c   Haselgrove, Christian ^d   Kennedy, David N ^d   Toga, Arthur W ^a   Van Horn, John Darrell ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF NURSING   (United States)

^c YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

Author keywords

Autism spectrum disorder; Brain structure; Functional MRI diffusion imaging; Neuroimaging; Provenance; Workflows

Indexed keywords

ARTICLE; AUTISM; BRAIN CORTEX; DATA BASE; DIRECTORY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGING SOFTWARE; MEDICAL RESEARCH; NEUROIMAGING; PRIORITY JOURNAL; WORKFLOW; CLOUD COMPUTING; FACTUAL DATABASE; INFORMATION DISSEMINATION; INFORMATION RETRIEVAL; PATHOLOGY; PROCEDURES; SOFTWARE; STATISTICS AND NUMERICAL DATA;

AUTISM SPECTRUM DISORDER; CLOUD COMPUTING; DATABASES, FACTUAL; HUMANS; INFORMATION DISSEMINATION; INFORMATION STORAGE AND RETRIEVAL; NEUROIMAGING; SOFTWARE; WORKFLOW;

EID: 84925518292     PISSN: 19317557     EISSN: 19317565     Source Type: Journal    
DOI: 10.1007/s11682-015-9354-z     Document Type: Article

References (43)

1. Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M. C., Sahl, R., O’Boyle, J. G., Schultz, R. T., & Pearlson, G. D. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage, 53(1), 247–256.
2. Atkinson, J., & Braddick, O. (2011). From genes to brain development to phenotypic behavior: “dorsal-stream vulnerability” in relation to spatial cognition, attention, and planning of actions in Williams syndrome (WS) and other developmental disorders. Progress in Brain Research, 189, 261–283.
3. Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23(2), 137–152.
4. Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 360(1457), 1001–1013.
5. Billeci, L., Calderoni, S., Tosetti, M., Catani, M., & Muratori, F. (2012). White matter connectivity in children with autism spectrum disorders: a tract-based spatial statistics study. BMC Neurology, 12, 148.
6. Breeze, J. L., Poline, J. B., & Kennedy, D. N. (2012). Data sharing and publishing in the field of neuroimaging. Gigascience, 1(1), 9.
7. Cody, H., Pelphrey, K., & Piven, J. (2002). Structural and functional magnetic resonance imaging of autism. International Journal of Developmental Neuroscience, 20(3–5), 421–438.
8. Delmonte, S., Gallagher, L., O’Hanlon, E., McGrath, J., & Balsters, J. H. (2013). Functional and structural connectivity of frontostriatal circuitry in Autism Spectrum Disorder. Frontiers in Human Neuroscience, 7, 430.
9. Dinov, I. D., Van Horn, J. D., Lozev, K. M., Magsipoc, R., Petrosyan, P., Liu, Z., MacKenzie-Graham, A., Eggert, P., Parker, D. S., & Toga, A. W. (2009). Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline. Frontiers in Neuroinformatics, 3.
10. Dinov, I., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., Zamanyan, A., Chakrapani, S., Van Horn, J., Parker, D. S., Magsipoc, R., Leung, K., Gutman, B., Woods, R., & Toga, A. (2010). Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS ONE, 5(9).
11. Doyle-Thomas, K. A., Kushki, A., Duerden, E. G., Taylor, M. J., Lerch, J. P., Soorya, L. V., Wang, A. T., Fan, J., & Anagnostou, E. (2013). The effect of diagnosis, age, and symptom severity on cortical surface area in the cingulate cortex and insula in autism spectrum disorders. Journal of Child Neurology, 28(6), 732–739.
12. Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics, 5, 13.
13. Hall, D., Huerta, M. F., McAuliffe, M. J., & Farber, G. K. (2012). Sharing heterogeneous data: the national database for autism research. Neuroinformatics, 10(4), 331–339.
14. Insel, T. R., Volkow, N. D., Landis, S. C., Li, T. K., Battey, J. F., & Sieving, P. (2004). Limits to growth: why neuroscience needs large-scale science. Nature Neuroscience, 7(5), 426–427.
15. Kennedy, D. N. (2012). The benefits of preparing data for sharing even when you don’t. Neuroinformatics, 10(3), 223–224.
16. LaConte, S., Anderson, J., Muley, S., Ashe, J., Frutiger, S., Rehm, K., Hansen, L. K., Yacoub, E., Hu, X., Rottenberg, D., & Strother, S. (2003). The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. NeuroImage, 18(1), 10–27.
17. Leung, K., Parker, D. S., Cunha, A., Dinov, I. D. and Toga, A. W. (2008). IRMA: an image registration meta-algorithm - evaluating alternative algorithms with multiple metrics. SSDBM.
18. Luo, X.-Z. J., Kennedy, D. N., & Cohen, Z. (2009). Neuroimaging informatics tools and resources clearinghouse (NITRC) resource announcement. Neuroinformatics, 7(1), 55–56.
19. MacKenzie-Graham, A., Payan, A., Dinov, I., Van Horn, J. D., & Toga, A. W. (2008a). Neuroimaging data provenance using the LONI pipeline workflow environment. Provenance and annotation of data international provenance and annotation workshop, IPAW 2008. Salt Lake City: University of Utah.
20. Mackenzie-Graham, A. J., Van Horn, J. D., Woods, R. P., Crawford, K. L., & Toga, A. W. (2008b). Provenance in neuroimaging. NeuroImage, 42(1), 178–195.
21. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Le Goualher, G., Feidler, J., Smith, K., Boomsma, D., Pol, H. H., Cannon, T., Kawashima, R., & Mazoyer, B. (2001). A four-dimensional probabilistic atlas of the human brain. Journal of the American Medical Informatics Association, 8(5), 401–430.
22. McPartland, J. C., Coffman, M., & Pelphrey, K. A. (2011). Recent advances in understanding the neural bases of autism spectrum disorder. Current Opinion in Pediatrics, 23(6), 628–632.
23. Mori, S., & van Zijl, P. C. (2002). Fiber tracking: principles and strategies - a technical review. NMR in Biomedicine, 15(7–8), 468–480.
24. National Autism Center (2011). Evidence-based practice and Autism in the schools. Randolph.
25. Nebel, M.B., Joel, S.E., Muschelli, J., Barber, A.D., Caffo, B.S., Pekar, J.J. and Mostofsky, S.H. (2012). Disruption of functional organization within the primary motor cortex in children with autism. Human Brain Mappang.
26. Pelphrey, K. A., Morris, J. P., & McCarthy, G. (2005). Neural basis of eye gaze processing deficits in autism. Brain, 128(Pt 5), 1038–1048.
27. Pelphrey, K. A., Morris, J. P., McCarthy, G., & Labar, K. S. (2007). Perception of dynamic changes in facial affect and identity in autism. Social Cognitive and Affective Neuroscience, 2(2), 140–149.
28. Poline, J. B., Breeze, J. L., Ghosh, S., Gorgolewski, K., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., Poldrack, R. A., Schwartz, Y., Ashburner, J., & Kennedy, D. N. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6, 9.
29. Redcay, E., Moran, J. M., Mavros, P. L., Tager-Flusberg, H., Gabrieli, J. D., & Whitfield-Gabrieli, S. (2013). Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder. Frontiers in Human Neuroscience, 7, 573.
30. Rex, D. E., Shattuck, D. W., Woods, R. P., Narr, K. L., Luders, E., Rehm, K., Stolzner, S. E., Rottenberg, D. E., & Toga, A. W. (2004). A meta-algorithm for brain extraction in MRI. NeuroImage, 23(2), 625–637.
31. Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: an automated cortical surface identification tool. Medical Image Analysis, 6(2), 129–142.
32. Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M. and Toga, A.W. (2007). Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage.
33. Shokouhi, M., Williams, J. H., Waiter, G. D., & Condon, B. (2012). Changes in the sulcal size associated with autism spectrum disorder revealed by sulcal morphometry. Autism Research, 5(4), 245–252.
34. Stef-Praun, T., Clifford, B., Foster, I., Hasson, U., Hategan, M., Small, S. L., Wilde, M., & Zhao, Y. (2007). Accelerating medical research using the swift workflow system. Studies in Health Technology and Informatics, 126, 207–216.
35. Strother, S. C., Anderson, J., Hansen, L. K., Kjems, U., Kustra, R., Sidtis, J., Frutiger, S., Muley, S., LaConte, S., & Rottenberg, D. (2002). The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework. NeuroImage, 15(4), 747–771.
36. Tu, Z., & Toga, A. W. (2007). Towards whole brain segmentation by a hybrid model. Medical Image Computing and Computer-Assisted Intervention International Conference on Medical Image Computing and Computer-Assisted Intervention, 10(Pt 2), 169–177.
37. Tu, Z., Zheng, S., Yuille, A. L., Reiss, A. L., Dutton, R. A., Lee, A. D., Galaburda, A. M., Dinov, I., Thompson, P. M., & Toga, A. W. (2007). Automated extraction of the cortical sulci based on a supervised learning approach. IEEE Transactions on Medical Imaging, 26(4), 541–552.
38. Van Horn, J. D., Grethe, J. S., Kostelec, P., Woodward, J. B., Aslam, J. A., Rus, D., Rockmore, D., & Gazzaniga, M. S. (2001). The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1412), 1323–1339.
39. Van Horn, J.D., Dobson, J., Woodward, J., Wilde, M., Zhao, Y, Voeckler, J. and Foster, I. (2006). Grid-based computing and the future of neuroscience computation. Methods in mind. C. Senior, T. Russell; M. S. Gazzaniga (141–170). MIT Press: Cambridge.
40. von dem Hagen, E. A., Stoyanova, R. S., Baron-Cohen, S., & Calder, A. J. (2013). Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions. Social Cognitive and Affective Neuroscience, 8(6), 694–701.
41. Wallace, G. L., Robustelli, B., Dankner, N., Kenworthy, L., Giedd, J. N., & Martin, A. (2013). Increased gyrification, but comparable surface area in adolescents with autism spectrum disorders. Brain, 136(Pt 6), 1956–1967.
42. Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R., & Mazziotta, J. C. (1998a). Automated image registration: I. General methods and intrasubject, intramodality validation. Journal of Computer Assisted Tomography, 22(1), 139–152.
43. Woods, R. P., Grafton, S. T., Watson, J. D., Sicotte, N. L., & Mazziotta, J. C. (1998b). Automated image registration: II. Intersubject validation of linear and nonlinear models. Journal of Computer Assisted Tomography, 22(1), 153–165.