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Computational neuroanatomy: ontology-based representation of neural components and connectivity
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Institution: |
1Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. 2Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA, USA. 3Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. |
Publisher: |
BMC Bioinformatics |
Publication Date: |
Feb-2009 |
Citation: |
BMC Bioinformatics 2009; 10(Suppl 2):S3. |
PubMed ID: |
19208191 |
PMCID: |
PMC2646240 |
Appears in Collections: |
SPL, NAC, NCIGT |
Sponsors: |
NIH U54 HG004028 NIH P41 RR13218 NIH U41 RR019703 |
Generated Citation: |
Rubin D, Talos I, Halle M, Musen M, Kikinis R. Computational neuroanatomy: ontology-based representation of neural components and connectivity. BMC Bioinformatics 2009; 10(Suppl 2):S3. PMID: 19208191. PMCID: PMC2646240. |
| Downloaded: | 309 times. [view map] |
| Paper: | Download, View online |
| Export citation: |
A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. Results: We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Conclusion: Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.
Additional Material
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