atlasBREX: Automated template-derived brain extraction in animal MRI

RS2-Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI

Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissue contrast, significant variations in image resolution, and blurred boundaries between the brain and surrounding tissues, particularly in rodents. In this study, we have developed a lightweight framework based on Swin-UNETR to automate the skull stripping process in MRI scans of mice and rats. The primary objective of this framework is to eliminate the need for preprocessing, reduce the workload, and provide an out-of-the-box solution capable of adapting to various MRI image resolutions. By employing a lightweight neural network, we aim to lower the performance requirements of the framework. To validate the effectiveness of our approach, we trained and evaluated the network using publicly available multi-center data, encompassing 1,037 rodents and 1,142 images from 89 centers, resulting in a preliminary mean Dice coefficient of 0.9914. The framework, data, and pre-trained models can be found on the following link: https://github.com/VitoLin21/Rodent-Skull-Stripping.

Brief isoflurane administration as an adjunct treatment to control organophosphate-induced convulsions and neuropathology

Organophosphate-based chemical agents (OP), including nerve agents and certain pesticides such as paraoxon, are potent acetylcholinesterase inhibitors that cause severe convulsions and seizures, leading to permanent central nervous system (CNS) damage if not treated promptly. The current treatment regimen for OP poisoning is intramuscular injection of atropine sulfate with an oxime such as pralidoxime (2-PAM) to mitigate cholinergic over-activation of the somatic musculature and autonomic nervous system. This treatment does not provide protection against CNS cholinergic overactivation and therefore convulsions require additional medication. Benzodiazepines are the currently accepted treatment for OP-induced convulsions, but the convulsions become refractory to these GABAA agonists and repeated dosing has diminishing effectiveness. As such, adjunct anticonvulsant treatments are needed to provide improved protection against recurrent and prolonged convulsions and the associated excitotoxic CNS damage that results from them. Previously we have shown that brief, 4-min administration of 3%-5% isoflurane in 100% oxygen has profound anticonvulsant and CNS protective effects when administered 30 min after a lethal dose of paraoxon. In this report we provide an extended time course of the effectiveness of 5% isoflurane delivered for 5 min, ranging from 60 to 180 min after a lethal dose of paraoxon in rats. We observed substantial effectiveness in preventing neuronal loss as shown by Fluoro-Jade B staining when isoflurane was administered 1 h after paraoxon, with diminishing effectiveness at 90, 120 and 180 min. In vivo magnetic resonance imaging (MRI) derived T2 and mean diffusivity (MD) values showed that 5-min isoflurane administration at a concentration of 5% prevents brain edema and tissue damage when administered 1 h after a lethal dose of paraoxon. We also observed reduced astrogliosis as shown by GFAP immunohistochemistry. Studies with continuous EEG monitoring are ongoing to demonstrate effectiveness in animal models of soman poisoning.

Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient's image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.

Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease

Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.

HC-Net: A hybrid convolutional network for non-human primate brain extraction

Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.

Fibrin-targeting molecular MRI in inflammatory CNS disorders

BACKGROUND

Fibrin deposition is a fundamental pathophysiological event in the inflammatory component of various CNS disorders, such as multiple sclerosis (MS) and Alzheimer's disease. Beyond its traditional role in coagulation, fibrin elicits immunoinflammatory changes with oxidative stress response and activation of CNS-resident/peripheral immune cells contributing to CNS injury.

PURPOSE

To investigate if CNS fibrin deposition can be determined using molecular MRI, and to assess its capacity as a non-invasive imaging biomarker that corresponds to inflammatory response and barrier impairment.

MATERIALS AND METHODS

Specificity and efficacy of a peptide-conjugated Gd-based molecular MRI probe (EP2104-R) to visualise and quantify CNS fibrin deposition were evaluated. Probe efficacy to specifically target CNS fibrin deposition in murine adoptive-transfer experimental autoimmune encephalomyelitis (EAE), a pre-clinical model for MS (n = 12), was assessed. Findings were validated using immunohistochemistry and laser ablation inductively coupled plasma mass spectrometry. Deposition of fibrin in neuroinflammatory conditions was investigated and its diagnostic capacity for disease staging and monitoring as well as quantification of immunoinflammatory response was determined. Results were compared using t-tests (two groups) or one-way ANOVA with multiple comparisons test. Linear regression was used to model the relationship between variables.

RESULTS

For the first time (to our knowledge), CNS fibrin deposition was visualised and quantified in vivo using molecular imaging. Signal enhancement was apparent in EAE lesions even 12-h after administration of EP2104-R due to targeted binding (M ± SD, 1.07 ± 0.10 (baseline) vs. 0.73 ± 0.09 (EP2104-R), p = .008), which could be inhibited with an MRI-silent analogue (M ± SD, 0.60 ± 0.14 (EP2104-R) vs. 0.96 ± 0.13 (EP2104-La), p = .006). CNS fibrin deposition corresponded to immunoinflammatory activity (R2 = 0.85, p < .001) and disability (R2 = 0.81, p < .001) in a model for MS, which suggests a clinical role for staging and monitoring. Additionally, EP2104-R showed substantially higher SNR (M ± SD, 6.6 ± 1 (EP2104-R) vs. 2.7 ± 0.4 (gadobutrol), p = .004) than clinically used contrast media, which increases sensitivity for lesion detection.

CONCLUSIONS

Molecular imaging of CNS fibrin deposition provides an imaging biomarker for inflammatory CNS pathology, which corresponds to pathophysiological ECM remodelling and disease activity, and yields high signal-to-noise ratio, which can improve diagnostic neuroimaging across several neurological diseases with variable degrees of barrier impairment.

In vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats

Spontaneously hypertensive rats (SHRs) are a valuable animal model of essential hypertension. The increasing use of SHRs in neuroimaging has generated an urgent demand for a template set that provides a reference for advanced data analysis. Structural T2-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and functional MRI scans that were used to build the template set were obtained from 8 SHRs longitudinally scanned in vivo at 10, 24 and 52 weeks of age. These symmetric multi-contrast templates were constructed by iterative registration and averaging. The cortical atlas was derived from the Tohoku atlas, and the subcortical regions were manually delineated based on the templates. A set of SHR brain images named the Hebei Medical University rat brain template set (HRT) comprised 3D symmetric T2WI, raw T2-weighted signal with no added diffusion weighting (B0), fractional anisotropy (FA), mean diffusivity (MD) and blood oxygen level-dependent (BOLD) templates; tissue probability maps (TPMs) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF); and a whole-brain atlas with 163 labels. We quantitatively validated the template and characterized the longitudinal changes in brain morphology in different brain tissues as SHRs aged. To our knowledge, the HRT is the first MRI template set for SHRs. We believe that the HRT can serve as a beneficial tool for precise analysis of the SHR brain using structural and functional MRI, which can promote neuroimaging studies on essential hypertension.

Patch-based 3D U-Net and transfer learning for longitudinal piglet brain segmentation on MRI

BACKGROUND AND OBJECTIVES

In order to study neural plasticity in immature brain following early brain lesion, large animal model are needed. Because of its morphological similarities with the human developmental brain, piglet is a suitable but little used one. Its study from Magnetic Resonance Imaging (MRI) requires the development of automatic algorithms for the segmentation of the different structures and tissues. A crucial preliminary step consists in automatically segmenting the brain.

METHODS

We propose a fully automatic brain segmentation method applied to piglets by combining a 3D patch-based U-Net and a post-processing pipeline for spatial regularization and elimination of false positives. Our approach also integrates a transfer-learning strategy for managing an automated longitudinal monitoring evaluated for four developmental stages (2, 6, 10 and 18 weeks), facing the issue of MRI changes resulting from the rapid brain development. It is compared to a 2D approach and the Brain Extraction Tool (BET) as well as techniques adapted to other animals (rodents, macaques). The influence of training patches size and distribution is studied as well as the benefits of spatial regularization.

RESULTS

Results show that our approach is efficient in terms of average Dice score (0.952) and Hausdorff distance (8.51), outperforming the use of a 2D U-Net (Dice: 0.919, Hausdorff distance: 11.06) and BET (Dice: 0.764, Hausdorff distance: 25.91). The transfer-learning strategy achieves a good performance on older piglets (Dice of 0.934 at 6 weeks, 0.956 at 10 weeks and 0.958 at 18 weeks) compared to a standard training strategy with few data (Dice of 0.636 at 6 weeks, 0.907 at 10 weeks, not calculable at 18 weeks because of too few training piglets).

CONCLUSIONS

In conclusion, we provide a method for longitudinal MRI piglet brain segmentation based on 3D U-Net and transfer learning which can be used for future morphometric studies and applied to other animals.

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans

Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications.

U-net model for brain extraction: Trained on humans for transfer to non-human primates

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Early postnatal irradiation-induced age-dependent changes in adult mouse brain: MRI based characterization

Background

Brain radiation exposure, in particular, radiotherapy, can induce cognitive impairment in patients, with significant effects persisting for the rest of their life. However, the main mechanisms leading to this adverse event remain largely unknown. A study of radiation-induced injury to multiple brain regions, focused on the hippocampus, may shed light on neuroanatomic bases of neurocognitive impairments in patients. Hence, we irradiated BALB/c mice (male and female) at postnatal day 3 (P3), day 10 (P10), and day 21 (P21) and investigated the long-term radiation effect on brain MRI changes and hippocampal neurogenesis.

Results

We found characteristic brain volume reductions in the hippocampus, olfactory bulbs, the cerebellar hemisphere, cerebellar white matter (WM) and cerebellar vermis WM, cingulate, occipital and frontal cortices, cerebellar flocculonodular WM, parietal region, endopiriform claustrum, and entorhinal cortex after irradiation with 5 Gy at P3. Irradiation at P10 induced significant volume reduction in the cerebellum, parietal region, cingulate region, and olfactory bulbs, whereas the reduction of the volume in the entorhinal, parietal, insular, and frontal cortices was demonstrated after irradiation at P21. Immunohistochemical study with cell division marker Ki67 and immature marker doublecortin (DCX) indicated the reduced cell division and genesis of new neurons in the subgranular zone of the dentate gyrus in the hippocampus after irradiation at all three postnatal days, but the reduction of total granule cells in the stratum granulosun was found after irradiation at P3 and P10.

Conclusions

The early life radiation exposure during different developmental stages induces varied brain pathophysiological changes which may be related to the development of neurological and neuropsychological disorders later in life.

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.

DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques

As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.

Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates

The standard anatomical brain template provides a common space and coordinate system for visualizing and analyzing neuroimaging data from large cohorts of subjects. Previous templates and atlases for the common marmoset brain were either based on data from a single individual or lacked essential functionalities for neuroimaging analysis. Here, we present new population-based in-vivo standard templates and tools derived from multi-modal data of 27 marmosets, including multiple types of T1w and T2w contrast images, DTI contrasts, and large field-of-view MRI and CT images. We performed multi-atlas labeling of anatomical structures on the new templates and constructed highly accurate tissue-type segmentation maps to facilitate volumetric studies. We built fully featured brain surfaces and cortical flat maps to facilitate 3D visualization and surface-based analyses, which are compatible with most surface analyzing tools, including FreeSurfer, AFNI/SUMA, and the Connectome Workbench. Analysis of the MRI and CT datasets revealed significant variations in brain shapes, sizes, and regional volumes of brain structures, highlighting substantial individual variabilities in the marmoset population. Thus, our population-based template and associated tools provide a versatile analysis platform and standard coordinate system for a wide range of MRI and connectome studies of common marmosets. These new template tools comprise version 3 of our Marmoset Brain Mapping Project and are publicly available via marmosetbrainmapping.org/v3.html.

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.

Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2^∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using data extracted from magnetic resonance imaging (MRI). This protocol allows for the generation of 3D printed anatomically accurate physical models of the brain and skull, as well as an agarose gel model of the brain modeling some of the mechanical properties of the brain. These models can be extracted from MRI using brain extraction software for the model of the brain, and custom code for the model of the skull. The preparation protocol takes advantage of state-of-the-art 3D printing technology to make interfacing brains, skulls, and molds for gel brain models. The skull and brain models can be used to visualize brain tissue inside the skull with the addition of a craniotomy in the custom code, allowing for better preparation for surgeries directly involving the brain. The applications of these methods are designed for surgeries involved in neurological stimulation and recording as well as injection, but the versatility of the system allows for future expansion of the protocol, extraction techniques, and models to a wider scope of surgeries.