1. ¹UIIBP, Service Hospitalier Frederic Joliot, CEA, Orsay, France
2. ²Computational Radiology Laboratory, Harvard Medical School, Boston, Massachusetts, USA
3. ³Service Hospitalier Frederic Joliot, CEA CNRS URA 2210, Service Hospitalier Frederic Joliot, Orsay, France
4. ⁴MIRCen Program, Fontenay-Aux-Roses, France

Correspondence: Dr T Delzescaux, Service Hospitalier Frédéric Joliot, 4, place du Général Leclerc, 91401 Orsay Cedex, France. E-mail: thierry.delzescaux@cea.fr

Received 21 September 2006; Revised 12 January 2007; Accepted 12 January 2007; Published online 21 March 2007.

Abstract

Besides the newly developed positron emission tomography scanners (microPET) dedicated to the in vivo functional study of small animals, autoradiography remains the reference technique widely used for functional brain imaging and the gold standard for the validation of in vivo results. The analysis of autoradiographic data is classically achieved in two dimensions (2D) using a section-by-section approach, is often limited to few sections and the delineation of the regions of interest to be analysed is directly performed on autoradiographic sections. In addition, such approach of analysis does not accommodate the possible anatomical shifts linked to dissymmetry associated with the sectioning process. This classic analysis is time-consuming, operator-dependent and can therefore lead to non-objective and non-reproducible results. In this paper, we have developed an automated and generic toolbox for processing of autoradiographic and corresponding histological rat brain sections based on a three-step approach, which involves: (1) an optimized digitization dealing with hundreds of autoradiographic and histological sections; (2) a robust reconstruction of the volumes based on a reliable registration method; and (3) an original 3D-geometry-based approach to analysis of anatomical and functional post-mortem data. The integration of the toolbox under a unified environment (in-house software BrainVISA, http://brainvisa.info) with a graphic interface enabled a robust and operator-independent exploitation of the overall anatomical and functional information. We illustrated the substantial qualitative and quantitative benefits obtained by applying our methodology to an activation study (rats, n=5, under unilateral visual stimulation).