cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/ympg-3af
title: Density measurements of different receptors for CA1 (Hippocampus) [rat, v2.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA1 (hippocampus) 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_ **muscarinic M<sub>1</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]pirenzepine_ **muscarinic M<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]oxotremorine-M_ **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_ 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/g20b-5qy
title: Whole-brain images of neuronal activation

description: Whole-brain datasets showing neurons activated in different phases of fear memory. The step-through inhibitory avoidance paradigm is used. In this paradigm, a cage with two compartments (one bright and one dark) is used. The mouse is placed in the bright compartment and has a natural tendency to move into the dark one. Here, in the training phase, it receives a mild electric foot shock. In the test phase, the mouse is placed again in the bright compartment, and the latency time to move into the dark one is considered as a measure of memory. No foot shock occurs during test. Four different experimental classes are studied: homecage (control), training, test 24h after training, test 1 week after training. Neuronal activation is visualized through the expression of the immediate early gene c-fos, using the FosTRAP transgenic line (Guenthner et al., Neuron 2013). Images are then obtained by using light-sheet microscopy in combination with tissue clearing. Native resolution of data is 0.65x0.65x2 microns; downloadable data are downscaled to 10x10x10 microns to keep data size reasonable. Data are from both male and female mice, of age between 3 and 6 months.
DOI: 10.25493/7ezg-3xv
title: Probabilistic cytoarchitectonic map of LB (Amygdala) (v8.0)

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) Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/C3X0-NV3](https://doi.org/10.25493%2FC3X0-NV3) 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) 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/fq3r-3jx
title: Probabilistic cytoarchitectonic map of Area s24 (sACC) (v16.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area s24 (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 s24 (sACC). The probability map of Area s24 (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 s24 (sACC): Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/HXWM-NRX](https://doi.org/10.25493%2FHXWM-NRX) 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/wvnr-spt
title: Probabilistic cytoarchitectonic map of Area 6ma (preSMA, mesial SFG) (v9.1)

description: This dataset contains the distinct architectonic Area 6ma (preSMA, mesial SFG) in the MNI Colin 27 and MNI ICBM 152 reference spaces. 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. 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 6ma (preSMA, mesial SFG). The probability map of Area 6ma (preSMA, mesial SFG) 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 6ma (preSMA, mesial SFG): Ruan et al. (2019) [Data set, v9.0] [DOI: 10.25493/HPX1-1CT](https://doi.org/10.25493%2FHPX1-1CT) The most probable delineation of Area 6ma (preSMA, mesial SFG) 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: 10.25493/1cc5-0be
title: Homology and Specificity of Natural Sound-Encoding in Monkey Auditory Cortex

description: The dataset contains fMRI nifti files for three monkey subjects involved in the experiment. Using fMRI responses to natural sounds, we investigated the representation of multiple acoustic features in auditory cortex of awake macaques and humans. Comparative analyses revealed homologous large-scale topographies not only for frequency but also for temporal and spectral modulations. In both species, posterior regions preferably encoded relatively fast temporal and coarse spectral information, whereas anterior regions encoded slow temporal and fine spectral modulations. Conversely, we observed a striking interspecies difference in cortical sensitivity to temporal modulations: While decoding from macaque auditory cortex was most accurate at fast rates (&gt; 30 Hz), humans had highest sensitivity to ~3 Hz, a relevant rate for speech analysis. These findings suggest that characteristic tuning of human auditory cortex to slow temporal modulations is unique and may have emerged as a critical step in the evolution of speech and language.
DOI: 10.25493/bpts-crr
title: Input Impedance Recordings in Neocortical Pyramidal cells

description: Recordings of Cellular Input Impedance in Layer V Pyramidal cells of Mouse V1 using patch-clamp recordings in brain slices. We performed intracellular recordings of visually identified pyramidal cells located in the layer V of mouse cortex using the perforated‐patch technique. To determine the input impedance at the soma, we injected sinusoidal currents in the current-clamp mode, we recorded the membrane potential response to a current input and we varied the frequencies and amplitudes.
DOI: 10.25493/8dp8-8he
title: Probabilistic cytoarchitectonic map of Area PFcm (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PFcm (IPL) 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 PFcm (IPL). The probability map of Area PFcm (IPL) 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 PFcm (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/G279-H6V](https://doi.org/10.25493%2FG279-H6V) The most probable delineation of Area PFcm (IPL) 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/b3h2-vyt
title: Probabilistic cytoarchitectonic map of Area hIP2 (IPS) (v7.0)

description: This dataset contains the distinct architectonic Area hIP2 (IPS) 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 hIP2 (IPS). The probability map of Area hIP2 (IPS) 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 hIP2 (IPS): Choi et al. (2018) [Data set, v6.0] [DOI: 10.25493/FR6P-6HW](https://doi.org/10.25493%2FFR6P-6HW) Choi et al. (2019) [Data set, v6.1] [DOI: 10.25493/EJTM-NDY](https://doi.org/10.25493%2FEJTM-NDY) The most probable delineation of Area hIP2 (IPS) 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/d0qk-ysx
title: Probabilistic cytoarchitectonic map of Area FG3 (FusG) (v7.0)

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) Lorenz et al. (2019) [Data set, v6.1] [DOI: 10.25493/Z0F6-0SY](https://doi.org/10.25493%2FZ0F6-0SY) 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) 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: 10.34810/data72/357
title: 01CO_1.MR.0004.0109.2016.02.04.15.44.11.109375.67138262.IMA
DOI: 10.25493/13rg-fyv
title: Probabilistic cytoarchitectonic map of Area FG4 (FusG) (v6.1)

description: This dataset contains the distinct architectonic Area FG4 (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 FG4 (FusG). The probability map of Area FG4 (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 FG4 (FusG): Lorenz et al. (2018) [Data set, v6.0] [DOI: 10.25493/6A73-MZS](https://doi.org/10.25493%2F6A73-MZS) The most probable delineation of Area FG4 (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/k89v-1cg
title: Probability map of bundle lh_RAC-SF_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 Rostral Anterior Cingulate and Superior Frontal 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/cs0y-54j
title: Probabilistic cytoarchitectonic map of Area OP7 (Frontal Operculum) (v3.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP7 (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 OP7 (Frontal Operculum). The probability map of Area OP7 (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 OP7 (Frontal Operculum): Unger et al. (2020) [Data set, v2.0] DOI: 10.25493/T2M3-2ST](https://doi.org/10.25493%2FT2M3-2ST) The most probable delineation of Area OP7 (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: 10.25493/4spm-v00
title: Large scale multi-channel EEG in rats

description: The aim of this task is to develop a protocol and a set-up for recording EEG signal from a large number of cortical areas in head-restrained rats exposed to anesthesia and during wakefulness. In order to quantify cortical complexity and its relation to (un)consciousness, a large scale EEG that samples activity from most of cortical areas is needed (Massimini et al. Science, 2005; Casali et al. Sci Transl Med, 2013). Unfortunately, only few existing probes allow this large scale recording in rodents (Megévand et al. Neuroimage, 2008; Choi et al. J Neurophysiol, 2010). Besides, since those probes are designed to be laid down on the skull surface, the obtained scalp EEG is subjected to the filtering action of the skull tissue, which separates the brain surface from the electrode. This would reduce the amplitude and the high frequency resolution of the cortical signal (Buzsáki et al. Nat Rev Neuroscience, 2012). Alternatively, epidural screw electrodes, by directly contacting the outer surface of the dura mater, showed a high signal amplitude and resolution, even for high frequencies (Arena et al. J Physiol, 2016). Therefore, we expanded the spatial density of epidural electrodes and optimized a protocol for the surgical implantation of a chronic array of 17 stainless steel electrodes by covering most of the cortical areas of a rat brain. Then, in combination with protocol for head restraining (Moore et al. Nat Protocols, 2014), we obtained multi-channel spontaneous EEG recordings from rats during wakefulness and during the exposure to low and high doses of 3 different anesthetics (propofol, sevoflurane and ketamine).