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OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform

Lydie EDWARD1, 2*, Grégory Operto2, 3, Sylvain Poret1, 2, Yann Cointepas2, 3, Nader Cheaib2, 3, Leila Makkaoui2, 3 and Bénédicte M. Batrancourt1, 2
  • 1 CRICM UPMC/INSERM UMR_S975, France
  • 2 CATI, France
  • 3 CEA, UNATI/NeuroSpin/I2BM, France

CATI (Acquisition Centre and Image Processing, a French national platform supported by the Alzheimer’s Foundation [1]) provides software solutions for automatic measurement of different biomarkers related to specific modalities (Figure 1). CATI provides assistance for acquiring, analyzing, organizing and sharing neuroimaging data in the context of multi-centric clinical research studies. Our goal is to support the scientific study of the different working groups (neurologist, MRI PET physician, quality check operations engineer, engineer developing brain imaging tools) and help them to model the knowledge linked to their domain of interest.

We proposed a structured metadata hierarchy for storing information relevant to various aspects of a project (e.g. study, subject, dataset, etc.) along with derived data and process in order to develop improved semantic request and reasoning tools for knowledge extraction on the derived data (for example we would like to compare the hippocampal volumes obtained on different time points). We developed a database schema CATISchema and an ontology OntoCATI [2]. At this stage, the first module of OntoCATI supports the information concerning acquisition, raw data (clinical and neuroimaging), and image processing. This module uses DOLCE [3] as its upper ontology. The image processing generates a multitude of data and derived measures. These measures are potential biomarkers for the Alzheimer’s disease (AD).

In order to build the second module of OntoCATI dedicated to these measurements we first focus on the data issued from the modality T1 MRI and on three types of measures: rate of hippocampal atrophy with the longitudinal version of SACHA [4], cortical thickness reduction with Freesurfer (http://ftp.nmr.mgh.harvard.edu/) and opening of primary cortical folds using the longitudinal version of Morphologist-2012 pipeline [5]. An important point in our modeling consists in distinguishing the concept of measure and biomarker. For example, hippocampal volume is considered as the most widely used and admitted biomarkers in the AD to monitor its progression [6]. Therefore in OntoCATI this measure belongs to the sub-class of biomarker. Concerning the other measures the ontology will allow us to determine what measures become biomarkers using queries.

To model the different measurements that are the assignment of numbers to anatomical entities, we need to describe the brain anatomy (e.g., hippocampus), the measure (magnitude, dimensions (units) and uncertainty) and the disease concepts and their codification. This module of OntoCATI uses the Basic Formal Ontology (BFO) as its upper level ontology [7]. For the anatomy domain we refer to the Foundational Model of Anatomy ontology (FMA) [8], which adopts and extends into BFO [9], a domain-independent, spatio-temporal theory that provides a rigorous ontological framework. Concerning the pathology domain we rely on ICD-10 [10] and UMLS standard [11]. For example to describe the hippocampus, according to FMA, we define hippocampus as a ‘gyrus of limbic lobe' that is a ‘segment of cerebral hemisphere’ that is a subClass of ‘Anatomical Entity’ that is a subclass of the BFO concept 'independent continuant'. To represent the volume of the hippocampus we create the relation has for volume that is linking the concept of ‘hippocampus’ to the BFO concept ‘three dimensional region’ (Figure 2).

This is the first step in our work. In the future, we will integrate other concepts to represent the other measures (cortical thickness, opening of primary cortical folds, etc.). We will also work on the other modalities presented in Figure 1 and we will develop the queries to cross the frontier between measures and biomarkers.

Figure 1
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Figure 2
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Acknowledgements

This work is funded by the French Foundation Plan Alzheimer and was realized in the CATI study group. The authors gratefully acknowledge all contributors.

References

1. http://www.fondation-alzheimer.org/
2. Edward L, Poret S, Batrancourt B. An Ontology for Knowledge Management on Shared Imaging Databases and Tools in the CATI Neuroscience Platform. International conference on Knowledge Management, Information and Knowledge Systems (KMIKS) 2013.
3. Masolo C, Borgo S, Gangemi A, Guarino N, Oltramari A, Schneider L. The WonderWeb Library of Foundational Ontologies and the DOLCE ontology. WonderWeb Deliverable D18, Final Report (vr. 1.0, 31-12-2003).
4. Chupin M, Mukuna-Bantumbakulu AR, Hasboun D, Bardinet E, Baillet S, Kinkingnéhun S, Lemieux L, Dubois B, Garnero L. Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala: Method and validation on controls and patients with Alzheimer's disease. Neuroimage. 2007 Feb 1;34(3):996-1019.
5. Fischer C, Operto G, Laguitton S, Perrot M, Denghien I, Rivière D, Mangin J-F. Morphologist 2012: the new morphological pipeline of BrainVISA, Human Brain Mapping Conference, 2012
6. Sarazin M, De Souza L C, Lehericy S, Dubois B. Clinical and research diagnostic criteria for Alzheimer's disease. Neuroimaging Clin N Am., Feb 2012, vol 22 no 1, p. 23-32
7. http://www.ifomis.org/bfo
8. Rosse C, Mejino J L V. The Foundational Model of Anatomy Ontology. In A. Burger, D. Davidson, and R. Baldock, editors, Anatomy Ontologies for Bioinformatics: Principles and Practice, vol. 6, p. 59-117, London, 2007. Springer
9. G Grenon P, Smith B, and Goldberg L. Biodynamic ontology: applying BFO in the biomedical domain. In P.M. Pisannelli, editor, Ontologies in Medicine. Studies in Health technology and Informatics, volume 102, pages 20–38, Amsterdam, 2004. IOS Press.
10. http://apps.who.int/classifications/icd10/browse/2010/en
11. http://www.nlm.nih.gov/research/umls/

Keywords: ontology of biomarker, biomarker, Alzheimer Disease, Hippocampus, neuroscience platform

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: Neuroimaging

Citation: EDWARD L, Operto G, Poret S, Cointepas Y, Cheaib N, Makkaoui L and Batrancourt BM (2013). OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00103

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Received: 29 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence: Dr. Lydie EDWARD, CRICM UPMC/INSERM UMR_S975, Paris, 75013, France, lydie.edward@upmc.fr