cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/z0f6-0sy
title: Probabilistic cytoarchitectonic map of Area FG3 (FusG) (v6.1)

description: This dataset contains the distinct architectonic Area FG3 (FusG) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area FG3 (FusG). The probability map of Area FG3 (FusG) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area FG3 (FusG): Lorenz et al. (2018) [Data set, v6.0] [DOI: 10.25493/E7VG-4NG](https://doi.org/10.25493%2FE7VG-4NG) The most probable delineation of Area FG3 (FusG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/bnyj-fr4
title: Probability map of bundle lh_PoC-PrC_1 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Postcentral and Precentral regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/8h4f-0dh
title: Behavior of mice on the AirTrack floating plus-maze

description: We tracked behavior of head-fixed mice running freely on a floating plus maze. We painted body parts of interest (i.e. whiskers on the both sides of the snout, and the nose in some cases) using UV paint, and performed color-keyed offline tracking, based on the videos acquired by a high-speed camera running at 190 Hz. In addition, we performed manual annotation of behavioral states of the animal in a frame-wise manner in the videos.
DOI: 10.25493/hjmy-zzp
title: Probabilistic cytoarchitectonic map of Area Fo5 (OFC) (v2.1)

description: This dataset contains the distinct architectonic Area Fo5 (OFC) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area Fo5 (OFC). The probability map of Area Fo5 (OFC) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. The most probable delineation of Area Fo5 (OFC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/dtb3-0kp
title: Pituitary homeobox 3 tetracycline-transactivator expression: coronal sections (case 6517)

description: Bright-field microscopy images of serial coronal brain sections showing Pituitary homeobox 3 (Pitx3) promoter expression in a bigenic Pitx3-tetracycline-transactivator (tTA) mouse brain (case 6517, adult female), using a driver-reporter construct in which the Pitx3-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data show that the Pitx3-tTA promoter is spatially restricted to the substantia nigra, ventral tegmental area, and a few other regions.
DOI: 10.25493/by0j-9p9
title: Trans-synaptic signaling and rhythmogenesis in the hippocampus

description: The CA2 region of the hippocampus has recently emerged as a central structure for social memory processing. Here, in vivo recordings from anesthetized animals have been used to assess the dynamics of neuronal signaling in the CA2 and CA3 hippocampal area. In vivo experiments from NLG3R451C knock-in mice, NLG3 knock-out mice and littermate controls have unveiled a selective impairment of spike-related slow wave activity in the CA2 area and a significant reduction in oscillatory activity in the theta and gamma frequencies range in both CA2 and CA3 regions of the hippocampus. Since transgenic mice are widely used as non-syndromic animal models of Autism Spectrum Disorders (ASDs), data obtained in our experiments can be used by researchers interested in modeling brain activity underlying these neurodevelopmental disorders.
DOI: 10.25493/5h13-1q0
title: Neuropsin tetracycline-transactivator expression: coronal sections (case 1952)

description: Bright-field microscopy images of serial coronal brain sections showing neuropsin (Nop) promoter expression in a bigenic Nop-tetracycline-transactivator (tTA) mouse brain (case 1952 adult male), using a driver-reporter construct in which the Nop-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Methodological details are provided in Yetman et al., Brain Struct Funct 221:2231-49, 2016. Our data show that the Nop-tTA promoter mainly is expressed in the entorhinal cortex, but also in several other cortical regions.
DOI: 10.25493/9yjb-115
title: Anterograde visualization of projections from ventral orbitofrontal cortex in rat (case: F14)

description: High-resolution bright-field microscopy images (44) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. The anterograde tracer biotinylated dextran amine (BDA) was injected into the ventral orbitofrontal cortex (VOC). Brain sections are 300 µm apart and 50 µm thick. The dataset contains a complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/anr2-929
title: Probability map of bundle lh_PrC-SM_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Precentral and Supramarginal regions of the Desikan-Killiany atlas, in the MNI ICBM152 reference brain. This bundle was identified using a hybrid approach, incorporating anatomical information (from cortical regions of interest) and fibre shape (fibre clustering), from the tractography datasets of 78 subjects in the Neurospin’s ARCHI database. The map shows the probability of finding a fibre belonging to the bundle in each voxel of the reference brain. The maximum probability corresponds to the voxels with the highest number of putative fibres going through.
DOI: 10.25493/4fr1-ymq
title: DiFuMo atlas (256 dimensions)

description: DiFuMo (Dictionary of Functional Modes) 256-dimensional atlas presents a parcellation of the brain into 256 elementary structures using sparse dictionary learning. The atlas contains two main components: (1) an image of the 256 spatial components, sampled in MNI space, and (2) anatomical names for these 256 regions. It is meant to serve as an atlas to extract functional signals, eg to create image-derived phenotypes. These modes are optimized to represent well raw BOLD time series, over a with range of experimental conditions.
DOI: 10.25493/z6ng-4mu
title: Filter Activations of Convolutional Neuronal Networks Used in Cytoarchitectonic Brain Mapping

description: We studied the internal structure of two specific Convolutional Neural Networks (CNNs) which were trained to segment the primary (hOc1, V1) and secondary visual cortex (hOc2, V2) in microscopic scans of brain tissue sections with a resolution of 1 micrometer. All tissue sections correspond to those of the BigBrain dataset ([Amunts et al., 2013](https://science.sciencemag.org/content/340/6139/1472)). To analyze the internal feature representations learned by the model, 5184 filter activations from the batch-normalized output of each Rectified Linear Unit (ReLU) of the CNNs were calculated for section number 1021 of the histological stack. We described and analyzed these filter activations to better understand the internal feature representations of the trained networks. This enables a direct comparison with the underlying histology and a direct assessment of cytoarchitectonic features reflected inside the networks. **Additional information:** The corresponding reference delineations were published in: Kiwitz et al. (2019) [DOI: 10.25493/3GSV-T4A](https://kg.ebrains.eu/search/instances/Dataset/87c6dea7-bdf7-4049-9975-6a9925df393f) Kiwitz et al. (2019) [DOI: 10.25493/8MKD-D77](https://kg.ebrains.eu/search/instances/Dataset/02b56db7-a083-44f3-91dc-72bb67f3fd0a)
DOI: 10.25493/c3x0-nv3
title: Probabilistic cytoarchitectonic map of LB (Amygdala) (v6.4)

description: This dataset contains the distinct architectonic LB (Amygdala) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to LB (Amygdala). The probability map of LB (Amygdala) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of LB (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/E7QC-B3Y](https://doi.org/10.25493%2FE7QC-B3Y) The most probable delineation of LB (Amygdala) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/1gh6-qfv
title: Immunocytochemical detection of excitatory and inhibitory terminals in the mouse hippocampus (CA1) by confocal microscopy

description: The densities of Glutamatergic and GABAergic axon terminals have been estimated in the different strata of the CA1 hippocampal region in five mice.
DOI: 10.34810/data72/385
title: 01CO_1.MR.0004.0018.2016.02.04.15.44.11.109375.67134612.IMA
DOI: 10.25493/gx3r-2et
title: Reference delineations of Area ifs4 (IFS) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic maps of Area ifs4 (IFS) in the BigBrain. The mappings were created using cytoarchitectonic criteria applied on digitized histological sections of 1 µm resolution, cut in coronal plane. Areal borders have been detected by an oberserver-independent border definition (Schleicher 2000). Mappings are available on sections of the BigBrain and have been transformed to the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013. From these delineations, a preliminary 3D map of Area ifs4 (IFS) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below.
DOI: 10.25493/av5j-m46
title: RAPID-enabled light-sheet microscopy

description: These datasets support the manuscript “Universal autofocus for quantitative volumetric microscopy of whole mouse brains”, which describes RAPID, a real-time image-based autofocus for light-sheet microscopy. The different components of this data collection refer to different staining or clearing methods used to prepare entire mouse brains for imaging. In some of the cases, results of quantitative image analysis are reported. Furthermore, manual annotations of cell positions are included, that could potentially be used to train or validate software tools for neuronal localization.
DOI: 10.25493/sgng-av1
title: Probabilistic cytoarchitectonic map of Area hPO1 (POS) (v7.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area hPO1 (POS) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area hPO1 (POS). The probability map of Area hPO1 (POS) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area hPO1 (POS): Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/W50A-FAP](https://doi.org/10.25493%2FW50A-FAP) The most probable delineation of Area hPO1 (POS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/93yq-6qm
title: Electrophysiological data of cortical layer 6 neurons and synaptically coupled neuronal pairs

description: Electrophysiological characterisation of cortical neurons and synaptically coupled neuronal pairs is essential to understand their functions in brain microcircuits. Electrophysiological data were collected by using single or dual patch-clamp recordings from primary somatosensory (barrel) cortical layer 6 neurons in acute rat brain slices. These data are used for analysing neuronal and synaptic properties and could also be reused for modelling the electrical behaviour of individual neurons and synaptic transmission between them.
DOI: 10.25493/brnz-sxw
title: Probabilistic cytoarchitectonic map of SF (Amygdala) (v6.1)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of SF (Amygdala) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to SF (Amygdala). The probability map of SF (Amygdala) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of SF (Amygdala): Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/WD31-SEA](https://doi.org/10.25493%2FWD31-SEA) The most probable delineation of SF (Amygdala) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/hxwm-nrx
title: Probabilistic cytoarchitectonic map of Area s24 (sACC) (v16.1)

description: This dataset contains the distinct architectonic Area s24 (sACC) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area s24 (sACC). The probability map of Area s24 (sACC) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area s24 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/FQ3R-3JX](https://doi.org/10.25493%2FFQ3R-3JX) The most probable delineation of Area s24 (sACC) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/exha-95s
title: Maps of GABA receptors in human cortex

description: This dataset contains the image of spatial distribution of GABA receptor in human cortex using serial two photons microscopy. The individual 3D tiles acquired with the two photon fluorescence microscope were fused together to produce the final image.
DOI: 10.25493/c5qq-efb
title: Probabilistic cytoarchitectonic map of Area 3a (PostCG) (v8.4)

description: This dataset contains the distinct architectonic Area 3a (PostCG) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area 3a (PostCG). The probability map of Area 3a (PostCG) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area 3a (PostCG): Geyer et al. (2018) [Data set, v8.2] [DOI: 10.25493/R19N-B8E](https://doi.org/10.25493%2FR19N-B8E) The most probable delineation of Area 3a (PostCG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/sv03-mzw
title: Tissue-Cleared Wild-Type P0 Whole Mouse Brains Imaged by Lightsheet Microscopy

description: 3D imaging of intact, tissue-cleared mouse brains enables cellular quantification across the whole brain for applications ranging from tracing normal neurodevelopment to disease models. To facilitate comparison across mouse models, 3D atlases and wild-type controls are required for the appropriate strain, sex, and age. We have generated 3D reconstructions of the Allen Developing Mouse Brain Atlas to fill in missing sections, correct artifacts, and optimize alignment with the underlying histology. To validate these atlases, we generated 15 volumetric images of C57BL/6J wild-type mouse pup brains at postnatal day 0 (10 male, 5 female) using lightsheet microscopy. Mouse brains were cleared using the CUBIC clearing technique given its balance of transparency, morphological preservation, and ease of handling. SYTO 16 nuclear dye labeled cells throughout each brain, while 5x objective magnification provided cellular resolution. We registered our 3D reconstructed E18.5 atlas [(DOI: 10.25493/X4ZT-ARE)](https://doi.org/10.25493%2FX4ZT-ARE) to each of these brains for quantitative cellular analysis by region. These whole-brain images can be reused for further evaluation of 3D atlases, characterization of wild-type neuroanatomy, or additional controls for mouse-based brain-wide neurological studies.