Rubin Daniel L, Talos Ion-Florin, Halle Michael, Musen Mark A, Kikinis Ron
Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
BMC Bioinformatics. 2009 Feb 5;10 Suppl 2(Suppl 2):S3. doi: 10.1186/1471-2105-10-S2-S3.
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.
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.
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.
神经科学面临的一项关键挑战是如何组织、管理和获取神经科学知识的爆炸式增长，尤其是解剖学知识。我们认为，采用基于明确知识的方法使神经科学知识能够通过计算进行访问，将有助于应对这一挑战，并能实现各种利用这些知识的应用，如手术规划。
我们开发了基于本体的神经解剖学模型，以实现神经系统的符号查找、逻辑推理和数学建模。我们构建了一个运动系统的原型模型，该模型整合了描述性解剖学和定性功能性神经解剖学知识。除了对正常神经解剖学进行建模外，我们的方法还提供了疾病状态下异常神经连接的明确表示，如常见的运动障碍。基于本体的表示编码了神经解剖学的结构和功能方面。基于本体的模型可以通过计算进行评估，从而实现自动化计算机推理应用的开发。
神经解剖学知识可以使用本体以机器可访问的格式进行表示。本文所述的计算神经解剖学方法可能成为转化信息学的关键工具，从而在未来产生为神经疾病的手术规划和个性化护理提供信息和指导的决策支持应用。
