giRAff: an automated atlas segmentation tool adapted to single histological slices.

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases. Code is available at: https://github.com/MIRCen/NeuronInstanceSeg.

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain. HIGHLIGHTS: Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.

Automated Atlas-based Segmentation of Single Coronal Mouse Brain Slices using Linear 2D-2D Registration.

A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automatically and accurately segment single 2D coronal slices within a 3D volume of atlas, using linear registration. We validated its robustness and performance using an exploratory approach at whole-brain scale.

Evaluation of Deep Learning Topcoders Method for Neuron Individualization in Histological Macaque Brain Section.

Cell individualization has a vital role in digital pathology image analysis. Deep Learning is considered as an efficient tool for instance segmentation tasks, including cell individualization. However, the precision of the Deep Learning model relies on massive unbiased dataset and manual pixel-level annotations, which is labor intensive. Moreover, most applications of Deep Learning have been developed for processing oncological data. To overcome these challenges, i) we established a pipeline to synthesize pixel-level labels with only point annotations provided; ii) we tested an ensemble Deep Learning algorithm to perform cell individualization on neurological data. Results suggest that the proposed method successfully segments neuronal cells in both object-level and pixel-level, with an average detection accuracy of 0.93.

First evidence that emerging pinnatoxin-G, a contaminant of shellfish, reaches the brain and crosses the placental barrier.

Massive proliferation of some toxic marine dinoflagellates is responsible for the occurrence of harmful algal blooms and the contamination of fish and shellfish worldwide. Pinnatoxins (PnTx) (A-H) comprise an emerging phycotoxin family belonging to the cyclic imine toxin group. Interest has been focused on these lipophilic, fast-acting and highly potent toxins because they are widely found in contaminated shellfish, and can represent a risk for seafood consumers. PnTx display a potent antagonist effect on nicotinic acetylcholine receptors (nAChR), and in this study we assessed in vivo the ability of PnTx-G to cross physiological barriers to reach its molecular target. Radiolabeled [3H]-PnTx-G synthesized with good radiochemical purity and yield retained the high affinity of the natural toxin. Oral gavage or intravenous administration to adult rats and digital autoradiographic analyses revealed the biodistribution and toxicokinetics of [3H]-PnTx-G, which is rapidly cleared from blood, and accumulates in the liver and small intestine. The labeling of peripheral and brain adult/embryo rat tissues highlights its ability to cross the intestinal, blood-brain and placental barriers. High-resolution 3D-imaging and in vitro competition studies on rat embryo sections revealed the specificity of [3H]-PnTx-G binding and its selectivity for muscle and neuronal nAChR subtypes (such as α7 subtype). The use of a human perfused cotyledon model and mass spectrometry analyses disclosed that PnTx-G crosses the human placental barrier. The increasing worldwide occurrence of both the dinoflagellate Vulcanodinium rugosum and PnTx-contaminated shellfish, due to climate warming, raises concerns about the potential adverse impact that exposure to pinnatoxins may have for human health.

Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.

Accurate cerebral neuron segmentation is required before neuron counting and neuron morphological analysis. Numerous algorithms for neuron segmentation have been published, but they are mainly evaluated using limited subsets from a specific anatomical region, targeting neurons of clear contrast and/or neurons with similar staining intensity. It is thus unclear how these algorithms perform on cerebral neurons in diverse anatomical regions. In this article, we introduce and reliably evaluate existing machine learning algorithms using a data set of microscopy images of macaque brain. This data set highlights various anatomical regions (e.g., cortex, caudate, thalamus, claustrum, putamen, hippocampus, subiculum, lateral geniculate, globus pallidus, etc.), poor contrast, and staining intensity differences of neurons. The evaluation was performed using 10 architectures of six classic machine learning algorithms in terms of typical Recall, Precision, F-score, aggregated Jaccard index (AJI), as well as a performance ranking of algorithms. F-score of most of the algorithms is superior to 0.7. Deep learning algorithms facilitate generally higher F-scores. U-net with suitable layer depth has been evaluated to be excellent classifiers with F-score of 0.846 and 0.837 when performing cross validation. The evaluation and analysis indicate the performance gap among algorithms in various anatomical regions and the strengths and limitations of each algorithm. The comparative result highlights at the same time the importance and difficulty of neuron segmentation and provides clues for future improvement. To the best of our knowledge, this work is the first comprehensive study for neuron segmentation in such large-scale anatomical regions. Neuron segmentation plays a critical role in extracting cerebral information, such as neuron counting and neuron morphological analysis. Accurate automated cerebral neuron segmentation is a challenging task due to different kinds, poor contrast, staining intensity differences, and fuzzy boundaries of neurons. The comprehensive evaluation and analysis of performance among existing machine learning algorithms in diverse anatomical regions allows to make clear of the strengths and limitations of state-of-the-art algorithm. The comprehensive study provides clues for future improvement and creation of automated methods.

A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps.

Similarly to human population imaging, there are several well-founded motivations for animal population imaging, the most notable being the improvement of the validity of statistical results by pooling a sufficient number of animal data provided by different imaging centers. In this paper, we demonstrate the feasibility of such a multicenter animal study, sharing raw data from forty rats and processing pipelines between four imaging centers. As specific use case, we focused on T1 and T2 mapping of the healthy rat brain at 7T. We quantitatively report about the variability observed across two MR data providers and evaluate the influence of image processing steps on the final maps, using three fitting algorithms from three centers. Finally, to derive relaxation times from different brain areas, two multi-atlas segmentation pipelines from different centers were performed on two different platforms. Differences between the two data providers were 2.21% for T1 and 9.52% for T2. Differences between processing pipelines were 1.04% for T1 and 3.33% for T2. These maps, obtained in healthy conditions, may be used in the future as reference when exploring alterations in animal models of pathology.

Impairment of Glycolysis-Derived l-Serine Production in Astrocytes Contributes to Cognitive Deficits in Alzheimer's Disease.

Alteration of brain aerobic glycolysis is often observed early in the course of Alzheimer's disease (AD). Whether and how such metabolic dysregulation contributes to both synaptic plasticity and behavioral deficits in AD is not known. Here, we show that the astrocytic l-serine biosynthesis pathway, which branches from glycolysis, is impaired in young AD mice and in AD patients. l-serine is the precursor of d-serine, a co-agonist of synaptic NMDA receptors (NMDARs) required for synaptic plasticity. Accordingly, AD mice display a lower occupancy of the NMDAR co-agonist site as well as synaptic and behavioral deficits. Similar deficits are observed following inactivation of the l-serine synthetic pathway in hippocampal astrocytes, supporting the key role of astrocytic l-serine. Supplementation with l-serine in the diet prevents both synaptic and behavioral deficits in AD mice. Our findings reveal that astrocytic glycolysis controls cognitive functions and suggest oral l-serine as a ready-to-use therapy for AD.

Assessment of simplified methods for quantification of [18F]-DPA-714 using 3D whole-brain TSPO immunohistochemistry in a non-human primate.

The 18 kDa translocator protein (TSPO) is the main molecular target to image neuroinflammation by positron emission tomography (PET). However, TSPO-PET quantification is complex and none of the kinetic modelling approaches has been validated using a voxel-by-voxel comparison of TSPO-PET data with the actual TSPO levels of expression. Here, we present a single case study of binary classification of in vivo PET data to evaluate the statistical performance of different TSPO-PET quantification methods. To that end, we induced a localized and adjustable increase of TSPO levels in a non-human primate brain through a viral-vector strategy. We then performed a voxel-wise comparison of the different TSPO-PET quantification approaches providing parametric [18F]-DPA-714 PET images, with co-registered in vitro three-dimensional TSPO immunohistochemistry (3D-IHC) data. A data matrix was extracted from each brain hemisphere, containing the TSPO-IHC and TSPO-PET data for each voxel position. Each voxel was then classified as false or true, positive or negative after comparison of the TSPO-PET measure to the reference 3D-IHC method. Finally, receiver operating characteristic curves (ROC) were calculated for each TSPO-PET quantification method. Our results show that standard uptake value ratios using cerebellum as a reference region (SUVCBL) has the most optimal ROC score amongst all non-invasive approaches.

Longitudinal characterization of cognitive and motor deficits in an excitotoxic lesion model of striatal dysfunction in non-human primates.

As research progresses in the understanding of the molecular and cellular mechanisms underlying neurodegenerative diseases like Huntington's disease (HD) and expands towards preclinical work for the development of new therapies, highly relevant animal models are increasingly needed to test new hypotheses and to validate new therapeutic approaches. In this light, we characterized an excitotoxic lesion model of striatal dysfunction in non-human primates (NHPs) using cognitive and motor behaviour assessment as well as functional imaging and post-mortem anatomical analyses. NHPs received intra-striatal stereotaxic injections of quinolinic acid bilaterally in the caudate nucleus and unilaterally in the left sensorimotor putamen. Post-operative MRI scans showed atrophy of the caudate nucleus and a large ventricular enlargement in all 6 NHPs that correlated with post-mortem measurements. Behavioral analysis showed deficits in 2 analogues of the Wisconsin card sorting test (perseverative behavior) and in an executive task, while no deficits were observed in a visual recognition or an episodic memory task at 6 months following surgery. Spontaneous locomotor activity was decreased after lesion and the incidence of apomorphine-induced dyskinesias was significantly increased at 3 and 6 months following lesion. Positron emission tomography scans obtained at end-point showed a major deficit in glucose metabolism and D2 receptor density limited to the lesioned striatum of all NHPs compared to controls. Post-mortem analyses revealed a significant loss of medium-sized spiny neurons in the striatum, a loss of neurons and fibers in the globus pallidus, a unilateral decrease in dopaminergic neurons of the substantia nigra and a loss of neurons in the motor and dorsolateral prefrontal cortex. Overall, we show that this robust NHP model presents specific behavioral (learning, execution and retention of cognitive tests) and metabolic functional deficits that, to the best of our knowledge, are currently not mimicked in any available large animal model of striatal dysfunction. Moreover, we used non-invasive, translational techniques like behavior and imaging to quantify such deficits and found that they correlate to a significant cell loss in the striatum and its main input and output structures. This model can thus significantly contribute to the pre-clinical longitudinal evaluation of the ability of new therapeutic cell, gene or pharmacotherapy approaches in restoring the functionality of the striatal circuitry.

Tridimensional mapping of Phox2b expressing neurons in the brainstem of adult Macaca fascicularis and identification of the retrotrapezoid nucleus.

Chemosensitivity is a key mechanism for the regulation of breathing in vertebrates. The retrotrapezoid nucleus is a crucial hub for respiratory chemoreception within the brainstem. It integrates chemosensory information that are both peripheral from the carotid bodies (via the nucleus of the solitary tract) and central through the direct sensing of extracellular protons. To date, the location of a genetically defined RTN has only been ascertained in rodents. We first demonstrated that Phox2b, a key determinant for the development of the visceral nervous system and branchiomotor nuclei in the brainstem including the RTN, had a similar distribution in the brainstem of adult macaques compared to adult rats. Second, based on previous description of a specific molecular signature for the RTN in rats, and on an innovative technique for duplex in situ hybridization, we identified parafacial neurons which coexpressed Phox2b and ppGal mRNAs. They were located ventrally to the nucleus of the facial nerve and extended from the caudal part of the nucleus of the superior olive to the rostral tip of the inferior olive. Using the previously described blockface technique, deformations were corrected to allow the proper alignment and stacking of digitized sections, hence providing for the first time a 3D reconstruction of the macaque brainstem, Phox2b distribution and the primate retrotrapezoid nucleus. This description should help bridging the gap between rodents and humans for the description of key respiratory structures in the brainstem.

Automated Individualization of Size-Varying and Touching Neurons in Macaque Cerebral Microscopic Images.

In biomedical research, cell analysis is important to assess physiological and pathophysiological information. Virtual microscopy offers the unique possibility to study the compositions of tissues at a cellular scale. However, images acquired at such high spatial resolution are massive, contain complex information, and are therefore difficult to analyze automatically. In this article, we address the problem of individualization of size-varying and touching neurons in optical microscopy two-dimensional (2-D) images. Our approach is based on a series of processing steps that incorporate increasingly more information. (1) After a step of segmentation of neuron class using a Random Forest classifier, a novel min-max filter is used to enhance neurons' centroids and boundaries, enabling the use of region growing process based on a contour-based model to drive it to neuron boundary and achieve individualization of touching neurons. (2) Taking into account size-varying neurons, an adaptive multiscale procedure aiming at individualizing touching neurons is proposed. This protocol was evaluated in 17 major anatomical regions from three NeuN-stained macaque brain sections presenting diverse and comprehensive neuron densities. Qualitative and quantitative analyses demonstrate that the proposed method provides satisfactory results in most regions (e.g., caudate, cortex, subiculum, and putamen) and outperforms a baseline Watershed algorithm. Neuron counts obtained with our method show high correlation with an adapted stereology technique performed by two experts (respectively, 0.983 and 0.975 for the two experts). Neuron diameters obtained with our method ranged between 2 and 28.6 µm, matching values reported in the literature. Further works will aim to evaluate the impact of staining and interindividual variability on our protocol.

Voxel-Based Statistical Analysis of 3D Immunostained Tissue Imaging.

Recently developed techniques to visualize immunostained tissues in 3D and in large samples have expanded the scope of microscopic investigations at the level of the whole brain. Here, we propose to adapt voxel-based statistical analysis to 3D high-resolution images of the immunostained rodent brain. The proposed approach was first validated with a simulation dataset with known cluster locations. Then, it was applied to characterize the effect of ADAM30, a gene involved in the metabolism of the amyloid precursor protein, in a mouse model of Alzheimer's disease. This work introduces voxel-based analysis of 3D immunostained microscopic brain images and, therefore, opens the door to localized whole-brain exploratory investigation of pathological markers and cellular alterations.

A validation dataset for Macaque brain MRI segmentation.

Validation data for segmentation algorithms dedicated to preclinical images is fiercely lacking, especially when compared to the large number of databases of Human brain images and segmentations available to the academic community. Not only is such data essential for validating methods, it is also needed for objectively comparing concurrent algorithms and detect promising paths, as segmentation challenges have shown for clinical images. The dataset we present here is a first step in this direction. It comprises 10 T2-weighted MRIs of healthy adult macaque brains, acquired on a 7 T magnet, along with corresponding manual segmentations into 17 brain anatomic labelled regions spread over 5 hierarchical levels based on a previously published macaque atlas (Calabrese et al., 2015) [1]. By giving access to this unique dataset, we hope to provide a reference needed by the non-human primate imaging community. This dataset was used in an article presenting a new primate brain morphology analysis pipeline, Primatologist (Balbastre et al., 2017) [2]. Data is available through a NITRC repository (https://www.nitrc.org/projects/mircen_macset).

ßAPP Processing Drives Gradual Tau Pathology in an Age-Dependent Amyloid Rat Model of Alzheimer's Disease.

The treatment of Alzheimer's disease (AD) remains challenging and requires a better in depth understanding of AD progression. Particularly, the link between amyloid protein precursor (APP) processing and Tau pathology development remains poorly understood. Growing evidences suggest that APP processing and amyloid-ß (Aß) release are upstream of Tau pathology but the lack of animal models mimicking the slow progression of human AD raised questions around this mechanism. Here, we described that an AD-like ßAPP processing in adults wild-type rats, yielding to human APP, ßCTF and Aß levels similar to those observed in AD patients, is sufficient to trigger gradual Tauopathy. The Tau hyperphosphorylation begins several months before the formation of both amyloid plaques and tangle-like aggregates in aged rats and without associated inflammation. Based on a longitudinal characterization over 30 months, we showed that extrasynaptic and emotional impairments appear before long-term potentiation deficits and memory decline and so before Aß and Tau aggregations. These compelling data allowed us to (1) experimentally confirm the causal relationship between ßAPP processing and Tau pathology in vivo and without Tau transgene overexpression, (2) support the amyloidogenic cascade and (3) propose a 4-step hypothesis of prodromal AD progression.

Primatologist: A modular segmentation pipeline for macaque brain morphometry.

Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Primatologist, for multi-region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation-maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre-processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2-weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non-linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated.

Cortical areas involved in behavioral expression of external pallidum dysfunctions: A PET imaging study in non-human primates.

The external pallidum (GPe) is a component of the indirect pathway centrally placed in the basal ganglia. Studies already demonstrated that the pharmacological disinhibition of the sensorimotor, associative, and limbic GPe produced dyskinesia, hyperactivity, and compulsive behaviors, respectively. The aim of this study was to investigate the cortical regions altered by the disinhibition of each GPe functional territory. Thus, 5 macaques were injected with bicuculline in sensorimotor, associative, and limbic sites of the GPe producing dyskinesia, hyperactivity, and compulsive behaviors, and underwent in vivo positron tomography with 18F-2-fluoro-2-deoxy-D-glucose to identify cortical dysfunctions related to GPe disinhibition. Blood cortisol levels were also quantified as a biomarker of anxiety for each condition. Our results showed that pallidal bicuculline injections in anesthetized animals reproducibly modified the activity of specific ipsilateral and contralateral cortical areas depending on the pallidal territory targeted. Bicuculline injections in the limbic GPe led to increased ipsilateral activations in limbic cortical regions (anterior insula, amygdala, and hippocampus). Injections in the associative vs. sensorimotor GPe increased the activity in the ipsilateral midcingulate vs. somatosensory and parietal cortices. Moreover, bicuculline injections increased blood cortisol levels only in animals injected in their limbic GPe. These are the first functional results supporting the model of opened cortico-striato-thalamo-cortical loops where modifications in a functional pallidal territory can impact cortical activities of the same functional territory but also cortical activities of other functional territories. This highlights the importance of the GPe as a crucial node in the top-down control of the cortico-striato-thalamo-cortical circuits from the frontal cortex to influence the perception, attention, and emotional processes at downstream (or non-frontal) cortical levels. Finally, we showed the implication of the ventral pallidum with the amygdala and the insular cortex in a circuit related to aversive processing that should be crucial for the production of anxious disorders.

ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's Disease.

Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amyloid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic analyses centred on metalloprotease genes unraveled a 50% decrease in ADAM30 expression that inversely correlates with amyloid load in Alzheimer's disease brains. Accordingly, in vitro down- or up-regulation of ADAM30 expression triggered an increase/decrease in Aß peptides levels whereas expression of a biologically inactive ADAM30 (ADAM30(mut)) did not affect Aß secretion. Proteomics/cell-based experiments showed that ADAM30-dependent regulation of APP metabolism required both cathepsin D (CTSD) activation and APP sorting to lysosomes. Accordingly, in Alzheimer-like transgenic mice, neuronal ADAM30 over-expression lowered Aß42 secretion in neuron primary cultures, soluble Aß42 and amyloid plaque load levels in the brain and concomitantly enhanced CTSD activity and finally rescued long term potentiation alterations. Our data thus indicate that lowering ADAM30 expression may favor Aß production, thereby contributing to Alzheimer's disease development.

In Vivo Detection of Amyloid Plaques by Gadolinium-Stained MRI Can Be Used to Demonstrate the Efficacy of an Anti-amyloid Immunotherapy.

Extracellular deposition of ß 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 Aß. 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.