Evaluation of Atlas based Mouse Brain Segmentation

Development and advancements in rodent MRI-based brain atlases

Rodents, particularly mice and rats, are extensively utilized in fundamental neuroscience research. Brain atlases have played a pivotal role in this field, evolving from traditional printed histology atlases to digital atlases incorporating diverse imaging datasets. Magnetic resonance imaging (MRI)-based brain atlases, also known as brain maps, have been employed in specific studies. However, the existence of numerous versions of MRI-based brain atlases has impeded their standardized application and widespread use, despite the consensus within the academic community regarding their significance in mice and rats. Furthermore, there is a dearth of comprehensive and systematic reviews on MRI-based brain atlases for rodents. This review aims to bridge this gap by providing a comprehensive overview of the advancements in MRI-based brain atlases for rodents, with a specific focus on mice and rats. It seeks to explore the advantages and disadvantages of histologically printed brain atlases in comparison to MRI brain atlases, delineate the standardized methods for creating MRI brain atlases, and summarize their primary applications in neuroscience research. Additionally, this review aims to assist researchers in selecting appropriate versions of MRI brain atlases for their studies or refining existing MRI brain atlas resources, thereby facilitating the development and widespread adoption of standardized MRI-based brain atlases in rodents.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Automatic Brain Extraction for Rodent MRI Images

Rodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel method, termed SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), which only requires a brain template mask as the input and is capable of automatically and reliably extracting the brain tissue in both rat and mouse MRI images. The method identifies a set of brain mask candidates, extracted from MRI images morphologically opened and closed sequentially with multiple kernel sizes, that match the shape of the brain template. These brain mask candidates are then merged to generate the brain mask. This method, along with four other state-of-the-art rodent brain extraction methods, were benchmarked on four separate datasets including both rat and mouse MRI images. Without involving any parameter tuning, our method performed comparably to the other four methods on all datasets, and its performance was robust with stably high true positive rates and low false positive rates. Taken together, this study provides a reliable automatic brain extraction method that can contribute to the establishment of automatic pipelines for rodent neuroimaging data analysis.

A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L₀ regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome

We describe a fully automated pipeline for the morphometric phenotyping of mouse brains from μMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes in the brain of this first transchromosomic animal carrying human chromosome 21. We incorporate an accessible approach for simultaneously scanning multiple ex vivo brains, requiring only a 3D-printed brain holder, and novel image processing steps for their separation and orientation. We employ clinically established multi-atlas techniques–superior to single-atlas methods–together with publicly-available atlas databases for automatic skull-stripping and tissue segmentation, providing high-quality, subject-specific tissue maps. We follow these steps with group-wise registration, structural parcellation and both Voxel- and Tensor-Based Morphometry–advantageous for their ability to highlight morphological differences without the laborious delineation of regions of interest. We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and parameters suitable for the preclinical paradigm. We used this pipeline to compare 29 Tc1 brains with 26 wild-type littermate controls, imaged ex vivo at 9.4T. We show an unexpected increase in Tc1 total intracranial volume and, controlling for this, local volume and grey matter density reductions in the Tc1 brain compared to the wild-types, most prominently in the cerebellum, in agreement with human DS and previous histological findings.

Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping

Intensive international efforts are underway toward phenotyping the entire mouse genome by modifying all its [Formula: see text] genes one-by-one for comparative studies. A workload of this scale has triggered numerous studies harnessing image informatics for the identification of morphological defects. However, existing work in this line primarily rests on abnormality detection via structural volumetrics between wild-type and gene-modified mice, which generally fails when the pathology involves no severe volume changes, such as ventricular septal defects (VSDs) in the heart. Furthermore, in embryo cardiac phenotyping, the lack of relevant work in embryonic heart segmentation, the limited availability of public atlases, and the general requirement of manual labor for the actual phenotype classification after abnormality detection, along with other limitations, have collectively restricted existing practices from meeting the high-throughput demands. This study proposes, to the best of our knowledge, the first fully automatic VSD classification framework in mouse embryo imaging. Our approach leverages a combination of atlas-based segmentation and snake evolution techniques to derive the segmentation of heart ventricles, where VSD classification is achieved by checking whether the left and right ventricles border or overlap with each other. A pilot study has validated our approach at a proof-of-concept level and achieved a classification accuracy of 100% through a series of empirical experiments on a database of 15 images.

Automatic structural parcellation of mouse brain MRI using multi-atlas label fusion

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.

A midas plugin to enable construction of reproducible web-based image processing pipelines

Image processing is an important quantitative technique for neuroscience researchers, but difficult for those who lack experience in the field. In this paper we present a web-based platform that allows an expert to create a brain image processing pipeline, enabling execution of that pipeline even by those biomedical researchers with limited image processing knowledge. These tools are implemented as a plugin for Midas, an open-source toolkit for creating web based scientific data storage and processing platforms. Using this plugin, an image processing expert can construct a pipeline, create a web-based User Interface, manage jobs, and visualize intermediate results. Pipelines are executed on a grid computing platform using BatchMake and HTCondor. This represents a new capability for biomedical researchers and offers an innovative platform for scientific collaboration. Current tools work well, but can be inaccessible for those lacking image processing expertise. Using this plugin, researchers in collaboration with image processing experts can create workflows with reasonable default settings and streamlined user interfaces, and data can be processed easily from a lab environment without the need for a powerful desktop computer. This platform allows simplified troubleshooting, centralized maintenance, and easy data sharing with collaborators. These capabilities enable reproducible science by sharing datasets and processing pipelines between collaborators. In this paper, we present a description of this innovative Midas plugin, along with results obtained from building and executing several ITK based image processing workflows for diffusion weighted MRI (DW MRI) of rodent brain images, as well as recommendations for building automated image processing pipelines. Although the particular image processing pipelines developed were focused on rodent brain MRI, the presented plugin can be used to support any executable or script-based pipeline.

RATS: Rapid Automatic Tissue Segmentation in rodent brain MRI

BACKGROUND

High-field MRI is a popular technique for the study of rodent brains. These datasets, while similar to human brain MRI in many aspects, present unique image processing challenges. We address a very common preprocessing step, skull-stripping, which refers to the segmentation of the brain tissue from the image for further processing. While several methods exist for addressing this problem, they are computationally expensive and often require interactive post-processing by an expert to clean up poorly segmented areas. This further increases total processing time per subject.

NEW METHOD

We propose a novel algorithm, based on grayscale mathematical morphology and LOGISMOS-based graph segmentation, which is rapid, robust and highly accurate.

RESULTS

Comparative results obtained on two challenging in vivo datasets, consisting of 22 T1-weighted rat brain images and 10 T2-weighted mouse brain images illustrate the robustness and excellent performance of the proposed algorithm, in a fraction of the computational time needed by existing algorithms.

COMPARISON WITH EXISTING METHODS

In comparison to current state-of-the-art methods, our approach achieved average Dice similarity coefficient of 0.92 ± 0.02 and average Hausdorff distance of 13.6 ± 5.2 voxels (vs. 0.85 ± 0.20, p<0.05 and 42.6 ± 22.9, p << 0.001) for the rat dataset, and 0.96 ± 0.01 and average Hausdorff distance of 21.6 ± 12.7 voxels (vs. 0.93 ± 0.01, p <<0.001 and 33.7 ± 3.5, p <<0.001) for the mouse dataset. The proposed algorithm took approximately 90s per subject, compared to 10-20 min for the neural-network based method and 30-90 min for the atlas-based method.

CONCLUSIONS

RATS is a robust and computationally efficient method for accurate rodent brain skull-stripping even in challenging data.

Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics

Magnetic resonance imaging (MRI) of rodent brains enables study of the development and the integrity of the brain under certain conditions (alcohol, drugs etc.). However, these images are difficult to analyze for biomedical researchers with limited image processing experience. In this paper we present an image processing pipeline running on a Midas server, a web-based data storage system. It is composed of the following steps: rigid registration, skull-stripping, average computation, average parcellation, parcellation propagation to individual subjects, and computation of region-based statistics on each image. The pipeline is easy to configure and requires very little image processing knowledge. We present results obtained by processing a data set using this pipeline and demonstrate how this pipeline can be used to find differences between populations.

Comparison of magnetic resonance imaging in live vs. post mortem rat brains

Magnetic Resonance Imaging (MRI) is an increasingly popular technique for examining neurobiology in rodents because it is both noninvasive and nondestructive. MRI scans can be acquired from either live or post mortem specimens. In vivo scans have a key advantage in that subjects can be scanned at multiple time-points in longitudinal studies. However, repeated exposure to anesthesia and stress may confound studies. In contrast, post mortem scans offer improved image quality and increased signal-to-noise ratio (SNR) due to several key advantages: First, the images are not disrupted by motion and pulsation artifacts. Second, they allow the brain tissue to be perfused with contrast agents, enhancing tissue contrast. Third, they allow longer image acquisition times, yielding higher resolution and/or improved SNR. Fourth, they allow assessment of groups of animals at the same age without scheduling complications. Despite these advantages, researchers are often skeptical of post mortem MRI scans because of uncertainty about whether the fixation process alters the MRI measurements. To address these concerns, we present a thorough comparative study of in vivo and post mortem MRI scans in healthy male Wistar rats at three age points throughout adolescence (postnatal days 28 through 80). For each subject, an in vivo scan was acquired, followed by perfusion and two post mortem scans at two different MRI facilities. The goal was to assess robustness of measurements, to detect any changes in volumetric measurements after fixation, and to investigate any differential bias that may exist between image acquisition techniques. We present this volumetric analysis for comparison of 22 anatomical structures between in vivo and post mortem scans. No significant changes in volumetric measurements were detected; however, as hypothesized, the image quality is dramatically improved in post mortem scans. These findings illustrate the validity and utility of using post mortem scans in volumetric neurobiological studies.

3-dimensional diffusion tensor imaging (DTI) atlas of the rat brain

Anatomical atlases play an important role in the analysis of neuroimaging data in rodent neuroimaging studies. Having a high resolution, detailed atlas not only can expand understanding of rodent brain anatomy, but also enables automatic segmentation of new images, thus greatly increasing the efficiency of future analysis when applied to new data. These atlases can be used to analyze new scans of individual cases using a variety of automated segmentation methods. This project seeks to develop a set of detailed 3D anatomical atlases of the brain at postnatal day 5 (P5), 14 (P14), and adults (P72) in Sprague-Dawley rats. Our methods consisted of first creating a template image based on fixed scans of control rats, then manually segmenting various individual brain regions on the template. Using itk-SNAP software, subcortical and cortical regions, including both white matter and gray matter structures, were manually segmented in the axial, sagittal, and coronal planes. The P5, P14, and P72 atlases had 39, 45, and 29 regions segmented, respectively. These atlases have been made available to the broader research community.

Automated segmentation of mouse brain images using multi-atlas multi-ROI deformation and label fusion

We propose an automated multi-atlas and multi-ROI based segmentation method for both skull-stripping of mouse brain and the ROI-labeling of mouse brain structures from the three dimensional (3D) magnetic resonance images (MRI). Three main steps are involved in our method. First, a region of interest (ROI) guided warping algorithm is designed to register multi-atlas images to the subject space, by considering more on the matching of image contents around the ROI boundaries which are more important for ROI labeling. Then, a multi-atlas and multi-ROI based deformable segmentation method is adopted to refine the ROI labeling result by deforming each ROI surface via boundary recognizers (i.e., SVM classifiers) trained on local surface patches. Finally, a local-mutual-information (MI) based multi-label fusion technique is proposed for allowing the atlases with better local image similarity with the subject to have more contributions in label fusion. The experimental results show that our method works better than the conventional methods on both in vitro and in vivo mouse brain datasets.

Peri-adolescent ethanol vapor exposure produces reductions in hippocampal volume that are correlated with deficits in prepulse inhibition of the startle

BACKGROUND

Epidemiological studies suggest that excessive alcohol consumption is prevalent among adolescents and may have lasting neurobehavioral consequences. The use of animal models allows for the separation of the effects of adolescent ethanol (EtOH) exposure from genetic background and other environmental insults. In this study, the effects of moderate EtOH vapor exposure, during adolescence, on structural diffusion tensor imaging (DTI) and behavioral measures were evaluated in adulthood.

METHODS

A total of 53 Wistar rats were received at postnatal day (PD) 21 and were randomly assigned to EtOH vapor (14 hours on/10 hours off/day) or air exposure for 35 days from PD 23 to 58 (average blood ethanol concentration: 169 mg%). Animals were received in 2 groups that were subsequently sacrificed at 2 time points following withdrawal from EtOH vapor: (i) at 72 days of age, 2 weeks following withdrawal or (ii) at day 128, 10 weeks following withdrawal. In the second group, behavior in the light/dark box and prepulse inhibition (PPI) of the startle was also evaluated. Fifteen animals in each group were scanned, postmortem, for structural DTI.

RESULTS

There were no significant differences in body weight between EtOH and control animals. Volumetric data demonstrated that total brain, hippocampal, corpus callosum but not ventricular volume were significantly larger in the 128-day-sacrificed animals as compared to the 72 day animals. The hippocampus was smaller and the ventricles larger at 128 days as compared to 72 days, in the EtOH-exposed animals, leading to a significant group × time effect. EtOH-exposed animals sacrificed at 128 days also had diminished PPI, and more rears in the light box were significantly correlated with hippocampal size.

CONCLUSIONS

These studies demonstrate that DTI volumetric measures of hippocampus are significantly impacted by age and peri-adolescent EtOH exposure and withdrawal in Wistar rats.

Postnatal day 7 ethanol treatment causes persistent reductions in adult mouse brain volume and cortical neurons with sex specific effects on neurogenesis

Ethanol treatment on postnatal day seven (P7) causes robust brain cell death and is a model of late gestational alcohol exposure (Ikonomidou et al., 2000). To investigate the long-term effects of P7 ethanol treatment on adult brain, mice received either two doses of saline or ethanol on P7 (2.5 g/kg, s.c., 2 h apart) and were assessed as adults (P82) for brain volume (using postmortem MRI) and cellular architecture (using immunohistochemistry). Adult mice that received P7 ethanol had reduced MRI total brain volume (4%) with multiple brain regions being reduced in both males and females. Immunohistochemistry indicated reduced frontal cortical parvalbumin immunoreactive (PV + IR) interneurons (18-33%) and reduced Cux1+IR layer II pyramidal neurons (15%) in both sexes. Interestingly, markers of adult hippocampal neurogenesis differed between sexes, with only ethanol treated males showing increased doublecortin and Ki67 expression (52 and 57% respectively) in the dentate gyrus, consistent with increased neurogenesis compared to controls. These findings suggest that P7 ethanol treatment causes persistent reductions in adult brain volume and frontal cortical neurons in both males and females. Increased adult neurogenesis in males, but not females, is consistent with differential adaptive responses to P7 ethanol toxicity between the sexes. One day of ethanol exposure, e.g. P7, causes persistent adult brain dysmorphology.

Quantitative mouse brain phenotyping based on single and multispectral MR protocols

Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83% for hippocampus and caudate putamen (Hc and CPu), but only 54% in white matter tracts, and 44% for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by >5%), white matter (10%), and ventricles (15%). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain.

The translational role of diffusion tensor image analysis in animal models of developmental pathologies

Diffusion tensor magnetic resonance imaging (DTI) has proven itself a powerful technique for clinical investigation of the neurobiological targets and mechanisms underlying developmental pathologies. The success of DTI in clinical studies has demonstrated its great potential for understanding translational animal models of clinical disorders, and preclinical animal researchers are beginning to embrace this new technology to study developmental pathologies. In animal models, genetics can be effectively controlled, drugs consistently administered, subject compliance ensured, and image acquisition times dramatically increased to reduce between-subject variability and improve image quality. When pairing these strengths with the many positive attributes of DTI, such as the ability to investigate microstructural brain organization and connectivity, it becomes possible to delve deeper into the study of both normal and abnormal development. The purpose of this review is to provide new preclinical investigators with an introductory source of information about the analysis of data resulting from small animal DTI studies to facilitate the translation of these studies to clinical data. In addition to an in-depth review of translational analysis techniques, we present a number of relevant clinical and animal studies using DTI to investigate developmental insults in order to further illustrate techniques and to highlight where small animal DTI could potentially provide a wealth of translational data to inform clinical researchers.

Synergy of image analysis for animal and human neuroimaging supports translational research on drug abuse

The use of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) in animal models of neuropathology is of increasing interest to the neuroscience community. In this work, we present our approach to create optimal translational studies that include both animal and human neuroimaging data within the frameworks of a study of post-natal neuro-development in intra-uterine cocaine-exposure. We propose the use of non-invasive neuroimaging to study developmental brain structural and white matter pathway abnormalities via sMRI and DTI, as advanced MR imaging technology is readily available and automated image analysis methodology have recently been transferred from the human to animal imaging setting. For this purpose, we developed a synergistic, parallel approach to imaging and image analysis for the human and the rodent branch of our study. We propose an equivalent design in both the selection of the developmental assessment stage and the neuroimaging setup. This approach brings significant advantages to study neurobiological features of early brain development that are common to animals and humans but also preserve analysis capabilities only possible in animal research. This paper presents the main framework and individual methods for the proposed cross-species study design, as well as preliminary DTI cross-species comparative results in the intra-uterine cocaine-exposure study.

Automatic cortical thickness analysis on rodent brain

Localized difference in the cortex is one of the most useful morphometric traits in human and animal brain studies. There are many tools and methods already developed to automatically measure and analyze cortical thickness for the human brain. However, these tools cannot be directly applied to rodent brains due to the different scales; even adult rodent brains are 50 to 100 times smaller than humans. This paper describes an algorithm for automatically measuring the cortical thickness of mouse and rat brains. The algorithm consists of three steps: segmentation, thickness measurement, and statistical analysis among experimental groups. The segmentation step provides the neocortex separation from other brain structures and thus is a preprocessing step for the thickness measurement. In the thickness measurement step, the thickness is computed by solving a Laplacian PDE and a transport equation. The Laplacian PDE first creates streamlines as an analogy of cortical columns; the transport equation computes the length of the streamlines. The result is stored as a thickness map over the neocortex surface. For the statistical analysis, it is important to sample thickness at corresponding points. This is achieved by the particle correspondence algorithm which minimizes entropy between dynamically moving sample points called particles. Since the computational cost of the correspondence algorithm may limit the number of corresponding points, we use thin-plate spline based interpolation to increase the number of corresponding sample points. As a driving application, we measured the thickness difference to assess the effects of adolescent intermittent ethanol exposure that persist into adulthood and performed t-test between the control and exposed rat groups. We found significantly differing regions in both hemispheres.

Automatic Skull-stripping of Rat MRI/DTI Scans and Atlas Building

3D Magnetic Resonance (MR) and Diffusion Tensor Imaging (DTI) have become important noninvasive tools for the study of animal models of brain development and neuropathologies. Fully automated analysis methods adapted to rodent scale for these images will allow high-throughput studies. A fundamental first step for most quantitative analysis algorithms is skull-stripping, which refers to the segmentation of the image into two tissue categories, brain and non-brain. In this manuscript, we present a fully automatic skull-stripping algorithm in an atlas-based manner. We also demonstrate how to either modify an external atlas or to build an atlas from the population itself to present a self-contained approach. We applied our method to three datasets of rat brain scans, at different ages (PND5, PND14 and adult), different study groups (control, ethanol exposed), as well as different image acquisition parameters. We validated our method by comparing the automated skull-strip results to manual delineations performed by our expert, which showed a discrepancy of less than a single voxel on average. We thus demonstrate that our algorithm can robustly and accurately perform the skull-stripping within one voxel of the manual delineation, and in a fraction of the time it takes a human expert.

Adolescent binge drinking alters adult brain neurotransmitter gene expression, behavior, brain regional volumes, and neurochemistry in mice

BACKGROUND

Binge drinking is common in human adolescents. The adolescent brain is undergoing structural maturation and has a unique sensitivity to alcohol neurotoxicity. Therefore, adolescent binge ethanol may have long-term effects on the adult brain that alter brain structure and behaviors that are relevant to alcohol-use disorders.

METHODS

To determine whether adolescent ethanol (AE) binge drinking alters the adult brain, male C57BL/6 mice were treated with either water or ethanol during adolescence (5 g/kg/d, i.g., postnatal days P28 to P37) and assessed during adulthood (P60 to P88). An array of neurotransmitter-specific genes, behavioral tests (i.e., reversal learning, prepulse inhibition, and open field), and postmortem brain structure using magnetic resonance imaging (MRI) and immunohistochemistry, were employed to assess persistent alterations in adult brain.

RESULTS

At P38, 24 hours after AE binge, many neurotransmitter genes, particularly cholinergic and dopaminergic, were reduced by ethanol treatment. Interestingly, dopamine receptor type 4 mRNA was reduced and confirmed using immunohistochemistry. Normal control maturation (P38 to P88) resulted in decreased neurotransmitter mRNA, e.g., an average decrease of 56%. Following AE treatment, adults showed greater gene expression reductions than controls, averaging 73%. Adult spatial learning assessed in the Morris water maze was not changed by AE treatment, but reversal learning experiments revealed deficits. Assessment of adult brain region volumes using MRI indicated that the olfactory bulb and basal forebrain were smaller in adults following AE. Immunohistochemical analyses found reduced basal forebrain area and fewer basal forebrain cholinergic neurons.

CONCLUSIONS

Adolescent binge ethanol treatment reduces adult neurotransmitter gene expression, particularly cholinergic genes, reduces basal forebrain and olfactory bulb volumes, and causes a reduction in the density of basal forebrain acetylcholine neurons. Loss of cholinergic neurons and forebrain structure could underlie adult reversal learning deficits following adolescent binge drinking.