To reference the NKI-RS, please cite the following article:

  1. Nooner et al,. The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Frontiers in neuroscience 6 (2012).

The list of publications using NKI-RS data is rapidly growing.  Below are the first 100 publications, curated by our group.  For a comprehensive set, including recent publications we recommend this link to Google Scholar with search term: “NKI Rockland Sample”

The following publications discuss NKI-RS in the context of large-scale data-sharing efforts:

  1. 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.
  2. Craddock, R. C., Tungaraza, R. L., & Milham, M. P. (2015). Connectomics and new approaches for analyzing human brain functional connectivity.GigaScience, 4(1), 1.
  3. Di Martino, A., Fair, D. A., Kelly, C., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., … & Milham, M. P. (2014). Unraveling the miswired connectome: a developmental perspective. Neuron, 83(6), 1335-1353.
  4. Gorgolewski, K. J., Margulies, D.S., Milham, M. P. (2013). Making data sharing count: A publication-based solution. Frontiers in neuroscience 7, 9.
  5. GOTO, M., ABE, O., MIYATI, T., YAMASUE, H., GOMI, T., & TAKEDA, T. (2016). Head Motion and Correction Methods in Resting-state Functional MRI. Magnetic Resonance in Medical Sciences, 15(2), 178-186.
  6. Keator, D.B., Helmer, K., Steffener, J., Turner, J.A., Van Erp, T. G., Gadde, S., Ashish, N., Burns, G. A., Nichols, B. N. (2013). Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage 82: 647-661.
  7. King, M. D., Wood, D., Miller, B., Kelly, R., Landis, D., Courtney, W., … & Calhoun, V. D. (2014). Automated collection of imaging and phenotypic data to centralized and distributed data repositories.
  8. Lavagnino, L., Mwangi, B., Bauer, I. E., Cao, B., Selvaraj, S., Prossin, A., & Soares, J. C. (2016). Reduced Inhibitory Control Mediates the Relationship Between Cortical Thickness in the Right Superior Frontal Gyrus and Body Mass Index. Neuropsychopharmacology.
  9. Milham, M. P. (2012). Open Neuroscience Solutions for the Connectome-wide Association Era. Neuron 73, no. 2: 214-218.
  10. Mennes, M., Biswal, B. B., Castellanos, F. X., Milham, M. P. (2013). Making Data Sharing Work: The FCP/INDI Experience. Neuroimage 82: 683-691.
  11. Nichols, B. N., Mejino, J. L., Detwiler, L. T., Nilsen, T. T., Martone, M. E., Turner, J. A., … & Brinkley, J. F. (2014). Neuroanatomical domain of the foundational model of anatomy ontology. Journal of biomedical semantics,5(1), 1.
  12. Panta, S. R., Wang, R., Fries, J., Kalyanam, R., Speer, N., Banich, M., … & Turner, J. A. (2016). A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Frontiers in neuroinformatics, 10.
  13. Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T.D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O., Milham, M. P. (2013). Toward Open Sharing of Task-based FMRI Data: The OpenfMRI Project.Frontiers in neuroinformatics 7.
  14. Poldrack, R. A., Gorgolewski, K.J. (2013). Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517.
  15. Pool, E. M., Rehme, A. K., Eickhoff, S. B., Fink, G. R., & Grefkes, C. (2015). Functional resting-state connectivity of the human motor network: Differences between right-and left-handers. NeuroImage, 109, 298-306.
  16. Puccio, B., Pooley, J. P., Pellman, J. S., Taverna, E. C., & Craddock, R. C. (2016). The Preprocessed Connectomes Project Repository of Manually Corrected Skull-stripped T1-weighted Anatomical MRI Data. bioRxiv, 067017.
  17. Somandepalli, K., Kelly, C., Reiss, P. T., Zuo, X. N., Craddock, R. C., Yan, C. G., … & Di Martino, A. (2015). Short-term test–retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder. Developmental Cognitive Neuroscience, 15, 83-93.

The following publications from researchers around the world have utilized data from the NKI-RS:

  1. Amft, M., Bzdok, D., Laird, A. R., Fox, P. T., Schilbach, L., & Eickhoff, S. B. (2014). Definition and characterization of an extended social-affective default network. Brain Structure and Function, Advance online publication. doi: 10.1007/s00429-013-0698-0.
  2. Basu, A. P., Taylor, P. N., Lowther, E., Forsyth, E. O., Blamire, A. M., & Forsyth, R. J. (2015). Structural connectivity in a paediatric case of anarchic hand syndrome. BMC neurology, 15(1), 234.
  3. Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., De Reus, M. A., Griffa, A., … & Van Den Heuvel, M. (2016). Generative models of the human connectome. Neuroimage, 124, 1054-1064.
  4. Betzel, R. F., Byrge, L., He, Y., Goni, J., Zuo, X. N., & Sporns, O. (2014). Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage, in press.
  5. Betzel, R. F., Mišić, B., He, Y., Rumschlag, J., Zuo, X. N., & Sporns, O. (2015). Functional brain modules reconfigure at multiple scales across the human lifespan. arXiv preprint arXiv:1510.08045.
  6. Bhushan, C., Haldar, J. P., Choi, S., Joshi, A. A., Shattuck, D. W.,& Leahy, R. M. (2015). Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization. Neuroimage,115, 269-280.
  7. Billings, J. C., Medda, A., & Keilholz, S. D. (2013, November). Agglomerative clustering for resting state MRI. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on (pp. 553-556). IEEE.
  8. Bottger, J., Schurade, R., Jakobsen, E., Schaefer, A., & Margulies, D. S. (2014). Connexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using braingl.Frontiers in neuroscience, 8, 15.
  9. Brown, J. A., Rudie, J. D., Bandrowski, A., Van Horn, J. D., & Bookheimer, S. Y. (2012). The ucla multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis.Frontiers in neuroinformatics, 6, 28.
  10. Bzdok, D. et al. (2014). Subspecialization in the human posterior medial cortex. NeuroImage
  11. Bzdok, D., Langner, R., Schilbach, L., Engemann, D. A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2013). Segregation of the human medial prefrontal cortex in social cognition. Frontiers in human neuroscience, 7, 232.
  12. Camilleri, J. A., Reid, A. T., Müller, V. I., Grefkes, C., Amunts, K., & Eickhoff, S. B. (2015). Multi-modal imaging of neural correlates of motor speed performance in the Trail Making Test. Frontiers in neurology, 6.
  13. Cao, M., Wang, J. H., Dai, Z. J., Cao, X. Y., Jiang, L. L., Fan, F. M., Song, X., Xia, M., Shu, N., Dong, Q., Milham, M.P., Castellanos, F. X., Zuo, X., & He, Y. (2014). Topological organization of the human brain functional connectome across the lifespan. Developmental cognitive neuroscience, 7, 76-93.
  14. Chase, H. W., Clos, M., Dibble, S., Fox, P., Grace, A. A., Phillips, M. L., & Eickhoff, S. B. (2015). Evidence for an anterior–posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: Focus on the subiculum. NeuroImage, 113, 44-60.
  15. Chen, R., Nixon, E., & Herskovits, E. (2016). Advanced connectivity analysis (ACA): a large scale functional connectivity data mining environment. Neuroinformatics, 14(2), 191-199.
  16. Chodkowski, B. A., Cowan, R. L., & Niswender, K. D. (2016). Imbalance in resting state functional connectivity is associated with eating behaviors and adiposity in children. Heliyon, 2(1), e00058.
  17. Chen, H., Kelly, C., Castellanos, F. X., He, Y., Zuo, X. N., & Reiss, P. T. (2015). Quantile rank maps: A new tool for understanding individual brain development. NeuroImage, 111, 454-463.
  18. Cieslik, E. C., Seidler, I., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2016). Different involvement of subregions within dorsal premotor and medial frontal cortex for pro-and antisaccades. Neuroscience & Biobehavioral Reviews, 68, 256-269.
  19. Clewett, D., Bachman, S., & Mather, M. (2014). Age-related reduced prefrontal-amygdala structural connectivity is associated with lower trait anxiety. Neuropsychology, 28(4), 631-642.
  20. Clos, M., Amunts, K., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2013). Tackling the multifunctional nature of broca’s region meta-analytically: co-activation-based parcellation of area 44. Neuroimage, 83, 174-188.
  21. Clos, M., Rottschy, C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014). Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula.Neuroimage, 99, 269-280.
  22. Corcoran, C. M., Keilp, J. G., Kayser, J., Klim, C., Butler, P. D., Bruder, G. E., … & Javitt, D. C. (2015). Emotion recognition deficits as predictors of transition in individuals at clinical high risk for schizophrenia: a neurodevelopmental perspective. Psychological medicine, 45(14), 2959-2973.
  23. Davey, J., Cornelissen, P. L., Thompson, H. E., Sonkusare, S., Hallam, G., Smallwood, J., & Jefferies, E. (2015). Automatic and controlled semantic retrieval: TMS reveals distinct contributions of posterior middle temporal gyrus and angular gyrus. The Journal of Neuroscience, 35(46), 15230-15239.
  24. Di, X., & Biswal, B. B. (2015). Characterizations of resting-state modulatory interactions in the human brain. Journal of neurophysiology, 114(5), 2785-2796.
  25. Di, X., Gohel, S., Kim, E. H., & Biswal, B. B. (2013). Task vs. rest, different network configurations between the coactivation and the resting-state brain networks. Frontiers in human neuroscience, 7, 493.
  26. Di, X., Fu, Z., Chan, S. C., Hung, Y. S., Biswal, B. B., & Zhang, Z. (2015). Task-related functional connectivity dynamics in a block-designed visual experiment. Frontiers in human neuroscience, 9.
  27. Eickhoff, S. B., Laird, A. R., Fox, P. T., Bzdok, D., & Hensel, L. (2014). Functional segregation of the human dorsomedial prefrontal cortex. Cerebral cortex, bhu250.
  28. Fiori, M., Sprechmann, P., Vogelstein, J., Musé, P., & Sapiro, G. (2013). Robust multimodal graph matching: Sparse coding meets graph matching.Advances in Neural Information Processing Systems, 127-135.
  29. Fu, Zening, Xin Di, Shing-Chow Chan, Yeung-Sam Hung, Bharat B Biswal, and Zhiguo Zhang. (2013). Time-varying correlation coefficients estimation and its application to dynamic connectivity analysis of fmri. 35th Annual International Conference of the IEEE EMBS, 2944-2947.
  30. Fukushima, M., Betzel, R. F., He, Y., Zuo, X. N., & Sporns, O. (2015). Characterizing Spatial Patterns and Flow Dynamics in Functional Connectivity States and Their Changes across the Human Lifespan. arXiv preprint arXiv:1511.06427.
  31. Gastner, M. T., & Ódor, G. (2015). The topology of large Open Connectome networks for the human brain. arXiv preprint arXiv:1512.01197.
  32. Genon, S., Müller, V. I., Cieslik, E., Hoffstaedter, F., Langner, R., Fox, P. T., & Eickhoff, S. B. (2014). Examining the right dorsal premotor mosaic: a connectivity-based parcellation approach. In OHBM Annual Meeting.
  33. Gohel, S. R., & Biswal, B.B. (2014). Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain connectivity, Advance online publication. doi:10.1089/brain.2013.0210.
  34. Gorgolewski, K. J., Lurie, D., Urchs, S., Kipping, J. A., Craddock, R. C., Milham, M. P., Margulies, D. S., & Smallwood, J. (2014). A correspondence between individual differences in the brain’s intrinsic functional architecture and the content and form of self-generated thoughts. PloS one, 9(5), e97176.
  35. Goulden, N., Khusnulina, A., Davis, N. J., Bracewell, R. M., Bokde, A. L., McNulty, J. P., & Mullins, P. G. (2014). The salience network is responsible for switching between the default mode network and the central executive network: replication from dcm. Neuroimage, 99, 180-190.
  36. Grandy, T. H., Garrett, D. D., Schmiedek, F., & Werkle-Bergner, M. (2016). On the estimation of brain signal entropy from sparse neuroimaging data. Scientific reports, 6.
  37. Grothe, M., Heinsen, H., & Teipel, S. (2012). Reduced network switching in aging correlates with atrophy of the cholinergic basal forebrain. Klinische Neurophysiologie, 43(01), P047.
  38. Han, C. E., Peraza, L. R., Taylor, J.-P. & Kaiser, M. (2014). Predicting age of human subjects based on structural connectivity from diffusion tensor imaging. ArXiv Prepr. ArXiv14055260
  39. Hardwick, R. M., Lesage, E., Eickhoff, C. R., Clos, M., Fox, P., & Eickhoff, S. B. (2015). Multimodal connectivity of motor learning-related dorsal premotor cortex. NeuroImage, 123, 114-128.
  40. He, Y., Xu, T., Zhang, W., & Zuo, X. N. (2015). Lifespan anxiety is reflected in human amygdala cortical connectivity. Human brain mapping.
  41. Heuer, K. et al. (2014). Browsing the connectome: 3D functional and structural brainnetworks in the cloud. 20th Annual Meeting of the Organization for Human Brain Mapping (OHBM).
  42. Hok, P., Opavský, R., Hluštík, P., & Tüdös, Z. (2015). 29. Meta-analytic and resting-state functional connectivity of the claustrum. Clinical Neurophysiology, 126(3), e39-e40.
  43. Hoffstaedter, F., Grefkes, C., Roski, C., Caspers, S., Zilles, K., & Eickhoff, S. B. (2014). Age-related decrease of functional connectivity additional to gray matter atrophy in a network for movement initiation.Brain Structure and Function, Advance online publication. doi: 10.1007/s00429-013-0696-2.
  44. Horn, A., & Blankenburg, F. (2016). Toward a standardized structural–functional group connectome in MNI space. NeuroImage, 124, 310-322.
  45. Hwang, K., Bertolero, M. A., Liu, W., & D’Esposito, M. (2016). The human thalamus is an integrative hub for functional brain networks. bioRxiv, 056630.
  46. Jakab, A., Blanc, R., & Berenyi, E. L. (2012). Mapping changes of in vivo connectivity patterns in the human mediodorsal thalamus: correlations with higher cognitive and executive functions. Brain imaging and behavior, 6(3), 472-483.
  47. Jakab, A., Emri, M., Spisak, T., Szeman-Nagy, A., Beres, M., Kis, S. A., Molnar, P., & Berenyi, E. (2013). Autistic traits in neurotypical adults: correlates of graph theoretical functional network topology and white matter anisotropy patterns. PloS one, 8(4), e60982.
  48. Jiang, L., Xu, T., He, Y., Hou, X. H., Wang, J., Cao, X. Y., Wei, G. X., Yang, Z., Yong, H., & Zuo, X. N. (2014). Toward neurobiological characterization of functional homogeneity in the human cortex: regional variation, morphological association and functional covariance network organization. Brain Structure and Function, Advance online publication. doi: 10.1007/s00429-014-0795-8.
  49. Jiang, L., & Zuo, X. N. (2015). Regional homogeneity a multimodal, multiscale neuroimaging marker of the human connectome. The Neuroscientist, 1073858415595004.
  50. 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 Cogn. Sci. 16, 181–188
  51. King, M. D. et al. (2014). Automated collection of imaging and phenotypic data to centralized and distributed data repositories. Front. Neuroinformatics 8, 60
  52. King, M. D., Wood, D., Miller, B., Kelly, R., Landis, D., Courtney, W., … & Calhoun, V. D. (2014). Automated collection of imaging and phenotypic data to centralized and distributed data repositories.
  53. Klein, A., & Tourville, J. (2012). 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in neuroscience, 6, 171.
  54. Kogler, L., Müller, V. I., Chang, A., Eickhoff, S. B., Fox, P. T., Gur, R. C., & Derntl, B. (2015). Psychosocial versus physiological stress—Meta-analyses on deactivations and activations of the neural correlates of stress reactions.Neuroimage, 119, 235-251.
  55. Kong, X. Z. (2014). Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study. PeerJ, 2, e366.
  56. Krall, S. C., Rottschy, C., Oberwelland, E., Bzdok, D., Fox, P. T., Eickhoff, S. B., Fink, G.R., & Konrad, K. (2014). The role of the right temporoparietal junction in attention and social interaction as revealed by ale meta-analysis. Brain Structure and Function, Advance online publication. doi: 0.1007/s00429-014-0803-z.
  57. Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013). Networks of task co-activations. Neuroimage, 80, 505-514.
  58. Li, K., Langley, J., Li, Z.,& Hu, X. (2014). Connectomic profiles for individualized resting state networks and rois. Brain connectivity, Advance online publication. doi: 0.1089/brain.2014.0229.
  59. Li, Q., Song, M., Fan, L., Liu, Y., & Jiang, T. (2015). Parcellation of the primary cerebral cortices based on local connectivity profiles. Frontiers in neuroanatomy, 9.
  60. Liao, Xu-Hong, Ming-Rui Xia, Ting Xu, Zheng-Jia Dai, Xiao-Yan Cao, Hai-Jing Niu, Xi-Nian Zuo, Yu-Feng Zang, and Yong He. (2013). Functional brain hubs and their test-retest reliability: a multiband resting-state functional mri study. Neuroimage, 83, 969-982.
  61. Liao, X., Yuan, L., Zhao, T., Dai, Z., Shu, N., Xia, M., … & He, Y. (2015). Spontaneous functional network dynamics and associated structural substrates in the human brain. Frontiers in human neuroscience, 9.
  62. Lim, S., Han, C. E., Uhlhaas, P. J., & Kaiser, M. (2013). Preferential detachment during human brain development: age-and sex-specific structural connectivity in diffusion tensor imaging (dti) data. Cerebral Cortex, bht333.
  63. Lo, Y. P., O’Dea, R., Crofts, J. J., Han, C. E., & Kaiser, M. (2015). A geometric network model of intrinsic grey-matter connectivity of the human brain.Scientific reports, 5.
  64. Luo, Q., Lu, W., Cheng, W., Valdes-Sosa, P. A., Wen, X., Ding, M., & Feng, J. (2013). Spatio-temporal granger causality: a new framework. Neuroimage, 79, 241-263.
  65. Malpas, C. B., Genc, S., Saling, M. M., Velakoulis, D., Desmond, P. M., & O’Brien, T. J. (2016). MRI correlates of general intelligence in neurotypical adults. Journal of Clinical Neuroscience, 24, 128-134.
  66. Mao, D., Ding, Z., Jia, W., Liao, W., Li, X., Huang, H., … & Zhang, H. (2015). Low-frequency fluctuations of the resting brain: high magnitude does not equal high reliability. PloS one, 10(6), e0128117.
  67. McDonald, A., Muraskin, J., Van Dam, N. T., Froehlich, C., Puccio, B., Pellman, J., … & Carter, S. (2016). The Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation Neuroimaging Data Repository. bioRxiv, 075275.
  68. Mennes, M., Jenkinson, M., Valabregue, R., Buitelaar, J. K., Beckmann, C., & Smith, S. (2014). Optimizing full-brain coverage in human brain MRI through population distributions of brain size. NeuroImage, 98, 513-520.
  69. Muller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2013). Dysregulated left inferior parietal activity in schizophrenia and depression: functional connectivity and characterization. Frontiers in human neuroscience, 7, 68.
  70. Muller, V. I., Langner, R., Cieslik, E. C., Rottschy, C., & Eickhoff, S. B. (2014). Interindividual differences in cognitive flexibility: influence of gray matter volume, functional connectivity and trait impulsivity. Brain Structure and Function, Advance online publication. doi: 10.1007/s00429-014-0797-6.
  71. Murray, R. J., Debbane, M., Fox, P. T., Bzdok, D., & Eickhoff, S. B. (2015). Functional connectivity mapping of regions associated with self‐and other‐processing. Human brain mapping, 36(4), 1304-1324.
  72. Mwangi, B., Hasan, K. M., & Soares, J. C. (2013). Prediction of individual subject’s age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage, 75, 58-67.
  73. Nickl-Jockschat, T., Rottschy, C., Thommes, J., Schneider, F., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2014). Neural networks related to dysfunctional face processing in autism spectrum disorder. Brain Structure and Function, Advance online publication. doi: 10.1007/s00429-014-0791-z.
  74. Nooner, K. B., Mennes, M., Brown, S., Castellanos, F. X., Leventhal, B., Milham, M. P., & Colcombe, S. J. (2013). Relationship of trauma symptoms to amygdala based functional brain changes in adolescents.Journal of traumatic stress, 26(6), 784-787.
  75. Oler, J. A., Birn, R. M., Patriat, R., Fox, A. S., Shelton, S. E., Burghy, C. A., Stodola, D.E., Essex, M. J., Davidson, R. J., & Kalin, N. H. (2012). Evidence for coordinated functional activity within the extended amygdala of non-human and human primates. Neuroimage, 61(4), 1059-1066.
  76. O’Muircheartaigh, J., Keller, S. S., Barker, G. J., & Richardson, M. P. (2015). White matter connectivity of the thalamus delineates the functional architecture of competing thalamocortical systems. Cerebral Cortex, 25(11), 4477-4489.
  77. Ovadia-Caro, S., Nir, Y., Soddu, A., Ramot, M., Hesselmann, G., Vanhaudenhuyse, A., Dinstein, I., Tshibanda, J. L., Harel, M., Laureys, S., & Malach, R. (2012). Reduction in inter-hemispheric connectivity in disorders of consciousness. PloS one, 7(5), e37238.
  78. Park, B. Y., Seo, J., & Park, H. (2016). Functional brain networks associated with eating behaviors in obesity. Scientific reports, 6.
  79. Potvin, O., Mouiha, A., Dieumegarde, L., Duchesne, S., & Alzheimer’s Disease Neuroimaging Initiative. (2016). Normative data for subcortical regional volumes over the lifetime of the adult human brain. NeuroImage.
  80. Qin, J., Chen, S. G., Hu, D., Zeng, L. L., Fan, Y. M., Chen, X. P., & Shen, H. (2015). Predicting individual brain maturity using dynamic functional connectivity. Frontiers in human neuroscience, 9.
  81. Reetz, K., Dogan, I., Rolfs, A., Binkofski, F., Schulz, J. B., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2012). Investigating function and connectivity of morphometric findings exemplified on cerebellar atrophy in spinocerebellar ataxia 17 (sca17). Neuroimage, 62(3), 1354-1366.
  82. Reid, A. T., Bzdok, D., Langner, R., Fox, P. T., Laird, A. R., Amunts, K., … & Eickhoff, C. R. (2015). Multimodal connectivity mapping of the human left anterior and posterior lateral prefrontal cortex. Brain Structure and Function, 1-17.
  83. Reid, A. T., Hoffstaedter, F., Gong, G., Laird, A. R., Fox, P., Evans, A. C., … & Eickhoff, S. B. (2016). A seed-based cross-modal comparison of brain connectivity measures. Brain Structure and Function, 1-21.
  84. Reid, A. T., Lewis, J., Bezgin, G., Khundrakpam, B., Eickhoff, S. B., McIntosh, A. R., … & Evans, A. C. (2016). A cross-modal, cross-species comparison of connectivity measures in the primate brain. NeuroImage, 125, 311-331.
  85. Roncal, W. G., Koterba, Z. H., Mhembere, D., Kleissas, D. M., Vogelstein, J. T., Burns, R., … & Wu, L. (2013, December). MIGRAINE: MRI graph reliability analysis and inference for connectomics. In Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE (pp. 313-316). IEEE.
  86. Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human brain mapping, 35, 4566-4582.
  87. Schaefer, A., Margulies, D. S., Lohmann, G., Gorgolewski, K. J., Smallwood, J., Kiebel, S. J., & Villringer, A. (2014). Dynamic network participation of functional connectivity hubs assessed by resting-state fmri. Frontiers in human neuroscience, 8, 195.
  88. Scheel, N., Chang, C., & Mamlouk, A. M. (2014, September). The Importance of Physiological Noise Regression in High Temporal Resolution fMRI. In International Conference on Artificial Neural Networks (pp. 829-836). Springer International Publishing.
  89. Scheel, N., Essenwanger, A., Münte, T. F., Heldmann, M., Krämer, U. M., & Mamlouk, A. M. (2015). Selection of Seeds for Resting-State fMRI-Based Prediction of Individual Brain Maturity. Bildverarbeitung für die Medizin 2015, 371-376.
  90. Shehzad, Z., Kelly, C., Reiss, P. T., Cameron Craddock, R., Emerson, J. W., McMahon, K., Copland, D. A., Castellanos, F. X., & Milham, M. P. (2014). A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage, 93, 74-94.
  91. Shine, J. M., Bell, P. T., Koyejo, O., Bissett, P. G., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2015). Dynamic fluctuations in global brain network topology characterize functional. Neuron, 88(1), 207-19.
  92. Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., … & Poldrack, R. A. (2016). The dynamics of functional brain networks: Integrated network states during cognitive function. arXiv preprint arXiv:1511.02976.
  93. Singh, S. S., Khundrakpam, B., Reid, A. T., Lewis, J. D., Evans, A. C., Ishrat, R., … & Singh, R. B. (2016). Scaling in topological properties of brain networks. Scientific reports, 6.
  94. Sochat, V., Supekar, K., Bustillo, J., Calhoun, V., Turner, J. A., & Rubin, D. L. (2014). A robust classifier to distinguish noise from fmri independent components. PloS one, 9(4), e95493.
  95. Stramaglia, S., Pellicoro, M., Angelini, L., Amico, E., Aerts, H., Cortés, J., … & Marinazzo, D. (2015). Conserved Ising Model on the Human Connectome.arXiv preprint arXiv:1509.02697.
  96. Tao, C., & Feng, J. (2016). Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies. Journal of neuroscience methods, 262, 110-132.
  97. Tarquino, J., Rueda, A., & Romero, E. (2014). A multiscale/sparse representation for diffusion weighted imaging (dwi) super-resolution. In Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium, 983-986.
  98. Taylor, P. N., & Forsyth, R. (2016). Heterogeneity of trans-callosal structural connectivity and effects on resting state subnetwork integrity may underlie both wanted and unwanted effects of therapeutic corpus callostomy.NeuroImage: Clinical, 12, 341-347.
  99. Taylor, P., Hobbs, J. N., Burroni, J., & Siegelmann, H. T. (2015). The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions. Scientific reports, 5.
  100. Tian, L., Ma, L., & Wang, L. (2016). Alterations of functional connectivities from early to middle adulthood: Clues from multivariate pattern analysis of resting-state fMRI data. NeuroImage, 129, 389-400.
  101. Tustison, N. J., Avants, B. B., Cook, P. A., Kim, J., Whyte, J., Gee, J. C. and Stone, J. R. (2014) Logical circularity in voxel-based analysis: normalization strategy may induce statistical bias. Hum. Brain Mapp., 35: 745–759. doi: 10.1002/hbm.22211.
  102. Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., Avants, B. B. (2014). Large-scale evaluation of ants and freesurfer cortical thickness measurements. Neuroimage, 99, 166-179.
  103. Uddin, L. Q., Supekar, K. S., Ryali, S., & Menon, V. (2011). Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. The Journal of Neuroscience, 31(50), 18578-18589.
  104. Vadovičová, K. (2014). Affective and cognitive prefrontal cortex projections to the lateral habenula in humans. Frontiers in human neuroscience, 8.
  105. Van Dam, N., O’Connor, D., Marcelle, E. T., Ho, E. J., Craddock, R. C., Tobe, R. H., … & Milham, M. P. (2016). Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels. bioRxiv, 051789.
  106. Wang, J., Fan, L., Wang, Y., Xu, W., Jiang, T., Fox, P. T., … & Jiang, T. (2015). Determination of the posterior boundary of Wernicke’s area based on multimodal connectivity profiles. Human brain mapping, 36(5), 1908-1924.
  107. Wang, X, Y Jiao, T Tang, H Wang, and Z Lu. (2013). Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.Neuroscience, 254, 404-426.
  108. Wang, X., Yang, N., He, Y., Zhang, Z., Zhu, X., Dong, H., Hou, X., Li, H., Zuo, X. (2014). The developmental trajectory of hippocampus across the human lifespan based on multimodal neuroimaging. Chinese Journal of Contemporary Neurology and Neurosurgery, 14(4), 291-297.
  109. Wu, G. R., & Marinazzo, D. (2016). Sensitivity of the resting-state haemodynamic response function estimation to autonomic nervous system fluctuations. Phil. Trans. R. Soc. A, 374(2067), 20150190.
  110. Xu, T., Yang, Z., Jiang, L., Xing, X. X., & Zuo, X. N. (2015). A connectome computation system for discovery science of brain. Science Bulletin, 60(1), 86-95.
  111. Yan, C. G., Yang, Z., Colcombe, S., Zuo, X. N., & Milham, M. (2016). Concordance Among Indices of Intrinsic Brain Function: Inter-Individual Variation and Temporal Dynamics Perspectives. bioRxiv, 048405.
  112. Yang, Z., Chang, C., Xu, T., Jiang, L., Handwerker, D. A., Castellanos, F. X., Milham, M.P., Bandettini, P.A., & Zuo, X. N. (2014). Connectivity trajectory across lifespan differentiates the precuneus from the default network. Neuroimage, 89, 45-56.
  113. Yang, Z., Craddock, R. C., & Milham, M. P. (2014). Impact of hematocrit on measurements of the intrinsic brain. Frontiers in neuroscience, 8.
  114. Yang, Z., Craddock, R. C., Margulies, D. S., Yan, C. G., & Milham, M. P. (2014). Common intrinsic connectivity states among posteromedial cortex subdivisions: insights from analysis of temporal dynamics.Neuroimage, 93, 124-137.
  115. Yao, L., Li, W., Dai, Z., & Dong, C. (2016). Eating behavior associated with gray matter volume alternations: A voxel based morphometry study. Appetite,96, 572-579.
  116. Zhang, S., Hu, S., Chao, H. H., & Li, C. S. R. (2015). Resting-state functional connectivity of the locus coeruleus in humans: in comparison with the ventral tegmental area/substantia nigra pars compacta and the effects of age. Cerebral Cortex, bhv172.
  117. Zhao, J., Li, M., Zhang, Y., Song, H., von Deneen, K. M., Shi, Y., … & He, D. (2016). Intrinsic brain subsystem associated with dietary restraint, disinhibition and hunger: an fMRI study. Brain imaging and behavior, 1-14.
  118. Zhao, T., Cao, M., Niu, H., Zuo, X. N., Evans, A., He, Y., … & Shu, N. (2015). Age‐related changes in the topological organization of the white matter structural connectome across the human lifespan. Human brain mapping, 36(10), 3777-3792.
  119. Zuo, Xi-Nian, Ting Xu, Lili Jiang, Zhi Yang, Xiao-Yan Cao, Yong He, Yu-Feng Zang, F Xavier Castellanos, and Michael P Milham. (2013). Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage, 65, 374-386.
  120. 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 & Biobehavioral Reviews, 45, 100-118.
  121. 颜志雄. (2016). 社会认知静息态脑网络的毕生发展轨线.