Publications

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

  1. Nooner et al, (2012). The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry. Frontiers in neuroscience 6, 152.

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. Airan, R. D., Vogelstein, J. T., Pillai, J. J., Caffo, B., Pekar, J. J., & Sair, H. I. (2016). Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI. Human brain mapping, 37(5), 1986-1997.
  2. 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.
  3. Ball, G., Beare, R., & Seal, M. L. (2017). Network component analysis reveals developmental trajectories of structural connectivity and specific alterations in autism spectrum disorder. Human brain mapping, 38(8), 4169-4184.
  4. 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.
  5. Bathelt, J., Johnson, A., Zhang, M., & Astle, D. E. (2018). Data-driven brain-types and their cognitive consequences. BioRxiv, 237859.
  6. Bathelt, J., Johnson, A., Zhang, M., & Astle, D. E. (2019). The cingulum as a marker of individual differences in neurocognitive development. Scientific reports, 9(1), 2281.
  7. Bernstein, J.P.K., DeVito, A. & Calamia, M. (In Press). Subjectively and Objectively Measured Sleep Predict Differing Aspects of Cognitive Functioning in Adults. Archives of Clinical Neuropsychology.
  8. 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.
  9. 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.
  10. Betzel, R. F., Fukushima, M., He, Y., Zuo, X. N., & Sporns, O. (2016). Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage, 127, 287-297.
  11. 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.
  12. 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.
  13. Billings, J. C., Medda, A., & Keilholz, S. D. (2013, November). Agglomerative clustering for resting state MRI. In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 553-556). IEEE.
  14. Billings, J. C., Medda, A., & Keilholz, S. D. (2015, December). Multiscale FC analysis refines functional connectivity networks in individual brains. In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 557-561). IEEE.
  15. Billings, J., & Keilholz, S. (2018). The Not-So-Global Blood Oxygen Level-Dependent Signal. Brain connectivity, 8(3), 121-128.
  16. Blumfield, M. L., Bei, B., Zimberg, I. Z., & Cain, S. W. (2018). Dietary disinhibition mediates the relationship between poor sleep quality and body weight. Appetite, 120, 602-608.
  17. 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.
  18. Boyne, P., Maloney, T., DiFrancesco, M., Fox, M. D., Awosika, O., Aggarwal, P., ... & Vannest, J. (2018). Resting-state functional connectivity of subcortical locomotor centers explains variance in walking capacity. Human brain mapping, 39(12), 4831-4843.
  19. Brown, J. A. (2013). Multimodality MRI-based Brain Network Analysis: Applications to Genetic Risk for Alzheimer's Disease. (Doctoral dissertation, UCLA).
  20. 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.
  21. Burroni, J., Taylor, P., Corey, C., Vachnadze, T., & Siegelmann, H. T. (2017). Energetic constraints produce self-sustained oscillatory dynamics in neuronal networks. Frontiers in neuroscience, 11, 80.
  22. Bzdok, D. et al. (2014). Subspecialization in the human posterior medial cortex. NeuroImage
  23. Bzdok, D., Hartwigsen, G., Reid, A., Laird, A. R., Fox, P. T., & Eickhoff, S. B. (2016). Left inferior parietal lobe engagement in social cognition and language. Neuroscience & Biobehavioral Reviews, 68, 319-334.
  24. 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.
  25. Callahan, B. L., & Plamondon, A. (2018). Examining the validity of the ADHD concept in adults and older adults. CNS spectrums, 1-8.
  26. 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, 219.
  27. Canale, A. , Durante, D. , Paci, L. and Scarpa, B. (2018). Connecting statistical brains. Significance, 15: 38-40.
  28. Cao, B., Mwangi, B., Passos, I. C., Wu, M. J., Keser, Z., Zunta-Soares, G. B., ... & Soares, J. C. (2017). Lifespan gyrification trajectories of human brain in healthy individuals and patients with major psychiatric disorders. Scientific reports, 7(1), 511.
  29. 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.
  30. Castagna, P. J., Roye, S., Calamia, M., Owens-French, J., Davis, T. E., & Greening, S. G. (2018). Parsing the neural correlates of anxious apprehension and anxious arousal in the grey-matter of healthy youth. Brain imaging and behavior, 12(4), 1084-1098.
  31. 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.
  32. 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.
  33. Chen, R., Nixon, E., & Herskovits, E. (2016). Advanced connectivity analysis (ACA): A large scale functional connectivity data mining environment. Neuroinformatics, 14(2), 191-199.
  34. Chen, Y., Zhao, X., Zhang, X., Liu, Y. N., Zhou, P., Ni, H., ... & Ming, D. (2018). Age-related early/late variations of functional connectivity across the human lifespan. Neuroradiology, 60(4), 403-412.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. Colvin, L. E., Malgaroli, M., Chapman, S., MacKay-Brandt, A., & Cosentino, S. (2018). Mood and personality characteristics are associated with metamemory knowledge accuracy in a community-based cohort of older adults. Journal of the International Neuropsychological Society, 24(5), 498-510.
  41. 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.
  42. Corcoran, C. M., Stoops, A., Lee, M., Martinez, A., Sehatpour, P., Dias, E. C., & Javitt, D. C. (2018). Developmental trajectory of mismatch negativity and visual event-related potentials in healthy controls: Implications for neurodevelopmental vs. neurodegenerative models of schizophrenia. Schizophrenia research, 191, 101-108.
  43. Crispino, M., D'Angelo, S., Ranciati, S., & Mira, A. (2017, June). Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data. In START UP RESEARCH (pp. 1-22). Springer, Cham.
  44. 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.
  45. Davey, J. M. (2015). The Cognitive and Neural Architecture of Semantic Cognition: Evidence for Dissociable Distributed Systems from Multiple Methods. (Doctoral dissertation, University of York).
  46. Davey, J., Thompson, H. E., Hallam, G., Karapanagiotidis, T., Murphy, C., De Caso, I., ... & Jefferies, E. (2016). Exploring the role of the posterior middle temporal gyrus in semantic cognition: Integration of anterior temporal lobe with executive processes. NeuroImage, 137, 165-177.
  47. de Vito, A., Calamia, M., Greening, S., & Roye, S. (2017). The association of anxiety, depression, and worry symptoms on cognitive performance in older adults. Aging, Neuropsychology, and Cognition, 1-13.
  48. Díaz Parra, A. (2018). A network science approach of the macroscopic organization of the brain: analysis of structural and functional brain networks in health and disease [Tesis doctoral no publicada]. Universitat Politècnica de València. doi:10.4995/Thesis/10251/106966
  49. Di, X., & Biswal, B. B. (2015). Characterizations of resting-state modulatory interactions in the human brain. Journal of neurophysiology, 114(5), 2785-2796.
  50. 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.
  51. 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.
  52. Di, X., Reynolds, R. C., & Biswal, B. B. (2017). Imperfect (de) convolution may introduce spurious psychophysiological interactions and how to avoid it. Human brain mapping, 38(4), 1723-1740.
  53. Dogan, I., Eickhoff, C. R., Fox, P. T., Laird, A. R., Schulz, J. B., Eickhoff, S. B., & Reetz, K. (2015). Functional connectivity modeling of consistent cortico-striatal degeneration in Huntington's disease. NeuroImage: Clinical, 7, 640-652.
  54. Dong, J., Jing, B., Ma, X., Liu, H., Mo, X., & Li, H. (2018). Hurst exponent analysis of resting-state fMRI signal complexity across the adult lifespan. Frontiers in Neuroscience, 12, 34.
  55. DuPre, E., & Spreng, R. N. (2017). Structural covariance networks across the life span, from 6 to 94 years of age. Network Neuroscience, 1(3), 302-323.
  56. 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.
  57. Faskowitz, J., Yan, X., Zuo, X. N., & Sporns, O. (2018). Weighted Stochastic Block Models of the Human Connectome across the Life Span. Scientific reports, 8(1), 12997.
  58. File, B., Klimaj, Z., Somogyvári, Z., Kozák, L. R., Gyebnár, G., Tóth, B., ... & Molnár, M. (2016, June). Age-related changes of the representative modular structure in the brain. In 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (pp. 1-4). IEEE.
  59. 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.
  60. Fritz, H. C. J., Ray, N., Dyrba, M., Sorg, C., Teipel, S., & Grothe, M. J. (2019). The corticotopic organization of the human basal forebrain as revealed by regionally selective functional connectivity profiles. Human brain mapping, 40(3), 868-878.
  61. Fuhrmann, D., Simpson-Kent, I. L., Bathelt, J., & Kievit, R. A. (2018). The neurocognitive architecture of fluid ability in children and adolescents. bioRxiv, 435719.
  62. Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X. N., ... & Fukushima, M. (2016). Individual variability and connectivity dynamics in modular organization of human cortical functional networks. arXiv preprint arXiv:1511.06427.
  63. Fukushima, M., Betzel, R. F., He, Y., de Reus, M. A., van den Heuvel, M. P., Zuo, X. N., & Sporns, O. (2018). Fluctuations between high-and low-modularity topology in time-resolved functional connectivity. NeuroImage, 180, 406-416.
  64. Fukushima, M., Betzel, R. F., He, Y., van den Heuvel, M. P., Zuo, X. N., & Sporns, O. (2018). Structure-function relationships during segregated and integrated network states of human brain functional connectivity. Brain Structure and Function, 223(3), 1091-1106.
  65. 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.
  66. Fu, Z. (2016). A study of dynamic functional brain connectivity using functional magnetic resonance imaging (fMRI): method and applications. HKU Theses Online (HKUTO).
  67. Fu, Z., Chan, S. C., Di, X., Biswal, B., & Zhang, Z. (2014). Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI. IEEE transactions on biomedical circuits and systems, 8(2), 228-239.
  68. 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.
  69. Garrett, D. D., Epp, S. M., Perry, A., & Lindenberger, U. (2018). Local temporal variability reflects functional integration in the human brain. NeuroImage, 183, 776-787.
  70. Gasperoni, F., & Luati, A. (2017, June). Robust Methods for Detecting Spontaneous Activations in fMRI Data. In START UP RESEARCH (pp. 91-110). Springer, Cham.
  71. Gastner, M. T., & Ódor, G. (2015). The topology of large Open Connectome networks for the human brain. arXiv preprint arXiv:1512.01197.
  72. Geha, P., Cecchi, G., Todd Constable, R., Abdallah, C., & Small, D. M. (2017). Reorganization of brain connectivity in obesity. Human brain mapping, 38(3), 1403-1420.
  73. 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.
  74. Genon, S., Reid, A., Li, H., Fan, L., Müller, V. I., Cieslik, E. C., ... & Fox, P. T. (2018). The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. Neuroimage, 170, 400-411.
  75. 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.
  76. Golchert, J. (2018). Structural and functional brain organization underlying spontaneous and deliberate mind-wandering . (Doctoral dissertation).
  77. Golchert, J., Smallwood, J., Jefferies, E., Liem, F., Huntenburg, J. M., Falkiewicz, M., ... & Margulies, D. S. (2017). In need of constraint: Understanding the role of the cingulate cortex in the impulsive mind. NeuroImage, 146, 804-813.
  78. Golchert, J., Smallwood, J., Jefferies, E., Seli, P., Huntenburg, J. M., Liem, F., ... & Margulies, D. S. (2017). Individual variation in intentionality in the mind-wandering state is reflected in the integration of the default-mode, fronto-parietal, and limbic networks. Neuroimage, 146, 226-235.
  79. 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.
  80. Goto, M., Abe, O., Miyati, T., Yamasue, H., Gomi, T., & Takeda, T. (2015). Head motion and correction methods in resting-state functional MRI. Magnetic Resonance in Medical Sciences, rev-2015.
  81. 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.
  82. 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.
  83. 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.
  84. Hallam, G. P., Thompson, H. E., Hymers, M., Millman, R. E., Rodd, J. M., Ralph, M. A. L., ... & Jefferies, E. (2018). Task-based and resting-state fMRI reveal compensatory network changes following damage to left inferior frontal gyrus. Cortex, 99, 150-165.
  85. 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
  86. Han, C. E., Peraza, L. R., Taylor, J. P., & Kaiser, M. (2014, October). Predicting age across human lifespan based on structural connectivity from diffusion tensor imaging. In 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings (pp. 137-140). IEEE.
  87. 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.
  88. 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).
  89. He, Y., Xu, T., Zhang, W., & Zuo, X. N. (2015). Lifespan anxiety is reflected in human amygdala cortical connectivity. Human brain mapping.
  90. Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific reports, 7(1), 16088.
  91. Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10-25.
  92. Hilger, K., & Fiebach, C. J. (2018). ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks in a Representative Sample of Healthy Adults. bioRxiv, 505891.
  93. 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.
  94. 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.
  95. Horn, A., & Blankenburg, F. (2016). Toward a standardized structural–functional group connectome in MNI space. NeuroImage, 124, 310-322.
  96. Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social cognitive and affective neuroscience, 13(2), 224-232.
  97. Huntenburg, J. M. (2014). Evaluating nonlinear coregistration of BOLD EPI and T1w images. (Doctoral dissertation, Freie Universität Berlin).
  98. Huo, Y., Aboud, K., Kang, H., Cutting, L. E., & Landman, B. A. (2016, October). Mapping lifetime brain volumetry with covariate-adjusted restricted cubic spline regression from cross-sectional multi-site MRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 81-88). Springer, Cham.
  99. Huo, Y., Swett, K., Resnick, S. M., Cutting, L. E., & Landman, B. A. (2018). Data-driven probabilistic atlases capture whole-brain individual variation. arXiv preprint arXiv:1806.02300.
  100. Hu, S., Ide, J. S., Chao, H. H., Zhornitsky, S., Fischer, K. A., Wang, W., ... & Chiang-shan, R. L. (2018). Resting state functional connectivity of the amygdala and problem drinking in non-dependent alcohol drinkers. Drug and alcohol dependence, 185, 173-180.
  101. Hwang, K., Bertolero, M. A., Liu, W., & D’Esposito, M. (2016). The human thalamus is an integrative hub for functional brain networks. bioRxiv, 056630.
  102. Ikuta, T., Frith, E., Ponce, P., & Loprinzi, P. D. (2018). Association of physical activity on the functional connectivity of the hippocampal-orbitofrontal pathway. The Physician and sportsmedicine, 1-5.
  103. Ikuta, T., & Loprinzi, P. D. (2019). Association of Cardiorespiratory Fitness on Interhemispheric Hippocampal and Parahippocampal Functional Connectivity. European Journal of Neuroscience.
  104. Ikuta, T., del Arco, A. & Karlsgodt, K.H. (2018). White matter integrity in the fronto-striatal accumbofrontal tract predicts impulsivity. Brain Imaging and Behavior, 12(5), 1524-1528.
  105. Jakab, A. (2012).Characterization of normal and pathological patterns of diffusion anisotropy with diffusion tensor imaging. (Doctoral dissertation, University of Debrecen).
  106. 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.
  107. 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.
  108. Jakobsen, E., Liem, F., Klados, M. A., Bayrak, Ş., Petrides, M., & Margulies, D. S. (2018). Automated individual-level parcellation of Broca's region based on functional connectivity. NeuroImage, 170, 41-53.
  109. Jiang, L., & Zuo, X. N. (2015). Regional homogeneity a multimodal, multiscale neuroimaging marker of the human connectome. The Neuroscientist, 1073858415595004.
  110. 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.
  111. Jiang, L., & Zuo, X. N. (2016). Regional homogeneity: a multimodal, multiscale neuroimaging marker of the human connectome. The Neuroscientist, 22(5), 486-505.
  112. Karapanagiotidis, T., Bernhardt, B. C., Jefferies, E., & Smallwood, J. (2017). Tracking thoughts: Exploring the neural architecture of mental time travel during mind-wandering. Neuroimage, 147, 272-281.
  113. 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
  114. Kerestes, R., Chase, H. W., Phillips, M. L., Ladouceur, C. D., & Eickhoff, S. B. (2017). Multimodal evaluation of the amygdala's functional connectivity. Neuroimage, 148, 219-229.
  115. King, M. D. et al. (2014). Automated collection of imaging and phenotypic data to centralized and distributed data repositories. Front. Neuroinformatics 8, 60
  116. 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.
  117. Klein, A., & Tourville, J. (2012). 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in neuroscience, 6, 171.
  118. 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.
  119. Kong, X. Z. (2014). Association between in-scanner head motion with cerebral white matter microstructure: a multiband diffusion-weighted MRI study. PeerJ, 2, e366.
  120. Kovács, K., Jakab, A., & Berényi, E.L. (2013). Exploring the neural correlates of psychometric scales: a tract-based analysis of diffusion anisotropy. ECR 2013
  121. Koyama, M. S., O'Connor, D., Shehzad, Z., & Milham, M. P. (2017). Differential contributions of the middle frontal gyrus functional connectivity to literacy and numeracy. Scientific reports, 7(1), 17548.
  122. 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.
  123. Krieger-Redwood, K., Jefferies, E., Karapanagiotidis, T., Seymour, R., Nunes, A., Ang, J. W. A., ... & Smallwood, J. (2016). Down but not out in posterior cingulate cortex: Deactivation yet functional coupling with prefrontal cortex during demanding semantic cognition. Neuroimage, 141, 366-377.
  124. 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.
  125. 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, 41(9), 2275.
  126. Lee, T. W., & Xue, S. W. (2017). Examination of the validity of the atlas-informed approach to functional parcellation: a resting functional MRI study. NeuroReport, 28(11), 649-653.
  127. Lee, T. W., & Xue, S. W. (2017). Linking graph features of anatomical architecture to regional brain activity: A multi-modal MRI study. Neuroscience letters, 651, 123-127.
  128. Lee, T. W., & Xue, S. W. (2018). Functional connectivity maps based on hippocampal and thalamic dynamics may account for the default-mode network. European Journal of Neuroscience, 47(5), 388-398.
  129. Lee, T. W., & Xue, S. W. (2018). Revisiting the Functional and Structural Connectivity of Large-Scale Cortical Networks. Brain connectivity, 8(3), 129-138.
  130. 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.
  131. 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.
  132. Liem, F., Varoquaux, G., Kynast, J., Beyer, F., Masouleh, S. K., Huntenburg, J. M., ... & Riedel-Heller, S. (2017). Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage, 148, 179-188.
  133. 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.
  134. 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.
  135. 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.
  136. 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.
  137. 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.
  138. Mace, R. A., Waters, A. B., Sawyer, K. S., Turrisi, T., & Gansler, D. A. (2018). Components of Executive Function Predict Regional Prefrontal Volumes. bioRxiv, 374009.
  139. Madan, C. R. (2017). Advances in studying brain morphology: the benefits of open-access data. Frontiers in human neuroscience, 11, 405.
  140. Mallela, A. N., Peck, K. K., Petrovich-Brennan, N. M., Zhang, Z., Lou, W., & Holodny, A. I. (2016). Altered resting-state functional connectivity in the hand motor network in glioma patients. Brain connectivity, 6(8), 587-595.
  141. 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.
  142. 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.
  143. Marchetti, I., Shumake, J., Grahek, I., & Koster, E. H. (2018). Temperamental factors in remitted depression: The role of effortful control and attentional mechanisms. Journal of Affective Disorders, 235, 499-505.
  144. 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.
  145. Medda, A., Billings, J. C., & Keilholz, S. D. (2014, November). Multiscale functional networks in human resting state functional MRI. In 2014 48th Asilomar Conference on Signals, Systems and Computers (pp. 415-419). IEEE.
  146. 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.
  147. 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.
  148. 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.
  149. 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.
  150. 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.
  151. Nakamura, Y., & Ikuta, T. (2017). Caudate-Precuneus Functional Connectivity Is Associated with Obesity Preventive Eating Tendency. Brain connectivity, 7(3), 211-217.
  152. Nashiro, K., Sakaki, M., Braskie, M. N., & Mather, M. (2017). Resting-state networks associated with cognitive processing show more age-related decline than those associated with emotional processing. Neurobiology of aging, 54, 152-162.
  153. 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.
  154. 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.
  155. 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.
  156. 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.
  157. Olszowy, W., Aston, J., Rua, C., & Williams, G. B. (2017). Accurate autocorrelation modeling substantially improves fMRI reliability. arXiv preprint arXiv:1711.09877.
  158. Olszowy, W., Williams, G. B., Rua, C., & Aston, J. (2017). Autocorrelation bias still exists in FMRI results. arXiv preprint ArXiv:1711.09877 [q-Bio].
  159. 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.
  160. 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, 9.
  161. Pardoe, H. R., & Kuzniecky, R. (2018). NAPR: a cloud-based framework for neuroanatomical age prediction. Neuroinformatics, 16(1), 43-49.
  162. Park, B. Y., Moon, T., & Park, H. (2018). Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis. Behavioural brain research, 337, 114-121.
  163. Park, B. Y., Seo, J., & Park, H. (2016). Functional brain networks associated with eating behaviors in obesity. Scientific reports, 6.
  164. Peraza, L. R., Díaz-Parra, A., Kennion, O., Moratal, D., Taylor, J. P., Kaiser, M., ... & Alzheimer's Disease Neuroimaging Initiative. (2019). Structural connectivity centrality changes mark the path toward Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 11, 98-107.
  165. Petrican, R., Taylor, M. J., & Grady, C. L. (2017). Trajectories of brain system maturation from childhood to older adulthood: implications for lifespan cognitive functioning. Neuroimage, 163, 125-149.
  166. Phinyomark, A., Ibanez-Marcelo, E., & Petri, G. (2017). Resting-state fmri functional connectivity: Big data preprocessing pipelines and topological data analysis. IEEE Transactions on Big Data, 3(4), 415-428.
  167. 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.
  168. 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. GigaScience, 5(1), 45.
  169. 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.
  170. Qin, W., Jin, L., & Tian, J. (2018). Prospects of Acupuncture Research in the Future. In Multi-Modality Neuroimaging Study on Neurobiological Mechanisms of Acupuncture (pp. 125-138). Springer, Singapore.
  171. Rasero, J., Pellicoro, M., Angelini, L., Cortes, J. M., Marinazzo, D., & Stramaglia, S. (2017). Consensus clustering approach to group brain connectivity matrices. Network Neuroscience, 1(3), 242-253.
  172. 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.
  173. 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.
  174. 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.
  175. 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.
  176. Robinson, P. A., Zhao, X., Aquino, K. M., Griffiths, J. D., Sarkar, S., & Mehta-Pandejee, G. (2016). Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment. NeuroImage, 142, 79-98.
  177. 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.
  178. Rucker, E. P. (2017). Pituitary Gland Functional Connectivity and BMI. (Doctoral dissertation, The University of Mississippi).
  179. 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.
  180. Santarnecchi, E., Rossi, S., & Rossi, A. (2015). The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults. Cortex, 64, 293-309.
  181. Santarnecchi, E., Tatti, E., Rossi, S., Serino, V., & Rossi, A. (2015). Intelligence-related differences in the asymmetry of spontaneous cerebral activity. Human brain mapping, 36(9), 3586-3602.
  182. 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.
  183. 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.
  184. 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.
  185. Scheel, N., Franke, E., Münte, T. F., & Mamlouk, A. M. (2018). Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE. Frontiers in human neuroscience, 12.
  186. 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.
  187. 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.
  188. Shine, J. M., Bell, P. T., Koyejo, O., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2015). Dynamic fluctuations in integration and segregation within the human functional connectome. arXiv preprint arXiv:1511.02976.
  189. 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.
  190. Sidtis, J. J., Mubeen, M. A., Asaei, A., Ardekani, B., & Van Lancker Sidtis, D. (2018). Performance and function meet structure: A white matter connection tuned for vocal production. Brain connectivity, 8(10), 628-636.
  191. 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.
  192. 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.
  193. 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.
  194. Takerkart, S., Berton, G., Malfait, N., & Dupé, F. X. (2017, May). Learning from Diffusion-Weighted Magnetic Resonance Images using graph kernels. In International Workshop on Graph-Based Representations in Pattern Recognition (pp. 39-48). Springer, Cham.
  195. 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.
  196. 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.
  197. Tarquino, J., Rueda, A., & Romero, E. (2014, October). Shearlet-based sparse representation for super-resolution in diffusion weighted imaging (DWI). In 2014 IEEE International Conference on Image Processing (ICIP). (pp. 3897-3900). IEEE.
  198. 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.
  199. 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.
  200. Tian, L., Li, Q., Wang, C., & Yu, J. (2018). Changes in dynamic functional connections with aging. Neuroimage, 172, 31-39.
  201. Tian, L., & Ma, L. (2017). Microstructural changes of the human brain from early to mid-adulthood. Frontiers in human neuroscience, 11, 393.
  202. 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.
  203. Tillman, R. M., Stockbridge, M. D., Nacewicz, B. M., Torrisi, S., Fox, A. S., Smith, J. F., & Shackman, A. J. (2018). Intrinsic functional connectivity of the central extended amygdala. Human brain mapping, 39(3), 1291-1312.
  204. Tustison, N. J., Avants, B. B., Cook, P. A., Kim, J., Whyte, J., Gee, J. C., & Stone, J. R. (2014). Logical circularity in voxel-based analysis: Normalization strategy may induce statistical bias. Human brain mapping, 35(3), 745-759.
  205. 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.
  206. 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.
  207. Um, M. (2017). Resting-state neural circuit correlates of negative urgency: a comparison between tobacco users and non-tobacco users. (Doctoral dissertation).
  208. Vadovičová, K. (2014). Affective and cognitive prefrontal cortex projections to the lateral habenula in humans. Frontiers in human neuroscience, 8, 819.
  209. 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.
  210. Vieira, B. H. (2018). Brain functional connectivity in regions that exhibit age-related cortical thinning. (Doctoral dissertation, Universidade de São Paulo).
  211. Vieira, B. H., & Salmon, C. E. G. (2019). A principled multivariate intersubject analysis of generalized partial directed coherence with Dirichlet regression: Application to healthy aging in areas exhibiting cortical thinning. Journal of neuroscience methods, 311, 243-252.
  212. Vij, S. G., Nomi, J., Dajani, D. R., & Uddin, L. Q. (2017). Age-related changes in spatial and temporal features of resting state fMRI. bioRxiv, 109181.
  213. Vij, S. G., Nomi, J. S., Dajani, D. R., & Uddin, L. Q. (2018). Evolution of spatial and temporal features of functional brain networks across the lifespan. NeuroImage, 173, 498-508.
  214. Villena-Gonzalez, M., Wang, H. T., Sormaz, M., Mollo, G., Margulies, D. S., Jefferies, E. A., & Smallwood, J. (2018). Individual variation in the propensity for prospective thought is associated with functional integration between visual and retrosplenial cortex. Cortex, 99, 224-234.
  215. Wang, H. T., Bzdok, D., Margulies, D., Craddock, C., Milham, M., Jefferies, E., & Smallwood, J. (2018). Patterns of thought: population variation in the associations between large-scale network organisation and self-reported experiences at rest. NeuroImage, 176, 518-527.
  216. Wang, H., Wen, B., Cheng, J., & Li, H. (2017). Brain structural differences between normal and obese adults and their links with lack of perseverance, negative urgency, and sensation seeking. Scientific reports, 7, 40595.
  217. 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.
  218. Wang, X. H., & Li, L. (2016, March). Predicting human age using regional morphometry and inter-regional morphological similarity. In Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 9788, p. 978821). International Society for Optics and Photonics.
  219. 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.
  220. 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.
  221. Wang, Y., Necus, J., Kaiser, M., & Mota, B. (2016). Universality in human cortical folding in health and disease. Proceedings of the National Academy of Sciences, 113(45), 12820-12825.
  222. Waters, A. B., Mace, R. A., Sawyer, K. S., & Gansler, D. A. (2018). Identifying errors in Freesurfer automated skull stripping and the incremental utility of manual intervention. Brain imaging and behavior, 1-11.
  223. Waters, A. B., Sawyer, K. S., & Gansler, D. A. (2018). On the impact of interhemispheric white matter: Age, executive functioning, and dedifferentiation in the frontal lobes. International journal of geriatric psychiatry, 33(9), 1271-1279.
  224. Waters, A., Mace, R., Sawyer, K., & Gansler, D. (2017). Using Outliers in Freesurfer Segmentation Statistics to Identify Cortical Reconstruction Errors in Structural Scans. bioRxiv, 176818.
  225. Wei, L., Chen, H., & Wu, G. R. (2018). Structural covariance of the prefrontal-amygdala pathways associated with heart rate variability. Frontiers in human neuroscience, 12, 2.
  226. Wei, L., Chen, H., & Wu, G. R. (2018). Heart rate variability associated with grey matter volumes in striatal and limbic structures of the central autonomic network. Brain research, 1681, 14-20.
  227. Wong, J., & Zhang, Z. (2015, June). Exploratory analysis of time-varying functional connectivity in a visual task. In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 1-5). IEEE.
  228. Wong, T. Y., Sid, A., Wensing, T., Eickhoff, S. B., Habel, U., Gur, R. C., & Nickl-Jockschat, T. (2018). Neural networks of aggression: ALE meta-analyses on trait and elicited aggression. Brain Structure and Function, 1-16.
  229. 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.
  230. Xue, S. W., Lee, T. W., & Guo, Y. H. (2018). Spontaneous activity in medial orbitofrontal cortex correlates with trait anxiety in healthy male adults. Journal of Zhejiang University-SCIENCE B, 19(8), 643-653.
  231. Xu, T., Opitz, A., Craddock, R. C., Wright, M. J., Zuo, X. N., & Milham, M. P. (2016). Assessing variations in areal organization for the intrinsic brain: from fingerprints to reliability. Cerebral Cortex, 26(11), 4192-4211.
  232. Xu, T., Opitz, A., Craddock, R. C., Zuo, X. N., Milham, M., & Milham, M. P. (2016). Intrinsic Areal Organization in the Individual Brain: Unique and Reliable. bioRxiv, 035790.
  233. 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.
  234. 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.
  235. 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.
  236. Yang, Z., Craddock, R. C., & Milham, M. P. (2014). Impact of hematocrit on measurements of the intrinsic brain. Frontiers in neuroscience, 8.
  237. 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.
  238. 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.
  239. Yuan, L., Wei, X., Shen, H., Zeng, L. L., & Hu, D. (2018). Multi-Center Brain Imaging Classification Using a Novel 3D CNN Approach. IEEE Access, 6, 49925-49934.
  240. Zhang, F., Wu, Y., Norton, I., Rathi, Y., Makris, N., & O'Donnell, L. J. (2018). A data-driven groupwise fiber clustering atlas for consistent white matter parcellation and anatomical tract identification of subjects across the lifespan. In Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).
  241. Zhang, J., Li, C., & Jiang, T. (2016). New Insights into Signed Path Coefficient Granger Causality Analysis. Frontiers in neuroinformatics, 10, 47.
  242. Zhang, L., Fu, Z. N., Chan, S. C., Wu, H. C., & Zhang, Z. G. (2016, May). A new L1-regularized time-varying autoregressive model for brain connectivity estimation: A study using visual task-related fMRI data. In 2016 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 29-32). IEEE.
  243. Zhang, S., & Arfanakis, K. (2018). Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: Template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences. NeuroImage, 172, 40-50.
  244. Zhang, S., Hu, S., Chao, H. H., & Li, C. S. R. (2016). 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, 26(8), 3413-3427.
  245. 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.
  246. Zhao, J., Tomasi, D., Wiers, C. E., Shokri-Kojori, E., Demiral, Ş. B., Zhang, Y., ... & Wang, G. J. (2017). Correlation between Traits of Emotion-Based Impulsivity and Intrinsic Default-Mode Network Activity. Neural plasticity, 2017.
  247. 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.
  248. Zhao, Y., Zheng, Z. L., & Castellanos, F. X. (2017). Analysis of alcohol use disorders from the Nathan Kline Institute-Rockland Sample: Correlation of brain cortical thickness with neuroticism. Drug and alcohol dependence, 170, 66-73.
  249. Zimmermann, J., Griffiths, J., Schirner, M., Ritter, P., & McIntosh, A. R. (2018). Subject specificity of the correlation between large-scale structural and functional connectivity. Network Neuroscience, 3(1), 90-106.
  250. 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.
  251. 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.
  252. Zuo, X. N., He, Y., Betzel, R. F., Colcombe, S., Sporns, O., & Milham, M. P. (2017). Human connectomics across the life span. Trends in cognitive sciences, 21(1), 32-45.
  253. 颜志雄. (2016). 社会认知静息态脑网络的毕生发展轨线.

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