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International audience; Extracellular deposition of β amyloid plaques is an early event associated to Alzheimer's disease. Here, we have used in vivo gadolinium-stained high resolution (29 * 29 * 117 µm 3) magnetic resonance imaging (MRI) to follow-up in a longitudinal way individual amyloid plaques in APP/PS1 mice and evaluate the efficacy of a new immunotherapy (SAR255952) directed against protofibrillar and fibrillary forms of Aβ. APP/PS1 mice were treated for 5 months between the age of 3.5 and 8.5 months. SAR255952 reduced amyloid load in 8.5-months-old animals, but not in 5.5-months animals compared to mice treated with a control antibody (DM4). Histological evaluation confirmed the reduction of amyloid load and revealed a lower density of amyloid plaques in 8.5-months SAR255952-treated animals. The longitudinal follow-up of individual amyloid plaques by MRI revealed that plaques that were visible at 5.5 months were still visible at 8.5 months in both SAR255952 and DM4-treated mice. This suggests that the amyloid load reduction induced by SAR255952 is related to a slowing down in the formation of new plaques rather than to the clearance of already formed plaques.

Histology is the gold standard to unveil microscopic brain structures and pathological alterations in humans and animal models of disease. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Recently developed 3D microscopic imaging techniques have allowed in-depth study of neuroanatomy. However, quantitative methods are still lacking for whole-brain analysis of cellular and pathological markers. Here, we propose a ready-to-use, automated and scalable method to thoroughly quantify histopathological markers in 3D in rodent whole brains. It relies on block-face photography, serial histology and 3D-HAPi (Three Dimensional Histology Analysis Pipeline), an open source image analysis software. We illustrate our method in studies involving mouse models of Alzheimer’s disease and show that it can be broadly applied to characterize animal models of brain diseases, to evaluate therapeutic interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques.

Neuroendocrine diseases are a heterogeneous group of entities with the ability to take up amine precursors, such as L-DOPA, and convert them into biogenic amines, such as dopamine. Congenital hyperinsulinism of infancy (HI) is a neuroendocrine disease secondary to either focal adenomatous hyperplasia or a diffuse abnormal pancreatic insulin secretion. While focal hyperinsulinism may be reversed by selective surgical resection, diffuse forms require near-total pancreatectomy when resistant to medical treatment. Here, we report the diagnostic value of PET with [(18)F]fluoro-L-DOPA in distinguishing focal from diffuse HI.Forty-nine children were studied with [(18)F]fluoro-L-DOPA. A thoraco-abdominal scan was acquired 45-65 min after the injection of 4.2 +/- 1.0 MBq/kg of [(18)F]fluoro-L-DOPA. Additionally, 12 of the 49 children were submitted to pancreatic venous catheterisation for blood samples (PVS) and 31 were also investigated using MRI.We identified abnormal focal pancreatic uptake of [(18)F]fluoro-L-DOPA in 15 children, whereas diffuse radiotracer uptake was observed in the pancreatic area in the other 34 patients. In children studied with both PET and PVS, the results were concordant in 11/12 cases. All patients with focal radiotracer uptake and nine of the patients with diffuse pancreatic radiotracer accumulation, unresponsive to medical treatment, were submitted to surgery. In 21 of these 24 patients, the histopathological results confirmed the PET findings. In focal forms, selective surgery was followed by clinical remission without carbohydrate intolerance.These data demonstrate that PET with [(18)F]fluoro-L-DOPA is an accurate non-invasive technique allowing differential diagnosis between focal and diffuse forms of HI.

International audience; Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings.

International audience; The fine spatial resolution and novel contrasts offered by high-field magnetic resonance allow in vivo detection of histological layers in the cerebral cortex. This opens the way to in vivo analysis of cortical lamination, but the comparison of lamination profiles has proved challenging because the layers’ geometry is strongly influenced by cortical curvature. This paper introduces a model of the micro-structural organization of the cortex, which can compensate for the effect of cortical curvature. Layers are modelled by an equivolumic principle, while the vertical structure of the cortex is represented with a Laplacian model. In this framework, lamination profiles can be represented in a way that preserves the original voxel sampling of the acquisition. This model is validated on a magnetic resonance image of a post-mortem human brain acquired on a human 7 T scanner at 0.35 mm resolution.

International audience; Huntington's disease (HD) is caused by cytosine-adenine-guanine (CAG) repeat expansions in the huntingtin (Htt) gene. Although early energy metabolic alterations in HD are likely to contribute to later neurodegenerative processes, the cellular and molecular mechanisms responsible for these metabolic alterations are not well characterized. Using the BACHD mice that express the full-length mutant huntingtin (mHtt) protein with 97 glutamine repeats, we first demonstrated localized in vivo changes in brain glucose use reminiscent of what is observed in premanifest HD carriers. Using biochemical, molecular, and functional analyses on different primary cell culture models from BACHD mice, we observed that mHtt does not directly affect metabolic activity in a cell autonomous manner. However, coculture of neurons with astrocytes from wild-type or BACHD mice identified mutant astrocytes as a source of adverse non-cell autonomous effects on neuron energy metabolism possibly by increasing oxidative stress. These results suggest that astrocyte-to-neuron signaling is involved in early energy metabolic alterations in HD.

Huntington's disease (HD) is a neurodegenerative disorder caused by an abnormal expansion of a CAG repeat encoding a polyglutamine tract in the huntingtin (Htt) protein. The mutation leads to neuronal death through mechanisms which are still unknown. One hypothesis is that mitochondrial defects may play a key role. In support of this, the activity of mitochondrial complex II (C-II) is preferentially reduced in the striatum of HD patients. Here, we studied C-II expression in different genetic models of HD expressing N-terminal fragments of mutant Htt (mHtt). Western blot analysis showed that the expression of the 30 kDa Iron–Sulfur (Ip) subunit of C-II was significantly reduced in the striatum of the R6/1 transgenic mice, while the levels of the FAD containing catalytic 70 kDa subunit (Fp) were not significantly changed. Blue native gel analysis showed that the assembly of C-II in mitochondria was altered early in N171-82Q transgenic mice. Early loco-regional reduction in C-II activity and Ip protein expression was also demonstrated in a rat model of HD using intrastriatal injection of lentiviral vectors encoding mHtt. Infection of the rat striatum with a lentiviral vector coding the C-II Ip or Fp subunits induced a significant overexpression of these proteins that led to significant neuroprotection of striatal neurons against mHtt neurotoxicity. These results obtained in vivo support the hypothesis that structural and functional alterations of C-II induced by mHtt may play a critical role in the degeneration of striatal neurons in HD and that mitochondrial-targeted therapies may be useful in its treatment.

Brainstem 48.39 2.9 0.76 0.1 Cerebral cortex 166.24 1.38 9.78 0.64 Corpus callosum 12.37 0.1 3.52 0.25 Fimbria 3.92 0.06 1.47 0.26 Hippocampal region 26.97 0.46 5.6 0.39 Hypothalamus 7.66 0.4 0.33 0.18 Internal capsule 3.04 0.05 0.12 0.02 Olfactory bulbs NA NA 9.03 0.71 Striatum 32.83 0.46 0.93 0.05 Thalamus 36.72 1.02 0.86 0.14 Ventricular system 4.88 0.07 1.35 0.24 Brain 346.73 4.13 5.58 0.91 Michel Vandenberghe, Anne-Sophie H erard, Nicolas Souedet, Hantraye Philippe, Marc Dhenain, Thierry Delzescaux, Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Fontenay-aux-Roses, France; CNRS, Fontenay-aux-Roses, France. Contact e-mail: michel.vandenberghe@cea.fr

International audience; Alzheimer's disease (AD), the most common age-related neurodegenerative disorder, is characterized by the invariant cerebral accumulation of ␤-amyloid peptide. This event occurs early in the disease process. In humans, [18F]-fluoro-2-deoxy-D-glucose ([18F]-FDG) positron emission tomography (PET) is largely used to follow-up in vivo cerebral glucose utilization (CGU) and brain metabolism modifications associated with the Alzheimer's disease pathology. Here, [18F]-FDG positron emission tomography was used to study age-related changes of cerebral glucose utilization under resting conditions in 3-, 6-, and 12-month-old APP SweLon /PS1 M146L , a mouse model of amyloidosis. We showed an age-dependent increase of glucose uptake in several brain regions of APP/PS1 mice but not in control animals and a higher [18F]-FDG uptake in the cortex and the hippocampus of 12-month-old APP/PS1 mice as compared with age-matched control mice. We then developed a method of 3-D microscopic autoradiography to evaluate glucose uptake at the level of amyloid plaques and showed an increased glucose uptake close to the plaques rather than in amyloid-free cerebral tissues. These data suggest a macroscopic and microscopic reorganization of glucose uptake in relation to cerebral amyloidosis.

Astrocytes and microglia become reactive under most brain pathological conditions, making this neuroinflammation process a surrogate marker of neuronal dysfunction. Neuroinflammation is associated with increased levels of translocator protein 18 kDa (TSPO) and binding sites for TSPO ligands. Positron emission tomography (PET) imaging of TSPO is thus commonly used to monitor neuroinflammation in preclinical and clinical studies. It is widely considered that TSPO PET signal reveals reactive microglia, although a few studies suggested a potential contribution of reactive astrocytes. Because astrocytes and microglia play very different roles, it is crucial to determine whether reactive astrocytes can also overexpress TSPO and yield to a detectable TSPO PET signalin vivo. We used a model of selective astrocyte activation through lentiviral gene transfer of the cytokine ciliary neurotrophic factor (CNTF) into the rat striatum, in the absence of neurodegeneration. CNTF induced an extensive activation of astrocytes, which overexpressed GFAP and become hypertrophic, whereas microglia displayed minimal increase in reactive markers. Two TSPO radioligands, [18F]DPA-714 [N,N-diethyl-2-(2-(4-(2-[18F]fluoroethoxy)phenyl)-5,7-dimethylpyrazolo[1,5-a]pyrimidin-3-yl)acetamide] and [11C]SSR180575 (7-chloro-N,N-dimethyl-5-[11C]methyl-4-oxo-3-phenyl-3,5-dihydro-4H-pyridazino[4,5-b]indole-1-acetamide), showed a significant binding in the lenti-CNTF-injected striatum that was saturated and displaced by PK11195 [N-methyl-N-(1-methylpropyl)-1-(2-chlorophenyl)-isoquinoline-3-carboxamide]. The volume of radioligand binding matched the GFAP immunopositive volume. TSPO mRNA levels were significantly increased, and TSPO protein was overexpressed by CNTF-activated astrocytes. We show that reactive astrocytes overexpress TSPO, yielding to a significant and selective binding of TSPO radioligands. Therefore, caution must be used when interpreting TSPO PET imaging in animals or patients because reactive astrocytes can contribute to the signal in addition to reactive microglia.

International audience; 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.

Biomedical imaging has been increasingly utilized in medical research to study brain and its associated dysfunctions. Biomedical imaging provides valuable in vivo information particularly in diagnosing diseases and guiding therapies. In the past years, the development of dedicated small animal imaging systems has enabled anatomical (MRI, Morris, 1999; CT, Paulus et al. 1999), or even functional (SPECT, Weber et al. 1994; PET, Cherry et al. 1997; fMRI, Mandeville et al.

The work reported in this paper aimed at developing and testing an automated method to calculate the biodistribution of a specific PET tracer in mouse brain PET/CT images using an MRI-based 3D digital atlas. Surface-based registration strategy and affine transformation estimation were considered. Such an approach allowed overcoming the lack of anatomical information in the inner regions of PET/CT brain scans. Promising results were obtained in one mouse (on two scans) and will be extended to a neuroinflammation mouse model to characterize the pathology and its evolution. Major improvements are expected regarding automation, time computation, robustness and reproducibility of mouse brain segmentation. Due to its generic implementation, this method could be successfully applied to PET/CT brain scans of other species (rat, primate) for which 3D digital atlases are available.

International audience; Brain glucose uptake was examined in transgenic mice relevant to Alzheimer's disease (APP/PS1) and their control littermates (PS1). Glucose distribution in the brain of the resting animals was measured using 3D-reconstructed autoradiography and analysed by a voxel-wise approach using SPMMouse combined to an MRI-based 3D digital atlas. Prompt and direct indexation of metabolic changes between the two groups was achieved, showing both hypo-and hypermetabolism of glucose in the brain of APP/PS1 mice. We confirm and extend previous study, since we identified brain structures affected in this pathological model and demonstrate glucose uptake changes in structures like the olfactory bulb. Our results pave the way to complete and accurate examination of functional data from cerebral structures involved in models of neurodegenerative diseases.

International audience; Biomarkers and technologies similar to those used in humans are essential for the follow-up of Alzheimer's disease (AD) animal models, particularly for the clarification of mechanisms and the screening and validation of new candidate treatments. In humans, changes in brain metabolism can be detected by 1-deoxy-2-[ 18 F] fluoro-D-glucose PET (FDG-PET) and assessed in a user-independent manner with dedicated software, such as Statistical Parametric Mapping (SPM). FDG-PET can be carried out in small animals, but its resolution is low as compared to the size of rodent brain structures. In mouse models of AD, changes in cerebral glucose utilization are usually detected by [ 14 C]-2-deoxyglucose (2DG) autoradiography, but this requires prior manual outlining of regions of interest (ROI) on selected sections. Here, we evaluate the feasibility of applying the SPM method to 3D autoradiographic data sets mapping brain metabolic activity in a transgenic mouse model of AD. We report the preliminary results obtained with 4 APP/PS1 (64 ± 1 weeks) and 3 PS1 (65 ± 2 weeks) mice. We also describe new procedures for the acquisition and use of "blockface" photographs and provide the first demonstration of their value for the 3D reconstruction and spatial normalization of post mortem mouse brain volumes. Despite this limited sample size, our results appear to be meaningful, consistent, and more comprehensive than findings from previously published studies based on conventional ROI-based methods. The establishment of statistical significance at the voxel level, rather than with a user-defined ROI, makes it possible to detect more reliably subtle differences in geometrically complex regions, such as the hippocampus. Our approach is generic and could be easily applied to other biomarkers and extended to other species and applications.

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 (9.4 T). 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.

In Parkinson's disease, degeneration of specific neurons in the midbrain can cause severe motor deficits, including tremors and the inability to initiate movement. The standard treatment is administration of pharmacological agents that transiently increase concentrations of brain dopamine and thereby discontinuously modulate neuronal activity in the striatum, the primary target of dopaminergic neurons. The resulting intermittent dopamine alleviates parkinsonian symptoms but is also thought to cause abnormal involuntary movements, called dyskinesias. To investigate gene therapy for Parkinson's disease, we simulated the disease in macaque monkeys by treating them with the complex I mitochondrial inhibitor 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, which induces selective degeneration of dopamine-producing neurons. In this model, we demonstrated that injection of a tricistronic lentiviral vector encoding the critical genes for dopamine synthesis (tyrosine hydroxylase, aromatic L-amino acid decarboxylase, and guanosine 5'-triphosphate cyclohydrolase 1) into the striatum safely restored extracellular concentrations of dopamine and corrected the motor deficits for 12 months without associated dyskinesias. Gene therapy-mediated dopamine replacement may be able to correct Parkinsonism in patients without the complications of dyskinesias.

It is implicitly assumed in most non-rigid registration methods that either corresponding structures can be found in both images, or that the regularization associated with the class of transformations chosen produce pertinent deformations elsewhere. However, when the images to register have a different contrast and resolution (e.g. in vivo / post mortem), or to minimize the deformation of some specific tissues (e.g. tumor, bones), it is necessary to have local control on the displacement field in these regions. We propose in this work an original registration method based on cubic B-spline transformations which allows to bound the variation of volume induced by the estimated transformation (variation of the determinant of the Jacobian of the transformation relatively to 1) in a predefined region. Lagrange multipliers are used to perform the constrained minimization of mutual information. We tested our method on artificial images with different tolerances on the volume variation and on one real 3D image: it performed efficiently while respecting the constraints.

Many analyses in neurosciences are carried out on histological and autoradiographic datasets and performed by manually drawing regions of interest on these 2D post mortem data. Such task being time-consuming, we propose an automated segmentation strategy to analyze 3D post mortem brain images. This method is based on the co-registration of a MRI-based 3D digital atlas on 3D-reconstructed post mortem data. We first deformed the original MRI on post mortem volumes. We then applied deformation parameters to warp the digital atlas. The method was tested on APP/PS1 mouse models of Alzheimer's disease and PS1 control littermates. Its reliability was qualitatively evaluated and a quantitative assessment was performed by comparing atlas and manual segmentations. Our results show this approach is promising to investigate at organ scale post mortem mouse datasets faster and in a more robust manner than with classical methods.

As mouse imaging has become more challenging in preclinical research, efforts have been made to develop dedicated PET systems. Although these systems are currently used for the study of physiopathologic murine models, they present some drawbacks for brain studies, including a low temporal resolution that limits the pharmacokinetic study of radiotracers. The aim of this study was to demonstrate the ability of a radiosensitive intracerebral probe to measure the binding of a radiotracer in the mouse brain in vivo. Methods: The potential of a probe 0.25 mm in diameter for pharmacokinetic studies was assessed. First, Monte Carlo simulations followed by experimental studies were used to evaluate the detection volume and sensitivity of the probe and its adequacy for the size of loci in the mouse brain. Second, ex vivo autoradiography of 5-hydroxytryptamine receptor 1A (5-HT1A) receptors in the mouse brain was performed with the PET radiotracer 2′-methoxyphenyl-(N-2′- pyridinyl)-p-18F-fluorobenzamidoethylpiperazine (18F-MPPF) . Finally, the binding kinetics of 18F-MPPF were measured in vivo in both the hippocampus and the cerebellum of mice. Results: Both the simulations and the experimental studies demonstrated the feasibility of using small probes to measure radioactive concentrations in specific regions of the mouse brain. Ex vivo autoradiography showed a heterogeneous distribution of 18F-MPPF consistent with the known distribution of 5-HT1A in the mouse brain. Finally, the time-activity curves obtained in vivo were reproducible and validated the capacity of the new probe to accurately measure 18F-MPPF kinetics in the mouse hippocampus. Conclusion: Our results demonstrate the ability of the tested radiosensitive intracerebral probe to monitor binding of PET radiotracers in anesthetized mice in vivo, with high temporal resolution suited for compartmental modeling. Copyright © 2008 by the Society of Nuclear Medicine, Inc.

International audience; PET scanners devoted to in vivo functional study have recently been developed, but autoradiography remains the reference technique for assessing cerebral glucose metabolism (CMRGlu) in rodents. Auto-radiographs are conventionally subjected to region of interest (ROI) analysis, which is intrinsically hypothesis-driven and therefore not suitable for whole-brain investigation. Voxel-wise statistical methods of analysis have long been used to determine differences in brain activity during in vivo functional neuroimaging experiments. They have also recently been applied to 3D reconstructed autoradiographic volume images from rat brains. We present here a fully automated analysis for autoradiographic data combining (1) computerized procedures for the acquisition and 3D reconstruction of postmortem volume images and (2) spatial normalization followed by classical whole-brain voxel-wise statistical analysis. We also describe an additional procedure for characterizing functional differences between the right and left hemispheres of the brain. We compared two spatial normalization techniques and evaluated how the effect of choosing a particular normalization technique impacted on the statistical analysis. We also propose a small volume correction analysis to address the problem of multiple statistical comparisons. Lastly, we investigated the reliability of such analyses, by comparing their results qualitatively and quantitatively with those previously obtained with our semiautomated ROI-based analysis [Dubois, A., Dauguet, J., Herard, A.-S., Besret, L., Duchesnay, E., Frouin, V., Hantraye, P., Bonvento, G., Delzescaux, T., 2007. Automated three-dimensional analysis of histologic and autoradiographic rat brain sections: application to an activation study. J. Cereb. Blood Flow Metab. 27 (10), 1742-1755.]. Both voxel-wise statistical analyses led to the detection of consistent interhemispheric differences in CMRGlu. This work demonstrates the potential value and robustness of voxel-wise statistical methods for analyzing autoradiographic data sets.

High energy demands of neurons make them vulnerable to adverse effects of energy impairment. Recently, astrocytes were shown to regulate the flux of energy substrates to neurons. In pathological situations, astrocytes are activated but the consequences on brain energy metabolism are still poorly characterized. We found that local lentiviral-mediated gene transfer of ciliary neurotrophic factor (CNTF), a cytokine known to activate astrocytes, induced a stable decrease in the glycolytic flux in the rat striatumin vivoas measured by 2-[18F]-2-deoxy-d-glucose autoradiography and micro-positron emission tomography imaging. The activity of the mitochondrial complex IV enzyme cytochrome oxidase was not modified, suggesting maintenance of downstream oxidative steps of energy production. CNTF significantly increased the phosphorylation level of the intracellular energy sensor AMP-activated protein kinase (AMPK), supporting a specific reorganization of brain energy pathways. Indeed, we found that different key enzymes/transporters of fatty acids β-oxidation and ketolysis were overexpressed by CNTF-activated astrocytes within the striatum. In primary striatal neuron/astrocyte mixed cultures exposed to CNTF, the AMPK pathway was also activated, and the rate of oxidation of fatty acids and ketone bodies was significantly enhanced. This metabolic plasticity conferred partial glial and neuronal protection against prolonged palmitate exposure and glycolysis inhibition. We conclude that CNTF-activated astrocytes may have a strong protective potential to face severe metabolic insults.

A classical neural tract tracer, WGA-HRP, was injected at multiple sites within the brain of a macaque monkey. Histological sections of the labeled fiber tracts were reconstructed in 3D, and the fibers were segmented and registered with the anatomical post-mortem MRI from the same animal. Fiber tracing along the same pathways was performed on the DTI data using a classical diffusion tracing technique. The fibers derived from the DTI were compared with those segmented from the histology in order to evaluate the performance of DTI fiber tracing. While there was generally good agreement between the two methods, our results reveal certain limitations of DTI tractography, particularly at regions of fiber tract crossing or bifurcation.

Since the introduction of diffusion weighted imaging (DWI) as a method for examining neural connectivity, its accuracy has not been formally evaluated. In this study, we directly compared connections that were visualized using injected neural tract tracers (WGA-HRP) with those obtained using in-vivo diffusion tensor imaging (DTI) tractography. First, we injected the tracer at multiple sites in the brain of a macaque monkey; second, we reconstructed the histological sections of the labeled fiber tracts in 3D; third, we segmented and registered the fibers (somatosensory and motor tracts) with the anatomical in-vivo MRI from the same animal; and last, we conducted fiber tracing along the same pathways on the DTI data using a classical diffusion tracing technique with the injection sites as seeds. To evaluate the performance of DTI fiber tracing, we compared the fibers derived from the DTI tractography with those segmented from the histology. We also studied the influence of the parameters controlling the tractography by comparing Dice superimposition coefficients between histology and DTI segmentations. While there was generally good visual agreement between the two methods, our quantitative comparisons reveal certain limitations of DTI tractography, particularly for regions at remote locations from seeds. We have thus demonstrated the importance of appropriate settings for realistic tractography results.

The correlation between post-mortem data and in-vivo brain images is of high interest for studying neurodegenerative diseases. This paper describes a protocol that matches a series of stained histological slices of a baboon brain with an anatomical MRI scan of the same subject using an intermediate 3D-consistent volume of "blockface" photographs taken during the sectioning process. Each stained histological section of the baboon brain was first registered to its corresponding blockface photograph using a novel "hemi-rigid" transformation. This piecewise rigid 2D transformation was specifically adapted to the registration of slices which contained both hemispheres. Subsenquently, to correct the global 3D deformations of the brain caused by histological preparation and fixation, a 3D elastic transformation was estimated between the blockface volume and the MRI data. This 3D elastic transformation was then applied to the histological volume previously aligned using the hemi-rigid method to complete the registration of the series of stained histological slices with the MRI data. We assessed the efficacy of our method by evaluating the quality of matching of anatomical features as well as the difference of volume measurements between the MRI and the histological images. Two complete baboon brains (with the exception of cerebellum) were successfully processed using our protocol.

International audience; 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).

Automated detection of amyloid plaques (AP) in post mortem brain sections of patients with Alzheimer disease (AD) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. We propose a new segmentation method to automatically detect amyloid plaques in Congo Red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. A set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation. Original information concerning global amyloid load have been derived from 6 mouse brains which opens new perspectives for the extensive analysis of such a data in 3-D and the possibility to integrate in vivo-post mortem information for diagnosis purposes.

Congenital hyperinsulinism (HI) of infancy, the most frequent cause of hypoglycaemia in young children, is a neuro-endocrine disease secondary to either focal adenomatous hyperplasia or a diffuse abn

International audience; Automated detection of amyloid plaques (AP) in post mortem brain sections of patients with Alzheimer disease (AD) or in mouse models of the disease is a major issue to improve quantitative, standardized and accurate assessment of neuropathological lesions as well as of their modulation by treatment. We propose a new segmentation method to automatically detect amyloid plaques in Congo Red stained sections based on adaptive thresholds and a dedicated amyloid plaque/tissue modelling. A set of histological sections focusing on anatomical structures was used to validate the method in comparison to expert segmentation.

International audience; 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 ␤A4. 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.

During a specific task, an increase in glucose utilization anatomically restricted to the functionally activated region(s) is a landmark of brain physiology. While this response represents the biological bases for functional brain imaging, the underlying signalling pathway(s) are still not fully characterized. Recent evidence suggests that glial glutamate (re)uptake plays a key role. We provide evidence that the metabolic response to synaptic activation (i.e. enhancement of glucose uptake) is decreased in the superior colliculus during visual stimulation in young adult mice deficient in the glial glutamate transporter GLT-1. A similar reduction was not observed in the glial glutamate transporter GLAST-knockout mice. Consistent with our previous observation obtained in the somatosensory cortex, our data suggest that a metabolic crosstalk takes place between neurons and astrocytes in the adult brain which would be regulated by synaptic activity and mediated by GLT-1.

The glycolytic flux (cerebral metabolic rate of glucose CMRglc) and the TCA cycle flux ( VTCA) were measured in the same monkeys by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and 13C NMR spectroscopy, respectively. Registration of nuclear magnetic resonance (NMR) and PET data were used for comparison of CMRglc and VTCA in the exact same area of the brain. Both fluxes were in good agreement with literature values (CMR glc 0.23 ± 0.03 μmol/g min, VTCA = 0.53 ± 0.13 μmol/gmin). The resulting [ CMRglc/VTCA] ratio was 0.46 ± 0.12 ( n = 5, mean ± s.d.), not significantly different from the 0.5 expected when glucose is the sole fuel that is completely oxidized. Our results provide a cross-validation of both techniques. Comparison of CMRglc with VTCA is in agreement with a metabolic coupling between the TCA cycle and glycolysis under normal physiologic conditions.

Hyperinsulinism (HI) of infancy is a neuroendocrine disease secondary to either focal adenomatous hyperplasia or a diffuse abnormality of insulin secretion of the pancreas. HI with focal lesions can revert by selective surgical resection in contrast to the diffuse form, which requires subtotal pancreatectomy when resistant to medical treatment. Neuroendocrine diseases are a heterogeneous group of entities with the ability to take up amine precursors and to convert them into biogenic amines. Therefore, the aim of this study was (a) to evaluate the use of PET with 18F-fluoro-L-dihydroxyphenylalanine (18F-fluoro-L-DOPA) and (b) to distinguish between focal and diffuse HI.Fifteen patients (11 boys, 4 girls) with neonatal HI were enrolled in this study. All patients fasted for at least 6 h before the PET examination and their medication was discontinued for at least 72 h. The examination was performed under light sedation (pentobarbital associated with or without chloral). The dynamic acquisition started 45-65 min after the injection of 18F-fluoro-L-DOPA (4.0 MBq/kg weight). Four or 6 scans of 5 min each (2 or 3 steps according to the height of the patient) were acquired from the neck to the upper legs.An abnormal focal pancreatic uptake of 18F-fluoro-L-DOPA was observed in 5 patients, whereas a diffuse uptake of the radiotracer was observed in the pancreatic area of the other patients. All patients with focal radiotracer uptake and also 4 of 10 patients with pancreatic diffuse radiotracer accumulation, unresponsive to medical treatment, underwent surgery. The histopathologic results confirmed the PET findings--that is, focal versus diffuse HI.The results of this study suggest that 18F-fluoro-L-DOPA could be an accurate noninvasive technique to distinguish between focal and diffuse forms of HI.

This paper presents a robust method to correct for intensity differences across a series of aligned stained histological slices. The method is made up of two steps. First, for each slice, a scale-space analysis of the histogram provides a set of alternative interpretations in terms of tissue classes. Each of these interpretations can lead to a different classification of the related slice. A simple heuristics selects for each slice the most plausible interpretation. Then, an iterative procedure refines the interpretation selections across the series in order to maximize a score measuring the spatial consistency of the classifications across contiguous slices. Results are presented for a series of 121 baboon slices.

Purpose To validate the adaptive semiautomated self-evaluated registration technique (ASSERT) followed by factor analysis of medical image sequence (FAMIS) for analyzing myocardial perfusion using magnetic resonance imaging (MRI) images. Materials and Methods Eleven patients having a significant stenosis of at least one coronary artery detected by coronarography were examined by thallium tomoscintigraphy and perfusion MRI (first-pass of Gd-DTPA-BMA) at rest and under pharmacologic stress. The MRI images were analyzed by ASSERT to correct for rigid motion in the acquisition plane and to reject those images that were severely deformed or acquired outside the slice plane. The images thus obtained were analyzed by FAMIS. The resulting factor images representing myocardial perfusion were read to identify the ischemic territories corresponding to left anterior descending coronary arteries and right coronary arteries. Results ASSERT allowed automatic correction for motion and the exclusion of images that could not be registered. The ischemic territories, identified from the factor images of the myocardium, agreed with those identified by coronarography and tomoscintigraphy for 20 of the 22 territories. Conclusion The results demonstrate the feasibility of analyzing myocardial perfusion using MRI acquisition and treating the resulting images by ASSERT and FAMIS. Extending this method to multislice examinations will enable evaluation of the perfusion of the whole myocardium. J. Magn. Reson. Imaging 2003;18:681–690. © 2003 Wiley-Liss, Inc.

The aim of this study is to propose a protocol allowing to merge informations acquired in vivo (MRI) and post mortem (histology). Departing from the 3-D histological volume and MRI, we first estimated a rigid transformation. Then, an elastic registration method using Free Form Deformation (FFD) was applied to correct the deformations due to the extraction of the brain from skull and the fixation. The final validation of the registration was based on a visual analysis of the matching of internal structures of the brain and the superimposition of cortical sulci.

Neuron detection is a key step in individualizing and counting neurons which are important for assessing physiological and pathophysiological information. A large number of methods including deep learning networks have been proposed but mainly targeting regions with few aggregated neurons. The objective of this paper is to address an automated neuron detection problem in heterogeneous hippocampus region with different degrees of neuron aggregation. Since deep learning networks require a lot of ground truths hut neuron instance annotation is impossible in regions where numerous neurons are clustered, ground truth of centroids marked at the center of neurons is created for training. We propose a multiscale convolutional neural network (CNN) to regress neuron centroid mapping across image. Using multiscale information makes the proposed network applicable not only for single individual neurons, but also for a large number of aggregated neurons. Experimental results show that our method is superior to state-of-the-art deep learning -based algorithms. To our knowledge, this is the first deep learning study to detect neurons in regions of highly clustered neurons.

In preclinical research and more specifically in neurobiology, histology uses images produced by increasingly powerful optical microscopes digitizing entire sections at cell scale. Quantification of stained tissue such as neurons relies on machine learning driven segmentation. However such methods need a lot of additional information, or features, which are extracted from raw data multiplying the quantity of data to process. As a result, the quantity of features is becoming a drawback to process large series of histological images in a fast and robust manner. Feature selection methods could reduce the amount of required information but selected subsets lack of stability. 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 on high-resolution histological whole sections. This selection has two selection steps: first at feature families scale (an intermediate pool of features, between space and individual feature). Second, feature selection is performed on pre-selected feature families. In this work, the selected sets of features are stables for two different neurons staining. Furthermore the feature selection results in a significant reduction of computation time and memory cost. This methodology can potentially enable exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research settings., L’histologie produit des images à l’échelle cellulaire grâce à des microscopes optiques très performants. La quantification du tissu marqué comme les neurones s’appuie de plus en plus sur des segmentations par apprentissage automatique. Cependant, l’apprentissage automatique nécessite une grande quantité d’informations intermédiaires, ou caractéristiques, extraites de la donnée brute multipliant d’autant la quantité de données à traiter. Ainsi, le nombre important de ces caractéristiques est un obstacle au traitement robuste et rapide de séries d’images histologiques. Les algorithmes de sélection de caractéristiques pourraient réduire la quantité d’informations nécessaires mais les ensembles de caractéristiques sélectionnés sont peu reproductibles. Nous proposons une méthodologie originale fonctionnant sur des infrastructures de calcul haute-performance (CHP) visant à sélectionner des petits ensembles de caractéristiques stables afin de permettre des segmentations rapides et robustes sur des images histologiques acquises à très haute-résolution. Cette sélection se déroule en deux étapes : la première à l’échelle des familles de caractéristiques. La deuxième est appliquée directement sur les caractéristiques issues de ces familles. Dans ce travail, nous avons obtenu des ensembles généralisables et stables pour deux marquages neuronaux différents. Ces ensembles permettent des réductions significatives des temps de traitement et de la mémoire vive utilisée. Cette méthodologie rendra possible des études histologiques exhaustives à haute-résolution sur des infrastructures CHP que ce soit en recherche préclinique et possiblement clinique.

Understanding the mechanisms involved in healthy cases, neurodegenerative diseases and the development of new therapeutic approaches is based on the use of relevant experimental models and appropriate imaging techniques. In this context, virtual microscopy offers the unique possibility of analyzing these models at a cellular scale with a very wide variety of histological markers. My thesis project consists in carrying out and applying a method of analyzing colored histological images that can segment and synthesize information corresponding to neurons using the NeuN antibody on sections of the macaque brain. In this work, we first apply the Random Forest (RF) method to segment neurons as well as tissue and background. Then, we propose an original method to separate the touching or overlapping neurons in order to individualize them. This method is adapted to process neurons presenting a variable size (diameter varying between 5 and 30 μm). It is also effective not only for so-called "simple" regions characterized by a low density of neurons but also for so-called "complex" regions characterized by a very high density of several thousands of neurons. The next work focuses on the creation of parametric maps synthesizing the morphology and distribution of individualized neurons. For this purpose, a multiscale approach is implemented in order to produce maps with lower spatial resolutions (0.22 μm original resolution and created maps offering adaptive spatial resolution from a few dozens to a few hundred of micrometers). Several dozens of morphological parameters (mean radius, surface, orientation, etc.) are first computed as well as colorimetric parameters. Then, it is possible to synthesize this information in the form of lower-resolution parametric maps at the level of anatomical regions, sections and even, eventually, the entire brains. This step transforms qualitative color microscopic images to quantitative mesoscopic images, more informative and easier to analyze. This work makes it possible to statistically analyze very large volumes of data, to synthesize information in the form of quantitative maps, to analyze extremely complex problems such as neuronal death, to test new drugs and to compare this acquired information post mortem with data acquired in vivo.; La compréhension des mécanismes impliqués dans les cas sains, les maladies neurodégénératives ainsi que le développement de nouvelles approches thérapeutiques repose sur l’utilisation de modèles expérimentaux pertinents et de techniques d’imagerie adaptées. Dans ce contexte, la microscopie virtuelle offre la possibilité unique d’analyser ces modèles à l’échelle cellulaire avec une très grande variété de marquages histologiques. Mon projet de thèse consiste à mettre en place et à appliquer une méthode d’analyse d’images histologiques en couleur permettant de segmenter et de synthétiser l’information relative aux neurones à l’aide de l’anticorps NeuN sur des coupes de cerveau de macaque. Dans ce travail, nous appliquons d’abord la méthode de Random Forest (RF) pour segmenter les neurones ainsi que le tissu et le fond. Ensuite, nous proposons une méthode originale pour séparer les neurones qui sont accolés afin de les individualiser. Cette méthode s’adapte à l’ensemble des neurones présentant une taille variable (diamètre variant entre 5 et 30 μm). Elle est également efficace non seulement pour des régions dites « simples » caractérisées par une faible densité de neurones mais aussi pour des régions dites « complexes » caractérisées par une très forte densité de plusieurs milliers de neurones. Le travail suivant se concentre sur la création de cartes paramétriques synthétisant la morphologie et la distribution des neurones individualisés. Pour cela, un changement d’échelle est mis en œuvre afin de produire des cartographies présentant des résolutions spatiales plus faibles (résolution originale de 0,22 μm et cartographies créées proposant une résolution spatiale adaptative de quelques dizaines à quelques centaines de micromètres). Plusieurs dizaines de paramètres morphologiques (rayon moyen, surface, orientation, etc.) sont d’abord calculés pour chaque neurone ainsi que des paramètres colorimétriques. Ensuite, il est possible de synthétiser ces informations sous la forme de cartes paramétriques à plus basse résolution à l’échelle de régions anatomiques, de coupes voire, à terme, de cerveaux entiers. Cette étape transforme des images microscopiques qualitatives couleur en images mésoscopiques quantitatives, plus informatives et plus simples à analyser. Ce travail permet d’analyser statistiquement de très grands volumes de données, de synthétiser l’information sous la forme de cartographies quantitatives, d’analyser des problèmes extrêmement complexes tels que la mort neuronale et à terme de tester de nouveaux médicaments voire de confronter ces informations acquises post mortem avec des données acquises in vivo.

Understanding the mechanisms involved in neurodegenerative or developmental diseases and designing new therapeutic approaches are based on the use of relevant experimental models as well as appropriate imaging techniques. In this context, MRI is a prominent tool for in vivo investigation as it allows for longitudinal follow-up. Successful translation from bench to bedside calls for well-characterized models as well as transferable biomarkers. Yet, despite the existence of both clinical and preclinical scanners, analysis tools are hardly translational. In this work, inspired by standards developed in Humans, we've built and validated tools for the automated segmentation of neuroanatomical structures in the Macaque. This method is based on the registration of a digital probabilistic atlas followed by the fitting of a statistical model consisting of a gaussian mixture and Markov random fields. It was first validated in healthy adults and then applied to the study of neonatal brain development. Furthermore, to pave the way for comparisons with gold standard post mortem biomarkers, we developed a pipeline for the automated 3D reconstruction of histological volumes that we applied to the characterization of MRI contrast in a stem-cell graft following an excitotoxic lesion., La compréhension des mécanismes impliqués dans les maladies neurodégénératives ou développementales ainsi que la mise en place de nouvelles approches thérapeutiques reposent sur l’utilisation de modèles expérimentaux pertinents et de techniques d’imagerie adaptées. Dans ce contexte, l’IRM est un outil de choix pour l’exploration anatomique in vivo dans la mesure où elle permet d’effectuer un suivi longitudinal. Le succès translationnel des thérapies du laboratoire au patient repose sur une bonne caractérisation des modèles et une continuité des biomarqueurs utilisés. Or, si l'IRM est disponible en préclinique et en clinique, les outils d'analyse sont peu « génériques ». Au cours de cette thèse, en s'inspirant des travaux menés chez l'Homme, nous avons développé et validé des outils automatiques de segmentation des structures neuroanatomiques chez le Macaque. La méthode proposée repose sur la mise en registre avec l'IRM du sujet d'un atlas digital probabiliste suivi de l'optimisation d'un modèle statistique par mélanges de gaussiennes et champs aléatoires de Markov. Elle a été validée chez un ensemble de sujets sains adultes puis mise en application dans le contexte du développement néonatal normal du cerveau. Afin de poser les bases d'une évaluation permettant une comparaison des biomarqueurs IRM avec les biomarqueurs post mortem de référence, nous avons également mis au point une chaîne de traitement permettant la reconstruction 3D de volumes histologiques du cerveau de Macaque et l'avons appliqué à la caractérisation du contraste IRM au cours d'une greffe de cellules souches après lésion excitotoxique.

Histology is the gold standard to study the spatial distribution of the molecular building blocks of organs. In humans and in animal models of disease, histology is widely used to highlight neuropathological markers on brain tissue sections. This makes it particularly useful to investigate the pathophysiology of neurodegenerative diseases such as Alzheimer’s disease and to evaluate drug candidates. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Quantitative methods are lacking for whole-brain analysis of cellular and pathological markers. In this work, we propose an automated and scalable method to thoroughly quantify and analyze histopathological markers in 3D in rodent whole brains. Histology images are reconstructed in 3D using block-face photography as a spatial reference and the markers of interest are segmented via supervised machine learning. Two complimentary approaches are proposed to detect differences in histopathological marker load between groups of animals: an ontology-based approach is used to infer difference at the level of brain regions and a voxel-wise approach is used to detect local differences without spatial a priori. Several applications in mouse models of A-beta deposition are described to illustrate 3D histopathology usability to characterize animal models of brain diseases, to evaluate the effect of experimental interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques.; L’histologie est la méthode de choix pour l’étude ex vivo de la distribution spatiale des molécules qui composent les organes. En particulier, l’histologie permet de mettre en évidence les marqueurs neuropathologiques de la maladie d’Alzheimer ce qui en fait un outil incontournable pour étudier la physiopathologie de la maladie et pour évaluer l’efficacité de candidats médicaments. Classiquement, l’analyse de données histologiques implique de lourdes interventions manuelles, et de ce fait, est souvent limitée à l’analyse d’un nombre restreint de coupe histologiques et à quelques régions d’intérêts. Dans ce travail de thèse, nous proposons une méthode automatique pour l’analyse quantitative de marqueurs histopathologiques en trois dimensions dans le cerveau entier de rongeurs. Les images histologiques deux-dimensionnelles sont d’abord reconstruites en trois dimensions en utilisant l’imagerie photographique de bloc comme référence géométrique et les marqueurs d’intérêts sont segmentés par apprentissage automatique. Deux approches sont proposées pour détecter des différences entre groupes d’animaux: la première est basée sur l’utilisation d’une ontologie anatomique de cerveau qui permet détecter des différences à l’échelle de structures entières et la deuxième approche est basée sur la comparaison voxel-à-voxel afin de détecter des différences locales sans a priori spatial. Cette méthode a été appliquée dans plusieurs études chez des souris modèles de déposition amyloïde afin d’en démontrer l’utilisabilité.