A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy

Active learning of neuron morphology for accurate automated tracing of neurites

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.

Evaluation of coils for imaging histological slides: signal-to-noise ratio and filling factor

PURPOSE

To investigate the relative gain in sensitivity of five histology coils designed in-house to accommodate tissue sections of various sizes and compare with commercial mouse head coils.

METHODS

The coil set was tailored to house tissue sections ranging from 5 to1000 µm encased in either glass slides or coverslips.

RESULTS

Our simulations and experimental measurements demonstrated that although the sensitivity of this flat structure consistently underperforms relative to a birdcage head coil based on the gain expected from their respective filling factor ratios, our results demonstrate that it can still provide a remarkable gain in sensitivity. Our study also describes preparation protocols for freshly excised sections, as well as premounted tissue slides of both mouse and human specimens. Examples of the exceptional level of tissue detail and the near-perfect magnetic resonance imaging to light microscopic image coregistration are provided.

CONCLUSION

The increase in filling factor achieved by the histology radiofrequency (RF) probe overcomes the losses associated with electric leaks inherent to this structure, leading to a 6.7-fold improvement in performance for the smallest coil implemented. Alternatively, the largest histology coil design exhibited equal sensitivity to the mouse head coil while nearly doubling the RF planar area coverage.

Multi-atlas segmentation with particle-based group-wise image registration

We propose a novel multi-atlas segmentation method that employs a group-wise image registration method for the brain segmentation on rodent magnetic resonance (MR) images. The core element of the proposed segmentation is the use of a particle-guided image registration method that extends the concept of particle correspondence into the volumetric image domain. The registration method performs a group-wise image registration that simultaneously registers a set of images toward the space defined by the average of particles. The particle-guided image registration method is robust with low signal-to-noise ratio images as well as differing sizes and shapes observed in the developing rodent brain. Also, the use of an implicit common reference frame can prevent potential bias induced by the use of a single template in the segmentation process. We show that the use of a particle guided-image registration method can be naturally extended to a novel multi-atlas segmentation method and improves the registration method to explicitly use the provided template labels as an additional constraint. In the experiment, we show that our segmentation algorithm provides more accuracy with multi-atlas label fusion and stability against pair-wise image registration. The comparison with previous group-wise registration method is provided as well.

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.

Multimodal nanoprobes to target cerebrovascular amyloid in Alzheimer's disease brain

Cerebral amyloid angiopathy (CAA) results from the accumulation of Aβ proteins primarily within the media and adventitia of small arteries and capillaries of the cortex and leptomeninges. CAA affects a majority of Alzheimer's disease (AD) patients and is associated with a rapid decline in cognitive reserve. Unfortunately, there is no pre-mortem diagnosis available for CAA. Furthermore, treatment options are few and relatively ineffective. To combat this issue, we have designed nanovehicles (nanoparticles-IgG4.1) capable of targeting cerebrovascular amyloid (CVA) and serving as early diagnostic and therapeutic agents. These nanovehicles were loaded with Gadolinium (Gd) based (Magnevist(®)) magnetic resonance imaging contrast agents or single photon emission computed tomography (SPECT) agents, such as (125)I. In addition, the nanovehicles carry either anti-inflammatory and anti-amyloidogenic agents such as curcumin or immunosuppressants such as dexamethasone, which were previously shown to reduce cerebrovascular inflammation. Owing to the anti-amyloid antibody (IgG4.1) grafted on the surface, the nanovehicles are capable of specifically targeting CVA deposits. The nanovehicles effectively marginate from the blood flow to the vascular wall as determined by using quartz crystal microbalance with dissipation monitoring (QCM-D) technology. They demonstrate excellent distribution to the brain vasculature and target CVA, thus providing MRI and SPECT contrast specific to the CVA in the brain. In addition, they also display the potential to carry therapeutic agents to reduce cerebrovascular inflammation associated with CAA, which is believed to trigger hemorrhage in CAA patients.

Dipole source localization of mouse electroencephalogram using the Fieldtrip toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.

Visualization of mouse barrel cortex using ex-vivo track density imaging

We describe the visualization of the barrel cortex of the primary somatosensory area (S1) of ex vivo adult mouse brain with short-tracks track density imaging (stTDI). stTDI produced much higher definition of barrel structures than conventional fractional anisotropy (FA), directionally-encoded color FA maps, spin-echo T1- and T2-weighted imaging and gradient echo T1/T2*-weighted imaging. 3D high angular resolution diffusion imaging (HARDI) data were acquired at 48 micron isotropic resolution for a (3mm)(3) block of cortex containing the barrel field and reconstructed using stTDI at 10 micron isotropic resolution. HARDI data were also acquired at 100 micron isotropic resolution to image the whole brain and reconstructed using stTDI at 20 micron isotropic resolution. The 10 micron resolution stTDI maps showed exceptionally clear delineation of barrel structures. Individual barrels could also be distinguished in the 20 micron stTDI maps but the septa separating the individual barrels appeared thicker compared to the 10 micron maps, indicating that the ability of stTDI to produce high quality structural delineation is dependent upon acquisition resolution. Close homology was observed between the barrel structure delineated using stTDI and reconstructed histological data from the same samples. stTDI also detects barrel deletions in the posterior medial barrel sub-field in mice with infraorbital nerve cuts. The results demonstrate that stTDI is a novel imaging technique that enables three-dimensional characterization of complex structures such as the barrels in S1 and provides an important complementary non-invasive imaging tool for studying synaptic connectivity, development and plasticity of the sensory system.

Distribution of neural precursor cells in the adult mouse brain

Since its inception in 1992 [Reynolds and Weiss, Science 255:1707-10, 1992], the neurosphere assay (NSA) has proven an exceptionally useful tool in detecting neural stem cells (NSCs) in both the developing and adult mammalian brain. To date, over 1,300 manuscripts have been published employing the assay, attesting to the robustness of the assay, and its ease of use. However, a brief survey of the literature demonstrates that the number of primary neurospheres generated from essentially the same anatomical region (i.e., the periventricular region of the rostral lateral ventricle) ranges between 150 and 936 [Gritti et al., J Neurosci 22:437-445, 2002; Tropepe et al., J Neurosci 17:7850-59, 1997; Doetsch et al., Cell 97:703-16, 1999; Enwere et al., J Neurosci 24:8354-65, 2004]. Indeed, in our hands we typically generate approximately 1,800 primary spheres when harvesting tissue from the same region.

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.

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.

A magnetic resonance image based atlas of the rabbit brain for automatic parcellation

Rabbit brain has been used in several works for the analysis of neurodevelopment. However, there are not specific digital rabbit brain atlases that allow an automatic identification of brain regions, which is a crucial step for various neuroimage analyses, and, instead, manual delineation of areas of interest must be performed in order to evaluate a specific structure. For this reason, we propose an atlas of the rabbit brain based on magnetic resonance imaging, including both structural and diffusion weighted, that can be used for the automatic parcellation of the rabbit brain. Ten individual atlases, as well as an average template and probabilistic maps of the anatomical regions were built. In addition, an example of automatic segmentation based on this atlas is described.

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.

In vivo high-resolution diffusion tensor imaging of the mouse brain

Diffusion tensor imaging (DTI) of the laboratory mouse brain provides important macroscopic information for anatomical characterization of mouse models in basic research. Currently, in vivo DTI of the mouse brain is often limited by the available resolution. In this study, we demonstrate in vivo high-resolution DTI of the mouse brain using a cryogenic probe and a modified diffusion-weighted gradient and spin echo (GRASE) imaging sequence at 11.7 T. Three-dimensional (3D) DTI of the entire mouse brain at 0.125 mm isotropic resolution could be obtained in approximately 2 h. The high spatial resolution, which was previously only available with ex vivo imaging, enabled non-invasive examination of small structures in the adult and neonatal mouse brains. Based on data acquired from eight adult mice, a group-averaged DTI atlas of the in vivo adult mouse brain with 60 structure segmentations was developed. Comparisons between in vivo and ex vivo mouse brain DTI data showed significant differences in brain morphology and tissue contrasts, which indicate the importance of the in vivo DTI-based mouse brain atlas.

Mapping registration sensitivity in MR mouse brain images

Nonlinear registration algorithms provide a way to estimate structural (brain) differences based on magnetic resonance images. Their ability to align images of different individuals and across modalities has been well-researched, but the bounds of their sensitivity with respect to the recovery of salient morphological differences between groups are unclear. Here we develop a novel approach to simulate deformations on MR brain images to evaluate the ability of two registration algorithms to extract structural differences corresponding to biologically plausible atrophy and expansion. We show that at a neuroanatomical level registration accuracy is influenced by the size and compactness of structures, but do so differently depending on how much change is simulated. The size of structures has a small influence on the recovered accuracy. There is a trend for larger structures to be recovered more accurately, which becomes only significant as the amount of simulated change is large. More compact structures can be recovered more accurately regardless of the amount of simulated change. Both tested algorithms underestimate the full extent of the simulated atrophy and expansion. Finally we show that when multiple comparisons are corrected for at a voxelwise level, a very low rate of false positives is obtained. More interesting is that true positive rates average around 40%, indicating that the simulated changes are not fully recovered. Simulation experiments were run using two fundamentally different registration algorithms and we identified the same results, suggesting that our findings are generalizable across different classes of nonlinear registration algorithms.

3D model of frequency representation in the cochlear nucleus of the CBA/J mouse

The relationship between structure and function is an invaluable context with which to explore biological mechanisms of normal and dysfunctional hearing. The systematic and topographic representation of frequency originates at the cochlea, and is retained throughout much of the central auditory system. The cochlear nucleus (CN), which initiates all ascending auditory pathways, represents an essential link for understanding frequency organization. A model of the CN that maps frequency representation in 3D would facilitate investigations of possible frequency specializations and pathologic changes that disturb frequency organization. Toward this goal, we reconstructed in 3D the trajectories of labeled auditory nerve (AN) fibers following multiunit recordings and dye injections in the anteroventral CN of the CBA/J mouse. We observed that each injection produced a continuous sheet of labeled AN fibers. Individual cases were normalized to a template using 3D alignment procedures that revealed a systematic and tonotopic arrangement of AN fibers in each subdivision with a clear indication of isofrequency laminae. The combined dataset was used to mathematically derive a 3D quantitative map of frequency organization throughout the entire volume of the CN. This model, available online (http://3D.ryugolab.com/), can serve as a tool for quantitatively testing hypotheses concerning frequency and location in the CN.

Evaluation of serotonin 5-HT(1A) receptors in rodent models using [¹⁸F]mefway PET

INTRODUCTION

Serotonin 5-HT(1A) receptors have been investigated in various CNS disorders, including epilepsy, mood disorders, and neurodegeneration. [¹⁸F]Mefway (N-{2-[4-(2'-methoxyphenyl)piperazinyl]ethyl}-N-(2-pyridyl)-N-(cis/trans-4'-[¹⁸F]fluoromethylcyclohexane)-carboxamide) has been developed as a suitable positron emission tomography (PET) imaging agent for these receptors. We have now evaluated the suitability of [¹⁸F]trans-mefway in rat and mouse models using PET and computerized tomography (CT) imaging and corroborated with ex vivo and in vitro autoradiographic studies.

METHODS

Normal Sprague-Dawley rats and Balb/C mice were used for PET/CT imaging using intravenously injected [¹⁸F]trans-mefway. Brain PET data were coregistered with rat and mouse magnetic resonance imaging template and regional distribution of radioactivity was quantitated. Selected animals were used for ex vivo autoradiographic studies to confirm regional brain distribution and quantitative measures of binding, using brain region to cerebellum ratios. Binding affinity of trans-mefway and WAY-100635 was measured in rat brain homogenates. Distribution of [¹⁸F]trans-4-fluoromethylcyclohexane carboxylate ([¹⁸F]FMCHA), a major metabolite of [¹⁸F] trans-mefway, was assessed in the rat by PET/CT.

RESULTS

The inhibition constant, K(i) for trans-mefway was 0.84 nM and that for WAY-100635 was 1.07 nM. Rapid brain uptake of [¹⁸F]trans-mefway was observed in all rat brain regions and clearance from cerebellum was fast and was used as a reference region in all studies. Distribution of [¹⁸F]trans-mefway in various brain regions was consistent in PET and in vitro studies. The dorsal raphe was visualized and quantified in the rat PET but identification in the mouse was difficult. The rank order of binding to the various brain regions was hippocampus > frontal cortex > anterior cingulate cortex > lateral septal nuclei > dorsal raphe nuclei.

CONCLUSION

[¹⁸F]trans-Mefway appears to be an effective 5-HT(1A) receptor imaging agent in rodents for studies of various disease models.

A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex

The neocortex is the largest component of the mammalian cerebral cortex. It integrates sensory inputs with experiences and memory to produce sophisticated responses to an organism's internal and external environment. While areal patterning of the mouse neocortex has been mapped using histological techniques, the neocortex has not been comprehensively segmented in magnetic resonance images. This study presents a method for systematic segmentation of the C57BL/6J mouse neocortex. We created a minimum deformation atlas, which was hierarchically segmented into 74 neocortical and cortical-related regions, making it the most detailed atlas of the mouse neocortex currently available. In addition, we provide mean volumes and relative intensities for each structure as well as a nomenclature comparison between the two most cited histological atlases of the mouse brain. This MR atlas is available for download, and it should enable researchers to perform automated segmentation in genetic models of cortical disorders.

In vivo SPECT and ex vivo autoradiographic brain imaging of the novel selective CB1 receptor antagonist radioligand [125I]SD7015 in CB1 knock-out and wildtype mouse

We aimed to evaluate the novel high-affinity and relatively lipophilic CB(1) receptor (CB(1)R) antagonist radioligand [(125)I]SD7015 for SPECT imaging of CB(1)Rs in vivo using the multiplexed multipinhole dedicated small animal SPECT/CT system, NanoSPECT/CT(PLUS) (Mediso, Budapest, Hungary), in knock-out CB(1) receptor knock-out (CB(1)R-/-) and wildtype mice. In order to exclude possible differences in cerebral blood flow between the two types of animals, HMPAO SPECT scans were performed, whereas in order to confirm the brain uptake differences of the radioligand between knock-out mice and wildtype mice, in vivo scans were complemented with ex vivo autoradiographic measurements using the brains of the same animals. With SPECT/CT imaging, we measured the brain uptake of radioactivity, using %SUV (% standardised uptake values) in CB(1)R-/- mice (n=3) and C57BL6 wildtype mice (n=7) under urethane anaesthesia after injecting [(125)I]SD7015 intravenously or intraperitoneally. The Brookhaven Laboratory mouse MRI atlas was fused to the SPECT/CT images by using a combination of rigid and non-rigid algorithms in the Mediso Fusion™ (Mediso, Budapest, Hungary) and VivoQuant (inviCRO, Boston, MA, USA) softwares. Phosphor imager plate autoradiography (ARG) was performed on 4 μm-thin cryostat sections of the excised brains. %SUV was 8.6±3.6 (average±SD) in CB(1)R-/- mice and 22.1±12.4 in wildtype mice between 2 and 4 h after injection (p<0.05). ARG of identically taken sections from wildtype mouse brain showed moderate radioactivity uptake when compared with the in vivo images, with a clear difference between grey matter and white matter, whereas ARG in CB(1)R(-/-) mice showed practically no radioactivity uptake. [(125)I]SD7015 enters the mouse brain in sufficient amount to enable SPECT imaging. Brain radioactivity distribution largely coincides with that of the known CB(1)R expression pattern in rodent brain. We conclude that [(125)I]SD7015 should be a useful SPECT radioligand for studying brain CB(1)R in mouse and rat disease models.

An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI

The postnatal period of neurodevelopment has been implicated in a number of brain disorders including autism and schizophrenia. Rodent models have proven to be invaluable in advancing our understanding of the human brain, and will almost certainly play a pivotal role in future studies on postnatal neurodevelopment. The growing field of magnetic resonance microscopy has the potential to revolutionize our understanding of neurodevelopment, if it can be successfully and appropriately assimilated into the vast body of existing neuroscience research. In this study, we demonstrate the utility of a developmental neuro-ontology designed specifically for tracking regional changes in MR biomarkers throughout postnatal neurodevelopment. Using this ontological classification as a segmentation guide, we track regional changes in brain volume in rats between postnatal day zero and postnatal day 80 and demonstrate differential growth rates in axial versus paraxial brain regions. Both the ontology and the associated label volumes are provided as a foundation for future MR-based studies of postnatal neurodevelopment in normal and disease states.

Progressive supranuclear palsy: high-field-strength MR microscopy in the human substantia nigra and globus pallidus

PURPOSE

To characterize changes in the magnetic resonance (MR) relaxation properties of progressive supranuclear palsy (PSP) and tissue from neurologically normal brains by using high-resolution (21.1-T, 900-MHz) MR microscopy of postmortem human midbrain and basal ganglia.

MATERIALS AND METHODS

This HIPAA-compliant study was approved by the institutional review board at the Mayo Clinic and informed consent was obtained. Postmortem tissue from age-matched PSP (n = 6) and control (n = 3) brains was imaged by using three-dimensional fast low-angle shot MR imaging with isotropic resolution of 50 μm. Relaxation times and parametric relaxation maps were generated from spin-echo and gradient-recalled-echo sequences. MR findings were correlated with histologic features by evaluating the presence of iron by using Prussian blue and ferritin and microglia burden as determined by a custom-designed color deconvolution algorithm. T2 and T2*, signal intensities, percent pixels (that could not be fitted in a pixel-by-pixel regression analysis due to severe hypointensity), and histologic data (total iron, ferritin, and microglia burden) were statistically analyzed by using independent sample t tests (P < .05).

RESULTS

PSP specimens showed higher iron burden in the cerebral peduncles and substantia nigra than did controls. However, only the putamen was significantly different, and it correlated with a decrease of T2* compared with controls (-48%; P = .043). Similarly, substantia nigra showed a significant decrease of T2* signal in PSP compared with controls (-57%; P = .028). Compared with controls, cerebral peduncles showed increased T2 (38%; P = .026) and T2* (34%; P = .014), as well as higher T2 signal intensity (57%; P = .049). Ferritin immunoreactivity was the opposite from iron burden and was significantly lower compared with controls in the putamen (-74%; P = .025), red nucleus (-61%; P = .018), and entire basal ganglia section (-63%; P = .016).

CONCLUSION

High-field-strength MR microscopy yielded pronounced differences in substantia nigra and globus pallidus of PSP compared with control brains. Histologic data also suggested that the predominant iron in PSP is hemosiderin, not ferritin. Iron in the brain is a contrast enhancer and potential biomarker for PSP.

Stress-induced grey matter loss determined by MRI is primarily due to loss of dendrites and their synapses

Stress, unaccompanied by signs of post-traumatic stress disorder, is known to decrease grey matter volume (GMV) in the anterior cingulate cortex (ACC) and hippocampus but not the amygdala in humans. We sought to determine if this was the case in stressed mice using high-resolution magnetic resonance imaging (MRI) and to identify the cellular constituents of the grey matter that quantitatively give rise to such changes. Stressed mice showed grey matter losses of 10 and 15 % in the ACC and hippocampus, respectively but not in the amygdala or the retrosplenial granular area (RSG). Concurrently, no changes in the number or volumes of the somas of neurons, astrocytes or oligodendrocytes were detected. A loss of synaptic spine density of up to 60 % occurred on different-order dendrites in the ACC and hippocampus (CA1) but not in the amygdala or RSG. The loss of spines was accompanied by decreases in cumulative dendritic length of neurons of over 40 % in the ACC and hippocampus (CA1) giving rise to decreases in volume of dendrites of 2.6 mm(3) for the former and 0.6 mm(3) for the latter, with no change in the amygdala or RSG. These values are similar to the MRI-determined loss of GMV following stress of 3.0 and 0.8 mm(3) in ACC and hippocampus, respectively, with no changes in the amygdala or RSG. This quantitative study is the first to relate GMV changes in the cortex measured with MRI to volume changes in cellular constituents of the grey matter.

A multidimensional magnetic resonance histology atlas of the Wistar rat brain

We have produced a multidimensional atlas of the adult Wistar rat brain based on magnetic resonance histology (MRH). This MR atlas has been carefully aligned with the widely used Paxinos-Watson atlas based on optical sections to allow comparisons between histochemical and immuno-marker data, and the use of the Paxinos-Watson abbreviation set. Our MR atlas attempts to make a seamless connection with the advantageous features of the Paxinos-Watson atlas, and to extend the utility of the data through the unique capabilities of MR histology: a) ability to view the brain in the skull with limited distortion from shrinkage or sectioning; b) isotropic spatial resolution, which permits sectioning along any arbitrary axis without loss of detail; c) three-dimensional (3D) images preserving spatial relationships; and d) widely varied contrast dependent on the unique properties of water protons. 3D diffusion tensor images (DTI) at what we believe to be the highest resolution ever attained in the rat provide unique insight into white matter structures and connectivity. The 3D isotropic data allow registration of multiple data sets into a common reference space to provide average atlases not possible with conventional histology. The resulting multidimensional atlas that combines Paxinos-Watson with multidimensional MRH images from multiple specimens provides a new, comprehensive view of the neuroanatomy of the rat and offers a collaborative platform for future rat brain studies.

A volume birdcage coil with an adjustable sliding tuner ring for neuroimaging in high field vertical magnets: ex and in vivo applications at 21.1T

A tunable 900 MHz transmit/receive volume coil was constructed for ¹H MR imaging of biological samples in a 21.1 T vertical bore magnet. To accommodate a diverse range of specimen and RF loads at such a high frequency, a sliding-ring adaptation of a low-pass birdcage was implemented through simultaneous alteration of distributed capacitance. To make efficient use of the constrained space inside the vertical bore, a modular probe design was implemented with a bottom-adjustable tuning and matching apparatus. The sliding ring coil displays good homogeneity and sufficient tuning range for different samples of various dimensions representing large span of RF loads. High resolution in vivo and ex vivo images of large rats (up to 350 g), mice and human postmortem tissues were obtained to demonstrate coil functionality and to provide examples of potential applications at 21.1 T.

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.

Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images

The C57BL mouse is the centerpiece of efforts to use gene-targeting technology to understand cerebellar pathology, thus creating a need for a detailed magnetic resonance imaging (MRI) atlas of the cerebellum of this strain. In this study we present a methodology for systematic delineation of the vermal and hemispheric lobules of the C57BL/6J mouse cerebellum in magnetic resonance images. We have successfully delineated 38 cerebellar and cerebellar-related structures. The higher signal-to-noise ratio achieved by group averaging facilitated the identification of anatomical structures. In addition, we have calculated average region volumes and created probabilistic maps for each structure. The segmentation method and the probabilistic maps we have created will provide a foundation for future studies of cerebellar disorders using transgenic mouse models.

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.

Performing label-fusion-based segmentation using multiple automatically generated templates

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively).

Segmentation propagation using a 3D embryo atlas for high-throughput MRI phenotyping: comparison and validation with manual segmentation

Effective methods for high-throughput screening and morphometric analysis are crucial for phenotyping the increasing number of mouse mutants that are being generated. Automated segmentation propagation for embryo phenotyping is an emerging application that enables noninvasive and rapid quantification of substructure volumetric data for morphometric analysis. We present a study to assess and validate the accuracy of brain and kidney volumes generated via segmentation propagation in an ex vivo mouse embryo MRI atlas comprising three different groups against the current "gold standard"--manual segmentation. Morphometric assessment showed good agreement between automatically and manually segmented volumes, demonstrating that it is possible to assess volumes for phenotyping a population of embryos using segmentation propagation with the same variation as manual segmentation. As part of this study, we have made our average atlas and segmented volumes freely available to the community for use in mouse embryo phenotyping studies. These MRI datasets and automated methods of analyses will be essential for meeting the challenge of high-throughput, automated embryo phenotyping.

Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration

Although many atlas-based segmentation methods have been developed and validated for the human brain, limited work has been done for the mouse brain. This paper investigated roles of image registration and segmentation model complexity in the mouse brain segmentation. We employed four segmentation models [single atlas, multiatlas, simultaneous truth and performance level estimation (STAPLE) and Markov random field (MRF) via four different image registration algorithms (affine, B-spline free-form deformation (FFD), Demons and large deformation diffeomorphic metric mapping (LDDMM)] for delineating 19 structures from in vivo magnetic resonance microscopy images. We validated their accuracies against manual segmentation. Our results revealed that LDDMM outperformed Demons, FFD and affine in any of the segmentation models. Under the same registration, increasing segmentation model complexity from single atlas to multiatlas, STAPLE or MRF significantly improved the segmentation accuracy. Interestingly, the multiatlas-based segmentation using nonlinear registrations (FFD, Demons and LDDMM) had similar performance to their STAPLE counterparts, while they both outperformed their MRF counterparts. Furthermore, when the single-atlas affine segmentation was used as reference, the improvement due to nonlinear registrations (FFD, Demons and LDDMM) in the single-atlas segmentation model was greater than that due to increasing model complexity (multiatlas, STAPLE and MRF affine segmentation). Hence, we concluded that image registration plays a more crucial role in the atlas-based automatic mouse brain segmentation as compared to model complexity. Multiple atlases with LDDMM can best improve the segmentation accuracy in the mouse brain among all segmentation models tested in this study.

Rodent functional and anatomical imaging of pain

Human brain imaging has provided much information about pain processing and pain modulation, but brain imaging in rodents can provide information not attainable in human studies. First, the short lifespan of rats and mice, as well as the ability to have homogenous genetics and environments, allows for longitudinal studies of the effects of chronic pain on the brain. Second, brain imaging in animals allows for the testing of central actions of novel pharmacological and nonpharmacological analgesics before they can be tested in humans. The two most commonly used brain imaging methods in rodents are magnetic resonance imaging (MRI) and positron emission tomography (PET). MRI provides better spatial and temporal resolution than PET, but PET allows for the imaging of neurotransmitters and non-neuronal cells, such as astrocytes, in addition to functional imaging. One problem with rodent brain imaging involves methods for keeping the subject still in the scanner. Both anesthetic agents and restraint techniques have potential confounds. Some PET methods allow for tracer uptake before the animal is anesthetized, but imaging a moving animal also has potential confounds. Despite the challenges associated with the various techniques, the 31 studies using either functional MRI or PET to image pain processing in rodents have yielded surprisingly consistent results, with brain regions commonly activated in human pain imaging studies (somatosensory cortex, cingulate cortex, thalamus) also being activated in the majority of these studies. Pharmacological imaging in rodents shows overlapping activation patterns with pain and opiate analgesics, similar to what is found in humans. Despite the many structural imaging studies in human chronic pain patients, only one study has been performed in rodents, but that study confirmed human findings of decreased cortical thickness associated with chronic pain. Future directions in rodent pain imaging include miniaturized PET for the freely moving animal, as well as new MRI techniques that enable ongoing chronic pain imaging.

Chronic Toxoplasma infection modifies the structure and the risk of host behavior

The intracellular parasite Toxoplasma has an indirect life cycle, in which felids are the definitive host. It has been suggested that this parasite developed mechanisms for enhancing its transmission rate to felids by inducing behavioral modifications in the intermediate rodent host. For example, Toxoplasma-infected rodents display a reduction in the innate fear of predator odor. However, animals with Toxoplasma infection acquired in the wild are more often caught in traps, suggesting that there are manipulations of intermediate host behavior beyond those that increase predation by felids. We investigated the behavioral modifications of Toxoplasma-infected mice in environments with exposed versus non-exposed areas, and found that chronically infected mice with brain cysts display a plethora of behavioral alterations. Using principal component analysis, we discovered that most of the behavioral differences observed in cyst-containing animals reflected changes in the microstructure of exploratory behavior and risk/unconditioned fear. We next examined whether these behavioral changes were related to the presence and distribution of parasitic cysts in the brain of chronically infected mice. We found no strong cyst tropism for any particular brain area but found that the distribution of Toxoplasma cysts in the brain of infected animals was not random, and that particular combinations of cyst localizations changed risk/unconditioned fear in the host. These results suggest that brain cysts in animals chronically infected with Toxoplasma alter the fine structure of exploratory behavior and risk/unconditioned fear, which may result in greater capture probability of infected rodents. These data also raise the possibility that selective pressures acted on Toxoplasma to broaden its transmission between intermediate predator hosts, in addition to felid definitive hosts.

A 24-hour temporal profile of in vivo brain and heart pet imaging reveals a nocturnal peak in brain 18F-fluorodeoxyglucose uptake

Using positron emission tomography, we measured in vivo uptake of ¹⁸F-fluorodeoxyglucose (FDG) in the brain and heart of C57Bl/6 mice at intervals across a 24-hour light-dark cycle. Our data describe a significant, high amplitude rhythm in FDG uptake throughout the whole brain, peaking at the mid-dark phase of the light-dark cycle, which is the active phase for nocturnal mice. Under these conditions, heart FDG uptake did not vary with time of day, but did show biological variation throughout the 24-hour period for measurements within the same mice. FDG uptake was scanned at different times of day within an individual mouse, and also compared to different times of day between individuals, showing both biological and technical reproducibility of the 24-hour pattern in FDG uptake. Regional analysis of brain FDG uptake revealed especially high amplitude rhythms in the olfactory bulb and cortex, while low amplitude rhythms were observed in the amygdala, brain stem and hypothalamus. Low amplitude 24-hour rhythms in regional FDG uptake may be due to multiple rhythms with different phases in a single brain structure, quenching some of the amplitude. Our data show that the whole brain exhibits significant, high amplitude daily variation in glucose uptake in living mice. Reports applying the 2-deoxy-D[¹⁴C]-glucose method for the quantitative determination of the rates of local cerebral glucose utilization indicate only a small number of brain regions exhibiting a day versus night variation in glucose utilization. In contrast, our data show 24-hour patterns in glucose uptake in most of the brain regions examined, including several regions that do not show a difference in glucose utilization. Our data also emphasizes a methodological requirement of controlling for the time of day of scanning FDG uptake in the brain in both clinical and pre-clinical settings, and suggests waveform normalization of FDG measurements at different times of the day.

Nicotinic acetylcholine receptors in rat forebrain that bind ¹⁸F-nifene: relating PET imaging, autoradiography, and behavior

Nicotinic acetylcholine receptors (nAChRs) in the brain are important for cognitive function; however, their specific role in relevant brain regions remains unclear. In this study, we used the novel compound ¹⁸F-nifene to examine the distribution of nAChRs in the rat forebrain, and for individual animals related the results to behavioral performance on an auditory-cognitive task. We first show negligible binding of ¹⁸F-nifene in mice lacking the β2 nAChR subunit, consistent with previous findings that ¹⁸F-nifene binds to α4β2* nAChRs. We then examined the distribution of ¹⁸F-nifene in rat using three methods: in vivo PET, ex vivo PET and autoradiography. Generally, ¹⁸F-nifene labeled forebrain regions known to contain nAChRs, and the three methods produced similar relative binding among regions. Importantly, ¹⁸F-nifene also labeled some white matter (myelinated axon) tracts, most prominently in the temporal subcortical region that contains the auditory thalamocortical pathway. Finally, we related ¹⁸F-nifene binding in several forebrain regions to each animal's performance on an auditory-cued, active avoidance task. The strongest correlations with performance after 14 days training were found for ¹⁸F-nifene binding in the temporal subcortical white matter, subiculum, and medial frontal cortex (correlation coefficients, r > 0.8); there was no correlation with binding in the auditory thalamus or auditory cortex. These findings suggest that individual performance is linked to nicotinic functions in specific brain regions, and further support a role for nAChRs in sensory-cognitive function.

Large-scale neuroinformatics for in situ hybridization data in the mouse brain

Large-scale databases of the brain are providing content to the neuroscience community through molecular, cellular, functional, and connectomic data. Organization, presentation, and maintenance requirements are substantial given the complexity, diverse modalities, resolution, and scale. In addition to microarrays, magnetic resonance imaging, and RNA sequencing, several in situ hybridization databases have been constructed due to their value in spatially localizing cellular expression. Scalable techniques for processing and presenting these data for maximum utility in viewing and analysis are key for end user value. We describe methods and use cases for the Allen Brain Atlas resources of the adult and developing mouse.

Cortical atrophy in experimental autoimmune encephalomyelitis: in vivo imaging

There are strong correlations between cortical atrophy observed by MRI and clinical disability and disease duration in multiple sclerosis (MS). The objective of this study was to evaluate the progression of cortical atrophy over time in vivo in experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model for MS. Volumetric changes in brains of EAE mice and matched healthy controls were quantified by collecting high-resolution T2-weighted magnetic resonance images in vivo and labeling anatomical structures on the images. In vivo scanning permitted us to evaluate brain structure volumes in individual animals over time and we observed that though brain atrophy progressed differently in each individual animal, all mice with EAE demonstrated significant atrophy in whole brain, cerebral cortex, and whole cerebellum compared to normal controls. Furthermore, we found a strong correlation between cerebellar atrophy and cumulative disease score in mice with EAE. Ex vivo MRI showed a significant decrease in brain and cerebellar volume and a trend that did not reach significance in cerebral cortex volume in mice with EAE compared to controls. Cross modality correlations revealed a significant association between neuronal loss on neuropathology and in vivo atrophy of the cerebral cortex by neuroimaging. These results demonstrate that longitudinal in vivo imaging is more sensitive to changes that occur in neurodegenerative disease models than cross-sectional ex vivo imaging. This is the first report of progressive cortical atrophy in vivo in a mouse model of MS.

3-dimensional imaging modalities for phenotyping genetically engineered mice

A variety of 3-dimensional (3D) digital imaging modalities are available for whole-body assessment of genetically engineered mice: magnetic resonance microscopy (MRM), X-ray microcomputed tomography (microCT), optical projection tomography (OPT), episcopic and cryoimaging, and ultrasound biomicroscopy (UBM). Embryo and adult mouse phenotyping can be accomplished at microscopy or near microscopy spatial resolutions using these modalities. MRM and microCT are particularly well-suited for evaluating structural information at the organ level, whereas episcopic and OPT imaging provide structural and functional information from molecular fluorescence imaging at the cellular level. UBM can be used to monitor embryonic development longitudinally in utero. Specimens are not significantly altered during preparation, and structures can be viewed in their native orientations. Technologies for rapid automated data acquisition and high-throughput phenotyping have been developed and continually improve as this exciting field evolves.

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies.

A prior feature SVM-MRF based method for mouse brain segmentation

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.

Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN)

Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies.

Quantitative expression profile of distinct functional regions in the adult mouse brain

The adult mammalian brain is composed of distinct regions with specialized roles including regulation of circadian clocks, feeding, sleep/awake, and seasonal rhythms. To find quantitative differences of expression among such various brain regions, we conducted the BrainStars (B*) project, in which we profiled the genome-wide expression of ∼50 small brain regions, including sensory centers, and centers for motion, time, memory, fear, and feeding. To avoid confounds from temporal differences in gene expression, we sampled each region every 4 hours for 24 hours, and pooled the samples for DNA-microarray assays. Therefore, we focused on spatial differences in gene expression. We used informatics to identify candidate genes with expression changes showing high or low expression in specific regions. We also identified candidate genes with stable expression across brain regions that can be used as new internal control genes, and ligand-receptor interactions of neurohormones and neurotransmitters. Through these analyses, we found 8,159 multi-state genes, 2,212 regional marker gene candidates for 44 small brain regions, 915 internal control gene candidates, and 23,864 inferred ligand-receptor interactions. We also found that these sets include well-known genes as well as novel candidate genes that might be related to specific functions in brain regions. We used our findings to develop an integrated database (http://brainstars.org/) for exploring genome-wide expression in the adult mouse brain, and have made this database openly accessible. These new resources will help accelerate the functional analysis of the mammalian brain and the elucidation of its regulatory network systems.