Abstract
Beginning in the 1990’s, substantial advances have been made in the ability to image the living human brain. Functional MRI, PET, and other modalities have been developed to provide a rich means for assessing brain function and structure across spatial and temporal dimensions. Such methods are now the preferred means to examine the brain in vivo, with several thousand articles now appearing in the literature each year. The next era of human brain imaging is upon us now as technological developments reach a level where data can be processed quickly and combined with other biological information to provide fundamentally new applications and insights. This new era will involve and require the collaborative participation of leading research groups from around the world to share information and expertise for understanding observed effects and synthesizing these into new knowledge. One particular community that is gaining in its prominence in the field is that of the Pacific Rim, whose collective research efforts present an important corpus of research effort into brain structure and function. The Pacific Rim represents an important community of researchers interested in the greater sharing of ideas. In this special issue of Brain Imaging and Behavior, we focus on emerging areas of research that utilize brain imaging methodology, and discuss how current developments are driving the expansion of functional imaging research. Moreover, we focus on the robust interaction of researchers from around the Pacific Rim whose collaborations are significantly shaping the future of brain imaging.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Amari, S., Beltrame, F., et al. (2002). Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience. Journal of Integrative Neuroscience, 1(2), 117–128. doi:10.1142/S0219635202000128.
Bammer, R., Acar, B., et al. (2003). In vivo MR tractography using diffusion imaging. European Journal of Radiology, 45(3), 223–234. doi:10.1016/S0720-048X(02)00311-X.
Bandettini, P. (2007). Functional MRI today. International Journal of Psychophysiology, 63, 138–145. doi:10.1016/j.ijpsycho.2006.03.016.
Bandettini, P. A., Wong, E. C., et al. (1992). Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine, 25(2), 390–397. doi:10.1002/mrm.1910250220.
Basser, P. J., & Jones, D. K. (2002). Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR in Biomedicine, 15(7–8), 456–467. doi:10.1002/nbm.783.
Bellgowan, P. S., Bandettini, P. A., et al. (2006). Improved BOLD detection in the medial temporal region using parallel imaging and voxel volume reduction. NeuroImage, 29(4), 1244–1251. doi:10.1016/j.neuroimage.2005.08.042.
Boesz, C., & Lloyd, N. (2008). Collaborations: investigating international misconduct. Nature, 452(7188), 686−687. doi:10.1038/452686a.
Buxton, R. B. (2001). The elusive initial dip. NeuroImage, 13(6 Pt 1), 953−958. doi:10.1006/nimg.2001.0814.
Casey, B. J., Giedd, J. N., et al. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54(1−3), 241–257. doi:10.1016/S0301-0511(00)00058-2.
Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19(2 Pt 1), 261–270. doi:10.1016/S1053-8119(03)00049-1.
D’Esposito, M. (2000). Functional neuroimaging of cognition. Seminars in Neurology, 20(4), 487–498. doi:10.1055/s-2000-13182.
Dale, A. M., Liu, A. K., et al. (2000). Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron, 26(1), 55–67. doi:10.1016/S0896-6273(00)81138-1.
Friston, K. J., & Price, C. J. (2001). Generative models, brain function and neuroimaging. Scandinavian Journal of Psychology, 42(3), 167–177. doi:10.1111/1467-9450.00228.
Golay, X., Jiang, H., et al. (2002). High-resolution isotropic 3D diffusion tensor imaging of the human brain. Magnetic Resonance in Medicine, 47(5), 837–843. doi:10.1002/mrm.10143.
Halgren, E., Raij, T., et al. (2000). Cognitive response profile of the human fusiform face area as determined by MEG. Cerebral Cortex (New York, N.Y.), 10(1), 69–81. doi:10.1093/cercor/10.1.69.
Hariri, A. R., & Weinberger, D. R. (2003). Imaging genomics. British Medical Bulletin, 65, 259–270. doi:10.1093/bmb/65.1.259.
Jorm, A. F., Masaki, K. H., et al. (2005). Cognitive deficits 3 to 6 years before dementia onset in a population sample: the Honolulu-Asia aging study. Journal of the American Geriatrics Society, 53(3), 452–455. doi:10.1111/j.1532-5415.2005.53163.x.
Kondro, W. (2007). Research funding. Canadian institutes get windfall without the bother of competition. Science, 315(5820), 1779–1780. doi:10.1126/science.315.5820.1779a.
Madden, D. J., Spaniol, J., et al. (2007). Adult age differences in the functional neuroanatomy of visual attention: a combined fMRI and DTI study. Neurobiology of Aging, 28(3), 459–476. doi:10.1016/j.neurobiolaging.2006.01.005.
Mazziotta, J., Toga, A., et al. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 356(1412), 1293–1322. doi:10.1098/rstb.2001.0915.
Mitka, M. (2007). Scientists warn NIH funding squeeze hampering biomedical research. Journal of the American Medical Association, 297(17), 1867–1868. doi:10.1001/jama.297.17.1867.
Narr, K. L., Bilder, R. M., et al. (2005). Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cerebral Cortex (New York, N.Y.), 15(6), 708–719. doi:10.1093/cercor/bhh172.
Nature Editorial (2008). Science in retreat. Nature, 451(7181), 866. doi:10.1038/451866a.
Norman, K. A., Polyn, S. M., et al. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. doi:10.1016/j.tics.2006.07.005.
Ogawa, S., Lee, T. M., et al. (2000). An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11026–11031. doi:10.1073/pnas.97.20.11026.
Ogawa, S., Tank, D. W., et al. (1992). Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences of the United States of America, 89(13), 5951–5955. doi:10.1073/pnas.89.13.5951.
Pantelis, C., Velakoulis, D., et al. (2007). Neuroimaging and emerging psychotic disorders: the Melbourne ultra-high risk studies. International Review of Psychiatry (Abingdon, England), 19(4), 371–381. doi:10.1080/09540260701512079.
Rouse, R. (2000). Survey shows Australian scientists' discontent. Nature Medicine, 6(1), 10. doi:10.1038/71448.
Saczynski, J. S., Pfeifer, L. A., et al. (2006). The effect of social engagement on incident dementia: the Honolulu-Asia Aging Study. American Journal of Epidemiology, 163(5), 433–440. doi:10.1093/aje/kwj061.
Smith, S. M., Jenkinson, M., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208–S219. doi:10.1016/j.neuroimage.2004.07.051.
Spinney, L. (2004). China opens neuroimaging center. Scientist (Philadelphia, Pa.), 5(1), 3.
Stewart, R., White, L. R., et al. (2007). Twenty-six-year change in total cholesterol levels and incident dementia: the Honolulu-Asia Aging Study. Archives of Neurology, 64(1), 103–107. doi:10.1001/archneur.64.1.103.
Van Horn, J. D., Dobson, J., et al. (2006). Grid-Based computing and the future of neuroscience computation. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.), Methods in Mind (pp. 141–170). Cambridge: MIT.
Van Horn, J. D., & Gazzaniga, M. S. (2005). Maximizing information content in shared neuroimaging studies of cognitive function. In S. H. Koslow, & A. Subramanian (Eds.), Databasing the Brain: From Data to Knowledge. New York: Wiley and Sons.
Wells, W. A. (2007). The returning tide: how China, the world's most populous country, is building a competitive research base. The Journal of Cell Biology, 176(4), 376–401. doi:10.1083/jcb.200701150.
Yamashita, O., Sadato, N., et al. (2005). Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models. NeuroImage, 25(2), 478–490. doi:10.1016/j.neuroimage.2004.11.042.
Acknowledgements
The special issue editors wish to recognize the support of the International Brain Research Organization (IBRO), University of California Pacific Rim Institute, the National Center for Research Resources (NCRR), and the International Society for Magnetic Resonance in Medicine (ISMRM), The Institute for Collaborative Biotechnology (ICB) at UC Santa Barbara, The International Neuroinformatics Coordinating Facility (INCF), and Sun Microsystems. We also would like to thanks Ms. Amanda Hammond and Mr. Vaughan Greer of the Laboratory of Neuroimaging (LONI) for their efforts with preparing cover artwork. Finally, we extend our thanks to the Editor-in-Chief of Brain Imaging and Behavior, Professor Andrew Saykin, for his encouragement and enthusiasm for this special issue.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Van Horn, J.D., Bandettini, P.A., Cheng, K. et al. New Horizons for the Next Era of Human Brain Imaging, Cognitive, and Behavioral Research: Pacific Rim Interactivity. Brain Imaging and Behavior 2, 227–231 (2008). https://doi.org/10.1007/s11682-008-9045-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-008-9045-0