Primatologist: A modular segmentation pipeline for macaque brain morphometry

Functional and neuropathological changes induced by injection of distinct alpha-synuclein strains: A pilot study in non-human primates

The role of alpha-synuclein in Parkinson's disease has been heavily investigated since its discovery as a component of Lewy bodies. Recent rodent data demonstrate that alpha-synuclein strain structure is critical for differential propagation and toxicity. Based on these findings, we have compared, for the first time, in this pilot study, the capacity of two alpha-synuclein strains and patient-derived Lewy body extracts to model synucleinopathies after intra-putaminal injection in the non-human primate brain. Functional alterations triggered by these injections were evaluated in vivo using glucose positron emission tomography imaging. Post-mortem immunohistochemical and biochemical analyses were used to detect neuropathological alterations in the dopaminergic system and alpha-synuclein pathology propagation. In vivo results revealed a decrease in glucose metabolism more pronounced in alpha-synuclein strain-injected animals. Histology showed a decreased number of dopaminergic tyrosine hydroxylase-positive cells in the substantia nigra to different extents according to the inoculum used. Biochemistry revealed that alpha-synuclein-induced aggregation, phosphorylation, and propagation in different brain regions are strain-specific. Our findings show that distinct alpha-synuclein strains can induce specific patterns of synucleinopathy in the non-human primate, changes in the nigrostriatal pathway, and functional alterations that resemble early-stage Parkinson's disease.

PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface

Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images.

NEW METHOD

In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types.

RESULTS

Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC.

COMPARISON WITH EXISTING METHODS

Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning.

CONCLUSIONS

Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

A collaborative resource platform for non-human primate neuroimaging

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.

Comparative test-retest variability of outcome parameters derived from brain [18F]FDG PET studies in non-human primates

INTRODUCTION

Knowledge of the repeatability of quantitative parameters derived from [18F]FDG PET images is essential to define the group size and allow correct interpretation. Here we tested repeatability and accuracy of different [18F]FDG absolute and relative quantification parameters in a standardized preclinical setup in nonhuman primates (NHP).

MATERIAL AND METHODS

Repeated brain [18F]FDG scans were performed in 6 healthy NHP under controlled experimental factors likely to account for variability. Regional cerebral metabolic rate of glucose (CMRglu) was calculated using a Patlak plot with blood input function Semi-quantitative approaches measuring standard uptake values (SUV, SUV×glycemia and SUVR (SUV Ratio) using the pons or cerebellum as a reference region) were considered. Test-retest variability of all quantification parameters were compared in different brain regions in terms of absolute variability and intra-and-inter-subject variabilities. In an independent [18F]FDG PET experiment, robustness of these parameters was evaluated in 4 naive NHP.

RESULTS

Experimental conditions (injected dose, body weight, animal temperature) were the same at both imaging sessions (p >0.4). No significant difference in the [18F]FDG quantification parameters was found between test and retest sessions. Absolute variability of CMRglu, SUV, SUV×glycemia and normalized SUV ranged from 25 to 43%, 16 to 21%, 23 to 28%, and 7 to 14%, respectively. Intra-subject variability largely explained the absolute variability of all quantitative parameters. They were all significantly correlated to each other and they were all robust. Arterial and venous glycemia were highly correlated (r = 0.9691; p<0.0001).

CONCLUSION

[18F]FDG test-retest studies in NHP protocols need to be conducted under well-standardized experimental conditions to assess and select the most reliable and reproducible quantification approach. Furthermore, the choice of the quantification parameter has to account for the transversal or follow-up study design. If pons and cerebellum regions are not affected, non-invasive SUVR is the most favorable approach for both designs.

atlasBREX: Automated template-derived brain extraction in animal MRI

We proposed a generic template-derived approach for (semi-) automated brain extraction in animal MRI studies and evaluated our implementation with different animal models (macaque, marmoset, rodent) and MRI protocols (T1, T2). While conventional MR-neuroimaging studies perform brain extraction as an initial step priming subsequent image-registration from subject to template, our proposed approach propagates an anatomical template to (whole-head) individual subjects in reverse order, which is challenging due to the surrounding extracranial tissue, greater differences in contrast pattern and larger areas with field inhomogeneity. As a novel approach, the herein introduced brain extraction algorithm derives whole-brain segmentation using rigid and non-rigid deformation based on unbiased anatomical atlas building with a priori estimates from study-cohort and an initial approximate brain extraction. We evaluated our proposed method in comparison to several other technical approaches including “Marker based watershed scalper”, “Brain-Extraction-Tool”, “3dSkullStrip”, “Primatologist-Toolbox”, “Rapid Automatic Tissue Segmentation” and “Robust automatic rodent brain extraction using 3D pulse-coupled neural networks” with manual skull-stripping as reference standard. ABX demonstrated best performance with accurate (≥92%) and consistent results throughout datasets and across species, age and MRI protocols. ABX was made available to the public with documentation, templates and sample material (https://www.github.com/jlohmeier/atlasBREX).

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.

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.

An Open Resource for Non-human Primate Imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

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).