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A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

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Mazziotta, John, Toga, Arthur, Evans, Alan, Fox, Peter, Lancaster, Jack, Zilles, Karl, Woods, Roger, Paus, Tomas, Simpson, Gregory, Pike, Bruce, Holmes, Colin, Collins, Louis, Thompson, Paul, MacDonald, David, Iacoboni, Marco, Schormann, Thorsten, Amunts, Katrin, Palomero-Gallagher, Nicola, Geyer, Stefan, Parsons, Larry, Narr, Katherine, Kabani, Noor, Goualher, Georges Le, Boomsma, Dorret, Cannon, Tyrone, Kawashima, Ryuta, Mazoyer, Bernard·2001·Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences
DOI 10.1098/rstb.2001.0915
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Abstract

Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four–dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono– and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post–mortem tissue to determine the probabilistic distribution of microscopic cyto– and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the first large scale opportunity to gain meaningful insights into the concordance or discordance in micro– and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype–phenotype–behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.

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Metadata

Authors
Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., Parsons, L., Narr, K., Kabani, N., Goualher, G. L., Boomsma, D., Cannon, T., Kawashima, R., & Mazoyer, B.
Venue
Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences
Publisher
The Royal Society
Published
2001-08-29
Volume
356
Issue
1412
Pages
1293-1322
ISSN
  • 0962-8436 (ISSN-L)
  • 1471-2970
DOI
10.1098/rstb.2001.0915
OpenAlex
W2143895814
Cited by
2,347
References
7
FWCI
8.02
Type
journal article
Language
en
Open access
green
Licence
royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.