EBRAINS regional datasets

The modality “EbrainsRegionalDataset’ is different from the others. It returns any datasets in the EBRAINS Knowledge Graph which could be linked to the given atlas concept, and provides access to their metadata, as well as the link to the EBRAINS Knowledge Graph. The returned features can thus actually have different modalities, and might include datasets which are also supported as one of the explicitly supported modalities shown in previous examples.

We query regional features for the secondary visual cortex V2.

import siibra
atlas = siibra.atlases.MULTILEVEL_HUMAN_ATLAS
region = atlas.get_region("hoc2")
features = siibra.features.get(region, siibra.features.dataset.EbrainsDataFeature)
for feature in features:
    print(f" - {feature.name}")

- Ebrains Dataset: Response profiles to uni- and bilateral stimulation in higher-order visual cortex (v1)
- Ebrains Dataset: 3D reconstructions of human cortical pyramidal cells (v1)
- Ebrains Dataset: Ultrahigh resolution 3D cytoarchitectonic map of Area hOc2 (V2, 18) created by a Deep-Learning assisted workflow (v1)
- Ebrains Dataset: Layer-specific distributions of segmented cells in Area hOc2 (V2, 18) of BigBrain (v1)
- Ebrains Dataset: Filter Activations of Convolutional Neuronal Networks Used in Cytoarchitectonic Brain Mapping (v1)
- Ebrains Dataset: Structure-phenotype associations for Julich-Brain Cytoarchitectonic Atlas regions (v1)
- Ebrains Dataset: Reference delineations of Area hOc2 (V2, 18) in individual sections of the BigBrain (v1)
- Ebrains Dataset: Functional brain complexity changes in humans under the influence of psychedelic low doses of ketamine (v1)
- Ebrains Dataset: Phase-interaction matrices of the phase relationships of different pathological states of consciousness (v1)
- Ebrains Dataset: Intracranial electrophysiological recordings from the human brain during memory tasks with pupillometry (v1)
- Ebrains Dataset: Measures of states of consciousness during attentional and cognitive load (v1)
- Ebrains Dataset: Resolving nerve fiber crossings at micrometer resolution in rat, vervet monkey, and human brain samples (v1.0)
- Ebrains Dataset: Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG (v1)
- Ebrains Dataset: CEREBRUM-7T: Fast and Fully-volumetric Brain Segmentation of 7 Tesla MR Volumes (v1)
- Ebrains Dataset: Spontaneous EEG during sleep with intermittent awakenings (v1)
- Ebrains Dataset: Sleep deprivation in human electroencephalography (EEG) (v1)
- Ebrains Dataset: Human ultra-high resolution functional imaging data (7 Tesla) on multi-element event memories (general raw data) (v1)
- Ebrains Dataset: 3D reconstruction and analysis of four human brain cortex samples with two-photon fluorescence microscopy (v1.0)
- Ebrains Dataset: Density measurements of different receptors for dorsal part of Area hOc2 (V2, 18) [human] (v1)
- Ebrains Dataset: Probabilistic cytoarchitectonic map of Area hOc2 (V2, 18) (v4.2)
- Ebrains Dataset: Density measurements of different receptors for ventral part of Area hOc2 (V2, 18) [human] (v1)

Each EBRAINS feature provides access to the metadata from the EBRAINS Knowledge Graph. We view some of those for the last returned feature (which is accessible from the loop above). siibra implements a lazy loading mechanism here again: Once we access attributes which require deeper metadata, it will run a query to the Knowledge Graph to fetch it.

print(feature.name)
print(feature.description)

Ebrains Dataset: Density measurements of different receptors for ventral part of Area hOc2 (V2, 18) [human] (v1)
This dataset contains the densities (in fmol/mg protein) of receptors for classical neurotransmitters in the ventral part of Area hOc2 (V2, 18) 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. For exemplary samples, we also provide laminar _profiles_ (**pr**)  and/or color-coded laminar _autoradiography_ images (**ar**). The density profiles provide, for a single tissue sample, an exemplary density distribution for a single receptor from the pial surface to the border between layer VI and the white matter. The autoradiography images show an exemplary density distribution of a single receptor for one laminar cross-section in a single tissue sample. 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.

We can use the url of the feature to access their full information in the Knowledge Graph. Just click on the link to test it.

print(feature.url)

https://doi.org/10.25493/2E5C-PVM