cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/gbj2-cx6
title: Probabilistic cytoarchitectonic map of Area OP9 (Frontal Operculum) (v6.0)

description: This dataset contains the distinct architectonic Area OP9 (Frontal Operculum) 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 OP9 (Frontal Operculum). The probability map of Area OP9 (Frontal Operculum) 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 OP9 (Frontal Operculum): Saal et al. (2019) [Data set, v5.0] [DOI: 10.25493/QYFC-TQV](https://doi.org/10.25493%2FQYFC-TQV) Saal et al. (2019) [Data set, v5.1] [DOI: 10.25493/3A30-5E4](https://doi.org/10.25493%2F3A30-5E4) The most probable delineation of Area OP9 (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. (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: 10.25493/67tt-jtj
title: Probabilistic map of the anterior segment of the left arcuate fasciculus (atlas of deep white matter fibre bundles, version 2018)

description: This data contains the probability map of the anterior segment of the left arcuate 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/mjn4-sy2
title: 3D cyto- and receptor architectonic map of macaque area PGop of the inferior parietal lobe

description: A multimodal analysis of the macaque inferior parietal lobule 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 inferior parietal lobe, in combination with an in vitro quantitative receptor autoradiographic analysis of the regional and laminar distribution patterns of 15 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 area PGop 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/vhyh-aj1
title: Modulation of hippocampal GABAergic currents by anti-gephyrin intrabodies (delta2-188)

description: The efficiency of synaptic transmission relies on the temporally and spatially regulated expression of postsynaptic receptors localized in precise apposition to presynaptic release sites. At synapses, receptors are organized in clusters which are formed by highly regulated events, dynamically controlled by a number of proteins including scaffolds, adhesion molecules and active transport processes along the cytoskeleton (Kneussel and Loebrich, 2007). At inhibitory synapses, GABAergic signaling controls dendritic integration, neural excitability, circuit reorganization and fine tuning of network activity. Among different players, the tubulin-binding protein gephyrin, plays a key role in anchoring GABAA receptors to synaptic membranes, (Tyagarajan and Fritschy, 2014). Moreover, gephyrin is instrumental in establishing and maintaining a proper excitatory (E)/inhibitory (I) balance necessary for the correct functioning of neuronal networks (Pizzarelli and Cherubini, 2011). A disruption of the E/I balance is thought to be at the origin of several neuropsychiatric disorders including epilepsy, schizophrenia and autism (Penzes et al. 2013; Cellot and Cherubini, 2014; Nelson and Valakh, 2015). We tested the effects of truncated form of gephyrin (delta 2-188), acting as a dominant negative protein, on spontaneous inhibitory postsynaptic currents, sIPSCs, from hippocampal neurons in culture. In cells transfected with delta 2-188 fused to EGFP or with EGFP alone, the mean sIPSCs frequency was 0.91 ± 0.18 Hz in control (n=10) and 0.42 ± 0.09 Hz in the presence of delta 2-188 (n=9). The mean sIPSCs amplitude was 129.4 ± 18.8 pA in controls and 81.9 ±m13.2 pA in the presence of delta 2-188. Significant differences were found between controls and delta 2-188 regarding the frequency and amplitude of sIPSCs (p=0.03 and p=0.05, respectively). These data further support the hypothesis that gephyrin regulates the postsynaptic organization of synaptic GABAA receptors. Andjus, P R, Stevic‐Marinkovic, Z and Cherubini E (1997) Immunoglobulins from motoneurone disease patients enhance glutamate release from rat hippocampal neurones in culture. The Journal of Physiology, 504: 103-112 Cellot G, Cherubini E (2014) GABAergic signaling as therapeutic target for autism spectrum disorders. Front Pediatr 2:70. doi: 10.3389/fped.2014.00070 Kneussel M, Loebrich S (2007) Trafficking and synaptic anchoring of ionotropic inhibitory neurotransmitter receptors. Biol Cell 99: 297-309 Nelson SB, Valakh V (2015) Excitatory/Inhibitory Balance and Circuit Homeostasis in Autism Spectrum Disorders. Neuron 87: 684-698 Penzes P, Buonanno A, Passafaro M, Sala C, Sweet RA (2013) Developmental vulnerability of synapses and circuits associated with neuropsychiatric disorders. J Neurochem 126:165-82 Pizzarelli R, Cherubini E (2011) Alterations of GABAergic signaling in autism spectrum disorders. Neural Plast 297153. doi: 10.1155/2011/297153 Tyagarajan SK, Fritschy JM (2014) Gephyrin: a master regulator of neuronal function? Nat Rev Neurosci 15: 141-156
DOI: 10.25493/e7pm-fdc
title: Density measurements of different receptors for Area PFt (IPL) [human, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in Area PFt (IPL) 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_ **mGluR2/3** \| **_glutamate_** \| _[<sup>3</sup>H]LY341495_ **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_ **nicotinic α<sub>4</sub>β<sub>2</sub>** \| **_acetylcholine_** \| _[<sup>3</sup>H]epibatidine_ **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 for the receptor fingerprints, profiles and autoradiographs as well as a list of 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/k63g-89h
title: Probabilistic cytoarchitectonic map of Area Id4 (Insula) (v3.1)

description: This dataset contains the distinct architectonic Area Id4 (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 Id4 (Insula). The probability map of Area Id4 (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. The most probable delineation of Area Id4 (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)
DOI: 10.25493/mwas-3s6
title: Comparability of basal ganglia delineations across different mouse brain atlases

description: The dataset contains an evaluation of the comparability of basal ganglia delineations in three commonly used mouse brain atlases to similarly named, or in other ways corresponding, regions in the Allen Mouse Common Coordinate Framework (CCF). The degree of spatial correspondence between delineations was semi-quantitatively scored by inspection of serial atlas images from different atlases co-registered to the CCF. The dataset consists of an .xlxs file summarising spatial relationships and a .pdf file with accompanying descriptions. This provides an overview of the degree of overlap among basal ganglia delineations, which can be used to determine whether data defined by use of different atlases are fully or partly comparable.
DOI: 10.25493/fpfj-zcd
title: Probabilistic cytoarchitectonic map of Area PGp (IPL) (v9.4)

description: This dataset contains the distinct architectonic Area PGp (IPL) 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 PGp (IPL). The probability map of Area PGp (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 PGp (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/V9NJ-TBQ](https://doi.org/10.25493%2FV9NJ-TBQ) The most probable delineation of Area PGp (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/dvph-rde
title: Morphological reconstruction of striatal low threshold-spiking interneuron

description: Three-dimensional (3D) model of low threshold spiking interneuron is reconstructed from confocal scans of striatal slices.
DOI: 10.25493/h24f-2et
title: Comparison of morphological parameters of pyramidal neurons between rodents and humans

description: We have produced 3D reconstructions of pyramidal cells in the mouse and human hippocampal CA1 region using Neurolucida software from 3D confocal stack of images. Data from 3D reconstructions will be useful to compare and model anatomical differences with functional studies both in mouse and human hippocampus.
DOI: 10.34810/data72/362
title: 01CO_1.MR.0004.0197.2016.02.04.15.44.11.109375.67141790.IMA
DOI: 10.25493/5hsf-81j
title: Probabilistic cytoarchitectonic map of Area 4p (PreCG) (v9.4)

description: This dataset contains the distinct architectonic Area 4p (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 4p (PreCG). The probability map of Area 4p (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 4p (PreCG): Geyer et al. (2018) [Data set, v9.2] [DOI: 10.25493/ECYA-0D1](https://doi.org/10.25493%2FECYA-0D1) The most probable delineation of Area 4p (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)
DOI: 10.25493/w4ws-b3p
title: Probabilistic cytoarchitectonic map of Area 3b (PostCG) (v8.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 3b (PostCG) 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 3b (PostCG). The probability map of Area 3b (PostCG) 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 3b (PostCG): Geyer et al. (2019) [Data set, v8.4] [DOI: 10.25493/2JK3-QXR](https://doi.org/10.25493%2F2JK3-QXR) The most probable delineation of Area 3b (PostCG) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25493/2we9-ryf
title: Stimulation of the ventral tegmental area in macaques drives category-selective learning in the absence of attention

description: Perception improves by repeated practice with visual stimuli, a phenomenon known as visual perceptual learning (VPL). The interplay of attentional and neuromodulatory reward signals is hypothesized to cause these behavioral and associated neuronal changes, although VPL can occur without attention (i.e. task-irrelevant VPL). In addition, task-relevant VPL can be category-selective for simple attended oriented stimuli. Yet, it is unclear whether category-selective task-irrelevant VPL occurs, nor which brain centers mediate underlying form of adult cortical plasticity. Here, we show that pairing subliminal complex visual stimuli (faces and bodies) with electrical microstimulation of the ventral tegmental area (VTA-EM) causes category-selective task-irrelevant VPL. These perceptual improvements are accompanie by fMRI signal changes in early and late visual and frontal areas, as well as the cerebellum, hippocampus, claustrum and putamen. In conclusion, Pavlovian pairing of unattended complex stimuli with VTA-EM causes category-selective learning accompanied by changes of cortical and subcortical neural representations in macaques.
DOI: 10.25493/1kv8-b9g
title: Probabilistic cytoarchitectonic map of DG (Hippocampus) (v11b.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of DG (Hippocampus) 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 DG (Hippocampus). The probability map of DG (Hippocampus) 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 DG (Hippocampus): Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/M8JP-XQT](https://doi.org/10.25493%2FM8JP-XQT) The most probable delineation of DG (Hippocampus) 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/nvj5-jj
title: Probabilistic cytoarchitectonic map of Area 6d3 (SFS) (v4.1)

description: This dataset contains the distinct architectonic Area 6d3 (SFS) 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 6d3 (SFS). The probability map of Area 6d3 (SFS) 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 6d3 (SFS): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/40J8-TAK](https://doi.org/10.25493%2F40J8-TAK) The most probable delineation of Area 6d3 (SFS) 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)