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Neuroinformatics

, Volume 12, Issue 1, pp 39–62 | Cite as

An Informatics Approach to Integrating Genetic and Neurological Data in Speech and Language Neuroscience

  • Jason W. Bohland
  • Emma M. Myers
  • Esther Kim
Original Article

Abstract

A number of heritable disorders impair the normal development of speech and language processes and occur in large numbers within the general population. While candidate genes and loci have been identified, the gap between genotype and phenotype is vast, limiting current understanding of the biology of normal and disordered processes. This gap exists not only in our scientific knowledge, but also in our research communities, where genetics researchers and speech, language, and cognitive scientists tend to operate independently. Here we describe a web-based, domain-specific, curated database that represents information about genotype-phenotype relations specific to speech and language disorders, as well as neuroimaging results demonstrating focal brain differences in relevant patients versus controls. Bringing these two distinct data types into a common database (http://neurospeech.org/sldb) is a first step toward bringing molecular level information into cognitive and computational theories of speech and language function. One bridge between these data types is provided by densely sampled profiles of gene expression in the brain, such as those provided by the Allen Brain Atlases. Here we present results from exploratory analyses of human brain gene expression profiles for genes implicated in speech and language disorders, which are annotated in our database. We then discuss how such datasets can be useful in the development of computational models that bridge levels of analysis, necessary to provide a mechanistic understanding of heritable language disorders. We further describe our general approach to information integration, discuss important caveats and considerations, and offer a specific but speculative example based on genes implicated in stuttering and basal ganglia function in speech motor control.

Keywords

Speech Language Neuroinformatics Bioinformatics Imaging Neurogenetics 

Notes

Acknowledgments

Support for EM and EK was provided through the National Science Foundation (NSF) Center of Excellence for Learning in Education, Science, and Technology (CELEST; NSF SMA 0835976; PI Barbara Shinn-Cunningham). The authors are grateful to Drs. Michael Hawrylycz and Changkyu Lee from the Allen Institute for Brain Science for their support with gene expression datasets and to Prof. Michael Arbib for organizing the Workshop on Action, Language, and Neuroinformatics in July 2011, during which early versions of this work were presented and discussed. We also thank the two anonymous reviewers, whose critical feedback and recommendations greatly improved this manuscript and database.

Supplementary material

12021_2013_9201_MOESM1_ESM.pdf (244 kb)
Supplementary Figure 1 Expanded view of the information displayed to the web application user for a single GenePhenotype record for the gene GNPTG. (PDF 243 kb)

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Departments of Health Sciences and Speech, Language, and Hearing SciencesBoston UniversityBostonUSA
  2. 2.Graduate Program for NeuroscienceBoston UniversityBostonUSA
  3. 3.Biological SciencesUniversity of MarylandBaltimoreUSA

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