cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/s82a-b17
title: 3D cyto- and receptor architectonic map of macaque lateral premotor area F7s

description: A multimodal analysis of the macaque motor cortex was carried out to create a 3D atlas integrating and reconciling discrepancies between previously published maps. This involved a quantitative and statistically testable approach to the analysis of cytoarchitecture in the macaque monkey frontal agranular cortex, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 13 different neurotransmitter receptors. The borders of the identified areas were traced onto a reference macaque monkey brain in stereotaxic space. We provide here for the first time a map of lateral premotor area F7s of the macaque monkey brain integrating cyto- and receptor architectonic features in stereotaxic space. It constitutes a valuable resource for the analysis of functional experiments with non-human primates, and provides crucial data for modeling approaches with realistic synaptic dynamics.
DOI: 10.25493/q73s-bmj
title: Probabilistic cytoarchitectonic map of Area Fo7 (OFC) (v3.0)

description: This dataset contains the distinct architectonic Area Fo7 (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 Fo7 (OFC). The probability map of Area Fo7 (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. Other available data versions of Area Fo7 (OFC): Wojtasik et al. (2019) [Data set, v2.1] [DOI: 10.25493/3WEV-561](https://doi.org/10.25493%2F3WEV-561) The most probable delineation of Area Fo7 (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/10.25493/8EGG-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/xq7v-qn9
title: Three-dimensional somatotopic organization of rat pontine projections from major body representations of the primary somatosensory cortex

description: 3D reconstructed point coordinate data representing the spatial distribution of rat corticopontine projections, anterogradely labelled after tracer injections into discrete, electrophysiologically defined locations in the rat primary somatosensory cortex. The data show that spatial relationships of major body primary somatosensory projections are largely preserved in a 3D clustered pontine map.
DOI: 10.25493/5b62-88v
title: An optogenetic probe for the specific manipulation of newly potentiated dendritic spines

description: We created a pair of constructs aimed at expressing Channelrhodopsin (ChR) selectively at potentiated synapses. The constructs allow activity-dependent expression of the high-performance ChR isoform ChR-XXM in an activity-dependent fashion at dendritic spines. Moreover, the temporal window for the expression of ChR-XXM can be regulated via a tetON system. The constructs were validated using patch-clamp recordings and demonstrate the possibility to control neuronal firing by specifically acting on the subset of synapses recruited by structural plasticity. Once inserted in AAV vectors, these constructs will be distributed and reused by the scientific community for studies on the physiopathology of learning and memory.
DOI: 10.25493/y7js-8x9
title: 3D reconstructions of pyramidal cells in rat hippocampal CA1 region

description: We have produced 3D reconstructions of pyramidal cells in rat hippocampal CA1 region using Neurolucida software from 3D confocal stack of images. Data from 3D reconstructions will be useful to modeling and integration of anatomical data with functional studies in rat hippocampus.
DOI: 10.25493/99gw-n85
title: Probabilistic cytoarchitectonic map of Area OP4 (POperc) (v11.0)

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. (2018) [Data set, v9.2] [DOI: 10.25493/51S0-K7W](https://doi.org/10.25493%2F51S0-K7W) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/BVT0-H3U](https://doi.org/10.25493%2FBVT0-H3U) 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/m1aq-3ac
title: Whole cell patch-clamp recordings of cerebellar stellate cells

description: This dataset provides a characterization of the electrophysiological properties of the cerebellar stellate cells and investigation of their functions. Stellate cells are inhibitory interneurons that receive excitatory input from parallel fibers and make synaptic inhibitory contacts with Purkinje cells and other stellate cells. Whole-cell patch-clamp recordings were performed in order to reproduce the input/output relationships and to study the specific properties of the cells firing rate. The passive properties of the cells were extracted by analyzing current relaxation induced by step voltage changes (IV protocol), while the cellular spontaneous firing and the cellular responses to positive and negative current injections were acquired through gap free and CC step protocols, respectively. The synaptic responses were elicited through an electrical stimulation of the parallel fibers (EPSP and EPSC protocols). The experimental recordings provided data for the reconstruction of a biophysically realistic model of the stellate cell.
DOI: 10.6084/m9.figshare.15020052
title: Stakeholder Perceptions of Socio-Ecological System Improvements: A Place Based Study of Stream Habitat Enhancements

description: NRACStudyData.csv is raw data. NRACReadMe.txt is the readme for the raw data.
DOI: 10.6084/m9.figshare.15020052.v1
title: Stakeholder Perceptions of Socio-Ecological System Improvements: A Place Based Study of Stream Habitat Enhancements

description: NRACStudyData.csv is raw data. NRACReadMe.txt is the readme for the raw data.
DOI: 10.25493/8x89-5vb
title: Optimisation of photostimulation targeting muscarinic receptors

description: Behavior is driven by specific neuronal activity and can be directly associated with characteristic brain states. The oscillatory activity of neurons contains information about the mental state of an individual, and the transition between physiological brain states is largely controlled by neuromodulators. Manipulating neural activity, brain rhythms or synchronization is of significant therapeutic interest in several neurological disorders and can be achieved by different means such as transcranial current and magnetic stimulation techniques, and by light through optogenetics, although the clinical translation of the latter is hampered by the need of gene therapy. Here, we directly modulate brain rhythms with light using a novel photo-switchable muscarinic agonist. Synchronous slow wave activity is transformed into a higher frequency pattern in the cerebral cortex in in vitro ferret slices in vitro. These results open the way to the study of the neuromodulation and control of spatiotemporal patterns of activity and pharmacology of brain states, their transitions, and their links to cognition and behavior, in different organisms without requiring any genetic manipulation (Barbero-Castillo 2020).
DOI: 10.25493/mpv4-8g
title: Interpolated 3D map of Area TE 1.0 (HESCHL) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area TE 1.0 (HESCHL) 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 TE 1.0 (HESCHL) 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 TE 1.0 (HESCHL) of the JuBrain Cytoarchitectonic Atlas, published in: Amunts et al. (2018) [Data set] [DOI: 10.25493/CP2T-FYT](https://doi.org/10.25493%2FCP2T-FYT) Amunts et al. (2019) [Data set, v5.1] [DOI: 10.25493/MV3G-RET](https://doi.org/10.25493%2FMV3G-RET) In addition, the dataset of the probabilistic cytoarchitectonic map of Area TE 1.0 (HESCHL) is part of the following research publications: Rademacher, J., Morosan, P., Schormann, T., Schleicher, A., Werner, C., Freund, H.-J., & Zilles, K. (2001). Probabilistic Mapping and Volume Measurement of Human Primary Auditory Cortex. NeuroImage, 13(4), 669–683. [DOI: 10.1006/nimg.2000.0714](https://doi.org/10.1006%2Fnimg.2000.0714) Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T., & Zilles, K. (2001). Human Primary Auditory Cortex: Cytoarchitectonic Subdivisions and Mapping into a Spatial Reference System. NeuroImage, 13(4), 684–701. [DOI: 10.1006/nimg.2000.0715](https://doi.org/10.1006%2Fnimg.2000.0715)
DOI: 10.25493/7vdy-r10
title: Probabilistic cytoarchitectonic map of Area TE 1.2 (HESCHL) (v6.0)

description: This dataset contains the distinct architectonic Area TE 1.2 (HESCHL) 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 TE 1.2 (HESCHL). The probability map of Area TE 1.2 (HESCHL) 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 TE 1.2 (HESCHL): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/T1WA-MBT](https://doi.org/10.25493%2FT1WA-MBT) Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/R382-617](https://doi.org/10.25493%2FR382-617) The most probable delineation of Area TE 1.2 (HESCHL) 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) 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/t9vh-a00
title: Probabilistic cytoarchitectonic map of Area 4a (PreCG) (v11.0)

description: This dataset contains the distinct architectonic Area 4a (PreCG) 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 4a (PreCG). The probability map of Area 4a (PreCG) 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 4a (PreCG): Geyer et al. (2018) [Data set, v9.2] [DOI: 10.25493/5GXC-Z2U](https://doi.org/10.25493%2F5GXC-Z2U) Geyer et al. (2019) [Data set, v9.4] [DOI: 10.25493/PVPP-P3Q](https://doi.org/10.25493%2FPVPP-P3Q) The most probable delineation of Area 4a (PreCG) 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) 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/hzbg-p5r
title: DiFuMo atlas (512 dimensions)

description: DiFuMo (Dictionary of Functional Modes) 512-dimensional atlas presents a parcellation of the brain into 512 elementary structures using sparse dictionary learning. The atlas contains two main components: (1) an image of the 512 spatial components, sampled in MNI space, and (2) anatomical names for these 512 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/t6bj-tsn
title: Density measurements of different receptors for Area V1 [macaque, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of Area V1 obtained by means of quantitative _in vitro_ autoradiography. The receptor densities are visualized as _fingerprints_ (**fp**), which provide the mean density and standard deviation for each of the analyzed receptor types, averaged across samples. Overview of available measurements [ **receptor** \| **_neurotransmitter_** \| _labeling agent_ ]: **AMPA** \| **_glutamate_** \| _[<sup>3</sup>H]AMPA_ **kainate** \| **_glutamate_** \| _[<sup>3</sup>H]kainate_ **NMDA** \| **_glutamate_** \| _[<sup>3</sup>H]MK-801_ **GABA<sub>A</sub>** \| **_GABA_** \| _[<sup>3</sup>H]muscimol_ **GABA<sub>B</sub>** \| **_GABA_** \| _[<sup>3</sup>H]CGP54626_ **GABA<sub>A</sub>/BZ** \| **_GABA_** \| _[<sup>3</sup>H]flumazenil_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **muscarinic M<sub>3</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]4-DAMP_ **α<sub>1</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]prazosin_ **α<sub>2</sub>** \| **_noradrenalin/norepinephrine_** \| _[<sup>3</sup>H]UK-14,304_ **5-HT<sub>1A</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]8-OH-DPAT_ **5-HT<sub>2</sub>** \| **_serotonin_** \| _[<sup>3</sup>H]ketanserin_ **D<sub>1</sub>** \| **_dopamine_** \| _[<sup>3</sup>H]SCH23390_ Information on the used tissue samples and corresponding subjects, as well as analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387
DOI: 10.25493/t7rh-46c
title: Probabilistic cytoarchitectonic map of Area hOc3v (LingG) (v5.0)

description: This dataset contains the distinct architectonic Area hOc3v (LingG) 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 hOc3v (LingG). The probability map of Area hOc3v (LingG) 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 hOc3v (LingG): Rottschy et al. (2018) [Data set, v3.2] [DOI: 10.25493/3K39-DNC](https://doi.org/10.25493%2F3K39-DNC) Rottschy et al. (2019) [Data set, v3.4] [DOI: 10.25493/E5E8-1VV](https://doi.org/10.25493%2FE5E8-1VV) The most probable delineation of Area hOc3v (LingG) 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) 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/de24-4fc
title: Probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) (v4.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 123 (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 123 (Basal Forebrain). The probability map of Ch 123 (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 123 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/7SEP-P2V](https://doi.org/10.25493%2F7SEP-P2V) The most probable delineation of Ch 123 (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/jzq4-4ga
title: Densities and 3D distributions of synapses using FIB/SEM imaging in the mouse hippocampus (CA1)

description: Synapses have been identified, segmented and quantified in the adult mouse hippocampus with 3D electron microscopy (FIB-SEM). Three animals have been used (ID2, ID4 and ID28). Three layers of the CA1 area have been studied: stratum oriens, stratum radiatum and stratum lacunosum-moleculare. Data obtained include the number of asymmetric and symmetric synapses, their sizes and their spatial distribution.
DOI: 10.25493/hpvs-z0b
title: Probabilistic map of the left corticospinal tract (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the left corticospinal tract (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.