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Neuroimage databases: The good, the bad and the ugly

Nature Reviews Neuroscience volume 3, pages 302–309 (2002)Cite this article

Key Points

  • A spectacular explosion in the amount of available scientific data has accompanied the profound advances that we have made on our quest to understand brain function. To make better sense of all of this information, we need to develop appropriate information-management tools, such as databases. Data from neuroimaging studies are particularly suitable for databasing, and the imaging community has begun to make efforts towards the development of imaging databases.

  • The development of databases is a complex problem that has many different dimensions, from the technological to the sociological. There has been significant progress in some of these dimensions, particularly with regard to the technology that is required to create a useful database. So, there are databases of different classes; each is aimed at a specific level of analysis and serves a particular purpose. The development of appropriate tools and software, which continues to progress steadily, has accompanied the development of these databases.

  • But there are other issues that have not seen so much progress. One of these relates to the class of data that needs to be fed into the database. As it is possible to share information at different levels of processing, from raw to highly analysed data, it has been difficult to reach an agreement on the right level of sharing, and there is current debate on the pros and cons of making data publicly available. We also lack a data taxonomy that allows us to codify data in a standard format, and there are nomenclature problems that add a further level of complexity to the development of databases.

  • In addition to these problems, there are other issues that need to be addressed if databasing is to be successful. These include problems of curation and quality control (who is going to make sure that the database is maintained and that the data are of good quality?), and legal and ethical issues (how will the rights of the data producer be protected?). Until these issues have been solved, data sharing and the creation of databases will continue to be a challenging goal.

Abstract

The potential of neuroimage databases to accelerate the dissemination and use of information about brain structure and function is enormous and ever increasing. Numerous efforts are now underway to further develop the technology and sociology that are necessary to support this revolution. Each effort has its own approach and tackles some of the complex problems that are associated with creating and providing access to a database. This paper introduces many of the recent successes and future challenges that are faced by the developers and users of neuroimage databases.

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Figure 1: Spatial normalization is essential for comparing neuroimage data from different individuals.
Figure 2: Matrix of data access.
Figure 3: Spatial resolution of data can present considerable challenges for databases.
Figure 4: Reconstruction and potential recognition of facial features in MRI.

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Acknowledgements

This work was generously supported by research grants from the National Library of Medicine, the National Center for Research Resources, and by a Human Brain Project grant known as the International Consortium for Brain Mapping, which is funded jointly by the National Institute of Mental Health and the National Institute on Drug Abuse. The author also wishes to acknowledge his deep appreciation to the members of the Laboratory of Neuro Imaging and, especially, J. Bacheller and A. Lee for their graphical prowess.

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Authors and Affiliations

  1. Department of Neurology, Laboratory of Neuro Imaging, Room 4238, Reed Neurological Research Center, UCLA School of Medicine, 710 Westwood Plaza, Los Angeles, 90095-1769, California, USA

    Arthur W. Toga

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FURTHER INFORMATION

BrainMap

Directive 96/9/EC on the legal protection of databases

Encyclopedia of Life Sciences

bioinformatics

biological data centres

brain imaging: localization of brain functions

brain imaging: observing ongoing neural activity

computed tomography

ethics of research: protection of human subjects

magnetic resonance imaging

mining biological databases

FisWidgets

Image Analysis Tools Registry

International Consortium for Brain Mapping

Laboratory of Neuro Imaging (LONI)

MIT Encyclopedia of Cognitive Sciences

magnetic resonance imaging

positron emission tomography

NLM's Unified Medical Language System

Statement on H.R. 354 — the Collections of Information Antipiracy Act

The Genome Database

World Intellectual Property Organization

Glossary

BOOLEAN LOGIC

Named after the nineteenth-century mathematician George Boole, Boolean logic is a form of algebra in which all values are reduced to either true or false. Boolean logic is especially important for computer science because it fits nicely with its binary numbering system. Boolean logic depends on the use of three logical operators, AND, OR and NOT.

FUZZY LOGIC

A type of logic that recognizes more than true and false values. Propositions can be represented with degrees of truth and untruth. This characteristic of fuzzy logic has made it particularly useful in the field of artificial intelligence.

TALAIRACH SYSTEM

In 1988, Talairach and Tournoux published a stereotaxic atlas of the human brain that introduced three important innovations: a coordinate system to identify a particular brain location relative to anatomical landmarks; a spatial transformation to match one brain to another; and an atlas that describes a standard brain, with anatomical and cytoarchitectonic labels. The authors suggested that the brain be aligned according to the anterior and posterior commissures, two relatively invariant structures. The experimenter draws a line between the commissures and rotates the brain so that this line is on a horizontal plane. According to the Talairach system, a coordinate can now be defined relative to three orthogonal axes, with the anterior commissure as the origin.

PULSE SEQUENCE

A set of radiofrequency pulses that are applied to a sample to produce a specific form of nuclear magnetic resonance signal.

ONTOLOGY

The specification of unique relationships between words and the operationally defined concepts they represent. A neuroanatomical ontology defines the relations of neuroanatomical terms to structures in the brain.

MAGNETOENCEPHALOGRAPHY

A non-invasive technique that allows the detection of the changing magnetic fields that are associated with brain activity. As the magnetic fields of the brain are very weak, extremely sensitive magnetic detectors known as superconducting quantum interference devices, which work at very low, superconducting temperatures (−269 °C), are used to pick up the signal.

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Toga, A. Neuroimage databases: The good, the bad and the ugly. Nat Rev Neurosci 3, 302–309 (2002). https://doi.org/10.1038/nrn782

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