Reference Datasets

Important

Many of the datasets below are subject to attribution licenses. When fetching data via the API, NiSpace prints all required citations to the terminal. If you use multiple maps, all associated DOIs must be cited — use nispace.datasets.fetch_metadata() for per-map details.

NiSpace provides the following built-in reference datasets. Fetch them via nispace.datasets.fetch_reference():

from nispace.datasets import fetch_reference
pet = fetch_reference("pet", collection="UniqueTracers",
                      parcellation="Schaefer200")

Note

Linked items point to files or folders in the NiSpace data repository.

Would you like to use the brain plots shown below in your publication? You can! They were generated with nispace.plotting.brainplot() and can be fetched via the API with nispace.datasets.fetch_plot(). You are allowed to modify them further, but please attribute NiSpace as the source.

Key	Description	Maps	Precomputed parcellations	Collections
`bigbrain`	The NiSpace "BigBrain" dataset contains histological, microstructural, and functional differentiation maps.	13	19	3
`cortexfeatures`	The NiSpace "CortexFeatures" dataset consists of different features of cortical topology available via neuromaps.	23	19	4
`enigmaarea`	The NiSpace "ENIGMAarea" dataset is based on ENIGMA analyses of brain structure across several neuro-psychiatric disorders.	tab.	2	2
`enigmathick`	The NiSpace "ENIGMAthick" dataset is based on ENIGMA analyses of brain structure across several neuro-psychiatric disorders.	tab.	2	2
`grf`	The NiSpace "GRF" dataset consists of Gaussian Random Field Maps (GRFs) generated with varying spatial smoothness.	tab.	27	4
`magicc`	The NiSpace "MAGICC" dataset contains cortical gene expression maps for > 10,000 genes based on a continuous reconstruction.	tab.	19	16
`mitobrain`	The NiSpace "MitoBrain" datasets contains mitochondrial phenotypes reconstructed from histological data.	6	27	1
`mrna`	The NiSpace "mRNA" dataset contains whole-brain gene expression maps for > 10,000 genes based on parcel-wise mapping.	tab.	27	16
`neurosynth`	The NiSpace "Neurosynth" dataset contains > 1,000 meta-analytic maps for terms from the Neurosynth database.	tab.	27	2
`pet`	The NiSpace "PET" dataset contains a growing collection of nuclear imaging maps from different source publications.	49	27	4
`rsn`	The NiSpace "RSN" dataset is based on resting-state network probability maps generated by Dworetsky et al., 2021.	14	19	1
`rsn17`	The NiSpace "RSN17" dataset contains resting-state network probability maps describing the 17 "Kong" networks.	17	19	1
`tpm`	The NiSpace "TPM" dataset is based on openly available tissue probability maps collected from different sources.	5	27	1

See nispace.datasets.fetch_metadata() to retrieve detailed per-map metadata (tracers, publications, licenses) for datasets that carry it (e.g. pet).

Tip

Most datasets ship with collections — curated subsets of maps. Pass the collection argument to fetch_reference() to load only the maps in a given collection:

from nispace.datasets import fetch_reference, fetch_collection
pet = fetch_reference("pet", collection="UniqueTracers",
                      parcellation="Schaefer200")
colls = fetch_collection("UniqueTracers", dataset="pet")

BigBrain

The NiSpace "BigBrain" dataset contains histological, microstructural, and functional differentiation maps. The data is adopted from the BigBrainWarp toolbox (https://bigbrainwarp.readthedocs.io) by Paquola et al. The maps are fetched directly from the BigBrainWarp Sciebo folder (space="fsaverageOriginal") or downloaded from the NiSpace-data GitHub repo (space="fsLR" or "fsaverage"; recommended). The data is subject to a CC BY-NC-SA 4.0 license. Please cite the appropriate original publication for each map when used (see map info table).

Coverage: Cortex only
Individual maps: 13
Precomputed for: AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, Yan100, Yan1000, Yan200, Yan400
Collections: All, CorticalLayers, DifferentiationGradients
Metadata: metadata.csv
References: Amunts et al., 2013 · Paquola et al., 2021

Show all 13 maps

Map	Available spaces
`feature-funcgradient1_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-funcgradient2_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-funcgradient3_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-histogradient1_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-histogradient2_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-layer1_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-layer2_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-layer3_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-layer4_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-layer5_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-layer6_pub-wagstyl2020`	`fsaverage`, `fsLR`
`feature-microgradient1_pub-paquola2021`	`fsaverage`, `fsLR`
`feature-microgradient2_pub-paquola2021`	`fsaverage`, `fsLR`

CortexFeatures

The NiSpace "CortexFeatures" dataset consists of different features of cortical topology available via neuromaps. Many of these maps were used in Shafiei et al., 2023. The maps are fetched directly from neuromaps (space="fsLROriginal" or "fsaverageOriginal") or downloaded from the NiSpace-data GitHub repo (space="fsLR" or "fsaverage"; recommended). Please cite neuromaps (https://neuromaps-main.readthedocs.io/) and the original publication for each map when used.

Coverage: Cortex only
Individual maps: 23
Precomputed for: AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, Yan100, Yan1000, Yan200, Yan400
Collections: All, MEG, Metabolism, CortexOrganisation
Metadata: metadata.csv
References: Markello et al., 2022 · Shafiei et al., 2023

Show all 23 maps

Map	Available spaces
`feature-cbf_pub-vaishnavi2010`	`fsaverage`, `fsLR`
`feature-cbv_pub-vaishnavi2010`	`fsaverage`, `fsLR`
`feature-cmrglc_pub-vaishnavi2010`	`fsaverage`, `fsLR`
`feature-cmro2_pub-vaishnavi2010`	`fsaverage`, `fsLR`
`feature-develexpansion_pub-hill2010`	`fsaverage`, `fsLR`
`feature-evolexpansion_pub-hill2010`	`fsaverage`, `fsLR`
`feature-evolexpansion_pub-xu2020`	`fsaverage`, `fsLR`
`feature-fcgradient1_pub-margulies2016`	`fsaverage`, `fsLR`
`feature-fcgradient2_pub-margulies2016`	`fsaverage`, `fsLR`
`feature-fcgradient3_pub-margulies2016`	`fsaverage`, `fsLR`
`feature-geneexpr-abagen`	`fsaverage`, `fsLR`
`feature-glycindex_pub-vaishnavi2010`	`fsaverage`, `fsLR`
`feature-megpoweralpha_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megpowerbeta_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megpowerdelta_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megpowergamma1_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megpowergamma2_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megpowertheta_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-megtimescale_pub-shafiei2022`	`fsaverage`, `fsLR`
`feature-saaxis_pub-sydnor2021`	`fsaverage`, `fsLR`
`feature-specieshomology_pub-xu2020`	`fsaverage`, `fsLR`
`feature-t1t2_pub-hcps1200`	`fsaverage`, `fsLR`
`feature-thickness_pub-hcps1200`	`fsaverage`, `fsLR`

ENIGMAarea

The NiSpace "ENIGMAarea" dataset is based on ENIGMA analyses of brain structure across several neuro-psychiatric disorders. The original data is provided via the ENIGMA Toolbox v2.0.3 (https://github.com/MICA-MNI/ENIGMA). It contains Cohen's d effect sizes (d_icv, largely ICV-corrected) for case-vs-control differences in cortical surface area and subcortical volume. Cortical values are provided in the Desikan parcellation, subcortical values in the Aseg parcellation. For some disorders, effect size maps are split by subtype and/or age group. Use collection "Main" for a reduced collection of the main effect size maps. For each disorder, please cite the appropriate ENIGMA working group publication (see map info table).

Coverage: Cortex + subcortex
Precomputed for: DesikanKilliany, Aseg
Collections: All, Main (bold = default)
Metadata: metadata.csv
References: Larivière et al., 2021

ENIGMAthick

The NiSpace "ENIGMAthick" dataset is based on ENIGMA analyses of brain structure across several neuro-psychiatric disorders. The original data is provided via the ENIGMA Toolbox v2.0.3 (https://github.com/MICA-MNI/ENIGMA). It contains Cohen's d effect sizes (d_icv, largely ICV-corrected) for case-vs-control differences in cortical thickness and subcortical volume. Cortical values are provided in the Desikan parcellation, subcortical values in the Aseg parcellation. For some disorders, effect size maps are split by subtype and/or age group. Use collection "Main" for a reduced collection of the main effect size maps. For each disorder, please cite the appropriate ENIGMA working group publication (see map info table).

Coverage: Cortex + subcortex
Precomputed for: DesikanKilliany, Aseg
Collections: All, Main (bold = default)
Metadata: metadata.csv
References: Larivière et al., 2021

GRF

The NiSpace "GRF" dataset consists of Gaussian Random Field Maps (GRFs) generated with varying spatial smoothness. The parameter "alpha" controls the smoothness of the field and therefore the spatial autocorrelation of the resulting brain maps. The maps are mirrored across hemispheres exactly at MNI coordinate (0,0,0). There are 10,000 maps for alpha=0 (spatially unstructured, white noise) and 1,000 maps for each of the three smoothness levels (alpha=1, 2, 3). The code was adopted from Markello et al., 2021 and Burt et al., 2020.

Coverage: Cortex + subcortex
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All, Alpha0, ByAlpha, ByAlpha1000 (bold = default)
References: Markello et al., 2021 · Burt et al., 2020

MAGICC

The NiSpace "MAGICC" dataset contains cortical gene expression maps for > 10,000 genes based on a continuous reconstruction. The dataset consists of Allen Brain Atlas mRNA expression data from postmortem brain tissue of six donors (Hawrylycz et al., 2012), mapped onto continuous fsLR space as described in Wagstyl et al., 2024, and parcellated with integrated parcellation. Only genes that showed a high reproducibility (>= 0.5, see paper) were retained. Please cite the source publications, as well as publications associated with gene set collections as appropriate.

Coverage: Cortex only
Precomputed for: AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, Yan100, Yan1000, Yan200, Yan400
Collections: All, CellTypesPsychEncodeTPM, CellTypesPsychEncodeUMI, CellTypesSilettiClusters, CellTypesSilettiSuperclusters, Chromosome, SynGO, GOBiologicalProcess, GOCellularComponent, GOMolecularFunction, CorticalLayers, ProteinAtlas, BrainSpan, BrainSpanWeights, ASDModulesGandal2022, ASDModulesGandal2022Weights (bold = default)
References: Hawrylycz et al., 2012 · Wagstyl et al., 2024

MitoBrain

The NiSpace "MitoBrain" datasets contains mitochondrial phenotypes reconstructed from histological data. The source data is provided by MitoBrainMap (http://humanmitobrainmap.bcblab.com; Mosharov et al., 2025, Nature). Maps were generated by profiling OXPHOS enzyme activities and mitochondria density in a whole human hemisphere at 3mm voxel-like resolution and reconstruction into MNIs space. Original maps are retrieved from NeuroVault (https://identifiers.org/neurovault.collection:16418; space MNIOriginal), masked, resampled to 2mm MNI152NLin6Asym space, and transformed to MNI152NLin2009cAsym, fsLR, and fsaverage surface spaces.

Coverage: Cortex + subcortex
Individual maps: 6
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All
Metadata: metadata.csv
References: Mosharov et al., 2025

Show all 6 maps

Map	Available spaces
`feature-complexi_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`
`feature-complexii_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`
`feature-complexiv_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`
`feature-mitodensity_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`
`feature-mitoresp_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`
`feature-tissueresp_pub-mosharov2025`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsLR`, `fsaverage`

mRNA

The NiSpace "mRNA" dataset contains whole-brain gene expression maps for > 10,000 genes based on parcel-wise mapping. The dataset consists of Allen Brain Atlas mRNA expression data from postmortem brain tissue of six donors (Hawrylycz et al., 2012), mapped onto MNI or fsaverage parcels using the abagen toolbox (Markello et al., 2021; abagen.get_expression_data(..., lr_mirror="bidirectional", norm_matched=False)). Gene stability was assessed using a voxel-level atlas at 8mm isotropic resolution; genes were retained if their mean donor-to-donor Spearman rank correlation exceeded 0.2. Please cite the source publications, as well as publications associated with gene set collections as appropriate.

Coverage: Cortex + subcortex
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All, CellTypesPsychEncodeTPM, CellTypesPsychEncodeUMI, CellTypesSilettiClusters, CellTypesSilettiSuperclusters, Chromosome, SynGO, GOBiologicalProcess, GOCellularComponent, GOMolecularFunction, CorticalLayers, ProteinAtlas, BrainSpan, BrainSpanWeights, ASDModulesGandal2022, ASDModulesGandal2022Weights (bold = default)
References: Hawrylycz et al., 2012 · Markello et al., 2021

Neurosynth

The NiSpace "Neurosynth" dataset contains > 1,000 meta-analytic maps for terms from the Neurosynth database. For each Neurosynth term (https://neurosynth.org/) of interest for spatial correlation analysis, a "MKDAChi2" meta-analysis map was generated in MNI152NLin6Asym-2mm space using the nimare package. The resulting "z_desc-association" maps are available only as parcellation tables.

Coverage: Cortex + subcortex
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All, CognitiveFunctions (bold = default)
References: Yarkoni et al., 2011 · Salo et al., 2022 · Wager et al., 2007

PET

The NiSpace "PET" dataset contains a growing collection of nuclear imaging maps from different source publications. Most maps were originally accessed via neuromaps (https://neuromaps-main.readthedocs.io/) and can be downloaded directly via neuromaps using the "original" spaces "MNIOriginal", "fsaverageOriginal", or "fsLROriginal". Maps requested in a defined space ("MNI152NLin2009cAsym", "MNI152NLin6Asym", "fsaverage", or "fsLR") are sourced from the NiSpace-data GitHub repo. These maps were directly registered to 2mm MNI152NLin6Asym space, and transformed to 2mm MNI152NLin2009cAsym with a pre-estimated MNI-to-MNI transformation using SynthMorph v4. The resulting maps were masked with a liberal grey matter mask generated from the Harvard-Oxford atlas and scaled from 1e-6 to 1. The accompanying metadata table contains detailed information about tracers, source samples, original publications and data sources, as well as the publication licenses. Every map should be cited when used. The responsibility for this lies with the user!

Coverage: Cortex + subcortex
Individual maps: 49
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All, AllTargetSets, UniqueTracers, UniqueTracerSets (bold = default)
Metadata: metadata.csv
References: Markello et al., 2022 · Hansen et al., 2022 · Dukart et al., 2021 · Hoffmann et al., 2024

Show all 49 maps

Map	Available spaces
`target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-5HT1a_tracer-way100635_n-35_dx-hc_pub-savli2012`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HT1b_tracer-az10419369_n-36_dx-hc_pub-beliveau2017`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-5HT1b_tracer-p943_n-23_dx-hc_pub-savli2012`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HT1b_tracer-p943_n-65_dx-hc_pub-gallezot2010`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HT2a_tracer-altanserin_n-19_dx-hc_pub-savli2012`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HT2a_tracer-cimbi36_n-29_dx-hc_pub-beliveau2017`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-5HT4_tracer-sb207145_n-59_dx-hc_pub-beliveau2017`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-5HT6_tracer-gsk215083_n-30_dx-hc_pub-radhakrishnan2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HTT_tracer-dasb_n-100_dx-hc_pub-beliveau2017`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-5HTT_tracer-madam_n-10_dx-hc_pub-fazio2016`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-A4B2_tracer-flubatine_n-30_dx-hc_pub-hillmer2016`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-CB1_tracer-fmpepd2_n-22_dx-hc_pub-laurikainen2019`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-CB1_tracer-omar_n-77_dx-hc_pub-normandin2015`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-CMRglu_tracer-fdg_n-20_dx-hc_pub-castrillon2023`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-COX1_tracer-ps13_n-11_dx-hc_pub-kim2020`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D1_tracer-sch23390_n-13_dx-hc_pub-kaller2017`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D23_tracer-fallypride_n-49_dx-hc_pub-jaworska2020`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D23_tracer-flb457_n-37_dx-hc_pub-smith2017`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D23_tracer-flb457_n-55_dx-hc_pub-sandiego2015`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D23_tracer-raclopride_n-156_dx-hc_pub-malen2022`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-D23_tracer-raclopride_n-7_dx-hc_pub-alarkurtti2015`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-DAT_tracer-fepe2i_n-6_dx-hc_pub-sasaki2012`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-DAT_tracer-fpcit_n-30_dx-hc_pub-garciagomez2013`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lukow2022`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-GABAa_tracer-flumazenil_n-16_dx-hc_pub-norgaard2021`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`target-GABAa_tracer-flumazenil_n-6_dx-hc_pub-dukart2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallezot2017`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vijay2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-M1_tracer-lsn3172176_n-24_dx-hc_pub-naganawa2020`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-MOR_tracer-carfentanil_n-204_dx-hc_pub-kantonen2020`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-MOR_tracer-carfentanil_n-39_dx-hc_pub-turtonen2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-NET_tracer-mrb_n-77_dx-hc_pub-ding2010`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-NMDA_tracer-ge179_n-29_dx-hc_pub-galovic2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-SV2A_tracer-ucbj_n-76_dx-hc_pub-finnema2016`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-TSPO_tracer-pbr28_n-6_dx-hc_pub-lois2018`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghourian2017`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-VAChT_tracer-feobv_n-4_dx-hc_pub-tuominen`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-VAChT_tracer-feobv_n-5_dx-hc_pub-bedard2019`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-VMAT2_tracer-dtbz_n-76_dx-hc_pub-larsen2020`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-mGluR5_tracer-abp688_n-22_dx-hc_pub-rosaneto`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-mGluR5_tracer-abp688_n-28_dx-hc_pub-dubois2015`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smart2019`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`target-rCPS_tracer-leucine_n-42_dx-hc_pub-smith2023`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`

RSN

The NiSpace "RSN" dataset is based on resting-state network probability maps generated by Dworetsky et al., 2021. The maps were obtained from the associated GitHub repository and can be retrieved in their original form with space="MNIOriginal". These maps were downloaded in MNI152NLin6Asym space and transformed to MNI152NLin2009cAsym with a pre-estimated MNI-to-MNI transformation, divided by 100, masked with a liberal grey matter mask, and transformed to fsLR and fsaverage. Please cite the original publication when using these maps.

Coverage: Cortex only
Individual maps: 14
Precomputed for: AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, Yan100, Yan1000, Yan200, Yan400
Collections: All
References: Dworetsky et al., 2021

Show all 14 maps

Map	Available spaces
`nw-Auditory_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Cinguloopercular_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-DefaultMode_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-DorsalAttention_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Frontoparietal_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Language_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-MedialTemporal_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Parietomedial_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Parietooccipital_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Salience_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-SomatomotorDorsal_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-SomatomotorLateral_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-TemporalPole_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`
`nw-Visual_pub-dworetsky2021`	`MNI152NLin6Asym`, `MNI152NLin2009cAsym`, `fsaverage`, `fsLR`

RSN17

The NiSpace "RSN17" dataset contains resting-state network probability maps describing the 17 "Kong" networks. The maps were derived from individual-level cortical parcellations of 1029 HCP subjects into 17 functional networks, generated as described in Kong et al., 2021 and made available at https://github.com/ThomasYeoLab/Kong2022_ArealMSHBM. The maps are only available in fsLR and fsaverage spaces. Note that the networks follow the Kong et al. naming convention, which differs from the 17 "Yeo networks". Please cite the original publication when using these maps.

Coverage: Cortex only
Individual maps: 17
Precomputed for: AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, Yan100, Yan1000, Yan200, Yan400
Collections: All
References: Kong et al., 2021

Show all 17 maps

Map	Available spaces
`nw-Auditory_pub-kong2022`	`fsaverage`, `fsLR`
`nw-ControlA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-ControlB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-ControlC_pub-kong2022`	`fsaverage`, `fsLR`
`nw-DefaultA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-DefaultB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-DefaultC_pub-kong2022`	`fsaverage`, `fsLR`
`nw-DorsAttnA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-DorsAttnB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-Language_pub-kong2022`	`fsaverage`, `fsLR`
`nw-SalVenAttnA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-SalVenAttnB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-SomatomotorA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-SomatomotorB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-VisualA_pub-kong2022`	`fsaverage`, `fsLR`
`nw-VisualB_pub-kong2022`	`fsaverage`, `fsLR`
`nw-VisualC_pub-kong2022`	`fsaverage`, `fsLR`

TPM

The NiSpace "TPM" dataset is based on openly available tissue probability maps collected from different sources. Many of these maps were used in Bolt et al., 2025. Fetch the original maps by requesting space="MNIOriginal". The processed maps are downloaded from the NiSpace-data GitHub repo; data was downloaded in MNI152NLin6Asym space and transformed to MNI152NLin2009cAsym with a pre-estimated MNI-to-MNI transformation, and to fsLR/fsaverage using neuromaps. Please cite the appropriate original publication for each map when used (see map info table).

Coverage: Cortex + subcortex
Individual maps: 5
Precomputed for: AALCortical, AALSubcortical, Aseg, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Glasser, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer1000Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer100Parcels17Networks, Schaefer100Parcels7Networks, Schaefer200Parcels17Networks, Schaefer200Parcels7Networks, Schaefer400Parcels17Networks, Schaefer400Parcels7Networks, TianS1, TianS2, TianS3, TianS4, Yan100, Yan1000, Yan200, Yan400
Collections: All
Metadata: metadata.csv
References: Bolt et al., 2025

Show all 5 maps

Map	Available spaces
`tissue-arteries_pub-mouches2019`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`tissue-csf_pub-spm`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`tissue-gm_pub-spm`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`tissue-veins_pub-huck2019`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`
`tissue-wm_pub-spm`	`fsaverage`, `fsLR`, `MNI152NLin6Asym`, `MNI152NLin2009cAsym`