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Perception adapts to mismatching multisensory information, both when different cues appear simultaneously and when they appear sequentially. While both multisensory integration and adaptive trial-by-trial recalibration are central for behavior, it remains unknown whether they are mechanistically linked and arise from a common neural substrate. To relate the neural underpinnings of sensory integration and recalibration, we measured whole-brain magnetoencephalography while human participants performed an audio-visual ventriloquist task. Using single-trial multivariate analysis, we localized the perceptually-relevant encoding of multisensory information within and between trials. While we found neural signatures of multisensory integration within temporal and parietal regions, only medial superior parietal activity encoded past and current sensory information and mediated the perceptual recalibration within and between trials. These results highlight a common neural substrate of sensory integration and perceptual recalibration, and reveal a role of medial parietal regions in linking present and previous multisensory evidence to guide adaptive behavior. PARK_KAYSER_RecalMEG_2019Please see README file.

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 previous data version of “Molecular characterization of the interneurons in human temporal neocortex by two photon fluorescence microscopy” can be found here: Costantini et al. (2020) [Data set, v1.0] [DOI: 10.25493/V9GW-4JG]( https://doi.org/10.25493%2FV9GW-4JG)

This dataset contains the distinct architectonic Area OP2 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. 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 OP2 (POperc). The probability map of Area OP2 (POperc) 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 OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/SDW0-YEZ](https://doi.org/10.25493%2FSDW0-YEZ) The most probable delineation of Area OP2 (POperc) 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)

This dataset contains the distinct architectonic Area 7P (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 7P (SPL). The probability map of Area 7P (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 7P (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/AHQS-ZR8](https://doi.org/10.25493%2FAHQS-ZR8) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C3HS-8R7](https://doi.org/10.25493%2FC3HS-8R7) The most probable delineation of Area 7P (SPL) derived from the calculation of a maximum probability map of all currently released JuBrain 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)

Electrophysiological evidence suggested primarily the involvement of area MT in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/KO. To clarify this conundrum, we decoded monkey fMRI responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion and their combination, and we compared results with those from an identical experiment previously performed in humans.Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. data describing fig 1-8 and sfig 1-12data.zip

This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain). The probability map of Ch 123 (Basal Forebrain) 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 Ch 123 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/7SEP-P2V](https://doi.org/10.25493%2F7SEP-P2V) The most probable delineation of Ch 123 (Basal Forebrain) 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)

Research data supporting the paper: Bergholt, M.S. et al., "Correlated heterospectral lipidomics for biomolecular profiling of remyelination in multiple sclerosis", ACS Central Science, 2017, DOI: 10.1021/acscentsci.7b00367.

This dataset contains cortical image patches of the cytoarchitectonic Area FG3 (FusG), extracted from microscopic scans of the original tissue sections of the BigBrain model ([Amunts et al., 2013](https://www.science.org/doi/10.1126/science.1235381)), together with manual annotations of cortical layers and automatic segmentations of cell bodies, as well as individual and averaged reports of cortical thicknesses and densities of cells. The histological sections were prepared as described in the original publication of the BigBrain. The cell instance segmentations have been computed using openly accessible code ([https://github.com/FZJ-INM1-BDA/celldetection](https://github.com/FZJ-INM1-BDA/celldetection)) and a pre-trained model for Contour Proposal Networks (CPN; preprint at [https://arxiv.org/abs/2104.03393](https://arxiv.org/abs/2104.03393)).

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_** \| _[3H]AMPA_ **kainate** \| **_glutamate_** \| _[3H]kainate_ **NMDA** \| **_glutamate_** \| _[3H]MK-801_ **GABAA** \| **_GABA_** \| _[3H]muscimol_ **muscarinic M1** \| **_acetylcholine_** \| _[3H]pirenzepine_ **muscarinic M2** \| **_acetylcholine_** \| _[3H]oxotremorine-M_ **α1** \| **_noradrenalin/norepinephrine_** \| _[3H]prazosin_ **α2** \| **_noradrenalin/norepinephrine_** \| _[3H]UK-14,304_ **5-HT1A** \| **_serotonin_** \| _[3H]8-OH-DPAT_ **5-HT2** \| **_serotonin_** \| _[3H]ketanserin_ Information on the used tissue samples and corresponding subjects, as well as analyzed receptors accompanies the provided dataset. **For methodological details, see:** Zilles, K. et al. (2002). Quantitative analysis of cyto- and receptorarchitecture of the human brain, pp. 573-602. In: Brain Mapping: The Methods, 2nd edition (A.W. Toga and J.C. Mazziotta, eds.). San Diego, Academic Press. Palomero-Gallagher N, Zilles K. (2018) Cyto- and receptorarchitectonic mapping of the human brain. In: Handbook of Clinical Neurology 150: 355-387