cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/fsv3-h85
title: Probabilistic cytoarchitectonic map of Area 6ma (preSMA, mesial SFG) (v10.0)

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) Ruan et al. (2019) [Data set, v9.1] [DOI: 10.25493/WVNR-SPT](https://doi.org/10.25493%2FWVNR-SPT) 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) 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/rqew-ytw
title: Probabilistic cytoarchitectonic map of CM (Amygdala) (v8.0)

description: This dataset contains the distinct architectonic CM (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 CM (Amygdala). The probability map of CM (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 CM (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/X0CV-G7F](https://doi.org/10.25493%2FX0CV-G7F) 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) 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: 10.25493/1a21-d6x
title: Probability map of bundle rh_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 right 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: 10.25493/48gk-b7x
title: Probabilistic cytoarchitectonic map of Area hOc3d (Cuneus) (v4.0)

description: This dataset contains the distinct architectonic Area hOc3d (Cuneus) 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 hOc3d (Cuneus). The probability map of Area hOc3d (Cuneus) 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 hOc3d (Cuneus): Kujovic et al. (2018) [Data set, v2.2] [DOI: 10.25493/SAMM-YKZ](https://doi.org/10.25493%2FSAMM-YKZ) Kujovic et al. (2019) [Data set, v2.4] [DOI: 10.25493/F9X3-JVJ](https://doi.org/10.25493%2FF9X3-JVJ) The most probable delineation of Area hOc3d (Cuneus) 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/y147-2ce
title: Brain-wide distribution of glutamate type 1 transporter protein (GLT1)

description: Glutamate is the major excitatory transmitter in the central nervous system (Danbolt, Prog. Neurobiol. 65:1-105, 2001). It is inactivated by cellular uptake, mostly catalyzed by the glutamate transporters GLT1 (slc1a2, excitatory amino acid transporter [EAAT2]) subtype expressed at high levels in brain astrocytes and at lower levels in neurons. Three C-terminal variants of EAAT2 exist: GLT1a (Pines et al., Nature 360:464-467, 1992), GLT1b (Utsunomiya-Tate et al., FEBS Lett 416:312-326,1997), and GLT1c (Rauen et al., Neurochem. Int. 45:1095-1106, 2004). This dataset is brain-wide collection of microscopic images showing the brain-wide distribution of GLT1 in the mouse and rat brain, visualized by immunohistochemistry using antibodies against GLT1a and GLT1b. To facilitate identification of anatomical location adjacent section were stained to reveal cyto- and myeloarchitecture.
DOI: 10.25493/wka8-q4t
title: Propagation modes of slow waves in mouse cortex

description: The objective of the study is to investigate the propagation modes of slow waves, their statistical properties and their transformation in varying brain states at the surface of the cortex (anesthesia levels - physiological sleep/wake). We obtained multi-electrode recordings close to the surface of the cortex. The analysis is being performed in collaboration with T3.2.1<sup>[1]</sup> and T3.2.5<sup>[2]</sup>. The data is being integrated in models carried out in T3.2.1<sup>[1]</sup> and T3.2.5<sup>[2]</sup>, and we will perform new experiments suggested by the models. These data will also be used to derive mean-field and population density descriptions from point neuron models (T4.1.3<sup>[3]</sup>) and to validate large-scale mouse network models. <sup>[1]</sup> HBP funded projects with task ID T3.2.1 “Modelling and analysis of heterogeneous cortical spontaneous activity across brain states” (SGA2) <sup>[2]</sup> HBP funded projects with task ID T3.2.5 “High-efficiency multi-scale pipeline for analysis and simulation of experimental slow waves and wakefulness transition”(SGA2) <sup>[3]</sup> HBP funded projects with task ID T4.1.3 and title “Mean-Field and Population Models” (SGA2)
DOI: 10.25493/axts-en0
title: Lateral Inhibition by Martinotti Interneurons is Facilitated by cholinergic Inputs in Human Neocortex

description: This dataset includes electrophysiological recordings from pyramidal cells and Martinotti cells. Pyramidal cells can be connected di-synaptically via Martinotti cells. This type of connections leads to lateral inhibition of neighboring pyramidal cells. Up to 4 cells were recorded simultaneous to test connectivity between cells. This data includes recordings from pyramidal cells to Martinnoti cells and lateral inhibition of neighboring pyramidal cells.
DOI: 10.25493/2qr1-swn
title: Anterograde visualization of projections from lateral and ventral orbitofrontal cortex in rat (case: F10)

description: High-resolution bright-field microscopy images (86) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. Two anterograde tracers, biotinylated dextran amine (BDA) and Phaseolus vulgaris-leucoagglutinin (PHA-L), were injected into the lateral (LOC) and ventral orbitofrontal cortex (VOC), respectively. Brain sections are 300 µm apart and 50 µm thick. The dataset contains two complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/kqey-yf4
title: Anterograde visualization of projections from lateral orbitofrontal cortex in rat (case: F36)

description: High-resolution bright-field microscopy images (42) of serial coronal brain sections showing anterogradely labeled axons in orbitofrontal cortex (OFC) in a female adult Sprague Dawley rat. The anterograde tracer biotinylated dextran amine (BDA) was injected into the lateral orbitofrontal cortex (LOC). Brain sections are 300 µm apart and 50 µm thick. The dataset contains a complete series throughout the anterior-posterior extent of the brain, contributing to visualization of a topographically organized connectivity from OFC throughout the brain, including the published data on a topographically organized projection to the parahippocampal areas.
DOI: 10.25493/4b87-q8x
title: Probabilistic cytoarchitectonic map of Area hOc4lp (LOC) (v3.4)

description: This dataset contains the distinct architectonic Area hOc4lp (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 hOc4lp (LOC). The probability map of Area hOc4lp (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 hOc4lp (LOC): Malikovic et al. (2018) [Data set, v3.2] [DOI: 10.25493/W4H2-WTM](https://doi.org/10.25493%2FW4H2-WTM) The most probable delineation of Area hOc4lp (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)
DOI: 10.25493/dtc3-evm
title: Probabilistic cytoarchitectonic map of Area TeI (STG) (v5.1)

description: This dataset contains the distinct architectonic Area TeI (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 TeI (STG). The probability map of Area TeI (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. The most probable delineation of Area TeI (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. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/km37-91k
title: Interpolated 3D map of Area 6ma (preSMA, mesial SFG) in the BigBrain

description: This dataset contains cytoarchitectonic maps of Area 6ma (preSMA, mesial SFG) in the Big Brain 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 approximately every 15-60th section of this region. They were then aligned to the corresponding sections of the 3D reconstructed Big Brain space, using the transformations used in Amunts et al. 2013, kindly provided by Claude Lepage (McGill University). From these delineations, a preliminary 3D map of Area 6ma (preSMA, mesial SFG) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below. A full mapping of this area in every histological section using a Deep Learning approach is in progress. **Additional information:** The reference delineations used for this map are part of the work for the corresponding probabilistic map of Area 6ma (preSMA, mesial SFG) of the JuBrain Cytoarchitectonic Atlas, published in: Ruan et al. (2019) [Data set, v9.0] [DOI: 10.25493/HPX1-1CT](https://doi.org/10.25493%2FHPX1-1CT) Ruan et al. (2019) [Data set, v9.1] [DOI: 10.25493/WVNR-SPT](https://doi.org/10.25493%2FWVNR-SPT) In addition, the dataset of the probabilistic cytoarchitectonic map of Area 6ma (preSMA, mesial SFG) is part of the following research publication: Ruan, J. et al. (2018) Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas, 223(9):4169-4186. [DOI: 10.1007/s00429-018-1738-6](https://doi.org/10.1007%2Fs00429-018-1738-6)
DOI: 10.34810/data72/348
title: 01CO_1.MR.0004.0033.2016.02.04.15.44.11.109375.67135214.IMA
DOI: 10.25493/v9gw-4jg
title: Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy (v1.0)

description: The aim of this work is to study the 3D organization of some population of interneurons within a sample of interest, previously analyzed for physiological information, using two photon fluorescence microscopy (TPFM). We exploit the high axial and radial resolution of TPFM optical sectioning (0.44 x 0.44 x 2 μm³) in combination with a protocol for tissue clearing and labeling to perform the 3D reconstruction of 300 um thick brain sections. We clear the sections with the SWITCH/TDE clearing method and label the samples with three antibodies to co-stain three different populations of inhibitory interneurons: PV (Parvalbumin), SST(Somatostatin), and VIP (Vaso Intestinal Peptide). A new data version of “Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy” can be found here: Costantini et al. (2021) [Data set, v1.1] [DOI: 10.25493/ZM3J-6C5](https://doi.org/10.25493%2FZM3J-6C5)
DOI: 10.25493/rttn-r5f
title: Probabilistic cytoarchitectonic map of Area TE 2.2 (STG) (v5.1)

description: This dataset contains the distinct architectonic Area TE 2.2 (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 2.2 (STG). The probability map of Area TE 2.2 (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. The most probable delineation of Area TE 2.2 (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. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-64D)
DOI: 10.25493/s6pk-z7e
title: Probabilistic cytoarchitectonic map of Area 33 (ACC) (v16.0)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area 33 (ACC) 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 33 (ACC). The probability map of Area 33 (ACC) 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 33 (ACC): Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/X9QP-C6F](https://doi.org/10.25493%2FX9QP-C6F) The most probable delineation of Area 33 (ACC) 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/qzvt-mym
title: Disconnected hemispheres and brain areas

description: Disconnection syndromes are defined as dysfunctions arising when nerve tracts are disrupted. One specific case is the disconnection of corpus callosum or “split-brain” syndrome. This is characterized by either a partial or complete disruption of corpus callosum fiber, which is responsible for directly transferring information between the two cerebral hemispheres (Gazzaniga, 2000). Consequently, after a callosotomy, the otherwise integrated information processing of the intact brain is split between the two hemispheres (Sperry, Gazzaniga and Bogen, 1969). This might give rise to two conscious halves within the same brain, if each hemisphere is still intact and equally capable to integrate information as the intact brain (Gazzaniga, 2000; Volz and Gazzaniga, 2017). The aim of this task is to implement and test a surgical procedure for disconnecting the two brain hemispheres by sectioning the corpus callosum, without impairment to the cortex and minimizing damages to the vasculature in rats. The method will provide the background for testing perturbational complexity (Casali et al., 2013; Comolatti et al., 2019) after callosotomy, in order to test if complex cortical dynamics can survive in disconnected hemispheres. The method here described was inspired by previous works in callosotomy (Goodman and Russell, 1974; Crowne and Richardson, 1985) and has been set up by Alessandro Arena and Sarah H Thon. The method contributed in Sarah H Thon's Master Thesis, titled: “A new method for measuring perturbational complexity index in rat model in vivo”, University of Oslo, 2019. The procedures described here are extracted from her thesis.
DOI: 10.25493/x1hc-se9
title: Probability map of bundle lh_IP-SP_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 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.