cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/k54a-yc9
title: Probabilistic cytoarchitectonic map of Area Ia (Insula) (v4.0)

description: This dataset contains the distinct architectonic Area Ia (Insula) 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 Ia (Insula). The probability map of Area Ia (Insula) 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 Ia (Insula): Quabs et al. (2019) [Data set, v3.1] [DOI: 10.25493/WW8G-T2G](https://doi.org/10.25493%2FWW8G-T2G) The most probable delineation of Area Ia (Insula) 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/10.25493/8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)
DOI: 10.25493/repp-n9f
title: Subcortical modulation of hippocampal activity, multichannel recordings in head-restrained, behaving mice

description: Cortical circuits display different functional states under the influence of subcortical modulatory input. In our study we aimed to uncover fundamental aspects of this subcortical modulation by focusing on the effect of the median raphe nucleus on the hippocampal network. High channel count electrophysiological recordings were executed from dorsal hippocampus, along with the measurement of neuronal activity in the median raphe, in head-restrained, awake mice. The different cell types of the MR (vGAT, vGlut2, vGlut3) were optogenetically identified and multimodal sensory stimuli were applied to the mouse while monitoring both subcortical activity in the MR and activity in the dorsal CA1 region. These data allow us to characterize the properties of hippocampal neurons in the intact animal and how these properties are changed by subcortical inputs thus providing important constraints for network models of the hippocampus.
DOI: 10.25493/nzq5-san
title: Probabilistic cytoarchitectonic map of MF (Amygdala) (v8.1)

description: This dataset contains the distinct architectonic MF (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 MF (Amygdala). The probability map of MF (Amygdala) 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 LB (Amygdala): Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/9375-55V](https://doi.org/10.25493%2F9375-55V) The most probable delineation of MF (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, v1.18] [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) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/z9j0-zzq
title: Fluorescence cortical recording of mouse activity after stroke

description: The dataset includes wide-field fluorescence recordings from the dorsal cortex (right hemisphere) of Thy1-GCaMP6f mice (C57BL/6J-Tg(Thy1GCaMP6f)GP5.17Dkim/J; RRID:IMSR_JAX:025393) during active contralateral forelimb retraction in a robotic device (.tiff files). GCamMP6f indicator enables to visualize fluctuations in calcium concentration, which is an indirect reporter of neuronal spiking activity. The forces applied and the position of the forelimb (.txt and .avi files) are recorded simultaneously with the fluorescence emitted from the cortex. Samples named “GCaMP-ChR2-17” are recorded before stroke; whereas samples named “GCaMP-ChR2-17s” refer to recordings performed after stroke. In detail, we used a photothrombotic model of focal stroke applied to the right primary motor cortex. The mouse was trained for 4 weeks starting 25 days after injury. More detailed overview of the experimental design and data collection process can be found in [Allegra Mascaro et al., 2019].
DOI: 10.25493/qgay-d9s
title: Probabilistic map of the right inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the right inferior longitudinal fasciculus (atlas of deep white matter fibre bundles, version 2018), in the MNI ICBM152 reference brain. This bundle was identified by fibre clustering, from the tractography datasets of 78 subjects in 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: 10.25493/4cps-sbd
title: Anterograde visualization of projections from ventrolateral orbitofrontal cortex in rat (case: F30)

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 ventrolateral orbitofrontal cortex (VLOC). 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: 10.25493/51s0-k7w
title: Probabilistic cytoarchitectonic map of Area OP4 (POperc) (v9.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP4 (POperc) 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 Area OP4 (POperc). The probability map of Area OP4 (POperc) 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 OP4 (POperc): Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/BVT0-H3U](https://doi.org/10.25493%2FBVT0-H3U) Eickhoff et al. (2019) [Data set, v11.0] [DOI: 10.25493/99GW-N85](https://doi.org/10.25493%2F99GW-N85) The most probable delineation of Area OP4 (POperc) 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/j4k5-k85
title: Synthetic simultaneous EEG-fMRI resting state dataset simulated with The Virtual Brain

description: Computational modeling bears the promise of linking the neuroimaging signals to a coherent framework capturing our understanding of brain function and disease. This dataset provides synthetic simultaneous electroencephalography (EEG) + functional magnetic resonance imaging (fMRI) time series of resting state for two subjects from the Human Connectome Project simulated with a Brain Network Model implemented in The Virtual Brain.
DOI: 10.34810/data72/378
title: 01CO_1.MR.0004.0103.2016.02.04.15.44.11.109375.67138022.IMA
DOI: 10.5281/zenodo.5115260
title: Models for JGR-SE paper: Azimuthal anisotropy tomography of the Southeast Asia subduction system

description: 3-D models of isotropic Vp and azimuthal anisotropy tomography beneath the Southeast Asia subduction system.
DOI: 10.5281/zenodo.5115259
title: Models for JGR-SE paper: Azimuthal anisotropy tomography of the Southeast Asia subduction system

description: 3-D models of isotropic Vp and azimuthal anisotropy tomography beneath the Southeast Asia subduction system.
DOI: 10.25493/49m4-qhs
title: Probability map of bundle lh_MT-ST_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 Middle Temporal and Superior Temporal 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/sdw0-yez
title: Probabilistic cytoarchitectonic map of Area OP2 (POperc) (v11.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP2 (POperc) 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 Area OP2 (POperc). The probability map of Area OP2 (POperc) 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 OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/5KBV-36J](https://doi.org/10.25493%2F5KBV-36J) The most probable delineation of Area OP2 (POperc) 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/bwz1-5mv
title: Probabilistic cytoarchitectonic map of Ch 4 (Basal Forebrain) (v4.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 4 (Basal Forebrain) 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 Ch 4 (Basal Forebrain). The probability map of Ch 4 (Basal Forebrain) 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 Ch 4 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/VZJ5-8WJ](https://doi.org/10.25493%2FVZJ5-8WJ) The most probable delineation of Ch 4 (Basal Forebrain) 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: 10.25493/6fwa-99y
title: Probability map of bundle lh_ST-Ins_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 Superior Temporal and Insula 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: 10.25493/981d-a24
title: Probabilistic cytoarchitectonic map of IF (Amygdala) (v8.1)

description: This dataset contains the distinct architectonic Area IF (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 Area IF (Amygdala). The probability map of Area IF (Amygdala) 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 IF (Amygdala): Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/GWPR-G6K](https://doi.org/10.25493%2FGWPR-G6K) The most probable delineation of Area IF (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, v1.18] [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) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/kw1b-m7x
title: Simultaneous 2-photon calcium imaging in dendritic tufts & cell bodies of single pyramidal neurons in mouse V1

description: Although it is thought that cortical feedback is crucial for accurate sensory perception and awareness, it remains unclear how feedforward and feedback information is integrated at the level of individual neurons. It is believed that feedforward sensory information inputs mostly enter dendrites near the cell body, while feedback information about what is to be expected would enter at the dendritic tufts. We present a dataset containing semi-simultaneously recorded calcium signals in layer 5 pyramidal neurons as well as their dendritic tufts and apical dendrites. The used behavioral paradigm was aimed at separately inducing sensory and contextual responses in the mouse primary visual cortex for testing if feedforward inputs are modulated by feedback inputs providing contextual information. Our dataset comprises the raw calcium images and extracted neuronal activity of pre-selected regions-of-interest for apical tufts, apical dendrites, and layer 5 somata. In addition, we provide log files, trial events and behavioral data of the mouse such as pupil size, pupil location and locomotor activity.
DOI: 10.25493/3zyq-jwx
title: Probability map of bundle rh_PoCi-RAC_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the right hemisphere Posterior Cingulate and Rostral Anterior Cingulate 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: 10.25493/47h2-1hr
title: 3D reconstruction of the vascular system of the mouse brain.

description: The goal of the study is to produce the first high resolution, 3D reconstruction of the entire macrostructure and vascular system of the mouse brain. To that end previously synchrotron radiation-based X-ray microscopy images were collected. Additionally recently two-photon microscopy images were collected. And we are looking for additional alternative imaging sources. Then this data is translated into 3D mesh and volumetric models (graph). This dataset contains the model of part of cortical vasculature of the mouse brain as a graph structure (discrete mathematics). The data is a derived dataset using computational methods from the dataset “3D imaging of the vascular system of the mouse brain” ([doi: 10.25493/3248-7V](https://doi.org/10.25493/3248-7V)).
DOI: 10.25493/8wsn-jq8
title: Interpolated 3D map of Area 6d2 (PreCG) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area 6d2 (PreCG) in the Big Brain dataset [Amunts et al. 2013]. The mappings were created using the semi-automatic method presented in Schleicher et al. 1999, based on coronal histological sections on 1 micron resolution. Mappings are available on approximately every 15-60th section of this region. They were then aligned to the corresponding sections of the 3D reconstructed Big Brain space, using the transformations used in Amunts et al. 2013, kindly provided by Claude Lepage (McGill University). From these delineations, a preliminary 3D map of Area 6d2 (PreCG) 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. A full mapping of this area in every histological section using a Deep Learning approach is in progress. **Additional information:** The reference delineations used for this map are part of the work for the corresponding probabilistic map of Area 6d2 (PreCG) of the JuBrain Cytoarchitectonic Atlas, published in: Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4MMD-HEB](https://doi.org/10.25493%2F4MMD-HEB) Sigl et al. (2019) [Data set, v4.1] [DOI: 0.25493/KXHS-N90](https://doi.org/10.25493%2FKXHS-N90)