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

  1. Tobe et al, (2022). A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Scientific Data 9, 300.

You may also site the original NKI-RS publication:

  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.
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