, Volume 10, Issue 2, pp 129–140 | Cite as

The Internet Brain Volume Database: A Public Resource for Storage and Retrieval of Volumetric Data

  • David N. KennedyEmail author
  • Steven M. Hodge
  • Yong Gao
  • Jean A. Frazier
  • Christian Haselgrove
Original Article


Every month, numerous publications appear that include neuroanatomic volumetric observations. The current and past literature that includes volumetric measurements is vast, but variable with respect to specific species, structures, and subject characteristics (such as gender, age, pathology, etc.). In this report we introduce the Internet Brain Volume Database (IBVD),, a site devoted to facilitating access to and utilization of neuroanatomic volumetric observations as published in the literature. We review the design and functionality of the site. The IBVD is the first database dedicated to integrating, exposing and sharing brain volumetric observations across species and disease. It offers valuable functionality for quality assurance assessment of results as well as support for meta-analysis across large segments of the published literature that are obscured from traditional text-based search engines.


Brain Volume Quantitative neuroanatomy Morphometry Database Website 



This work was supported in part by grant NS34189 (PI Kennedy) as part of the Human Brain Project, MH083320 (Co PIs Kennedy and Frazier) and NS064354 (PI Gao). Thanks for tireless data entry efforts are owed to Julianne Steele, Aliya Dincer and Thomas Buckley. Marilyn Albert and Bruce Fischl provided access to individual data from their published work. Robert Williams provided group and individual mouse brain volumes from the Mouse Brain Library (MBL—


  1. Bowden, D. M., & Dubach, M. F. (2003). NeuroNames 2002. Neuroinformatics, 1(1), 43–60.PubMedCrossRefGoogle Scholar
  2. Bug, W. J., Ascoli, G. A., et al. (2008). The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience. Neuroinformatics, 6(3), 175–194.PubMedCrossRefGoogle Scholar
  3. Bzdok, D., & Langner, R., et al. (2011). ALE meta-analysis on facial judgments of trustworthiness and attractiveness. Brain Structure Function, 215(3–4), 209–223.Google Scholar
  4. Casey, B. J., Tottenham, N., et al. (2005). Imaging the developing brain: what have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104–110.PubMedCrossRefGoogle Scholar
  5. Caviness, V. S. J., Kennedy, D. N., et al. (1996). The developing human brain: A morphometric profile. In R. W. Thatcher, G. R. Lyon, J. Rumsey, N. Krasnegor, et al. (Eds.), Developmental neuroimaging: Mapping the development of brain and behavior (pp. 3–14). New York: Academic.Google Scholar
  6. Caviness, V. S., & Lange, J., N. T., et al. (1999). MRI-based brain volumetrics: emergence of a developmental brain science. Brain and Development, 21(5), 289–295.Google Scholar
  7. Courchesne, E., & Pierce, K. (2005). Brain overgrowth in autism during a critical time in development: implications for frontal pyramidal neuron and interneuron development and connectivity. International Journal of Developmental Neuroscience, 23(2–3), 153–170.PubMedCrossRefGoogle Scholar
  8. Filipek, P. A., Richelme, C., et al. (1994). The young adult human brain: an MRI-based morphometric analysis. Cerebral Cortex, 4(4), 344–360.PubMedCrossRefGoogle Scholar
  9. Gardner, D., Akil, H., et al. (2008). The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics, 6(3), 149–160.PubMedCrossRefGoogle Scholar
  10. Giedd, J. N., Clasen, L. S., et al. (2006). Puberty-related influences on brain development. Molecular and Cellular Endocrinology, 254–255, 154–162.PubMedCrossRefGoogle Scholar
  11. Herbert, M. R., Ziegler, D. A., et al. (2003). Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain, 126(Pt 5), 1182–1192.PubMedCrossRefGoogle Scholar
  12. Jack, C. R., Jr., Bernstein, M. A., et al. (2008). The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.PubMedCrossRefGoogle Scholar
  13. Jardri, R., Pouchet, A., et al. (2011). Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. The American Journal of Psychiatry, 168(1), 73–81.PubMedCrossRefGoogle Scholar
  14. Konrad, C., Ukas, T., et al. (2009). Defining the human hippocampus in cerebral magnetic resonance images–an overview of current segmentation protocols. NeuroImage, 47(4), 1185–1195.PubMedCrossRefGoogle Scholar
  15. Laird, A. R., Lancaster, J. L., et al. (2005). BrainMap: the social evolution of a human brain mapping database. Neuroinformatics, 3(1), 65–78.PubMedCrossRefGoogle Scholar
  16. Larson, S. D., & Martone, M. E. (2009). Ontologies for neuroscience: what are they and what are they good for? Frontiers in Neuroscience, 3(1), 60–67.PubMedCrossRefGoogle Scholar
  17. Lenroot, R. K., Gogtay, N., et al. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36(4), 1065–1073.PubMedCrossRefGoogle Scholar
  18. Leow, A. D., Yanovsky, I., et al. (2009). Alzheimer’s disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition. NeuroImage, 45(3), 645–655.PubMedCrossRefGoogle Scholar
  19. Lindberg, D. A., Humphreys, B. L., et al. (1993). The unified medical language system. Methods of Information in Medicine, 32(4), 281–291.PubMedGoogle Scholar
  20. Marcus, D. S., Wang, T. H., et al. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507.PubMedCrossRefGoogle Scholar
  21. Marenco, L., Ascoli, G. A., et al. (2008). The NIF LinkOut broker: a web resource to facilitate federated data integration using NCBI identifiers. Neuroinformatics, 6(3), 219–227.PubMedCrossRefGoogle Scholar
  22. 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.PubMedCrossRefGoogle Scholar
  23. Muller, H. M., Rangarajan, A., et al. (2008). Textpresso for neuroscience: searching the full text of thousands of neuroscience research papers. Neuroinformatics, 6(3), 195–204.PubMedCrossRefGoogle Scholar
  24. Rodionov, R., Chupin, M., et al. (2009). Evaluation of atlas-based segmentation of hippocampi in healthy humans. Magnetic Resonance Imaging, 27(8), 1104–1109.PubMedCrossRefGoogle Scholar
  25. Rosas, H. D., Hevelone, N. D., et al. (2005). Regional cortical thinning in preclinical Huntington disease and its relationship to cognition. Neurology, 65(5), 745–747.PubMedCrossRefGoogle Scholar
  26. Saitoh, O., Karns, C. M., et al. (2001). Development of the hippocampal formation from 2 to 42 years: MRI evidence of smaller area dentata in autism. Brain, 124(Pt 7), 1317–1324.PubMedCrossRefGoogle Scholar
  27. Salat, D. H., Buckner, R. L., et al. (2004). Thinning of the cerebral cortex in aging. Cerebral Cortex, 14(7), 721–730.PubMedCrossRefGoogle Scholar
  28. Schuff, N., Woerner, N., et al. (2009). MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain, 132(Pt 4), 1067–1077.PubMedGoogle Scholar
  29. Seidman, L. J., Faraone, S. V., et al. (2002). Left hippocampal volume as a vulnerability indicator for schizophrenia: a magnetic resonance imaging morphometric study of nonpsychotic first-degree relatives. Archives of General Psychiatry, 59(9), 839–849.PubMedCrossRefGoogle Scholar
  30. Seidman, L. J., Valera, E. M., et al. (2005). Structural brain imaging of attention-deficit/hyperactivity disorder. Biological Psychiatry, 57(11), 1263–1272.PubMedCrossRefGoogle Scholar
  31. Thompson, P. M., Giedd, J. N., et al. (2000). Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature, 404(6774), 190–193.PubMedCrossRefGoogle Scholar
  32. Van Essen, D. C. (2009). Lost in localization–but found with foci?! NeuroImage, 48(1), 14–17.PubMedCrossRefGoogle Scholar
  33. Waber, D. P., De Moor, C., et al. (2007). The NIH MRI study of normal brain development: performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the International Neuropsychological Society, 13(5), 729–746.PubMedCrossRefGoogle Scholar
  34. Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • David N. Kennedy
    • 1
    Email author
  • Steven M. Hodge
    • 1
  • Yong Gao
    • 2
  • Jean A. Frazier
    • 1
  • Christian Haselgrove
    • 1
  1. 1.Division of Neuroinformatics and the Child and Adolescent NeuroDevelopment Initiative (CANDI), Department of PsychiatryUniversity of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Department of NeurologyMassachusetts General HospitalBostonUSA

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