BrainAligner: 3D registration atlases of Drosophila brains.
Ontology highlight
ABSTRACT:
SUBMITTER: Peng H
PROVIDER: S-EPMC3104101 | biostudies-literature | 2011 Jun
REPOSITORIES: biostudies-literature
Publications
BrainAligner: 3D registration atlases of Drosophila brains.
Peng Hanchuan H Chung Phuong P Long Fuhui F Qu Lei L Jenett Arnim A Seeds Andrew M AM Myers Eugene W EW Simpson Julie H JH
Nature methods 20110501 6
Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibod ...[more]
Similar Datasets
Project description:Atlases provide a framework for spatially mapping information from diverse sources into a common reference space. Specifically, brain atlases allow annotation of gene expression, cell morphology, connectivity, and activity. In larval zebrafish, advances in genetics, imaging, and computational methods now allow the collection of such information brain-wide. However, due to technical considerations, disparate datasets may use different references and may not be aligned to the same coordinate space. Two recent larval zebrafish atlases exemplify this problem: Z-Brain, containing gene expression, neural activity, and neuroanatomical segmentations, was acquired using immunohistochemical stains, while the Zebrafish Brain Browser (ZBB) was constructed from live scans of fluorescent reporters in transgenic larvae. Although different references were used, the atlases included several common transgenic patterns that provide potential "bridges" for transforming each into the other's coordinate space. We tested multiple bridging channels and registration algorithms and found that the symmetric diffeomorphic normalization algorithm improved live brain registration precision while better preserving cell morphology than B-spline-based registrations. Symmetric diffeomorphic normalization also corrected for tissue distortion introduced during fixation. Multi-reference channel optimization provided a transformation that enabled Z-Brain and ZBB to be co-aligned with precision of approximately a single cell diameter and minimal perturbation of cell and tissue morphology. Finally, we developed software to visualize brain regions in 3 dimensions, including a virtual reality neuroanatomy explorer. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate reference templates and acquisition protocols, when sufficient information is present for bridging. Increased accuracy and interoperability of zebrafish digital brain atlases will facilitate neurobiological studies.
| S-EPMC5597853 | biostudies-literature
Project description:Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/ .
| S-EPMC6797784 | biostudies-literature
Project description:Using multi-atlas registration (MAR), information carried by atlases can be transferred onto a new input image for the tasks of region of interest (ROI) segmentation, anatomical landmark detection, and so on. Conventional atlases used in MAR methods are monomodal and contain only normal anatomical structures. Therefore, the majority of MAR methods cannot handle input multimodal pathological images, which are often collected in routine image-based diagnosis. This is because registering monomodal atlases with normal appearances to multimodal pathological images involves two major problems: (1) missing imaging modalities in the monomodal atlases, and (2) influence from pathological regions. In this paper, we propose a new MAR framework to tackle these problems. In this framework, a deep learning based image synthesizers are applied for synthesizing multimodal normal atlases from conventional monomodal normal atlases. To reduce the influence from pathological regions, we further propose a multimodal lowrank approach to recover multimodal normal-looking images from multimodal pathological images. Finally, the multimodal normal atlases can be registered to the recovered multimodal images in a multi-channel way. We evaluate our MAR framework via brain ROI segmentation of multimodal tumor brain images. Due to the utilization of multimodal information and the reduced influence from pathological regions, experimental results show that registration based on our method is more accurate and robust, leading to significantly improved brain ROI segmentation compared with state-of-the-art methods.
| S-EPMC6579720 | biostudies-literature
Project description:One of the challenges of modern neuroscience is integrating voluminous data of diferent modalities derived from a variety of specimens. This task requires a common spatial framework that can be provided by brain atlases. The first atlases were limited to two-dimentional presentation of structural data. Recently, attempts at creating 3D atlases have been made to offer navigation within non-standard anatomical planes and improve capability of localization of different types of data within the brain volume. The 3D atlases available so far have been created using frameworks which make it difficult for other researchers to replicate the results. To facilitate reproducible research and data sharing in the field we propose an SVG-based Common Atlas Format (CAF) to store 2D atlas delineations or other compatible data and 3D Brain Atlas Reconstructor (3dBAR), software dedicated to automated reconstruction of three-dimensional brain structures from 2D atlas data. The basic functionality is provided by (1) a set of parsers which translate various atlases from a number of formats into the CAF, and (2) a module generating 3D models from CAF datasets. The whole reconstruction process is reproducible and can easily be configured, tracked and reviewed, which facilitates fixing errors. Manual corrections can be made when automatic reconstruction is not sufficient. The software was designed to simplify interoperability with other neuroinformatics tools by using open file formats. The content can easily be exchanged at any stage of data processing. The framework allows for the addition of new public or proprietary content.
| S-EPMC3325030 | biostudies-literature
Project description:Modern high throughput brain wide profiling techniques for cells and their morphology, connectivity, and other properties, make the use of reference atlases with 3D coordinate frameworks essential. However, anatomical location of observations made in microscopic sectional images from rodent brains is typically determined by comparison with 2D anatomical reference atlases. A major challenge in this regard is that microscopic sections often are cut with orientations deviating from the standard planes used in the reference atlases, resulting in inaccuracies and a need for tedious correction steps. Overall, efficient tools for registration of large series of section images to reference atlases are currently not widely available. Here we present QuickNII, a stand-alone software tool for semi-automated affine spatial registration of sectional image data to a 3D reference atlas coordinate framework. A key feature in the tool is the capability to generate user defined cut planes through the reference atlas, matching the orientation of the cut plane of the sectional image data. The reference atlas is transformed to match anatomical landmarks in the corresponding experimental images. In this way, the spatial relationship between experimental image and atlas is defined, without introducing distortions in the original experimental images. Following anchoring of a limited number of sections containing key landmarks, transformations are propagated across the entire series of sectional images to reduce the amount of manual steps required. By having coordinates assigned to the experimental images, further analysis of the distribution of features extracted from the images is greatly facilitated.
| S-EPMC6541252 | biostudies-literature
Project description:We describe a method for ex vivo culturing of whole Drosophila brains. This can be used as a counterpoint to chronic genetic manipulations for investigating the cell biology and development of central brain structures by allowing acute pharmacological interventions and live imaging of cellular processes. As an example of the technique, prior work from our lab(1) has shown that a previously unrecognized subcellular compartment lies between the axonal and somatodendritic compartments of axons of the Drosophila central brain. The development of this compartment, referred to as the axon initial segment (AIS)(2), was shown genetically to depend on the neuron-specific cyclin-dependent kinase, Cdk5. We show here that ex vivo treatment of wild-type Drosophila larval brains with the Cdk5-specific pharmacological inhibitors roscovitine and olomoucine(3) causes acute changes in actin organization, and in localization of the cell-surface protein Fasciclin 2, that mimic the changes seen in mutants that lack Cdk5 activity genetically. A second example of the ex vivo culture technique is provided for remodeling of the connections of embryonic mushroom body (MB) gamma neurons during metamorphosis from larva to adult. The mushroom body is the center of olfactory learning and memory in the fly(4), and these gamma neurons prune their axonal and dendritic branches during pupal development and then re-extend branches at a later timepoint to establish the adult innervation pattern(5). Pruning of these neurons of the MB has been shown to occur via local degeneration of neurite branches(6), by a mechanism that is triggered by ecdysone, a steroid hormone, acting at the ecdysone receptor B1(7), and that is dependent on the activity of the ubiquitin-proteasome system(6). Our method of ex vivo culturing can be used to interrogate further the mechanism of developmental remodeling. We found that in the ex vivo culture setting, gamma neurons of the MB recapitulated the process of developmental pruning with a time course similar to that in vivo. It was essential, however, to wait until 1.5 hours after puparium formation before explanting the tissue in order for the cells to commit irreversibly to metamorphosis; dissection of animals at the onset of pupariation led to little or no metamorphosis in culture. Thus, with appropriate modification, the ex vivo culture approach can be applied to study dynamic as well as steady state aspects of central brain biology.
| S-EPMC3529496 | biostudies-literature
Project description:Neurons and glia are the two major cell types in the nervous system and work closely with each other to program neuronal interplay. Traditionally, neurons are thought to be the major cells that actively regulate processes like synapse formation, plasticity, and behavioral output. Glia, on the other hand, serve a more supporting role. To date, accumulating evidence has suggested that glia are active participants in virtually every aspect of neuronal function. Despite this, fundamental features of how glia interact with neurons, and their spatial relationships, remain elusive. Here, we describe the glial cell population in Drosophila adult brains. Glial cells extend and tightly associate their processes with major structures such as the mushroom body (MB), ellipsoid body (EB), and antennal lobe (AL) in the brain. Glial cells are distributed in a more concentrated manner in the MB. Furthermore, subsets of glia exhibit distinctive association patterns around different neuronal structures. Whereas processes extended by astrocyte-like glia and ensheathing glia wrap around the MB and infiltrate into the EB and AL, cortex glia stay where cell bodies of neurons are and remain outside of the synaptic regions structured by EB or AL.
| S-EPMC5563736 | biostudies-literature
Project description:Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.
| S-EPMC10884250 | biostudies-literature
Project description:Various insect species serve as valuable model systems for investigating the cellular and molecular mechanisms by which a brain controls sophisticated behaviors. In particular, the nervous system of Drosophila melanogaster has been extensively studied, yet experiments aimed at determining the number of neurons in the Drosophila brain are surprisingly lacking. Using isotropic fractionator coupled with immunohistochemistry, we counted the total number of neuronal and non-neuronal cells in the whole brain, central brain, and optic lobe of Drosophila melanogaster. For comparison, we also counted neuronal populations in three divergent mosquito species: Aedes aegypti, Anopheles coluzzii and Culex quinquefasciatus. The average number of neurons in a whole adult brain was determined to be 199,380 ±3,400 cells in D. melanogaster, 217,910 ±6,180 cells in Ae. aegypti, 223,020 ± 4,650 cells in An. coluzzii and 225,911±7,220 cells in C. quinquefasciatus. The mean neuronal cell count in the central brain vs. optic lobes for D. melanogaster (101,140 ±3,650 vs. 107,270 ± 2,720), Ae. aegypti (109,140 ± 3,550 vs. 112,000 ± 4,280), An. coluzzii (105,130 ± 3,670 vs. 107,140 ± 3,090), and C. quinquefasciatus (108,530 ±7,990 vs. 110,670 ± 3,950) was also estimated. Each insect brain was comprised of 89% ± 2% neurons out of its total cell population. Isotropic fractionation analyses did not identify obvious sexual dimorphism in the neuronal and non-neuronal cell population of these insects. Our study provides experimental evidence for the total number of neurons in Drosophila and mosquito brains.
| S-EPMC8121336 | biostudies-literature
Project description:US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.
| S-EPMC6420033 | biostudies-literature