BrainMap Taxonomy

BrainMap uses a structured standardized coding scheme which describes published human neuroimaging experimental results. Originally conceived in 1988, it has evolved with changes in the field during over 25 years of development.

This taxonomy has been used to describe over 3600 publications and 15000 experiments, drawing upon over 110,000 subjects and reporting over 120,000 coordinates as results. This has been estimated to be 20-30% of the compliant literature in the field. The quality of the coding of each of these papers has been verified by a BrainMap taxonomy expert.

While other neuroimaging coordinate-data archives exist, only BrainMap combines this volume of data with extensive meta-data coding and rigorous, ongoing quality control. More than 500 peer-reviewed publications have cited using BrainMap's data and software.

This site is intended to serve as a reference for those seeking to fully understand the taxonomy. It should be helpful for creating meta-analytic workspaces, searching our databases, correctly coding papers for submission to a database, or performing further analysis such as behavioral profiles.

Types of Papers

Eligibility Criteria

BrainMap accepts published human neuroimaging articles that report whole-brain coordinate-based results in a standard space. Publications containing sections that don't meet these criteria can be included by coding only the experiments and conditions which qualify.

Structural & Functional

The main division of the coding scheme is between Structural and Functional. The two types are each kept in their own database.

Due to the differences between anatomical and functional imaging, not all meta-data fields will apply to both types of papers. When there are meta-data fields that are only used in one or the other, they will be labeled with either (Structural) or (Functional).

Sparse versus Full

Within the functional papers, there is a second division - between Sparse and Full coding.

Sparse coding is a good option for meta-analyses that will not make use of deep meta-data fields. Full coding is recommended for meta-analyses that use fields that are not included in sparse coding, like behavioral domains or parts of conditions.

Paper-Level

The meta-data encoded at the paper-level is broad information that applies across the entire study, or at least across experiments.

Experiment-Level

An experiment is defined as the comparison of brain images that creates a statistical parametric image (SPI). This three-dimensional map of statistical data is meant to relate specific brain regions to the experimental design of the publication.

Experiment-level meta-data describes key aspects of the experiment, such as which subjects, sessions or conditions apply, as well as the following:

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