Reference Datasets

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.

KeyDescriptionMapsPrecomputed parcellationsCollections
petPET receptor/transporter density maps; 52 tracers across major neurotransmitter systems49274
mrnaAllen Human Brain Atlas mRNA expression; 15,000+ genes with gene-set collectionstabular (27 parcellations)2714
magiccAHBA gene expression mapped to continuous cortical space (Wagstyl et al., 2024)tabular (19 parcellations)1914
rsnResting-state network probability maps; 14 canonical networks (Dworetsky et al., 2021)14191
rsn1717191
grfGaussian Random Field maps with controlled spatial autocorrelation; for null model validationtabular (27 parcellations)273
neurosynthMeta-analytic z-maps for ~1000 cognitive terms from the Neurosynth databasetabular (27 parcellations)272
cortexfeaturesCortical topology features: thickness, T1w/T2w, SA axis, MEG, metabolism, FC gradients23194
bigbrainHistological depth features and layer thickness from the BigBrain atlas (Paquola et al., 2021)13193
tpmTissue probability maps: grey/white matter, CSF, arteries, veins5271
enigmathickCohen's d maps comparing cortical thickness between neuro-psychiatric disorders and controlstabular (2 parcellations)22
enigmaareaCohen's d maps comparing surface area between neuro-psychiatric disorders and controlstabular (2 parcellations)22

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")

pet

The NiSpace "PET" dataset is based on openly available nuclear imaging maps largely accessed via neuromaps (https://neuromaps-main.readthedocs.io/). If requested in the varying original spaces and resolutions (termed "MNI152", "fsaverageOriginal", or "fsLROriginal"), the maps are downloaded directly from the source and cached locally. If, as is highly recommended, the maps are requested in a defined space ("MNI152NLin2009cAsym", "MNI152NLin6Asym", "fsaverage", or "fsLR"), they are downloaded from the NiSpace-data GitHub repo (find them in `~HOME/nispace-data/reference/pet/map`). The NiSpace-hosted MNI maps were directly registered to 2mm MNI152NLin6Asym space, and transformed to 2mm MNI152NLin2009cAsym with a pre-estimated MNI-to-MNI transformation using SynthMorph v4 (https://martinos.org/malte/synthmorph/). 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 scaling was transferred from MNI to surface maps if both were available for the same source (e.g., maps from Beliveau et al.). 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! We additionally ask to cite:

  • Markello et al., 2022 (https://doi.org/10.1038/s41592-022-01625-w)
  • Hansen et al., 2022 (https://doi.org/10.1038/s41593-022-01186-3)
  • Dukart et al., 2021 (https://doi.org/10.1002/hbm.25244)
  • Hoffmann et al., 2024 (https://doi.org/10.1162/imag_a_00197; if NiSpace-processed maps are used)

Individual maps: 49
Precomputed for: TianS1, TianS2, TianS3, TianS4, Glasser, AALCortical, AALSubcortical, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Aseg, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, AllTargetSets, UniqueTracers, UniqueTracerSets
Metadata: metadata.csv

Show all 49 maps
MapAvailable spaces
target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-5HT1a_tracer-way100635_n-35_dx-hc_pub-savli2012MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HT1b_tracer-az10419369_n-36_dx-hc_pub-beliveau2017fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-5HT1b_tracer-p943_n-23_dx-hc_pub-savli2012MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HT1b_tracer-p943_n-65_dx-hc_pub-gallezot2010MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HT2a_tracer-altanserin_n-19_dx-hc_pub-savli2012MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HT2a_tracer-cimbi36_n-29_dx-hc_pub-beliveau2017fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-5HT4_tracer-sb207145_n-59_dx-hc_pub-beliveau2017fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-5HT6_tracer-gsk215083_n-30_dx-hc_pub-radhakrishnan2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HTT_tracer-dasb_n-100_dx-hc_pub-beliveau2017fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-5HTT_tracer-madam_n-10_dx-hc_pub-fazio2016MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-A4B2_tracer-flubatine_n-30_dx-hc_pub-hillmer2016MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-CB1_tracer-fmpepd2_n-22_dx-hc_pub-laurikainen2019MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-CB1_tracer-omar_n-77_dx-hc_pub-normandin2015MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-CMRglu_tracer-fdg_n-20_dx-hc_pub-castrillon2023MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-COX1_tracer-ps13_n-11_dx-hc_pub-kim2020MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D1_tracer-sch23390_n-13_dx-hc_pub-kaller2017MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D23_tracer-fallypride_n-49_dx-hc_pub-jaworska2020MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D23_tracer-flb457_n-37_dx-hc_pub-smith2017MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D23_tracer-flb457_n-55_dx-hc_pub-sandiego2015MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D23_tracer-raclopride_n-156_dx-hc_pub-malen2022MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-D23_tracer-raclopride_n-7_dx-hc_pub-alarkurtti2015MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-DAT_tracer-fepe2i_n-6_dx-hc_pub-sasaki2012MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-DAT_tracer-fpcit_n-30_dx-hc_pub-garciagomez2013MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lukow2022MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-GABAa_tracer-flumazenil_n-16_dx-hc_pub-norgaard2021fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
target-GABAa_tracer-flumazenil_n-6_dx-hc_pub-dukart2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallezot2017MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vijay2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-M1_tracer-lsn3172176_n-24_dx-hc_pub-naganawa2020MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-MOR_tracer-carfentanil_n-204_dx-hc_pub-kantonen2020MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-MOR_tracer-carfentanil_n-39_dx-hc_pub-turtonen2021MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-NET_tracer-mrb_n-77_dx-hc_pub-ding2010MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-NMDA_tracer-ge179_n-29_dx-hc_pub-galovic2021MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-SV2A_tracer-ucbj_n-76_dx-hc_pub-finnema2016MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-TSPO_tracer-pbr28_n-6_dx-hc_pub-lois2018MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghourian2017MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-VAChT_tracer-feobv_n-4_dx-hc_pub-tuominenMNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-VAChT_tracer-feobv_n-5_dx-hc_pub-bedard2019MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-VMAT2_tracer-dtbz_n-76_dx-hc_pub-larsen2020MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-mGluR5_tracer-abp688_n-22_dx-hc_pub-rosanetoMNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-mGluR5_tracer-abp688_n-28_dx-hc_pub-dubois2015MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smart2019MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
target-rCPS_tracer-leucine_n-42_dx-hc_pub-smith2023MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR

mrna

The NiSpace "mRNA" dataset is based on Allen Human Brain Atlas (AHBA) gene expression data published in Hawrylycz et al., 2012 (https://doi.org/10.1038/nature11405). The dataset consists of mRNA expression data from postmortem brain tissue of six donors, mapped onto MNI or fsaverage parcels using the abagen toolbox (Markello et al., 2021, https://doi.org/10.7554/eLife.72129). The data was extracted using abagen.get_expression_data(..., lr_mirror="bidirectional", norm_matched=False), considering parcel hemisphere and cortical vs. subcortical location. Gene stability was assessed independently of any specific parcellation using a voxel-level atlas constructed from Schaefer100 (cortex) and TianS1 (subcortex) at 8mm isotropic resolution (each voxel assigned a unique identifier; sample-to-voxel tolerance=8mm). Per-donor expression matrices were obtained via abagen and genes were retained if their mean donor-to-donor Spearman rank correlation exceeded 0.2 (abagen.keep_stable_genes, rank=True, threshold=0.2). This stable gene set was applied uniformly across all parcellations. In addition to the two publications listed above, please cite publications associated with gene set collections as appropriate.

Precomputed for: TianS1, TianS2, TianS3, TianS4, Glasser, AALCortical, AALSubcortical, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Aseg, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, CellTypesPsychEncodeTPM, CellTypesPsychEncodeUMI, CellTypesSilettiClusters, CellTypesSilettiSuperclusters, Chromosome, SynGO, GOBiologicalProcess, GOCellularComponent, GOMolecularFunction, CorticalLayers, ProteinAtlas, BrainSpan, BrainSpanWeights
Metadata: metadata.csv

magicc

The NiSpace "magicc" dataset is based on Allen Human Brain Atlas (AHBA) gene expression data published in Hawrylycz et al., 2012 (https://doi.org/10.1038/nature11405). The dataset consists of mRNA expression data from postmortem brain tissue of six donors, mapped onto continuous fsLR space as described in Wagstyl et al., 2024 (https://doi.org/10.7554/eLife.86933.1), and parcellated with integrated parcellation. Only genes that showed a high reproducibility (>= 0.5, see paper) were retained. In addition to the two publications listed above, please cite publications associated with gene set collections as appropriate.

Precomputed for: Glasser, AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, HarvardOxfordCortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, CellTypesPsychEncodeTPM, CellTypesPsychEncodeUMI, CellTypesSilettiClusters, CellTypesSilettiSuperclusters, Chromosome, SynGO, GOBiologicalProcess, GOCellularComponent, GOMolecularFunction, CorticalLayers, ProteinAtlas, BrainSpan, BrainSpanWeights
Metadata: metadata.csv

rsn

The NiSpace "RSN" dataset is based on resting-state network probability maps generated by Dworetsky et al., 2021 (https://doi.org/10.1016/j.neuroimage.2021.118164). The maps were obtained from the associated GitHub repository (https://github.com/GrattonLab/Dworetsky_etal_ConsensusNetworks/4521ead/Probabilistic_Network_Maps_t88_333.zip) and can be retrieved in their original form with space="MNI152". 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 (obtain with: "MNI152NLin2009cAsym", "MNI152NLin6Asym", "fsaverage", or "fsLR"). Please cite the original publication when using these maps.

Individual maps: 14
Precomputed for: Glasser, AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, HarvardOxfordCortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All
Metadata: metadata.csv

Show all 14 maps
MapAvailable spaces
nw-Auditory_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Cinguloopercular_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-DefaultMode_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-DorsalAttention_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Frontoparietal_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Language_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-MedialTemporal_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Parietomedial_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Parietooccipital_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Salience_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-SomatomotorDorsal_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-SomatomotorLateral_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-TemporalPole_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR
nw-Visual_pub-dworetsky2021MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR

rsn17

The NiSpace "RSN17" dataset contains probabilistic resting-state network maps derived from individual-level cortical parcellations of 1029 HCP subjects into 17 functional networks, generated as described in Kong et al., 2021 (https://doi.org/10.1093/cercor/bhab101) and made available at https://github.com/ThomasYeoLab/Kong2022_ArealMSHBM. The maps are only available in fsLR and fsaverage spaces. Note that the networks followe the Kong et al. naming convention, which differs from the 17 "Yeo networks". Please cite the original publication when using these maps.

Individual maps: 17
Precomputed for: Glasser, AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, HarvardOxfordCortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All

Show all 17 maps
MapAvailable spaces
nw-Auditory_pub-kong2022fsaverage, fsLR
nw-ControlA_pub-kong2022fsaverage, fsLR
nw-ControlB_pub-kong2022fsaverage, fsLR
nw-ControlC_pub-kong2022fsaverage, fsLR
nw-DefaultA_pub-kong2022fsaverage, fsLR
nw-DefaultB_pub-kong2022fsaverage, fsLR
nw-DefaultC_pub-kong2022fsaverage, fsLR
nw-DorsAttnA_pub-kong2022fsaverage, fsLR
nw-DorsAttnB_pub-kong2022fsaverage, fsLR
nw-Language_pub-kong2022fsaverage, fsLR
nw-SalVenAttnA_pub-kong2022fsaverage, fsLR
nw-SalVenAttnB_pub-kong2022fsaverage, fsLR
nw-SomatomotorA_pub-kong2022fsaverage, fsLR
nw-SomatomotorB_pub-kong2022fsaverage, fsLR
nw-VisualA_pub-kong2022fsaverage, fsLR
nw-VisualB_pub-kong2022fsaverage, fsLR
nw-VisualC_pub-kong2022fsaverage, fsLR

grf

The NiSpace "GRF" dataset consists of many thousands of "Gaussian Random Field Maps" (GRFs) generated with different levels of 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 1,000 maps for each alpha level. The code used was adopted from Markello et al., 2021 (https://doi.org/10.1016/j.neuroimage.2021.118052) and Burt et al., 2020 (https://doi.org/10.1016/j.neuroimage.2020.117038).

Precomputed for: TianS1, TianS2, TianS3, TianS4, Glasser, AALCortical, AALSubcortical, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Aseg, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, Alpha0, ByAlpha

neurosynth

The NiSpace "neurosynth" dataset is based on the Neurosynth database (https://neurosynth.org/). The dataset consists of all terms in the Neurosynth database as preselected by the authors (i.e., the same available via their website). For each term, a "MKDAChi2" meta-analysis map was generated in MNI152NLin6Asym-2mm space using the nimare package (https://nimare.readthedocs.io/). The resulting "z_desc-association" maps were parcellated and are available only as parcellation tables. We ask to cite:

  • Yarkoni et al., 2011 (https://doi.org/10.1038/nmeth.1635) for neurosynth
  • Salo et al., 2022 (https://doi.org/10.52294/001c.87681) for nimare
  • Wager et al., 2007 (https://doi.org/10.1093/scan/nsm015) for the MKDAChi2 algorithm

Precomputed for: TianS1, TianS2, TianS3, TianS4, Glasser, AALCortical, AALSubcortical, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Aseg, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, CognitiveFunctions
Metadata: metadata.csv

cortexfeatures

The NiSpace "cortexfeatures" dataset consists of different features of cortical topology available via neuromaps (https://neuromaps-main.readthedocs.io/). Many of these maps were used in Shafiei et al., 2023 (https://doi.org/10.1038/s41467-023-41689-6). The maps are fetched directly from neuromaps (space = "fsLROriginal" or "fsaverageOriginal") or downloaded from the NiSpace-data GitHub repo (space = "fsLR" | "fsaverage"; recommended). Please cite the original publication when using these maps:

  • thickness: Van Essen et al., 2013 (https://doi.org/10.1016/j.neuroimage.2013.05.041)
  • t1t2: Van Essen et al., 2013 (https://doi.org/10.1016/j.neuroimage.2013.05.041)
  • saaxis: Sydnor et al., 2021 (https://doi.org/10.1016/j.neuron.2021.06.016)
  • devel/evolexpansion: Hill et al., 2010 (https://doi.org/10.1073/pnas.1001229107)
  • evolexpansion/specieshomology: Xu et al., 2020 (https://doi.org/10.1016/j.neuroimage.2020.117346)
  • geneexpr (1. principal component): Markello et al., 2021 (https://doi.org/10.7554/eLife.72129)
  • fcgradient1/2/3: Margulies et al., 2016 (https://doi.org/10.1073/pnas.1608282113)
  • cbf/cbv/cmro2/cmrglc/glycindex: Vaishnavi et al., 2010 (https://doi.org/10.1073/pnas.1010459107)
  • meg: Shafiei et al., 2022 (https://doi.org/10.1371/journal.pbio.3001735)

Individual maps: 23
Precomputed for: Glasser, AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, HarvardOxfordCortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, MEG, Metabolism, CortexTopology

Show all 23 maps
MapAvailable spaces
feature-cbf_pub-vaishnavi2010fsaverage, fsLR
feature-cbv_pub-vaishnavi2010fsaverage, fsLR
feature-cmrglc_pub-vaishnavi2010fsaverage, fsLR
feature-cmro2_pub-vaishnavi2010fsaverage, fsLR
feature-develexpansion_pub-hill2010fsaverage, fsLR
feature-evolexpansion_pub-hill2010fsaverage, fsLR
feature-evolexpansion_pub-xu2020fsaverage, fsLR
feature-fcgradient1_pub-margulies2016fsaverage, fsLR
feature-fcgradient2_pub-margulies2016fsaverage, fsLR
feature-fcgradient3_pub-margulies2016fsaverage, fsLR
feature-geneexpr-abagenfsaverage, fsLR
feature-glycindex_pub-vaishnavi2010fsaverage, fsLR
feature-megpoweralpha_pub-shafiei2022fsaverage, fsLR
feature-megpowerbeta_pub-shafiei2022fsaverage, fsLR
feature-megpowerdelta_pub-shafiei2022fsaverage, fsLR
feature-megpowergamma1_pub-shafiei2022fsaverage, fsLR
feature-megpowergamma2_pub-shafiei2022fsaverage, fsLR
feature-megpowertheta_pub-shafiei2022fsaverage, fsLR
feature-megtimescale_pub-shafiei2022fsaverage, fsLR
feature-saaxis_pub-sydnor2021fsaverage, fsLR
feature-specieshomology_pub-xu2020fsaverage, fsLR
feature-t1t2_pub-hcps1200fsaverage, fsLR
feature-thickness_pub-hcps1200fsaverage, fsLR

bigbrain

The NiSpace "bigbrain" dataset is adopted from the BigBrainWarp toolbox (https://bigbrainwarp.readthedocs.io) by Paquola et al. (see below). Further information on the dataset: https://bigbrainwarp.readthedocs.io/en/latest/pages/toolbox_contents.html The maps are fetched directly from the BigBrainWarp Sciebo folder (space = "fsaverageOriginal") or downloaded from the NiSpace-data GitHub repo (space = "fsLR" | "fsaverage"; recommended). The data is subject to a CC BY-NC-SA 4.0 license. We thank Dr. Casey Paquola for her help in providing these data. Please cite the following publication when using these maps:

  • BigBrain: Amunts et al., 2013 (https://doi.org/10.1126/science.1235381)
  • BigBrainWarp toolbox: Paquola et al., 2021 (https://doi.org/10.7554/eLife.70119)
  • Approximate cortical layer thickness: Wagstyl et al., 2020 (https://doi.org/10.1371/journal.pbio.3000678)

Individual maps: 13
Precomputed for: Glasser, AALCortical, BrainnetomeCortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, HarvardOxfordCortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All, CorticalLayers, DifferentiationGradients

Show all 13 maps
MapAvailable spaces
feature-funcgradient1_pub-paquola2021fsaverage, fsLR
feature-funcgradient2_pub-paquola2021fsaverage, fsLR
feature-funcgradient3_pub-paquola2021fsaverage, fsLR
feature-histogradient1_pub-paquola2021fsaverage, fsLR
feature-histogradient2_pub-paquola2021fsaverage, fsLR
feature-layer1_pub-wagstyl2020fsaverage, fsLR
feature-layer2_pub-wagstyl2020fsaverage, fsLR
feature-layer3_pub-wagstyl2020fsaverage, fsLR
feature-layer4_pub-wagstyl2020fsaverage, fsLR
feature-layer5_pub-wagstyl2020fsaverage, fsLR
feature-layer6_pub-wagstyl2020fsaverage, fsLR
feature-microgradient1_pub-paquola2021fsaverage, fsLR
feature-microgradient2_pub-paquola2021fsaverage, 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 (https://doi.org/10.1038/s41593-025-01945-y). Fetch the original maps by requesting space = "MNI152". The processed maps (space = "MNI152NLin2009cAsym" | "MNI152NLin6Asym" | "fsaverage" | "fsLR") 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/fsa using neuromaps. Please cite the original publication when using these maps:

  • GM/WM/CSF: SPM (https://www.fil.ion.ucl.ac.uk/spm/)
  • Arteries: Mouches et al., 2019 (https://doi.org/10.1038/s41597-019-0034-5), license: CC0 1.0
  • Veins: Huck et al., 2019 (https://doi.org/10.1007/s00429-019-01919-4), license: CC BY 4.0

Individual maps: 5
Precomputed for: TianS1, TianS2, TianS3, TianS4, Glasser, AALCortical, AALSubcortical, BrainnetomeCortical, BrainnetomeSubcortical, DesikanKilliany, DesikanKillianyTourville, Destrieux, Aseg, HarvardOxfordCortical, HarvardOxfordSubcortical, Schaefer100Parcels7Networks, Schaefer100Parcels17Networks, Schaefer200Parcels7Networks, Schaefer200Parcels17Networks, Schaefer400Parcels7Networks, Schaefer400Parcels17Networks, Schaefer1000Parcels7Networks, Schaefer1000Parcels17Networks, Yan100, Yan200, Yan400, Yan1000
Collections: All

Show all 5 maps
MapAvailable spaces
tissue-arteries_pub-mouches2019fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
tissue-csf_pub-spmfsaverage, fsLR, MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym
tissue-gm_pub-spmfsaverage, fsLR, MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym
tissue-veins_pub-huck2019fsaverage, fsLR, MNI152NLin6Asym, MNI152NLin2009cAsym
tissue-wm_pub-spmfsaverage, fsLR, MNI152, MNI152NLin6Asym, MNI152NLin2009cAsym

enigmathick

The NiSpace "enigmathick" dataset is based on ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) working group summary statistics as 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 across several neurological and psychiatric disorders. Cortical values are provided in the Desikan parcellation, subcortical values in the Aseg parcellation. Inclusion of the subcortical values enable combined cortical+subcortical access via parcellation="DesikanKillianyAseg". For some disorders, effect size maps are split by subtype and/or age group. Use collection "Adult" for a reduced collection of only adult data as often used in related publications. For each disorder, please cite the appropriate ENIGMA working group publication (see metadata.csv). We additionally ask to cite the ENIGMA Toolbox; Larivière et al., 2021 (https://doi.org/10.1038/s41596-021-00579-z).

Precomputed for: DesikanKilliany, Aseg
Collections: All, Adult
Metadata: metadata.csv

enigmaarea

The NiSpace "enigmaarea" dataset is based on ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) working group summary statistics as 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 across several neurological and psychiatric disorders. Cortical values are provided in the Desikan parcellation, subcortical values in the Aseg parcellation. Inclusion of the subcortical values enable combined cortical+subcortical access via parcellation="DesikanKillianyAseg". For some disorders, effect size maps are split by subtype and/or age group. Use collection "Adult" for a reduced collection of only adult data as often used in related publications. For each disorder, please cite the appropriate ENIGMA working group publication (see metadata.csv). We additionally ask to cite the ENIGMA Toolbox; Larivière et al., 2021 (https://doi.org/10.1038/s41596-021-00579-z).

Precomputed for: DesikanKilliany, Aseg
Collections: All, Adult
Metadata: metadata.csv