cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/30w7-0wk
title: Coregistration of simultaneous HD-EEG and intracranial EEG during single pulse intracerebral stimulation in wakefulness and sleep

description: Complexity, defined as the coexistence of functional differentiation and functional integration is a general property of thalamo-cortical circuits that can be characterized at multiscale level. Previously, we mainly focused on the relationship between slow waves and complexity, explored by perturbations and electrophysiological recordings, from micro- to macro-scale. The final goal was to link dynamics occurring at the micro-scale, such as sleep-like neuronal bistability, to the collapse/emergence of global patterns of complex interactions among brain areas at the macroscale. At the mesoscale level, we combined for the first time in this dataset (Pigorini et al. in prep.) intracortical single pulse electrical stimulation (SPES) in humans undergoing pre-surgical evaluation, with simultaneous intracortical recordings (stereo-EEG) and high-density electroencephalography (hd-EEG, 256 channels) during both wakefulness and sleep. Local perturbations with SPES allow studying bistable dynamics (downstates in the Local Field Potential) and their effects on local cortico-cortical interactions (Pigorini et al. 2015). Adding simultaneous hd-EEG links these intracortical events to overall connectivity and complexity as assessed at the scalp level (Casali et al. 2013, Sci Tr Med). The data is organized in two conditions (sessions): wakefulness and sleep. Subjects were patients that were undergoing intracranial monitoring for surgical planning due to refractory epilepsy. The project was approved by the responsible ethics committee (Comitato Etico Milano Area C - Ospedale Niguarda, Milano, Italy), all subjects provided informed consent and the experiments were carried out in accordance to the Declaration of Helsinki. **References:** **Pigorini et al. (2015)** Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep. *NeuroImage*, vol: 112, pp:105--113; doi: [10.1016/j.neuroimage.2015.02.056](https://doi.org/10.1016/j.neuroimage.2015.02.056) **Casali et al. (2013)** A theoretically based index of consciousness independent of sensory processing and behavior. *Science Translational Medicine*, vol: 5, issue: 198, pp. 198ra105; doi: [10.1126/scitranslmed.3006294](https://doi.org/10.1126/scitranslmed.3006294 ) **Funding:** This project/research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
DOI: 10.25493/q0my-250
title: GapMap Frontal II (v9.2)

description: This dataset contains the “Gap Map Frontal II” in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “Gap Map Frontal II”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “Gap Map Frontal II” 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. New maps are continuously replacing parts of “Gap Map Frontal II” with progress in mapping. Other available data versions of Gap Map Frontal II: Amunts et al. (2020) [Data set, v9.0] [DOI: 10.25493/XCKH-NF9](https://doi.org/10.25493%2FXCKH-NF9) Amunts et al. (2020) [Data set, v9.1] [DOI: 10.25493/H54A-PEB](https://doi.org/10.25493%2FH54A-PEB) The most probable delineation of Gap Map Frontal II 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)
DOI: 10.25493/4wsq-8fm
title: Probabilistic cytoarchitectonic map of Area 6d1 (PreCG) (v4.1)

description: This dataset contains the distinct architectonic Area 6d1 (PreG) 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 6d1 (PreG). The probability map of Area 6d1 (PreG) 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 6d1 (PreCG): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4JDW-STM](https://doi.org/10.25493%2F4JDW-STM) The most probable delineation of Area 6d1 (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/g3w4-x3p
title: Spike time dependent plasticity (STDP) data from neonate C57BL/6 (wild-type) mice, negative pairing

description: Spike-time dependent plasticity (STDP is a particular form of Hebbian type of learning which consists in bidirectional modifications of synaptic strength according to the temporal order of pre- and postsynaptic spiking (*Dan Y1, Poo MM (2006) Spike timing-dependent plasticity: from synapse to perception. Physiol Rev 86:1033-1048*). Thus, positively correlated pre- and postsynaptic spiking (pre before post) within a critical window leads to long term potentiation (LTP), whereas a negative correlation (post before pre) leads to long term depression (LTD). At the neonatal stage, the hippocampal mossy fiber (MF)-CA3 is GABAergic and exhibits STDP. Our data demonstrate that, at the same age, positive pairing fails to induce STD-LTP at MF-CA3 synapses in hippocampal slices obtained from neuroligin-3 (NL3) knock-in (NL3<sup>R451C</sup>KI) and NL3 knock-out (KO) mice. Similarly, in NLR<sup>R451C</sup> KI mice, negative pairing failed to cause STD-LTD. In contrast, STD-LTP and STD-LTD can be readily produced in control age-matched WT littermates. In NLR<sup>R451C</sup> KI mice, the impairment in STD-LTP is maintained in adulthood when MF are glutamatergic. This set of data refers to the neonate, C57BL/6 (wild-type), negative pairing condition.
DOI: 10.25493/8mkd-d77
title: Reference delineations of Area hOc2 (V2, 18) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic maps of Area hOc2 (V2, 18) in the BigBrain 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 every 60th section of the visual system. They were then transformed to the sections of the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013, which were provided by Claude Lepage (McGill). For this brain area, a highly detailed 3D map has been computed based on automatic delineations in every histological section from a novel Deep-Learning algorithm. This dataset can be accessed here: [Ultrahigh resolution 3D cytoarchitectonic map of Area hOc2 (V2, 18) created by a Deep-Learning assisted workflow](https://kg.humanbrainproject.eu/instances/Dataset/63093617-9b72-45f5-88e6-f648ad05ae79) This ultrahigh resolution 3D cytoarchitectonic map of Area hOc2 (V2, 18) can be explored in the HBP interactive Atlas Viewer.
DOI: 10.25493/4mmd-heb
title: Probabilistic cytoarchitectonic map of Area 6d2 (PreCG) (v4.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 6d2 (PreCG) 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 6d2 (PreCG). The probability map of Area 6d2 (PreCG) 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 6d2 (PreCG): Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/KXHS-N90](https://doi.org/10.25493%2FKXHS-N90) The most probable delineation of Area 6d2 (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: 10.25493/7njq-anh
title: Hippocampal place cell recordings in a Four-Room navigation task

description: Hippocampal place cells show location-specific firing and are thought to be an essential component of the brain’s “cognitive map”, supporting flexible memorization of places and possibly of their connectivity. So far, only a few studies investigated whether these cells encode the connectivity between places, either by adding a barrier or removing a wall. We aimed to go one step further and investigated whether place cells can encode pure connectivity, i.e. changes in the “closed” or “open” aspect of an otherwise identical door. We performed extracellular tetrode recordings from the dorsal CA1 hippocampus of 5 male Lister-Hooded rats while they were foraging and navigating in a 4-compartment environment. Pushable doors were used by the rats to transition between compartments. At first, all 4 doors were pushable. In a test condition, either one of the doors was closed, or all of the doors were closed one-way. This data could be analysed further to investigate basic place cell firing properties as well as possible neural correlates of navigation in such an environment.
DOI: 10.25493/twfh-bjz
title: Probabilistic cytoarchitectonic map of Area OP5 (Frontal Operculum) (v2.0)

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. 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)
DOI: 10.25493/c8p7-6x0
title: Neocortex-wide fluorescence imaging of neuronal activity during a reach-to-grasp task

description: The dataset includes wide-field fluorescence recordings from the dorsal neocortex of both hemispheres of Thy1-GCaMP6f mice (C57BL/6J-Tg(Thy1GCaMP6f)GP5.17Dkim/J; RRID:IMSR_JAX:025393) during a reach-to-grasp task. The GCaMP6f indicator enables visualization of fluctuations in calcium concentration, which is an indirect reporter of neuronal activity, expressed in terms of dF/F0. The behavior of the mouse is recorded via high-speed cameras, synchronized with the fluorescence signal. For selected movements, we provide data about the position of the metacarpus (.csv files), which was extracted from the corresponding .avi file.
DOI: 10.34810/data72/361
title: 01CO_1.MR.0004.0166.2016.02.04.15.44.11.109375.67140547.IMA
DOI: 10.25493/wb5j-r87
title: Detailed dynamical laminar organisation in different cortical areas (in rats in vivo)

description: The objective of the study was to investigate the detailed dynamic laminar organisation in different cortical areas to highlight inter-area differences. Functional studies using electrophysiology will be paralleled with structural information from the same areas in collaboration with T1.4.1<sup>[1]</sup> devoted to the detailed cellular and anatomical structure. Data was obtained from the thalamocortical interaction on visual cortex spontaneous activity in vivo in Wistar rats. <sup>[1]</sup> HBP funded projects with task ID T1.4.1 “Microanatomical and functional design of the cortical column” (SGA2)
DOI: 10.25493/2jk3-qxr
title: Probabilistic cytoarchitectonic map of Area 3b (PostCG) (v8.4)

description: This dataset contains the distinct architectonic Area 3b (PostCG) 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 3b (PostCG). The probability map of Area 3b (PostCG) 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 3b (PostCG): Geyer et al. (2018) [Data set, v8.2] [DOI: 10.25493/W4WS-B3P](https://doi.org/10.25493%2FW4WS-B3P) 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: 10.25493/26wt-e3p
title: Probabilistic cytoarchitectonic map of Area Fp2 (FPole) (v2.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area Fp2 (FPole) 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 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 Fp2 (FPole). The probability map of Area Fp2 (FPole) 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 Fp2 (FPole): Bludau et al. (2019) [Data set, v2.4] [DOI: 10.25493/GZW1-7R3](https://doi.org/10.25493%2FGZW1-7R3) The most probable delineation of Area Fp2 (FPole) 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/e883-nfa
title: Electrophysiological recordings of striatal fast-spiking interneurons

description: Membrane voltage traces recorded from somata of striatal fast-spiking interneurons in response to square-pulse current injections.
DOI: 10.25493/fk9a-8x4
title: Navigation and generalisation in a four-rooms environment

description: Humans performed a computerized task. They learned to navigate a 3D maze by pressing buttons to obtain rewards that were hidden in boxes. Mirroring the classic problem in AI research the rooms were arranged into a square with connectors linking adjacent boxes. Participants were required to forage for two rewards within a fixed time period. They viewed a context cue prior to each trial onset which indicated that rewards were either to be found in two horizontally arranged (cue A) or vertically arranged (cue B) rooms. This allowed them to infer the location of the second reward after the first had been located. Participants were not informed of this contingency but required to learn it by trial-and-error before generalising to a new environment.
DOI: 10.25493/1qkp-bgs
title: Probability map of bundle rh_SM-Ins_0 (atlas of superficial white matter fibre bundles, version 2018)

description: This data contains the probability map of a short fibre bundle connecting the right hemisphere Supramarginal and Insula 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/krrn-e62
title: GapMap Frontal to Temporal (v9.0)

description: This dataset contains the “GapMap Frontal to Temporal" in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. In order to provide whole-brain coverage for the cortex within the Julich-Brain Atlas, yet uncharted parts of the frontal cortex have been combined to the brain region “GapMap Frontal to Temporal”. The distributions were modeled so that probabilistic gap maps were computed in analogy to other maps of the Julich-Brain Atlas. The probabilistic map of “GapMap Frontal to Temporal” 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. New maps are continuously replacing parts of “GapMap Frontal to Temporal” with progress in mapping. The most probable delineation of GapMap Frontal to Temporal 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)
DOI: 10.25493/kxhs-n90
title: Probabilistic cytoarchitectonic map of Area 6d2 (PreCG) (v4.1)

description: This dataset contains the distinct architectonic Area 6d2 (PreG) 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 6d2 (PreG). The probability map of Area 6d2 (PreG) 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 6d2 (PreCG): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4MMD-HEB](https://doi.org/10.25493%2F4MMD-HEB) The most probable delineation of Area 6d2 (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)