cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/a04z-e42
title: Probabilistic cytoarchitectonic map of Area hIP1 (IPS) (v7.0)

description: This dataset contains the distinct architectonic Area hIP1 (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 hIP1 (IPS). The probability map of Area hIP1 (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 hIP1 (IPS): Choi et al. (2018) [Data set, v6.0] [DOI: 10.25493/VWV1-FYY](https://doi.org/10.25493%2FVWV1-FYY) Choi et al. (2019) [Data set, v6.1] [DOI: 10.25493/92FE-7S6](https://doi.org/10.25493%2F92FE-7S6) The most probable delineation of Area hIP1 (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/xmph-mts
title: Two-photon fluorescence microscopy and calcium imaging of cerebellar slices

description: A completely scanless two-photon microscope (SLM-2PM) was developed and used to perform two-photon calcium imaging experiments on acute cerebellar slices. The system allows to monitor the activity of different selected cells simultaneously while maintaining the single-cell resolution; it was used to study the spatial organization of the cerebellar granular layer activity, both in control condition and in condition of blockage of inhibition (Gandolfi et al. 2014, Pozzi et al., 2015).
DOI: 10.25493/r3n8-3c1
title: A database of synaptic protein-protein interactions in rule-based format

description: The study of Protein-Protein interactions (PPI) involves the analysis and identification of protein complexes that may form under a variety of reaction conditions. Interacting biomolecules have the potential to interact in a myriad of different ways as the number of possible post-translational modifications grow exponentially when considering the binary interactions within the reaction network. Using traditional methods like ODEs requires large amounts of reaction specific details, which sets the limitation on the size of the modelled system. A rule-based model, on the other hand, comprises of a set of rules where the network specification is implicit, which allows to model large-scale protein complexes. For this, PPIs should be defined using rule-based specification languages like Kappa (1) or BioNetGen (2). Respective software tools enable further simulation of those rules in deterministic or stochastic way. Up to date, thousands of synaptic PPIs are identified and plenty of rule-based model are published, while no existing database stores the Protein-Protein interaction rules. The purpose of this project is to create and load a database with synaptic PPIs in a rule-based format to enable researchers to reuse the existing models and create new models on demand.
DOI: 10.25493/6r48-e3v
title: Loose-cell attached patch-clamp recordings from cerebellar granule cells

description: Study of the synaptic responses of granule cells.
DOI: 10.25493/xa4s-xxz
title: Resolving nerve fiber crossings at micrometer resolution in rat, vervet monkey, and human brain samples

description: Unstained histological sections obtained from a Wistar rat brain, two vervet monkey brains, and a human optic chiasm were measured with Scattered Light Imaging (SLI) to study nerve fiber crossings at micrometer resolution, e.g. in the corona radiata of the vervet monkey brain. The data provided here contains the original image stacks obtained from the SLI measurements as well as generated parameter maps showing different characteristics of the measured brain sections, e.g. the individual (in-plane) orientations for up to three (crossing) nerve fiber bundles for each measured image pixel (with a resolution down to 6.5 μm). For some of the measured brain sections, we also provide parameter maps generated from 3D-Polarized Light Imaging (3D-PLI) measurements for better comparison.
DOI: 10.25493/h9bb-tnm
title: Probability map of bundle lh_IP-SP_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Inferior Parietal and Superior Parietal 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: 10.25493/49qz-awz
title: Corrected 3-D reconstruction and surface parcellation of an infant brain [v1.0]

description: The version 1.0 of the Infant Brain Atlas is a corrected three-dimensional reconstruction of a single MRI brain scan of a single human infant (female, age: 7.1 weeks) with a high resolution (1.1 mm isotropic). The inner cortical surface was segmented using a semi-automatic segmentation pipeline dedicated to T2-weighted MRI images of the infant brain, followed by manual correction, when local inaccuracies were detected. On the basis of the segmented sulci, 47 anatomical regions were delineated in each hemisphere, using the same principles as the AAL atlas (Automated Anatomical Labelling).
DOI: 10.25493/15zs-bqb
title: Full hemisphere human immunohistochemistry for calcium binding proteins Parvalbumin, Calbindin and Calretinin - Raw images

description: We investigate the regional and laminar localization of inhibitory interneurons, identified by calcium-binding proteins (CBPs) parvalbumin (PV), calbindin (CB) and calretinin (CR), in the human brain. We use immunohistochemistry against CBPs in full hemisphere human brain sections, spanning visual areas from the occipital pole to the splenium of the corpus callosum. This data set contains RAW full hemisphere brightfield scans at 1µm resoltion. This is the first mult-label, full section cell-specific data-set availble in human. It can be used for mapping studies, and the provided images can be processed by other researchers according to their research planning.
DOI: 10.25493/e0et-ptn
title: Density measurements of different receptors for CA, stratum cellulare (Hippocampus) [rat, v1.0]

description: This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters of CA, stratum cellulare (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_ 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/eg2e-ek6
title: 3D cyto- and receptor architectonic map of macaque supplementary motor cortex, area F3

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 the supplementary motor cortex (SMA; area F3) 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: 10.25493/66pb-17w
title: Probability map of bundle lh_PoC-SM_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the left hemisphere Postcentral and Supramarginal 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/m8jp-xqt
title: Probabilistic cytoarchitectonic map of DG (Hippocampus) (v11.1)

description: This dataset contains the distinct architectonic DG (Hippocampus) 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 DG (Hippocampus). The probability map of DG (Hippocampus) 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 DG (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/1KV8-B9G](https://doi.org/10.25493%2F1KV8-B9G) 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) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9)
DOI: 10.25493/abky-t1y
title: Metadata Schema of the Common Data Elements for Depression (v1)

description: Data in the Medical Informatics Platform (MIP) resides in hospital servers and never leaves the hospital. Analyses and experiments are executed with respect to that principle while preserving patients’ anonymity making it infeasible for their identity to be inferred. Hospitals importing their data to the Medical Informatics Platform join a federation so as to run analyses on data from other hospital nodes as well. Each federation in the MIP refers to a specific Medical Condition. Here the metadata for the federation of hospitals that studies depression is presented. This metadata schema, consisting of a total of 147 variables, has the following main variable categories: **1. Diagnosis** - 4 variables concerning the diagnosis code in ICD10, the disease duration, the age of the patient and patient control **2. Brain Anatomy** - 135 volumetric variables based on a brain atlas. Values have been generated from the brain feature extraction pipeline which uses SPM12 **3. Scores** - 3 scores for the Montgomery-Asberg Depression Rating Scale as well as its score in and out **4. Demographics** - 5 variables for basic demographics information that does not reveal patient’s identity The metadata viewing and management is done by Data Catalogue, a central web portal of MIP. Data Catalogue offers presentation, search and hierarchical visualisation of metadata information for datasets imported into the MIP while providing metadata management features to authorized users. One of its features is parsing metadata descriptions in XLSX files and generating their equivalent in a hierarchical JSON format which the MIP uses. In this repository we upload metadata in both XLSX and the generated JSON format.
DOI: 10.34810/data72/360
title: 01CO_1.MR.0004.0185.2016.02.04.15.44.11.109375.67141309.IMA
DOI: 10.25493/c2cw-hfw
title: Averaged structural and functional connectivities of healthy cohorts based on whole-brain parcellations

description: Brain connectivity is one of the main objects of brain research reflecting the interaction between neuronal populations at a variety of spatial and temporal scales and modalities. The main approaches include the extraction and analysis of the structural connectivity (SC) representing the anatomical axonal tracts and functional connectivity (FC) representing the temporal correlation between neuronal activity of brain regions. Extraction of SC and FC is a complex process requiring a deep knowledge of the data processing methods. The current project is aimed at facilitation of this process and provides an access to the extracted brain connectivities ready for further usage. The data includes SC and resting-state FC for several brain atlases of different granularity, where the parcellation is performed based on functional, anatomical and cytoarchitectonic brain properties. The connectivity is extracted and averaged over several hundreds of healthy subjects from well-known public repositories of neuroimaging data, including the Human Connectome Project (HCP) [Van Essen et al., 2013](https://doi.org/10.1016/j.neuroimage.2013.05.041) and Enhanced Nathan Klein Institute Rockland Sample (eNKI) [Nooner et al., 2012](https://dx.doi.org/10.3389/fnins.2012.00152). The provided SC and FC can be used for group-level investigations of the connectivities and for data-driven mathematical modeling of the resting-state brain dynamics.
DOI: 10.25493/ycge-adc
title: Purkinje cell protein 2 tetracycline-transactivator expression: coronal sections (case 4340)

description: Bright-field microscopy images of serial coronal brain sections showing Purkinje cell protein 2 (Pcp2) promoter expression in a bigenic Pcp2-tetracycline-transactivator (tTA) mouse brain (case 4340, adult male), generated using a driver-reporter construct in which the Pcp2-tTA promoter regulates the expression of the E. coli derived LacZ reporter gene encoding β-galactosidase, which is visualized histologically using X-gal (5-Bromo-4-chloro-3-indolyl β-d-galactopyranoside) as a substrate. Our data confirm earlier reports that Pcp2-tTA promoter expression is restricted to cerebellar Purkinje cells.
DOI: 10.25493/c1fq-2f
title: Probabilistic cytoarchitectonic map of Area 5L (SPL) (v8.4)

description: This dataset contains the distinct architectonic Area 5L (SPL) 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 5L (SPL). The probability map of Area 5L (SPL) 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 5L (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/A5V4-HFH](https://doi.org/10.25493%2FA5V4-HFH) The most probable delineation of Area 5L (SPL)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/j2kz-azw
title: Probabilistic cytoarchitectonic map of Area 45 (IFG) (v7.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 45 (IFG) 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 45 (IFG). The probability map of Area 45 (IFG) 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 45 (IFG): Amunts et al. (2019) [Data set, v7.4] [DOI: 10.25493/MR1V-BJ3](https://doi.org/10.25493%2FMR1V-BJ3) The most probable delineation of Area 45 (IFG) 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/gpet-6nb
title: Optimisation of photostimulation targeting muscarinic and glutamate receptors

description: The cholinergic system is critically involved in the modulation of cognitive functions. The central role of acetylcholine in cognition seems to be related to the synchronization of gamma band oscillations: gamma band power increases in relation to working memory load and abnormal gamma oscillations are associated with cognitive deficits. It has been demonstrated that muscarinic agonists can improve the frequency, and decrease the duration and the power of slow waves (delta). This balance of neuromodulator levels plays a key role in sleep and memory (Vakalopoulos C et al., Front Neurosci 2014, 8, 63). Carbachol, a cholinergic agonist, has been shown to stabilize gamma frequency oscillations. To modulate brainwaves in neural circuits, IBEC, in collaboration with IDIBAPS, designed and prepared novel light-regulated agonists of muscarinic receptors, and tested their efficacy in cells and brain slices under different wavelengths of light irradiation. Data collected comes from three different experiments. The first one is a study which aims to characterize the effect produced on neural activity when the PAI molecule (Phthalimide-Azo-Iperoxo) in its cis and trans configuration is applied. Mainly, for the whole set of experiments, drugs are applied to the primary visual cortex coronal slices from ferret. The second experiment pretends to study the effect of an increasing concentration of both PAI molecule configurations: cis and trans. Finally, the third one tries to study the effect of another molecule (IPEROXO) on the same cortical slices under different concentrations.