cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/3ev4-tdg
title: Morphological reconstruction of cholinergic interneurons in the striatum

description: The study entails the collection of single cell patch-clamp recordings and morphological reconstruction from cholinergic interneurons in the striatum. The project also includes literature review of molecular composition in terms of ion channels and receptors and electrophysiological behaviors. The data consists of patch-clamp recordings and 3D- reconstructions using the Neurolucida system. Only data related to morphological reconstructions presented here.
DOI: 10.25493/5ge0-6mf
title: Electrophysiological recordings of striatal low threshold-spiking interneuron

description: Membrane voltage traces recorded from somata of striatal low-threshold-spiking interneuron in response to square-pulse current injections.
DOI: 10.25493/r7j6-s69
title: Two-photon calcium imaging of cortical layer 2/3 neurons in GCaMP6f mice at different anesthesia levels

description: Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in spatial resolution and cannot target specific neuronal populations. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions and will give new important insight on the SW phenomenon. Here we combined Two-photon fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW related layer 2/3 (L2/3) neurons activity under different levels of anesthesia.
DOI: 10.25493/ywvt-90c
title: Regional and laminar distribution of receptors for glutamate in the rat brain

description: The present dataset provides the quantitative regional and laminar distribution of key molecules of excitatory neurotransmission, namely the ionotropic glutamate receptors AMPA, kainate and NMDA, in five selected rostrocaudal levels of the rat brain. The receptors were visualized by means of quantitative *in vitro* receptor autoradiography and the selective tritiated ligands AMPA, kainate and MK-801. The high spatial resolution of this method enables quantification of receptor densities in anatomically identifiable cortical structures as detailed as the hippocampal regions and layers, or subcortical structures such as amygdalar nuclei.
DOI: 10.25493/3wev-561
title: Probabilistic cytoarchitectonic map of Area Fo7 (OFC) (v2.1)

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. 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)
DOI: 10.25493/hwwz-zbq
title: Probabilistic cytoarchitectonic map of Area OP5 (Frontal Operculum) (v3.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP5 (Frontal Operculum) 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 OP5 (Frontal Operculum). The probability map of Area OP5 (Frontal Operculum) 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): Unger et al. (2020) [Data set, v2.0] [DOI: 10.25493/TWFH-BJZ](https://doi.org/10.25493%2FTWFH-BJZ) The most probable delineation of Area OP5 (Frontal Operculum) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: 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/bn5j-jt8
title: Probabilistic cytoarchitectonic map of Area TE 3 (STG) (v5.1)

description: This dataset contains the distinct architectonic Area TE 3 (STG) 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 3 (STG). The probability map of Area TE 3 (STG) 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 3 (STG): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/V09X-3EW](https://doi.org/10.25493%2FV09X-3EW) The most probable delineation of Area TE 3 (STG) 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/qq55-63t
title: A configurable and empirical model of the synaptic proteome (SQLite database)

description: Integration of 57 published synaptic proteomic datasets reveals a stunningly complex picture involving over 7000 proteins. At the post-synaptic side, multiple studies over the past 19 years appear to be reaching a consensus in terms of gross number and identity of the constituent proteins. Presynaptically the story remains more dynamic with fewer studies and each new study adding new components to the landscape. Molecular complexes were reconstructed using evidence-based protein-protein interaction data available from public databases. The constructed molecular interaction network model is embedded into an SQLite implementation allowing queries that generate custom network models based on meta-data including species, synaptic compartment, brain region, and method of extraction.
DOI: 10.25493/3pbv-wh0
title: Probabilistic cytoarchitectonic map of Area s32 (sACC) (v16.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area s32 (sACC) 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 s32 (sACC). The probability map of Area s32 (sACC) 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 s32 (sACC): Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/XTRR-172](https://doi.org/10.25493%2FXTRR-172) The most probable delineation of Area s32 (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/x0cv-g7f
title: Probabilistic cytoarchitectonic map of CM (Amygdala) (v6.1)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of CM (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 CM (Amygdala). The probability map of CM (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 CM (Amygdala): Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/36FR-C95](https://doi.org/10.25493%2F36FR-C95) The most probable delineation of CM (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: 10.25493/4b2j-mwg
title: Investigation of spatial distribution of excitation in cerebellar cortical slices

description: The aim of this project is to describe the spatial distribution of excitation in the cerebellar cortex, in particular in the granular layer, following mossy fiber electrical stimulation. The granular layer of the cerebellar cortex is composed by densely packed granule cells and fewer Golgi cells. The excitatory input to this layer is provided by glutamatergic mossy fibers, which contact both granule and Golgi cells in peculiar structures called cerebellar glomeruli. While granule cells are excitatory neurons and project to the molecular and Purkinje cell layers of the cerebellar cortex, Golgi cells are inhibitory interneurons that inhibit granule cells and make contact with other Golgi cells (through synapses or gap-junctions). Since granular cell axons excites Golgi cell dendrites, the inhibition of granule cells is obtained through a feed-forward and a feed-back loops. This organization provides specific temporal and spatial properties to granular layer spread of excitation, once the mossy fiber input is active. For a comprehensive review on this subject see D’Angelo et al. (2013) and Mapelli et al. (2014). In this project, voltage sensitive dyes recordings are used to characterize the spread of excitation in the granular layer following single-pulse electrical activation of mossy fibers, in acute slices of the cerebellar vermis (as in Soda et al., 2019).
DOI: 10.25493/vkv1-x9c
title: Population and single cell receptive field maps from mouse visual cortex

description: In this study we use population receptive-field (pRF) mapping techniques, which allow estimates to be made of aggregate receptive field sizes, to map the visual cortex of mice using wide-field calcium imaging. We include data relating the position of the pRF to its size across visual cortex. These maps show a region of mouse visual cortex in which pRFs are considerably smaller. We investigated the source of the smaller pRFs by recording receptive-fields from multi-units in the different layers of V1 using laminar electrodes and by using two-photon imaging to tile almost the entirety of V1 at the single-cell level. We also examine the relationship between RF position and size in the higher visual areas that surround V1 including LM, RL, AL, Am and PM.
DOI: 10.34810/data72/370
title: 01CO_1.MR.0004.0228.2016.02.04.15.44.11.109375.67144057.IMA
DOI: 10.25493/85kf-jmz
title: Density measurements of different receptors for ventral part of Area V2 [macaque, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in the ventral part of Area V2 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/f2vk-mb4
title: Recordings of excitatory postsynaptic currents from cerebellar neurons

description: Study and characterization of the synaptic properties of different cerebellar neuronal types.
DOI: 10.25493/kxjv-k22
title: The essential role of feedback processing for figure-ground perception in mice

description: The segregation of figures from the background is an important step in generating a visual percept. In primary visual cortex (V1), figures evoke stronger neural firing than backgrounds during a delayed phase of the neuronal responses, but it is unknown how this figure-ground modulation (FGM) arises and whether it is necessary for perception. Here we show, using optogenetic silencing in mice, that the delayed V1 response phase is necessary for figure-ground segregation. Neurons in higher visual areas also exhibit FGM and optogenetic silencing of higher areas strongly reduced FGM in V1. In V1, figures elicited higher activity of vasoactive intestinal peptide-expressing (VIP) interneurons than the background, whereas figures suppressed somatostatin-positive interneurons, resulting in an increased activation of pyramidal cells. Indeed, optogenetic silencing of VIP neurons reduced FGM in V1, indicating that disinhibitory circuits contribute to the response modulation. Our results provide new insight in how lower and higher areas of the visual cortex interact with each other to shape visual perception.
DOI: 10.25493/v1jj-6wx
title: Probabilistic cytoarchitectonic map of Area hOc4la (LOC) (v5.0)

description: This dataset contains the distinct architectonic Area hOc4la (LOC) 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 hOc4la (LOC). The probability map of Area hOc4la (LOC) 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 hOc4la (LOC): Malikovic et al. (2018) [Data set, v3.2] [DOI: 10.25493/FCQW-EZU](https://doi.org/10.25493%2FFCQW-EZU) Malikovic et al. (2019) [Data set, v3.4] [DOI: 10.25493/Z9JX-WKB](https://doi.org/10.25493%2FZ9JX-WKB) The most probable delineation of Area hOc4la (LOC) 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/wp86-fe5
title: 3D cyto- and receptor architectonic map of macaque lateral premotor area F4s

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 F4s 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: 10.25493/8jke-m53
title: Whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas (v2.5)

description: This dataset contains the whole-brain parcellation of the Julich-Brain Cytoarchitectonic Atlas in the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space ([Evans et al., 2012](https://www.sciencedirect.com/science/article/pii/S1053811912000419?via%3Dihub)). The parcellation is derived from the individual probability maps (PMs) of the cytoarchitectonic regions released in the Julich-Brain Cytoarchitectonic Atlas, that are further combined into a Maximum Probability Map (MPM). The MPM is calculated by considering for each voxel the probability of all cytoarchitectonic areas released in the atlas, and determining the most probable assignment ([Eickhoff 2005](https://www.sciencedirect.com/science/article/pii/S105381190400792X?via%3Dihub)). Gap maps complement cortical maps to achieve full cortical coverage.