A combination of atlas-based and voxel-wise approaches to analyze metabolic changes in autoradiographic data from Alzheimer's mice.

Murine models are commonly used in neuroscience research to improve our knowledge of disease processes and to test drug effects. To accurately study brain glucose metabolism in these animals, ex vivo autoradiography remains the gold standard. The analysis of 3D-reconstructed autoradiographic volumes using a voxel-wise approach allows clusters of voxels representing metabolic differences between groups to be revealed. However, the spatial localization of these clusters requires careful visual identification by a neuroanatomist, a time-consuming task that is often subject to misinterpretation. Moreover, the large number of voxels to be computed in autoradiographic rodent images leads to many false positives. Here, we proposed an original automated indexation of the results of a voxel-wise approach using an MRI-based 3D digital atlas, followed by the restriction of the statistical analysis using atlas-based segmentation, thus taking advantage of the specific and complementary strengths of these two approaches. In a preliminary study of transgenic Alzheimer's mice (APP/PS1), and control littermates (PS1), we were able to achieve prompt and direct anatomical indexation of metabolic changes detected between the two groups, revealing both hypo- and hypermetabolism in the brain of APP/PS1 mice. Furthermore, statistical results were refined using atlas-based segmentation: most interesting results were obtained for the hippocampus. We thus confirmed and extended our previous results by identifying the brain structures affected in this pathological model and demonstrating modified glucose uptake in structures like the olfactory bulb. Our combined approach thus paves the way for a complete and accurate examination of functional data from cerebral structures involved in models of neurodegenerative diseases.

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

Validation of MRI-based 3D digital atlas registration with histological and autoradiographic volumes: an anatomofunctional transgenic mouse brain imaging study.

Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to test drug effects. To accurately study neuroanatomy and brain function in small animals, histological staining and ex vivo autoradiography remain the gold standards to date. These analyses are classically performed by manually tracing regions of interest, which is time-consuming. For this reason, only a few 2D tissue sections are usually processed, resulting in a loss of information. We therefore proposed to match a 3D digital atlas with previously 3D-reconstructed post mortem data to automatically evaluate morphology and function in mouse brain structures. We used a freely available MRI-based 3D digital atlas derived from C57Bl/6J mouse brain scans (9.4T). The histological and autoradiographic volumes used were obtained from a preliminary study in APP(SL)/PS1(M146L) transgenic mice, models of Alzheimer's disease, and their control littermates (PS1(M146L)). We first deformed the original 3D MR images to match our experimental volumes. We then applied deformation parameters to warp the 3D digital atlas to match the data to be studied. The reliability of our method was qualitatively and quantitatively assessed by comparing atlas-based and manual segmentations in 3D. Our approach yields faster and more robust results than standard methods in the investigation of post mortem mouse data sets at the level of brain structures. It also constitutes an original method for the validation of an MRI-based atlas using histology and autoradiography as anatomical and functional references, respectively.

siRNA targeted against amyloid precursor protein impairs synaptic activity in vivo.

The amyloid precursor protein (APP) plays a central role in Alzheimer's disease (AD) pathogenesis through its cleavage leading to the accumulation of the peptide betaA4. Diffusible oligomeric assemblies of amyloid beta peptide are thought to induce synaptic dysfunction, an early change in AD. We tested the hypothesis that a reduction in presynaptic APP could itself lead to a decrease in synaptic efficacy in vivo. Twenty-four hours after intraocular injection, siRNA targeted against APP accumulated in retinal cells and the APP in retinal terminals in the superior colliculus was significantly reduced. Surprisingly, the amyloid precursor-like protein 2 (APLP2) was reduced as well. Functional imaging experiments in rats during visual stimulation showed that knockdown of presynaptic APP/APLP2 significantly reduced the stimulation-induced glucose utilization in the superior colliculus. Our results suggest that perturbations in the amount of APP/APLP2 axonally transported to, and/or in their turnover in the nerve terminal alter synaptic function and could be a pathogenic mechanism in AD.