NeuroNames Brain Hierarchy

Bowden, Douglas M.; Martin, R. F.

doi:10.1006/nimg.1995.1009

Cited by 96 publications

(85 citation statements)

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“…org; examples are available in Appendices 1-5 and Metadata 1-5, which are published as supporting information on the PNAS web site.) In addition, the anatomical hierarchy of the Neuro Names taxonomy (16) has been included as a user-friendly query tool within the database.…”

Section: Methodsmentioning

confidence: 99%

Adult mouse brain gene expression patterns bear an embryologic imprint

Zapala

Hovatta²,

Ellison³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

176

162

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The current model to explain the organization of the mammalian nervous system is based on studies of anatomy, embryology, and evolution. To further investigate the molecular organization of the adult mammalian brain, we have built a gene expression-based brain map. We measured gene expression patterns for 24 neural tissues covering the mouse central nervous system and found, surprisingly, that the adult brain bears a transcriptional ''imprint'' consistent with both embryological origins and classic evolutionary relationships. Embryonic cellular position along the anteriorposterior axis of the neural tube was shown to be closely associated with, and possibly a determinant of, the gene expression patterns in adult structures. We also observed a significant number of embryonic patterning and homeobox genes with region-specific expression in the adult nervous system. The relationships between global expression patterns for different anatomical regions and the nature of the observed region-specific genes suggest that the adult brain retains a degree of overall gene expression established during embryogenesis that is important for regional specificity and the functional relationships between regions in the adult. The complete collection of extensively annotated gene expression data along with data mining and visualization tools have been made available on a publicly accessible web site (www.barlow-lockhartbrainmapnimhgrant.org).database ͉ development ͉ evolution ͉ gene expression profiling ͉ inbred strains of mice T he adult nervous system achieves its mature form as the result of neuroectodermal cells committing to a specific fate and then segregating into distinct regional collectives of neurons that become fully functional through establishment of connections to other neurons. Our current understanding of brain architecture and organization is based on studies of embryology, anatomy, and evolution in which direct observation of anatomic structures was the foundation for postulated models of brain structure (1). Recent models of brain development and maturation consider relationships between different regions based on the expression of specific genes in assigning developmental origins of adult structures (2, 3). Here, we have constructed a regional gene expression atlas of the adult mouse brain and analyzed the quantitative results by using molecular classification algorithms.Genome-wide gene expression profiling is a powerful technique for deriving information about specific brain regions (4, 5). This approach has been used to measure gene expression patterns in particular regions, subregions, or cell populations in the brain (6-11). Two previous studies have analyzed gene expression differences across multiple regions of the mammalian brain by using multiple strains or species (12,13). However, the current study is the most extensive to date in terms of the number of genes and the coverage of different neural tissues. Our goal was to create a publicly accessible gene-based brain map with data sets, metadata, datab...

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Section: Methodsmentioning

confidence: 99%

Adult mouse brain gene expression patterns bear an embryologic imprint

Zapala

Hovatta²,

Ellison³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

176

162

View full text Add to dashboard Cite

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“…For each publication indexed, the reported groups and individuals are characterized with respect to the demographic, clinical and anatomic measurements reported. To facilitate interdatabase federation, many elements of the database are related to 'concept' entries in the Unified Medical Language System (UMLS) [Hole & Srinivasan, 2003] including the anatomic nomenclature (through NeuroNames [Bowden and Martin, 1995;Bowden and Dubach, 2003]] and diagnosis codes (DSM-4 and ICD-10).…”

Section: Methodsmentioning

confidence: 99%

MRI‐based morphometric analysis of typical and atypical brain development

Kennedy

Haselgrove

McInerney

2003

Ment. Retard. Dev. Disabil. Res. Rev.

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The neuroinformatics landscape in which human brain morphometry occurs has advanced dramatically over the past few years. Rapid advancement in image acquisition methods, image analysis tools and interpretation of morphometric results make the study of in vivo anatomic analysis both challenging and rewarding. This has revolutionized our expectations for current and future diagnostic and investigative work with the developing brain. This paper will briefly cover the methods of morphometric analysis that available for neuroanatomic analysis, and tour some sample results from a prototype retrospective database of neuroanatomic volumetric information. From these observations, issues regarding the anatomic variability of developmental maturation of neuroanatomic structures in both typically and atypically developing populations can be discussed.

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“…On the theoretical level, one would need to establish a set of data types and characterize how these types relate to one other. Even then, it is unclear whether in practice such a general scheme would be adopted by researchers; e.g., different research centers would need to agree on a common nomenclature for naming cortical regions, possibly within a larger context of a hierarchy of brain structures (e.g., NeuroNames, Bowden & Martin, 1995). In absence of such agreements, any such implementation would need to incorporate flexibility, such as accommodating multiple anatomical labelings for the same data (cf.…”

Section: Standards Conventionsmentioning

confidence: 99%

Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing

et al. 2008

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The increasingly complex research questions addressed by neuroimaging research impose substantial demands on computational infrastructures. These infrastructures need to support management of massive amounts of data in a way that affords rapid and precise data analysis, to allow collaborative research, and to achieve these aims securely and with minimum management overhead. Here we present an approach that overcomes many current limitations in data analysis and data sharing. This approach is based on open source database management systems that support complex data queries as an integral part of data analysis, flexible data sharing, and parallel and distributed data processing using cluster computing and Grid computing resources. We assess the strengths of these approaches as compared to current frameworks based on storage of binary or text files. We then describe in detail the implementation of such a system and provide a concrete description of how it was used to enable a complex analysis of fMRI time series data.

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NeuroNames Brain Hierarchy

Cited by 96 publications

References 0 publications

Adult mouse brain gene expression patterns bear an embryologic imprint

Adult mouse brain gene expression patterns bear an embryologic imprint

MRI‐based morphometric analysis of typical and atypical brain development

Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing

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