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在实验变量和值本体(OoEVV)中对功能磁共振成像(fMRI)实验变量进行建模。

Modeling functional Magnetic Resonance Imaging (fMRI) experimental variables in the Ontology of Experimental Variables and Values (OoEVV).

机构信息

Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292-6695, USA.

出版信息

Neuroimage. 2013 Nov 15;82:662-70. doi: 10.1016/j.neuroimage.2013.05.024. Epub 2013 May 16.

Abstract

Neuroimaging data is raw material for cognitive neuroscience experiments, leading to scientific knowledge about human neurological and psychological disease, language, perception, attention and ultimately, cognition. The structure of the variables used in the experimental design defines the structure of the data gathered in the experiments; this in turn structures the interpretative assertions that may be presented as experimental conclusions. Representing these assertions and the experimental data which support them in a computable way means that they could be used in logical reasoning environments, i.e. for automated meta-analyses, or linking hypotheses and results across different levels of neuroscientific experiments. Therefore, a crucial first step in being able to represent neuroimaging results in a clear, computable way is to develop representations for the scientific variables involved in neuroimaging experiments. These representations should be expressive, computable, valid, extensible, and easy-to-use. They should also leverage existing semantic standards to interoperate easily with other systems. We present an ontology design pattern called the Ontology of Experimental Variables and Values (OoEVV). This is designed to provide a lightweight framework to capture mathematical properties of data, with appropriate 'hooks' to permit linkage to other ontology-driven projects (such as the Ontology of Biomedical Investigations, OBI). We instantiate the OoEVV system with a small number of functional Magnetic Resonance Imaging datasets, to demonstrate the system's ability to describe the variables of a neuroimaging experiment. OoEVV is designed to be compatible with the XCEDE neuroimaging data standard for data collection terminology, and with the Cognitive Paradigm Ontology (CogPO) for specific reasoning elements of neuroimaging experimental designs.

摘要

神经影像学数据是认知神经科学实验的原始材料,为人类神经和心理疾病、语言、感知、注意力,最终为认知科学的研究提供了科学知识。实验设计中使用的变量结构定义了实验中收集的数据结构;这反过来又构建了可以作为实验结论提出的解释性断言。以可计算的方式表示这些断言和支持它们的实验数据意味着它们可以在逻辑推理环境中使用,即用于自动化的元分析,或者在不同层次的神经科学实验中链接假设和结果。因此,能够以清晰、可计算的方式表示神经影像学结果的关键第一步是开发用于神经影像学实验的科学变量的表示形式。这些表示形式应该具有表现力、可计算性、有效性、可扩展性和易用性。它们还应该利用现有的语义标准,以便与其他系统轻松交互。我们提出了一种称为实验变量和值本体 (OoEVV) 的本体设计模式。这旨在提供一个轻量级框架来捕获数据的数学属性,并具有适当的“钩子”,以允许与其他本体驱动的项目(如生物医学研究本体,OBI)链接。我们使用少量功能磁共振成像数据集实例化了 OoEVV 系统,以展示该系统描述神经影像学实验变量的能力。OoEVV 旨在与 XCEDE 神经影像学数据标准的收集术语兼容,并且与神经影像学实验设计的特定推理元素的认知范式本体 (CogPO) 兼容。

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本文引用的文献

2
The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data.
BMC Res Notes. 2011 Sep 9;4:349. doi: 10.1186/1756-0500-4-349.
4
Large-scale automated synthesis of human functional neuroimaging data.
Nat Methods. 2011 Jun 26;8(8):665-70. doi: 10.1038/nmeth.1635.
5
The cognitive paradigm ontology: design and application.
Neuroinformatics. 2012 Jan;10(1):57-66. doi: 10.1007/s12021-011-9126-x.
6
Enabling collaborative research using the Biomedical Informatics Research Network (BIRN).
J Am Med Inform Assoc. 2011 Jul-Aug;18(4):416-22. doi: 10.1136/amiajnl-2010-000032. Epub 2011 Apr 22.
7
XCEDE: an extensible schema for biomedical data.
Neuroinformatics. 2012 Jan;10(1):19-32. doi: 10.1007/s12021-011-9119-9.
8
ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level.
Bioinformatics. 2010 Sep 15;26(18):2354-6. doi: 10.1093/bioinformatics/btq415. Epub 2010 Aug 2.
9
Modeling biomedical experimental processes with OBI.
J Biomed Semantics. 2010 Jun 22;1 Suppl 1(Suppl 1):S7. doi: 10.1186/2041-1480-1-S1-S7.
10
Modeling sample variables with an Experimental Factor Ontology.
Bioinformatics. 2010 Apr 15;26(8):1112-8. doi: 10.1093/bioinformatics/btq099. Epub 2010 Mar 3.

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