Neuroinformatics

, Volume 14, Issue 3, pp 339–351 | Cite as

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

  • Chao-Gan Yan
  • Xin-Di Wang
  • Xi-Nian Zuo
  • Yu-Feng Zang
Software Original Article

Abstract

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

Keywords

Data processing Quality control Resting-state fMRI Standardization Statistical analysis 

References

  1. ADHD-200-Consortium. (2012). The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 62. doi:10.3389/fnsys.2012.00062.Google Scholar
  2. Anderson, J. S., Druzgal, T. J., Froehlich, A., DuBray, M. B., Lange, N., Alexander, A. L., Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., Bigler, E. D., & Lainhart, J. E. (2011). Decreased interhemispheric functional connectivity in autism. Cerebral Cortex, 21(5), 1134–1146. doi:10.1093/cercor/bhq190.CrossRefPubMedGoogle Scholar
  3. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113. doi:10.1016/j.neuroimage.2007.07.007.CrossRefPubMedGoogle Scholar
  4. Ashburner, J. (2012). SPM: a history. NeuroImage, 62(2), 791–800. doi:10.1016/j.neuroimage.2011.10.025.CrossRefPubMedGoogle Scholar
  5. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851. doi:10.1016/j.neuroimage.2005.02.018.CrossRefPubMedGoogle Scholar
  6. Bandettini, P. A. (2012). Twenty years of functional MRI: the science and the stories. NeuroImage, 62(2), 575–588. doi:10.1016/j.neuroimage.2012.04.026.CrossRefPubMedGoogle Scholar
  7. Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90–101. doi:10.1016/j.neuroimage.2007.04.042.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bennett, C. M., Wolford, G. L., & Miller, M. B. (2009). The principled control of false positives in neuroimaging. Social Cognitive and Affective Neuroscience, 4(4), 417–422. doi:10.1093/scan/nsp053.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Birn, R. M. (2012). The role of physiological noise in resting-state functional connectivity. NeuroImage, 62(2), 864–870. doi:10.1016/j.neuroimage.2012.01.016.CrossRefPubMedGoogle Scholar
  10. Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541.CrossRefPubMedGoogle Scholar
  11. Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., Dogonowski, A. M., Ernst, M., Fair, D., Hampson, M., Hoptman, M. J., Hyde, J. S., Kiviniemi, V. J., Kotter, R., Li, S. J., Lin, C. P., Lowe, M. J., Mackay, C., Madden, D. J., Madsen, K. H., Margulies, D. S., Mayberg, H. S., McMahon, K., Monk, C. S., Mostofsky, S. H., Nagel, B. J., Pekar, J. J., Peltier, S. J., Petersen, S. E., Riedl, V., Rombouts, S. A., Rypma, B., Schlaggar, B. L., Schmidt, S., Seidler, R. D., Siegle, G. J., Sorg, C., Teng, G. J., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X. C., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y. F., Zhang, H. Y., Castellanos, F. X., & Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4734–4739. doi:10.1073/pnas.0911855107.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. Journal of Neuroscience, 29(6), 1860–1873. doi:10.1523/JNEUROSCI.5062-08.2009.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Buckner, R. L., Krienen, F. M., & Yeo, B. T. (2013). Opportunities and limitations of intrinsic functional connectivity MRI. Nature Neuroscience, 16(7), 832–837. doi:10.1038/nn.3423.CrossRefPubMedGoogle Scholar
  14. Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D., & Milham, M. P. (2013). Clinical applications of the functional connectome. NeuroImage, 80, 527–540. doi:10.1016/j.neuroimage.2013.04.083.CrossRefPubMedGoogle Scholar
  15. Chai, X. J., Castanon, A. N., Ongur, D., & Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. NeuroImage, 59(2), 1420–1428. doi:10.1016/j.neuroimage.2011.08.048.CrossRefPubMedGoogle Scholar
  16. Cole, D. M., Smith, S. M., & Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 8. doi:10.3389/fnsys.2010.00008.PubMedPubMedCentralGoogle Scholar
  17. Cox, R. W. (2012). AFNI: what a long strange trip it’s been. NeuroImage, 62(2), 743–747. doi:10.1016/j.neuroimage.2011.08.056.CrossRefPubMedGoogle Scholar
  18. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D. A., Gallagher, L., Kennedy, D. P., Keown, C. L., Keysers, C., Lainhart, J. E., Lord, C., Luna, B., Menon, V., Minshew, N. J., Monk, C. S., Mueller, S., Muller, R. A., Nebel, M. B., Nigg, J. T., O’Hearn, K., Pelphrey, K. A., Peltier, S. J., Rudie, J. D., Sunaert, S., Thioux, M., Tyszka, J. M., Uddin, L. Q., Verhoeven, J. S., Wenderoth, N., Wiggins, J. L., Mostofsky, S. H., & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667. doi:10.1038/mp.2013.78.CrossRefPubMedGoogle Scholar
  19. Eklund, A., Nichols, T., Knutsson, H. (2015). Can parametric statistical methods be trusted for fMRI based group studies? arXiv preprint arXiv:1511.01863.Google Scholar
  20. Fair, D., Nigg, J.T., Iyer, S., Bathula, D., Mills, K.L., Dosenbach, N.U., Schlaggar, B.L., Mennes, M., Gutman, D., Bangaru, S., Buitelaar, J.K., Dickstein, D.P., Di Martino, A., Kennedy, D.N., Kelly, C., Luna, B., Schweitzer, J.B., Velanova, K., Wang, Y.-F., Mostofsky, S.H., Castellanos, F.X., Milham, M.P. (2012). Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data. Front Syst Neurosci, 6, doi:10.3389/fnsys.2012.00080.
  21. Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19. doi:10.3389/fnsys.2010.00019.PubMedPubMedCentralGoogle Scholar
  22. Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Review Neuroscience, 8(9), 700–711.CrossRefGoogle Scholar
  23. Friedman, L., & Glover, G. H. (2006a). Reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences. NeuroImage, 33(2), 471–481. doi:10.1016/j.neuroimage.2006.07.012.CrossRefPubMedGoogle Scholar
  24. Friedman, L., & Glover, G. H. (2006b). Report on a multicenter fMRI quality assurance protocol. Journal of Magnetic Resonance Imaging, 23(6), 827–839. doi:10.1002/jmri.20583.CrossRefPubMedGoogle Scholar
  25. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346–355.CrossRefPubMedGoogle Scholar
  26. Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology, 21(4), 424–430. doi:10.1097/WCO.0b013e328306f2c5.CrossRefPubMedGoogle Scholar
  27. Huettel, S., Song, A., McCarthy, G. (2004). Functional magnetic resonance imaging: Sinauer Associates Sunderland, MA.Google Scholar
  28. Hutchison, R. M., & Everling, S. (2012). Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations. Frontiers in Neuroanatomy, 6, 29. doi:10.3389/fnana.2012.00029.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Ihalainen, T., Sipila, O., & Savolainen, S. (2004). MRI quality control: six imagers studied using eleven unified image quality parameters. European Radiology, 14(10), 1859–1865. doi:10.1007/s00330-004-2278-4.CrossRefPubMedGoogle Scholar
  30. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.CrossRefPubMedGoogle Scholar
  31. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. doi:10.1016/j.neuroimage.2011.09.015.CrossRefPubMedGoogle Scholar
  32. Kelly, C., Biswal, B. B., Craddock, R. C., Castellanos, F. X., & Milham, M. P. (2012). Characterizing variation in the functional connectome: promise and pitfalls. Trends in Cognitive Science, 16(3), 181–188. doi:10.1016/j.tics.2012.02.001.CrossRefGoogle Scholar
  33. Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998). Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. NeuroImage, 7(2), 119–132.CrossRefPubMedGoogle Scholar
  34. McLaren, D. G., Kosmatka, K. J., Oakes, T. R., Kroenke, C. D., Kohama, S. G., Matochik, J. A., Ingram, D. K., & Johnson, S. C. (2009). A population-average MRI-based atlas collection of the rhesus macaque. NeuroImage, 45(1), 52–59. doi:10.1016/j.neuroimage.2008.10.058.CrossRefPubMedGoogle Scholar
  35. McLaren, D. G., Kosmatka, K. J., Kastman, E. K., Bendlin, B. B., & Johnson, S. C. (2010). Rhesus macaque brain morphometry: a methodological comparison of voxel-wise approaches. Methods, 50(3), 157–165. doi:10.1016/j.ymeth.2009.10.003.CrossRefPubMedGoogle Scholar
  36. Mennes, M., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2013). Making data sharing work: the FCP/INDI experience. NeuroImage, 82, 683–691. doi:10.1016/j.neuroimage.2012.10.064.CrossRefPubMedGoogle Scholar
  37. Milham, M. P. (2012). Open neuroscience solutions for the connectome-wide association era. Neuron, 73(2), 214–218. doi:10.1016/j.neuron.2011.11.004.CrossRefPubMedGoogle Scholar
  38. Muller, R., & Buttner, P. (1994). A critical discussion of intraclass correlation coefficients. Statistics in Medicine, 13(23–24), 2465–2476.CrossRefPubMedGoogle Scholar
  39. Poldrack, R. A., & Poline, J. B. (2015). The publication and reproducibility challenges of shared data. Trends in Cognitive Science, 19(2), 59–61. doi:10.1016/j.tics.2014.11.008.CrossRefGoogle Scholar
  40. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012a). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. doi:10.1016/j.neuroimage.2011.10.018.CrossRefPubMedGoogle Scholar
  41. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012b). Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp. NeuroImage. doi:10.1016/j.neuroimage.2012.03.017.PubMedCentralGoogle Scholar
  42. Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84C, 320–341. doi:10.1016/j.neuroimage.2013.08.048.CrossRefGoogle Scholar
  43. Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551. doi:10.1016/j.neuroimage.2014.10.044.CrossRefPubMedGoogle Scholar
  44. Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012). Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. NeuroImage, 60(1), 623–632. doi:10.1016/j.neuroimage.2011.12.063.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H., Gur, R. C., Gur, R. E., & Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256. doi:10.1016/j.neuroimage.2012.08.052.CrossRefPubMedGoogle Scholar
  46. Sawiak, S., Williams, G., Wood, N., Morton, A., Carpenter, T. (2009). SPMMouse: A new toolbox for SPM in the animal brain. ISMRM 17th Scientific Meeting & Exhibition, April, pp. 18-24.Google Scholar
  47. Schwarz, A. J., Danckaert, A., Reese, T., Gozzi, A., Paxinos, G., Watson, C., Merlo-Pich, E. V., & Bifone, A. (2006). A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: application to pharmacological MRI. NeuroImage, 32(2), 538–550. doi:10.1016/j.neuroimage.2006.04.214.CrossRefPubMedGoogle Scholar
  48. Shannon, B. J., Dosenbach, R. A., Su, Y., Vlassenko, A. G., Larson-Prior, L. J., Nolan, T. S., Snyder, A. Z., & Raichle, M. E. (2013). Morning-evening variation in human brain metabolism and memory circuits. Journal of Neurophysiology, 109(5), 1444–1456. doi:10.1152/jn.00651.2012.CrossRefPubMedGoogle Scholar
  49. Shehzad, Z., Kelly, A. M., Reiss, P. T., Gee, D. G., Gotimer, K., Uddin, L. Q., Lee, S. H., Margulies, D. S., Roy, A. K., Biswal, B. B., Petkova, E., Castellanos, F. X., & Milham, M. P. (2009). The resting brain: unconstrained yet reliable. Cerebral Cortex, 19(10), 2209–2229. doi:10.1093/cercor/bhn256.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.CrossRefPubMedGoogle Scholar
  51. Simmons, A., Moore, E., & Williams, S. C. (1999). Quality control for functional magnetic resonance imaging using automated data analysis and Shewhart charting. Magnetic Resonance in Medicine, 41(6), 1274–1278.CrossRefPubMedGoogle Scholar
  52. Song, X. W., Dong, Z. Y., Long, X. Y., Li, S. F., Zuo, X. N., Zhu, C. Z., He, Y., Yan, C. G., & Zang, Y. F. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE, 6(9), e25031. doi:10.1371/journal.pone.0025031.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Taylor, P. A., & Saad, Z. S. (2013). FATCAT: (an efficient) functional and tractographic connectivity analysis toolbox. Brain Connectivity, 3(5), 523–535. doi:10.1089/brain.2013.0154.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Van Dijk, K. R., Sabuncu, M. R., & Buckner, R. L. (2012). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59(1), 431–438. doi:10.1016/j.neuroimage.2011.07.044.CrossRefPubMedGoogle Scholar
  55. Vanhoutte, G., Verhoye, M., & Van der Linden, A. (2006). Changing body temperature affects the T2* signal in the rat brain and reveals hypothalamic activity. Magnetic Resonance in Medicine, 55(5), 1006–1012. doi:10.1002/mrm.20861.CrossRefPubMedGoogle Scholar
  56. Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141. doi:10.1089/brain.2012.0073.CrossRefPubMedGoogle Scholar
  57. Woo, C. W., Krishnan, A., & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. NeuroImage, 91, 412–419. doi:10.1016/j.neuroimage.2013.12.058.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Xia, M., Wang, J., & He, Y. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE, 8(7), e68910. doi:10.1371/journal.pone.0068910.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Yan, C., & Zang, Y. (2010). DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13. doi:10.3389/fnsys.2010.00013.Google Scholar
  60. Yan, C., Liu, D., He, Y., Zou, Q., Zhu, C., Zuo, X., Long, X., & Zang, Y. (2009). Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS ONE, 4(5), e5743. doi:10.1371/journal.pone.0005743.CrossRefPubMedPubMedCentralGoogle Scholar
  61. Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X. N., Castellanos, F. X., & Milham, M. P. (2013a). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage, 76, 183–201. doi:10.1016/j.neuroimage.2013.03.004.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Yan, C. G., Craddock, R. C., Zuo, X. N., Zang, Y. F., & Milham, M. P. (2013b). Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage, 80, 246–262. doi:10.1016/j.neuroimage.2013.04.081.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Yan, C.G., Li, Q., Gao, L. (2014). PRN: a preprint service for catalyzing R-fMRI and neuroscience related studies. F1000Res, 3, 313, doi: 10.12688/f1000research.5951.2.
  64. Zang, Y. F., Jiang, T. Z., Lu, Y. L., He, Y., & Tian, L. X. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22(1), 394–400.CrossRefPubMedGoogle Scholar
  65. Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., Tian, L. X., Jiang, T. Z., & Wang, Y. F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev, 29(2), 83–91.CrossRefPubMedGoogle Scholar
  66. Zou, Q. H., Zhu, C. Z., Yang, Y., Zuo, X. N., Long, X. Y., Cao, Q. J., Wang, Y. F., & Zang, Y. F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141. doi:10.1016/j.jneumeth.2008.04.012.CrossRefPubMedPubMedCentralGoogle Scholar
  67. Zuo, X. N., & Xing, X. X. (2011). Effects of non-local diffusion on structural MRI preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion. PLoS ONE, 6(10), e26703. doi:10.1371/journal.pone.0026703.CrossRefPubMedPubMedCentralGoogle Scholar
  68. Zuo, X. N., & Xing, X. X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neuroscience and Biobehavioral Reviews, 45, 100–118. doi:10.1016/j.neubiorev.2014.05.009.CrossRefPubMedGoogle Scholar
  69. Zuo, X. N., Kelly, C., Adelstein, J. S., Klein, D. F., Castellanos, F. X., & Milham, M. P. (2010a). Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. NeuroImage, 49(3), 2163–2177. doi:10.1016/j.neuroimage.2009.10.080.CrossRefPubMedGoogle Scholar
  70. Zuo, X. N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S., Grzadzinski, R., Evans, A. C., Zang, Y. F., Castellanos, F. X., & Milham, M. P. (2010b). Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. Journal of Neuroscience, 30(45), 15034–15043. doi:10.1523/JNEUROSCI.2612-10.2010.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Zuo, X. N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., & Milham, M. P. (2012). Network centrality in the human functional connectome. Cerebral Cortex, 22(8), 1862–1875. doi:10.1093/cercor/bhr269.CrossRefPubMedGoogle Scholar
  72. Zuo, X. N., Xu, T., Jiang, L., Yang, Z., Cao, X. Y., He, Y., Zang, Y. F., Castellanos, F. X., & Milham, M. P. (2013). Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. NeuroImage, 65, 374–386. doi:10.1016/j.neuroimage.2012.10.017.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Chao-Gan Yan
    • 1
    • 2
  • Xin-Di Wang
    • 3
  • Xi-Nian Zuo
    • 1
  • Yu-Feng Zang
    • 4
    • 5
    • 6
  1. 1.Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of PsychologyChinese Academy of SciencesBeijingChina
  2. 2.Department of Child and Adolescent PsychiatryNYU Langone Medical Center School of MedicineNew YorkUSA
  3. 3.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  4. 4.Center for Cognition and Brain DisordersHangzhou Normal UniversityHangzhouChina
  5. 5.Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina
  6. 6.Department of Psychology, College of EducationHangzhou Normal UniversityHangzhouChina

Personalised recommendations