Fetching datasets#
We have several templates, parcellations, and datasets integrated in
NiSpace.These can be accessed through functions named
nispace.datasets.fetch_...(). On first access, data should should be downloaded to the user’s home directory into a folder called nispace-data. The user can however provide a custom data folder via the argument nispace_data_dir available for every fetch_ method.Fetch reference datasets#
We start with the reference datasets as these are most important for you.
With “reference data”, we refer to brain maps that are ususally used to compare a certain (set of) target map(s) to. The main reference maps for now are nuclear imaging maps (
pet), mRNA expression maps from the Allen Human Brain Atlas (mrna and magicc). Later, we will run systematic comparisons of user-defined target maps with with these reference maps, for example to answer the question if a group-comparison map (like a SPM t-map) spatially correlates (“colocalizes”) with certain
neurotransmitter receptors.We can fetch our reference data with: nispace.datasets.fetch_reference(...).
The first and only mandatory argument determines the reference dataset (e.g.,
petormrna).The second argument is used to subset the datasets; it can be a part of a name or, for the
petdataset, a dict with detailed sub-setting information (see below).The argument
parcellationwill cause the data to be returned in parcellated format as a pandas DataFrame. See below for included parcellations.The argument
collectioncan be a name of a map subset shipped with the toolbox. Very relevant forpetdata, as we have many maps targeting the same transmitter receptor, and more so formrnadata, as we ship the toolbox with, e.g., pre-defined cell-type marker gene sets.
When fetching datasets, the function will by default print detailed information on the sources of each dataset. The user is responsible for citing these sources when used in publications!
[1]:
# load local nispace, for testing
# COMMENT THIS OUT IF YOU RUN THIS LOCALLY AFTER INSTALLING NISPACE
import sys
sys.path.append("/Users/llotter/projects/nispace/")
[2]:
from nispace.datasets import fetch_reference
Nuclear imaging ("pet")#
Collections include:
"All": all maps"AllTargetSets": all maps, but sorted in sets by tracer target. If called without a parcellation, this will not have an influence. If called with a parcellation, i.e. returned as a table, it will have a 3-dimensional “multi”-index, with “sets” in the first, “map” in the second, and “weight” (= number of subjects) in the third place."UniqueTracers": one pre-selected map for each target. When called with a parcellation, returned as table with a 2d-index, sorting the maps into sets by target groups (e.g., “Dopamine”, or “Serotonin”)."UniqueTracerSets": one or more maps for each target, given they have the same tracer. If parcellation is provided, the output table is indexed as for “AllTargetSets”.
In many instances, "UniqueTracers" will be a good choice.
[3]:
# Get all pet maps as paths to volumes
print("All pet maps")
pet_maps = fetch_reference("pet")
print("Number of pet maps:", len(pet_maps))
print("First map path:", pet_maps[0])
All pet maps
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
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)
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
- target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017 Source: Beliveau2017 CC BY-NC-SA 4.0 https://doi.org/10.1523/JNEUROSCI.2830-16.2016
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-5HT1a_tracer-way100635_n-35_dx-hc_pub-savli2012 Source: Savli2012 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2012.07.001
- target-5HT1b_tracer-az10419369_n-36_dx-hc_pub-beliveau2017 Source: Beliveau2017 CC BY-NC-SA 4.0 https://doi.org/10.1523/JNEUROSCI.2830-16.2016
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-5HT1b_tracer-p943_n-23_dx-hc_pub-savli2012 Source: Savli2012 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2012.07.001
- target-5HT1b_tracer-p943_n-65_dx-hc_pub-gallezot2010 Source: Gallezot2010 CC BY-NC-SA 4.0 https://doi.org/10.1038/jcbfm.2009.195, https://doi.org/10.1007/s00213-010-1881-0, https://doi.org/10.1001/archgenpsychiatry.2011.91, https://doi.org/10.1016/j.biopsych.2013.11.022, https://doi.org/10.1016/j.jad.2016.02.021, https://doi.org/10.1007/s00259-014-2958-5, https://doi.org/10.1002/syn.22159
- target-5HT2a_tracer-altanserin_n-19_dx-hc_pub-savli2012 Source: Savli2012 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2012.07.001
- target-5HT2a_tracer-cimbi36_n-29_dx-hc_pub-beliveau2017 Source: Beliveau2017 CC BY-NC-SA 4.0 https://doi.org/10.1523/JNEUROSCI.2830-16.2016
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-5HT4_tracer-sb207145_n-59_dx-hc_pub-beliveau2017 Source: Beliveau2017 CC BY-NC-SA 4.0 https://doi.org/10.1523/JNEUROSCI.2830-16.2016
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-5HT6_tracer-gsk215083_n-30_dx-hc_pub-radhakrishnan2018 Source: Radhakrishnan2018 CC BY-NC-SA 4.0 https://doi.org/10.2967/jnumed.117.206516, https://doi.org/10.1016/j.pscychresns.2019.111007
- target-5HTT_tracer-dasb_n-100_dx-hc_pub-beliveau2017 Source: Beliveau2017 CC BY-NC-SA 4.0 https://doi.org/10.1523/JNEUROSCI.2830-16.2016
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012 Source: Savli2012 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2012.07.001
- target-5HTT_tracer-madam_n-10_dx-hc_pub-fazio2016 Source: Fazio2016 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2016.03.019
- target-A4B2_tracer-flubatine_n-30_dx-hc_pub-hillmer2016 Source: Hillmer2016 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2016.07.026, https://doi.org/10.1093/ntr/ntx091
- target-CB1_tracer-fmpepd2_n-22_dx-hc_pub-laurikainen2019 Source: Laurikainen2019 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2018.10.013
- target-CB1_tracer-omar_n-77_dx-hc_pub-normandin2015 Source: Normandin2015 CC BY-NC-SA 4.0 https://doi.org/10.1038/jcbfm.2015.46, https://doi.org/10.1016/j.bpsc.2015.09.008, https://doi.org/10.1016/j.biopsych.2015.08.021, https://doi.org/10.1111/j.1530-0277.2012.01815.x
- target-CMRglu_tracer-fdg_n-20_dx-hc_pub-castrillon2023 Source: Castrillon2023 CC BY-NC-SA 4.0 https://doi.org/10.1126/sciadv.adi7632
- target-COX1_tracer-ps13_n-11_dx-hc_pub-kim2020 Source: Kim2020 CC0 1.0 https://doi.org/10.1007/s00259-020-04855-2, https://doi.org/10.18112/openneuro.ds004401.v1.0.1
- target-D1_tracer-sch23390_n-13_dx-hc_pub-kaller2017 Source: Kaller2017 CC BY-NC-SA 4.0 https://doi.org/10.1007/s00259-017-3645-0
- target-D23_tracer-fallypride_n-49_dx-hc_pub-jaworska2020 Source: Jaworska2020 CC BY-NC-SA 4.0 https://doi.org/10.1038/s41386-020-0662-7
- target-D23_tracer-flb457_n-37_dx-hc_pub-smith2017 Source: Smith2017 CC BY-NC-SA 4.0 https://doi.org/10.1177/0271678X17737693
- target-D23_tracer-flb457_n-55_dx-hc_pub-sandiego2015 Source: Sandiego2015 CC BY-NC-SA 4.0 https://doi.org/10.1038/jcbfm.2014.237, https://doi.org/10.1177/0271678X17737693, https://doi.org/10.1038/s41386-019-0456-y, https://doi.org/10.1001/jamapsychiatry.2014.2414, https://doi.org/10.1038/npp.2017.223
- target-DAT_tracer-fepe2i_n-6_dx-hc_pub-sasaki2012 Source: Sasaki2012 CC BY-NC-SA 4.0 https://doi.org/10.2967/jnumed.111.101626
- target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018 Source: Dukart2018 CC BY-NC-SA 4.0 https://doi.org/10.1038/s41598-018-22444-0
CAVE: SPECT, not PET!
- target-DAT_tracer-fpcit_n-30_dx-hc_pub-garciagomez2013 Source: Garciagomez2013 free https://doi.org/10.1016/j.remn.2013.02.009
CAVE: SPECT, not PET!
- target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018 Source: Garciagomez2018 free https://doi.org/10.33588/imagendiagnostica.901.2
- target-GABAa_tracer-flumazenil_n-16_dx-hc_pub-norgaard2021 Source: Norgaard2021 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.neuroimage.2021.117878
CAVE: Processed in fsaverage space, use volumetric maps only for subcortex! (NiSpace tables contain only fsaverage-cortical data for now.)
- target-GABAa_tracer-flumazenil_n-6_dx-hc_pub-dukart2018 Source: Dukart2018 CC BY-NC-SA 4.0 https://doi.org/10.1038/s41598-018-22444-0
- target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lukow2022 Source: Lukow2022 CC BY 4.0 https://doi.org/10.1038/s42003-022-03268-1
- target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallezot2017 Source: Gallezot2017 CC BY-NC-SA 4.0 https://doi.org/10.1177/0271678X16650697, https://doi.org/10.1038/jcbfm.2009.195
- target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016 Source: Wey2016 CC0 1.0 https://doi.org/10.1126/scitranslmed.aaf7551
- target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vijay2018 Source: Vijay2018 CC BY-NC-SA 4.0 https://doi.org/10.1038/s41386-018-0199-1
- target-M1_tracer-lsn3172176_n-24_dx-hc_pub-naganawa2020 Source: Naganawa2020 CC BY-NC-SA 4.0 https://doi.org/10.2967/jnumed.120.246967
- target-mGluR5_tracer-abp688_n-22_dx-hc_pub-rosaneto Source: Rosaneto CC BY-NC-SA 4.0 https://doi.org/10.1101/2021.10.28.466336
- target-mGluR5_tracer-abp688_n-28_dx-hc_pub-dubois2015 Source: Dubois2015 CC BY-NC-SA 4.0 https://doi.org/10.1007/s00259-015-3167-6
- target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smart2019 Source: Smart2019 CC BY-NC-SA 4.0 https://doi.org/10.1007/s00259-018-4252-4
- target-MOR_tracer-carfentanil_n-204_dx-hc_pub-kantonen2020 Source: Kantonen2020 CC BY-NC-SA 4.0 https://doi.org/10.1038/mp.2017.183
- target-MOR_tracer-carfentanil_n-39_dx-hc_pub-turtonen2021 Source: Turtonen2021 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.bpsc.2020.10.013
- target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017 Source: Hesse2017 CC BY-NC-SA 4.0 https://doi.org/10.1007/s00259-016-3590-3
- target-NET_tracer-mrb_n-77_dx-hc_pub-ding2010 Source: Ding2010 CC BY-NC-SA 4.0 https://doi.org/10.1002/syn.20696, https://doi.org/10.1016/j.neuroimage.2013.10.004, https://doi.org/10.1038/s41366-019-0471-4, https://doi.org/10.1210/jc.2017-02717
- target-NMDA_tracer-ge179_n-29_dx-hc_pub-galovic2021 Source: Galovic2021 CC BY-NC-SA 4.0 https://doi.org/10.1001/jamaneurol.2022.4352, https://doi.org/10.1016/j.neuroimage.2021.118194, https://doi.org/10.2967/jnumed.113.130641
CAVE: Unlike other tracers, [18F]GE-179 binds to open (active) NMDA receptors!
- target-SV2A_tracer-ucbj_n-76_dx-hc_pub-finnema2016 Source: Finnema2016 CC BY-NC-SA 4.0 https://doi.org/10.1177/0271678X17724947, https://doi.org/10.2967/jnumed.120.246967, https://doi.org/10.1177/0271678X211004312, https://doi.org/10.1186/s13195-020-00742-y, https://doi.org/10.1177/0271678X20946198, https://doi.org/10.1093/cid/ciab484, https://doi.org/10.1038/s41380-021-01184-0, https://doi.org/10.1016/j.bpsc.2015.09.008, https://doi.org/10.1111/epi.16653, https://doi.org/10.1186/s13550-020-00670-w, https://doi.org/10.1002/alz.12097, https://doi.org/10.1111/epi.14701, https://doi.org/10.1038/s41467-019-09562-7, https://doi.org/10.1001/jamaneurol.2018.1836
- target-TSPO_tracer-pbr28_n-6_dx-hc_pub-lois2018 Source: Lois2018 MIT https://doi.org/10.1021/acschemneuro.8b00072, https://doi.org/10.5281/zenodo.1174364
- target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghourian2017 Source: Aghourian2017 CC BY-NC-SA 4.0 https://doi.org/10.1038/mp.2017.183
- target-VAChT_tracer-feobv_n-4_dx-hc_pub-tuominen Source: Tuominen CC BY-NC-SA 4.0 https://doi.org/10.1101/2021.10.28.466336
- target-VAChT_tracer-feobv_n-5_dx-hc_pub-bedard2019 Source: Bedard2019 CC BY-NC-SA 4.0 https://doi.org/10.1016/j.sleep.2018.12.020
- target-VMAT2_tracer-dtbz_n-76_dx-hc_pub-larsen2020 Source: Larsen2020 CC0 1.0 https://doi.org/10.18112/openneuro.ds002385.v1.1.0, https://doi.org/10.1038/s41467-020-14693-3
Number of pet maps: 48
First map path: /Users/llotter/nispace-data/reference/pet/map/target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017/target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017_space-MNI152NLin2009cAsym.nii.gz
This command returned the paths to all maps and printed citation information. We can turn off the latter with the ´print_references` argument.
This is how we get a single specific map:
[4]:
# Get a single map based on the exact name
print("Get a single map (path to volume)")
pet_maps = fetch_reference(
"pet",
maps="target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017",
print_references=False
)
print("Filtered map path:", pet_maps)
Get a single map (path to volume)
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Applying filter: target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017
INFO | 16/06/25 09:25:24 | nispace: Filtered from 48 to 1 maps.
Filtered map path: [PosixPath('/Users/llotter/nispace-data/reference/pet/map/target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017/target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017_space-MNI152NLin2009cAsym.nii.gz')]
We have more options for sub-setting the pet collection, e.g.:
[5]:
# Subset maps based on sub-string
print("Maps containing '5HT'")
pet_maps = fetch_reference(
"pet",
maps="5HT",
print_references=False
)
print("Number of maps:", len(pet_maps))
print()
# Subset maps based on detail dict
print("Maps containing `5HT` or 'GABA' with a sample sizes over 30")
pet_maps = fetch_reference(
"pet",
maps={"target": ["5HT", "GABA"], "n":">30"},
print_references=False
)
print("Number of maps:", len(pet_maps))
Maps containing '5HT'
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Applying filter: 5HT
INFO | 16/06/25 09:25:24 | nispace: Filtered from 48 to 12 maps.
Number of maps: 12
Maps containing `5HT` or 'GABA' with a sample sizes over 30
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Applying filter: {'target': ['5HT', 'GABA'], 'n': '>30'}
INFO | 16/06/25 09:25:24 | nispace: Filtered from 48 to 5 maps.
Number of maps: 5
If we pass a parcellation (see below for the available ones), we will get the data already parcellated as a pandas DataFrame.
[6]:
# Get all pet maps as table
print("All pet maps as table")
pet_tab = fetch_reference(
"pet",
parcellation="Destrieux",
print_references=False
)
print(pet_tab.shape) # pet maps x 148 parcels
display(pet_tab.head(5))
All pet maps as table
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Loading data parcellated with 'Destrieux'
(48, 148)
| hemi-L_lab-G+and+S+frontomargin | hemi-L_lab-G+and+S+occipital+inf | hemi-L_lab-G+and+S+paracentral | hemi-L_lab-G+and+S+subcentral | hemi-L_lab-G+and+S+transv+frontopol | hemi-L_lab-G+and+S+cingul+Ant | hemi-L_lab-G+and+S+cingul+Mid+Ant | hemi-L_lab-G+and+S+cingul+Mid+Post | hemi-L_lab-G+cingul+Post+dorsal | hemi-L_lab-G+cingul+Post+ventral | ... | hemi-R_lab-S+parieto+occipital | hemi-R_lab-S+pericallosal | hemi-R_lab-S+postcentral | hemi-R_lab-S+precentral+inf+part | hemi-R_lab-S+precentral+sup+part | hemi-R_lab-S+suborbital | hemi-R_lab-S+subparietal | hemi-R_lab-S+temporal+inf | hemi-R_lab-S+temporal+sup | hemi-R_lab-S+temporal+transverse | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| target-5HT1a_tracer-cumi101_n-8_dx-hc_pub-beliveau2017 | 0.166229 | 0.140417 | 0.243418 | 0.223326 | 0.251212 | 0.239098 | 0.217597 | 0.179494 | 0.154957 | 0.107641 | ... | 0.159265 | 0.115716 | 0.187204 | 0.208126 | 0.171509 | 0.250436 | 0.195168 | 0.278166 | 0.243162 | 0.175779 |
| target-5HT1a_tracer-way100635_n-35_dx-hc_pub-savli2012 | 0.343777 | 0.318346 | 0.228999 | 0.361994 | 0.322103 | 0.378598 | 0.313098 | 0.328132 | 0.340351 | 0.219930 | ... | 0.257297 | NaN | 0.251281 | 0.315240 | 0.254720 | 0.479086 | 0.298675 | 0.441795 | 0.387867 | 0.343412 |
| target-5HT1b_tracer-az10419369_n-36_dx-hc_pub-beliveau2017 | 0.372709 | 0.340716 | 0.393472 | 0.397098 | 0.395923 | 0.436471 | 0.384247 | 0.353049 | 0.198469 | 0.455205 | ... | 0.392033 | 0.242409 | 0.354596 | 0.432699 | 0.419893 | 0.356429 | 0.369909 | 0.354134 | 0.367515 | 0.384210 |
| target-5HT1b_tracer-p943_n-23_dx-hc_pub-savli2012 | 0.531205 | 0.383014 | 0.274941 | 0.453660 | 0.476328 | 0.411272 | 0.399859 | 0.390499 | 0.409591 | 0.168372 | ... | 0.402827 | NaN | 0.341502 | 0.482905 | 0.421282 | 0.366256 | 0.378882 | 0.314340 | 0.364261 | 0.398171 |
| target-5HT1b_tracer-p943_n-65_dx-hc_pub-gallezot2010 | 0.520292 | 0.448108 | 0.392367 | 0.481213 | 0.511100 | 0.492182 | 0.470508 | 0.450506 | 0.440909 | 0.307216 | ... | 0.416835 | NaN | 0.443536 | 0.541298 | 0.486467 | 0.457092 | 0.425547 | 0.474332 | 0.477182 | 0.488412 |
5 rows × 148 columns
“Collections” provide pre-defined subsets of a dataset. In part, they provide multi-indices to group maps into sets. This is mandatory for X-set enrichment analyses (see later notebooks). If a collection is requested without a parcellation, a list of unique maps in the collection will be returned.
[7]:
# Filter using pre-defined collection
print("Filter using pre-defined collection")
pet_tab = fetch_reference(
"pet",
collection="UniqueTracers",
parcellation="Destrieux",
print_references=False
)
print(pet_tab.shape) # pet maps x 148 parcels
display(pet_tab)
Filter using pre-defined collection
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Applying collection filter from: /Users/llotter/nispace-data/reference/pet/collection-UniqueTracers.collect.
INFO | 16/06/25 09:25:24 | nispace: Loading data parcellated with 'Destrieux'
(28, 148)
| hemi-L_lab-G+and+S+frontomargin | hemi-L_lab-G+and+S+occipital+inf | hemi-L_lab-G+and+S+paracentral | hemi-L_lab-G+and+S+subcentral | hemi-L_lab-G+and+S+transv+frontopol | hemi-L_lab-G+and+S+cingul+Ant | hemi-L_lab-G+and+S+cingul+Mid+Ant | hemi-L_lab-G+and+S+cingul+Mid+Post | hemi-L_lab-G+cingul+Post+dorsal | hemi-L_lab-G+cingul+Post+ventral | ... | hemi-R_lab-S+parieto+occipital | hemi-R_lab-S+pericallosal | hemi-R_lab-S+postcentral | hemi-R_lab-S+precentral+inf+part | hemi-R_lab-S+precentral+sup+part | hemi-R_lab-S+suborbital | hemi-R_lab-S+subparietal | hemi-R_lab-S+temporal+inf | hemi-R_lab-S+temporal+sup | hemi-R_lab-S+temporal+transverse | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| set | map | |||||||||||||||||||||
| General | target-CMRglu_tracer-fdg_n-20_dx-hc_pub-castrillon2023 | 0.637165 | 0.619877 | 0.567377 | 0.672438 | 0.595498 | 0.710548 | 0.681885 | 0.713068 | 0.902651 | 0.609607 | ... | 0.779698 | NaN | 0.649124 | 0.740246 | 0.705462 | 0.747042 | 0.800423 | 0.666042 | 0.706858 | 0.747902 |
| target-SV2A_tracer-ucbj_n-76_dx-hc_pub-finnema2016 | 0.657544 | 0.596147 | 0.494876 | 0.629006 | 0.643146 | 0.705044 | 0.652129 | 0.696556 | 0.744545 | 0.483915 | ... | 0.646438 | NaN | 0.616731 | 0.626233 | 0.571934 | 0.720553 | 0.708779 | 0.678673 | 0.693986 | 0.657282 | |
| target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016 | 0.723660 | 0.649211 | 0.569503 | 0.651314 | 0.668096 | 0.653438 | 0.663738 | 0.674450 | 0.701170 | 0.532489 | ... | 0.702607 | NaN | 0.651778 | 0.715930 | 0.699370 | 0.696435 | 0.708003 | 0.713312 | 0.708125 | 0.732933 | |
| target-VMAT2_tracer-dtbz_n-76_dx-hc_pub-larsen2020 | 0.018498 | 0.015489 | 0.009632 | 0.019396 | 0.016348 | 0.030043 | 0.032365 | 0.037730 | 0.043705 | 0.012664 | ... | 0.022411 | NaN | 0.010768 | 0.016225 | 0.013911 | 0.025913 | 0.037628 | 0.020601 | 0.022400 | 0.024775 | |
| Immunity | target-TSPO_tracer-pbr28_n-6_dx-hc_pub-lois2018 | 0.687424 | 0.660813 | 0.584524 | 0.647924 | 0.669249 | 0.688761 | 0.665796 | 0.682357 | 0.729417 | 0.657087 | ... | 0.611432 | NaN | 0.610236 | 0.632971 | 0.613377 | 0.696122 | 0.654375 | 0.694619 | 0.661497 | 0.704014 |
| target-COX1_tracer-ps13_n-11_dx-hc_pub-kim2020 | 0.499441 | 0.562536 | 0.541698 | 0.529926 | 0.483388 | 0.526488 | 0.520554 | 0.512171 | 0.511276 | 0.498973 | ... | 0.633066 | NaN | 0.547782 | 0.511331 | 0.515266 | 0.524606 | 0.519848 | 0.515943 | 0.521818 | 0.556394 | |
| Glutamate | target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smart2019 | 0.603225 | 0.587133 | 0.577452 | 0.788872 | 0.580190 | 0.834775 | 0.822082 | 0.829476 | 0.875077 | 0.550936 | ... | 0.713332 | NaN | 0.678623 | 0.759680 | 0.636508 | 0.800428 | 0.786214 | 0.737080 | 0.792729 | 0.808482 |
| target-NMDA_tracer-ge179_n-29_dx-hc_pub-galovic2021 | 0.588515 | 0.635087 | 0.539512 | 0.629076 | 0.551330 | 0.647853 | 0.625612 | 0.659034 | 0.703712 | 0.597561 | ... | 0.679042 | NaN | 0.631428 | 0.676052 | 0.662457 | 0.735381 | 0.717249 | 0.688368 | 0.681596 | 0.800036 | |
| GABA | target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lukow2022 | 0.412239 | 0.345132 | 0.287038 | 0.415120 | 0.419536 | 0.619151 | 0.567274 | 0.500065 | 0.469611 | 0.369955 | ... | 0.357666 | NaN | 0.350135 | 0.400915 | 0.372432 | 0.548175 | 0.423651 | 0.436350 | 0.417468 | 0.371995 |
| target-GABAa_tracer-flumazenil_n-6_dx-hc_pub-dukart2018 | 0.625535 | 0.675792 | 0.487560 | 0.690240 | 0.573373 | 0.767576 | 0.687402 | 0.711057 | 0.812428 | 0.552503 | ... | 0.737331 | NaN | 0.635869 | 0.673358 | 0.631843 | 0.788609 | 0.724558 | 0.734305 | 0.734722 | 0.709873 | |
| Dopamine | target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018 | 0.311773 | 0.310412 | 0.278725 | 0.311788 | 0.291158 | 0.381907 | 0.352170 | 0.334281 | 0.323056 | 0.275160 | ... | 0.299785 | NaN | 0.294830 | 0.312347 | 0.297550 | 0.384336 | 0.322066 | 0.335199 | 0.323028 | 0.308332 |
| target-D1_tracer-sch23390_n-13_dx-hc_pub-kaller2017 | 0.183397 | 0.107540 | 0.078948 | 0.134384 | 0.154039 | 0.212894 | 0.185288 | 0.188905 | 0.228113 | 0.140633 | ... | 0.160792 | NaN | 0.140606 | 0.166885 | 0.133613 | 0.253925 | 0.201628 | 0.179463 | 0.192266 | 0.180495 | |
| target-D23_tracer-flb457_n-55_dx-hc_pub-sandiego2015 | 0.124486 | 0.121408 | 0.084513 | 0.133352 | 0.109683 | 0.143691 | 0.134510 | 0.118684 | 0.120475 | 0.086851 | ... | 0.108762 | NaN | 0.117825 | 0.114013 | 0.100719 | 0.141583 | 0.118455 | 0.179718 | 0.158241 | 0.142994 | |
| target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018 | 0.216720 | 0.244649 | 0.203527 | 0.282351 | 0.197790 | 0.312059 | 0.312815 | 0.312701 | 0.309115 | 0.254075 | ... | 0.291758 | NaN | 0.268230 | 0.289977 | 0.260974 | 0.307850 | 0.302147 | 0.291319 | 0.300988 | 0.308747 | |
| Serotonin | target-5HT1a_tracer-way100635_n-35_dx-hc_pub-savli2012 | 0.343777 | 0.318346 | 0.228999 | 0.361994 | 0.322103 | 0.378598 | 0.313098 | 0.328132 | 0.340351 | 0.219930 | ... | 0.257297 | NaN | 0.251281 | 0.315240 | 0.254720 | 0.479086 | 0.298675 | 0.441795 | 0.387867 | 0.343412 |
| target-5HT1b_tracer-p943_n-23_dx-hc_pub-savli2012 | 0.531205 | 0.383014 | 0.274941 | 0.453660 | 0.476328 | 0.411272 | 0.399859 | 0.390499 | 0.409591 | 0.168372 | ... | 0.402827 | NaN | 0.341502 | 0.482905 | 0.421282 | 0.366256 | 0.378882 | 0.314340 | 0.364261 | 0.398171 | |
| target-5HT2a_tracer-altanserin_n-19_dx-hc_pub-savli2012 | 0.646100 | 0.664230 | 0.344610 | 0.643179 | 0.613207 | 0.672502 | 0.556656 | 0.590664 | 0.765490 | 0.419012 | ... | 0.643719 | NaN | 0.568004 | 0.670378 | 0.569513 | 0.742673 | 0.652110 | 0.816891 | 0.790372 | 0.694868 | |
| target-5HT4_tracer-sb207145_n-59_dx-hc_pub-beliveau2017 | 0.174944 | 0.119751 | 0.207285 | 0.194449 | 0.204750 | 0.195337 | 0.190975 | 0.206011 | 0.142586 | 0.147011 | ... | 0.186995 | 0.12081 | 0.171658 | 0.178548 | 0.157012 | 0.200668 | 0.214346 | 0.232763 | 0.223681 | 0.187331 | |
| target-5HT6_tracer-gsk215083_n-30_dx-hc_pub-radhakrishnan2018 | 0.467500 | 0.474760 | 0.369479 | 0.471615 | 0.466632 | 0.478962 | 0.427414 | 0.426780 | 0.478465 | 0.275282 | ... | 0.447684 | NaN | 0.398272 | 0.456344 | 0.401045 | 0.444029 | 0.452315 | 0.513601 | 0.486448 | 0.463709 | |
| target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012 | 0.075403 | 0.060895 | 0.047332 | 0.100048 | 0.071061 | 0.121359 | 0.117065 | 0.130216 | 0.137156 | 0.064386 | ... | 0.088269 | NaN | 0.044580 | 0.047651 | 0.034051 | 0.140805 | 0.097268 | 0.054309 | 0.058438 | 0.110957 | |
| Noradrenaline/Acetylcholine | target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017 | 0.125213 | 0.119253 | 0.182873 | 0.159184 | 0.118253 | 0.131935 | 0.164863 | 0.187228 | 0.145118 | 0.072495 | ... | 0.137826 | NaN | 0.131542 | 0.099739 | 0.133062 | 0.092092 | 0.164904 | 0.140657 | 0.130026 | 0.166982 |
| target-A4B2_tracer-flubatine_n-30_dx-hc_pub-hillmer2016 | 0.324428 | 0.293606 | 0.258438 | 0.313554 | 0.314013 | 0.321499 | 0.341065 | 0.345242 | 0.340055 | 0.245778 | ... | 0.319758 | NaN | 0.326021 | 0.364841 | 0.352878 | 0.316778 | 0.340708 | 0.327949 | 0.340424 | 0.326998 | |
| target-M1_tracer-lsn3172176_n-24_dx-hc_pub-naganawa2020 | 0.444522 | 0.414434 | 0.349799 | 0.456415 | 0.437987 | 0.461943 | 0.428506 | 0.441084 | 0.480004 | 0.321075 | ... | 0.439156 | NaN | 0.433537 | 0.440224 | 0.402316 | 0.466079 | 0.455077 | 0.494821 | 0.490704 | 0.476752 | |
| target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghourian2017 | 0.129291 | 0.116044 | 0.136455 | 0.155591 | 0.119098 | 0.151412 | 0.187293 | 0.196462 | 0.154970 | 0.112630 | ... | 0.125380 | NaN | 0.134102 | 0.148374 | 0.148903 | 0.140091 | 0.146624 | 0.130764 | 0.136900 | 0.174610 | |
| Opiods/Endocannabinoids | target-MOR_tracer-carfentanil_n-204_dx-hc_pub-kantonen2020 | 0.315304 | 0.106893 | 0.119487 | 0.248392 | 0.283774 | 0.384791 | 0.373470 | 0.325987 | 0.263722 | 0.093779 | ... | 0.122963 | NaN | 0.190894 | 0.262917 | 0.244298 | 0.354848 | 0.242357 | 0.256187 | 0.259966 | 0.160587 |
| target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vijay2018 | 0.636851 | 0.599297 | 0.680876 | 0.773017 | 0.592962 | 0.775930 | 0.815478 | 0.719906 | 0.625592 | 0.493672 | ... | 0.605544 | NaN | 0.680841 | 0.731199 | 0.703461 | 0.684780 | 0.618201 | 0.656212 | 0.679512 | 0.665723 | |
| target-CB1_tracer-omar_n-77_dx-hc_pub-normandin2015 | 0.689277 | 0.632309 | 0.587256 | 0.642378 | 0.698075 | 0.703207 | 0.659200 | 0.663820 | 0.628370 | 0.492806 | ... | 0.556511 | NaN | 0.571252 | 0.590105 | 0.562312 | 0.740709 | 0.594028 | 0.687437 | 0.644567 | 0.620364 | |
| Histamine | target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallezot2017 | 0.191803 | 0.159743 | 0.178721 | 0.212827 | 0.195197 | 0.284119 | 0.269029 | 0.249380 | 0.226593 | 0.154015 | ... | 0.177500 | NaN | 0.182234 | 0.214274 | 0.213985 | 0.239113 | 0.201344 | 0.174119 | 0.179242 | 0.209483 |
28 rows × 148 columns
We provide PET maps registered and/or transformed to different specific spaces. These are:
MNI152: Original space in which the PET maps were published. This should not be used if you don’t plan to run registration yourself.MNI152NLin6Asym: One of the two common volumetric spaces currently used in MRI analyses (2 mm isotropic voxels).Mni152NLin2009cAsym: The other common volumetric space (2 mm isotropic voxels).fsaverage: The fsaverage space as used by FreeSurfer (41k vertices).fsLR: The Human Connectome Project space (32k vertices).
Data is requested in specific spaces via the space argument. Parcellated data was obtained within the space optimal for each map.
[8]:
# Get all maps for the noradrenaline transporter in a specific space
print("Get all maps for the noradrenaline transporter in a specific space")
pet_maps = fetch_reference(
"pet",
maps={"target": "NET"},
space="MNI152NLin6Asym",
print_references=False
)
print("Filtered map path:", pet_maps)
Get all maps for the noradrenaline transporter in a specific space
INFO | 16/06/25 09:25:24 | nispace: Loading pet maps.
INFO | 16/06/25 09:25:24 | nispace: Applying filter: {'target': 'NET'}
INFO | 16/06/25 09:25:24 | nispace: Filtered from 48 to 2 maps.
Filtered map path: [PosixPath('/Users/llotter/nispace-data/reference/pet/map/target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017/target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017_space-MNI152NLin6Asym.nii.gz'), PosixPath('/Users/llotter/nispace-data/reference/pet/map/target-NET_tracer-mrb_n-77_dx-hc_pub-ding2010/target-NET_tracer-mrb_n-77_dx-hc_pub-ding2010_space-MNI152NLin6Asym.nii.gz')]
Gene expression ("mrna")#
The mrna dataset is only available in tabulated format, so you always have to pass a parcellation.
Collections:
"CellTypesPsychEncodeTPM": cell type marker sets from Lake 2016 or Darmanis 2015"CellTypesPsychEncodeUMI": cell type marker sets from Lake Lake 2018"SynGO": Synapse-function related gene sets from the SynGO database"Chromosome": Chromosome-wise gene sets
[9]:
# Get all genes for a given parcellation
mrna_tab = fetch_reference(
"mrna",
parcellation="Destrieux",
)
display(f"{mrna_tab.shape[0]} genes x {mrna_tab.shape[1]} parcels", mrna_tab.head(5))
INFO | 16/06/25 09:25:24 | nispace: Loading mrna maps.
INFO | 16/06/25 09:25:24 | nispace: Loading data parcellated with 'Destrieux'
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"), considering parcel hemisphere and
cortical vs. subcortical location. Only genes that showed a mean donor-to-donor Spearman correlation of > 0.1 were retained.
In addition to the two publications listed above, please cite publications associated with gene set collections as appropriate.
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
'9983 genes x 148 parcels'
| hemi-L_lab-G+and+S+frontomargin | hemi-L_lab-G+and+S+occipital+inf | hemi-L_lab-G+and+S+paracentral | hemi-L_lab-G+and+S+subcentral | hemi-L_lab-G+and+S+transv+frontopol | hemi-L_lab-G+and+S+cingul+Ant | hemi-L_lab-G+and+S+cingul+Mid+Ant | hemi-L_lab-G+and+S+cingul+Mid+Post | hemi-L_lab-G+cingul+Post+dorsal | hemi-L_lab-G+cingul+Post+ventral | ... | hemi-R_lab-S+parieto+occipital | hemi-R_lab-S+pericallosal | hemi-R_lab-S+postcentral | hemi-R_lab-S+precentral+inf+part | hemi-R_lab-S+precentral+sup+part | hemi-R_lab-S+suborbital | hemi-R_lab-S+subparietal | hemi-R_lab-S+temporal+inf | hemi-R_lab-S+temporal+sup | hemi-R_lab-S+temporal+transverse | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| A1BG | 0.603714 | 0.424911 | 0.354168 | 0.514914 | 0.581526 | 0.579099 | 0.590871 | 0.478636 | 0.326660 | 0.399391 | ... | 0.541757 | 0.535359 | 0.514457 | 0.571902 | 0.455186 | 0.596736 | 0.483036 | 0.532298 | 0.525557 | 0.675296 |
| A1BG-AS1 | 0.676432 | 0.642984 | 0.598713 | 0.677616 | 0.621552 | 0.692636 | 0.638342 | 0.665583 | 0.658907 | 0.582184 | ... | 0.641603 | 0.561390 | 0.653640 | 0.605653 | 0.594782 | 0.639442 | 0.675937 | 0.714092 | 0.690400 | 0.673898 |
| A2M | 0.389871 | 0.512336 | 0.452206 | 0.407705 | 0.371963 | 0.461573 | 0.480850 | 0.487001 | 0.499881 | 0.455021 | ... | 0.637554 | 0.506473 | 0.547387 | 0.562318 | 0.499580 | 0.408249 | 0.534483 | 0.438315 | 0.489514 | 0.549798 |
| AAAS | 0.423352 | 0.538288 | 0.540322 | 0.485360 | 0.411931 | 0.436433 | 0.386540 | 0.429897 | 0.572567 | 0.559250 | ... | 0.607518 | 0.505082 | 0.687997 | 0.556730 | 0.573323 | 0.394876 | 0.574625 | 0.403460 | 0.534128 | 0.863640 |
| AAK1 | 0.626127 | 0.576627 | 0.585677 | 0.575091 | 0.586051 | 0.454669 | 0.476326 | 0.504907 | 0.520796 | 0.560450 | ... | 0.461256 | 0.483384 | 0.552856 | 0.470787 | 0.573373 | 0.456328 | 0.517026 | 0.473286 | 0.448987 | 0.529261 |
5 rows × 148 columns
Collections will subset and sort genes.
[10]:
# Get all genes for a given parcellation
mrna_tab = fetch_reference(
"mrna",
parcellation="Destrieux",
collection="CellTypesPsychEncodeTPM"
)
display(f"{mrna_tab.shape[0]} genes x {mrna_tab.shape[1]} parcels", mrna_tab.head(5))
INFO | 16/06/25 09:25:24 | nispace: Loading mrna maps.
INFO | 16/06/25 09:25:24 | nispace: Applying collection filter from: /Users/llotter/nispace-data/reference/mrna/collection-CellTypesPsychEncodeTPM.collect.
INFO | 16/06/25 09:25:25 | nispace: Loading data parcellated with 'Destrieux'
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"), considering parcel hemisphere and
cortical vs. subcortical location. Only genes that showed a mean donor-to-donor Spearman correlation of > 0.1 were retained.
In addition to the two publications listed above, please cite publications associated with gene set collections as appropriate.
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
- CellTypesPsychEncodeTPM Source: Lake2016 https://doi.org/10.1126/science.aaf1204
- CellTypesPsychEncodeTPM Source: Darmanis2015 https://doi.org/10.1073/pnas.1507125112
- CellTypesPsychEncodeTPM Source: Wang2018 https://doi.org/10.1126/science.aat8464
'444 genes x 148 parcels'
| hemi-L_lab-G+and+S+frontomargin | hemi-L_lab-G+and+S+occipital+inf | hemi-L_lab-G+and+S+paracentral | hemi-L_lab-G+and+S+subcentral | hemi-L_lab-G+and+S+transv+frontopol | hemi-L_lab-G+and+S+cingul+Ant | hemi-L_lab-G+and+S+cingul+Mid+Ant | hemi-L_lab-G+and+S+cingul+Mid+Post | hemi-L_lab-G+cingul+Post+dorsal | hemi-L_lab-G+cingul+Post+ventral | ... | hemi-R_lab-S+parieto+occipital | hemi-R_lab-S+pericallosal | hemi-R_lab-S+postcentral | hemi-R_lab-S+precentral+inf+part | hemi-R_lab-S+precentral+sup+part | hemi-R_lab-S+suborbital | hemi-R_lab-S+subparietal | hemi-R_lab-S+temporal+inf | hemi-R_lab-S+temporal+sup | hemi-R_lab-S+temporal+transverse | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| set | map | |||||||||||||||||||||
| Adult-Ex1 | CAMK2A | 0.856693 | 0.832408 | 0.769181 | 0.796297 | 0.825396 | 0.799951 | 0.758317 | 0.791016 | 0.840002 | 0.768446 | ... | 0.814037 | 0.787670 | 0.842221 | 0.824618 | 0.799944 | 0.802145 | 0.804215 | 0.833016 | 0.825433 | 0.773056 |
| CCDC88C | 0.381734 | 0.444780 | 0.338015 | 0.384871 | 0.231575 | 0.438720 | 0.374331 | 0.414423 | 0.400377 | 0.428926 | ... | 0.503742 | 0.348189 | 0.377369 | 0.477814 | 0.431914 | 0.344379 | 0.439141 | 0.557285 | 0.540488 | 0.447918 | |
| CDH9 | 0.737575 | 0.658874 | 0.645671 | 0.707994 | 0.718000 | 0.783677 | 0.726552 | 0.725915 | 0.723024 | 0.656156 | ... | 0.664827 | 0.708056 | 0.625394 | 0.665168 | 0.688928 | 0.762926 | 0.705784 | 0.728381 | 0.713863 | 0.673886 | |
| GNAL | 0.491871 | 0.478382 | 0.516195 | 0.460755 | 0.349280 | 0.422342 | 0.486171 | 0.496494 | 0.475241 | 0.547364 | ... | 0.378101 | 0.428946 | 0.365687 | 0.438092 | 0.481262 | 0.417479 | 0.433718 | 0.493504 | 0.438263 | 0.303117 | |
| GPR83 | 0.491008 | 0.465609 | 0.397596 | 0.422830 | 0.388784 | 0.403003 | 0.435505 | 0.435573 | 0.397809 | 0.357583 | ... | 0.358643 | 0.393777 | 0.402178 | 0.309362 | 0.367687 | 0.365041 | 0.451380 | 0.518286 | 0.445866 | 0.491256 |
5 rows × 148 columns
Resting state networks ("rsn")#
The rsn dataset is a set of resting state network probability maps. It is handled as the pet dataset.
Collections:
"All": All behavioral domains sorted into sets based on higher-level domains.
[11]:
# load all maps
print("Load all maps")
rsn_maps = fetch_reference("rsn", space="MNI152")
# load a single pet map
print("Load a single RSN map")
rsn_maps = fetch_reference(
"rsn",
maps="Aud",
print_references=False,
space="MNI152"
)
print(rsn_maps)
# get all maps as table for a parcellation
print("Get all maps as table for a parcellation")
rsn_tab = fetch_reference(
"rsn",
parcellation="Destrieux",
print_references=False,
space="MNI152"
)
display(rsn_tab.head(2))
Load all maps
INFO | 16/06/25 09:25:25 | nispace: Loading rsn maps.
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 are downloaded from the associated GitHub repository
(https://github.com/GrattonLab/Dworetsky_etal_ConsensusNetworks) and cached locally (find them in
`~HOME/nispace-data/reference/rsn/map`). Please cite the original publication when using these maps.
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
Load a single RSN map
INFO | 16/06/25 09:25:25 | nispace: Loading rsn maps.
INFO | 16/06/25 09:25:25 | nispace: Applying filter: Aud
INFO | 16/06/25 09:25:25 | nispace: Filtered from 14 to 1 maps.
[PosixPath("/Users/llotter/nispace-data/reference/rsn/map/nw-Aud/nw-Aud_space-MNI152.nii']")]
Get all maps as table for a parcellation
INFO | 16/06/25 09:25:25 | nispace: Loading rsn maps.
INFO | 16/06/25 09:25:25 | nispace: Loading data parcellated with 'Destrieux'
| hemi-L_lab-G+and+S+frontomargin | hemi-L_lab-G+and+S+occipital+inf | hemi-L_lab-G+and+S+paracentral | hemi-L_lab-G+and+S+subcentral | hemi-L_lab-G+and+S+transv+frontopol | hemi-L_lab-G+and+S+cingul+Ant | hemi-L_lab-G+and+S+cingul+Mid+Ant | hemi-L_lab-G+and+S+cingul+Mid+Post | hemi-L_lab-G+cingul+Post+dorsal | hemi-L_lab-G+cingul+Post+ventral | ... | hemi-R_lab-S+parieto+occipital | hemi-R_lab-S+pericallosal | hemi-R_lab-S+postcentral | hemi-R_lab-S+precentral+inf+part | hemi-R_lab-S+precentral+sup+part | hemi-R_lab-S+suborbital | hemi-R_lab-S+subparietal | hemi-R_lab-S+temporal+inf | hemi-R_lab-S+temporal+sup | hemi-R_lab-S+temporal+transverse | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| nw-Aud | -0.119025 | 0.030076 | 0.138495 | 0.439149 | -0.124080 | -0.081502 | 0.103589 | 0.160210 | -0.159586 | -0.049191 | ... | 0.023141 | -0.043084 | 0.206432 | -0.036749 | 0.130652 | -0.064339 | -0.152281 | -0.057003 | 0.070081 | 0.613958 |
| nw-CO | -0.134179 | -0.000936 | 0.029942 | 0.222530 | -0.244543 | -0.094110 | 0.458779 | 0.218662 | -0.311963 | -0.284088 | ... | 0.012664 | 0.093555 | 0.127784 | 0.200611 | 0.180290 | -0.324147 | -0.208874 | -0.086683 | -0.029506 | 0.141854 |
2 rows × 148 columns
Neurosynth term maps ("neurosynth")#
The neurosynth dataset is only available in tabulated format, so you always have to pass a parcellation.
Collections:
"CognitiveFunctions": manually curated and categorized list of terms assoicated to broad cognitive domains
[12]:
# Get all genes for a given parcellation
neurosynth_tab = fetch_reference(
"neurosynth",
parcellation="Schaefer200TianS2",
)
display(f"{neurosynth_tab.shape[0]} terms x {neurosynth_tab.shape[1]} parcels", neurosynth_tab.head(5))
INFO | 16/06/25 09:25:25 | nispace: Loading neurosynth maps.
INFO | 16/06/25 09:25:25 | nispace: Loading and inner-merging data parcellated with 'Schaefer200' and 'TianS2'
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
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
'1334 terms x 232 parcels'
| hemi-L_div-Vis_lab-1 | hemi-L_div-Vis_lab-2 | hemi-L_div-Vis_lab-3 | hemi-L_div-Vis_lab-4 | hemi-L_div-Vis_lab-5 | hemi-L_div-Vis_lab-6 | hemi-L_div-Vis_lab-7 | hemi-L_div-Vis_lab-8 | hemi-L_div-Vis_lab-9 | hemi-L_div-Vis_lab-10 | ... | hemi-R_lab-THA+VA | hemi-R_lab-THA+DA | hemi-R_lab-NAc+shell | hemi-R_lab-NAc+core | hemi-R_lab-pGP | hemi-R_lab-aGP | hemi-R_lab-aPUT | hemi-R_lab-pPUT | hemi-R_lab-aCAU | hemi-R_lab-pCAU | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| abilities | 0.362992 | 0.411401 | 0.473653 | 0.819001 | 0.198036 | 0.978645 | 0.842301 | -0.422545 | 0.025033 | 0.223432 | ... | -0.508977 | -0.620085 | -1.338172 | -1.041403 | -0.323786 | -0.699422 | -1.209605 | -0.659254 | -1.620233 | -0.880869 |
| ability | 0.395189 | 0.187913 | 0.393415 | 0.023410 | 0.292252 | 0.517031 | 0.789485 | 0.213077 | 0.331388 | 0.738013 | ... | -0.497820 | -1.016025 | -2.251947 | -0.965339 | -1.071935 | -1.210142 | -1.187464 | -1.276916 | -0.446526 | -0.083585 |
| abstract | 0.019574 | 0.929748 | 3.285519 | 0.244171 | 0.978648 | -0.320235 | 1.881808 | 1.375193 | 1.245818 | 0.474525 | ... | -2.064765 | -0.921057 | -0.156552 | -0.468877 | -1.293330 | -0.983690 | -1.116213 | -0.780750 | -1.638573 | -1.596848 |
| abuse | -0.558509 | -1.086712 | -1.174378 | -0.249232 | -0.668564 | 1.143069 | -0.570804 | -1.053871 | -0.414536 | -0.144280 | ... | 0.827074 | 1.281954 | 3.376993 | 2.575085 | 2.509335 | 1.737999 | 1.600366 | -0.252831 | 2.204873 | 1.506879 |
| acc | -1.210659 | -1.947663 | -1.935868 | -0.380162 | -1.423609 | -0.493732 | -0.748570 | -2.050332 | -1.402867 | -0.470303 | ... | 1.869581 | 0.418041 | 0.671167 | 1.717349 | -0.586445 | -0.096006 | 0.913634 | 0.185387 | 0.971623 | -0.433070 |
5 rows × 232 columns
Collections will subset and sort terms.
[13]:
# Get all genes for a given parcellation
neurosynth_tab = fetch_reference(
"neurosynth",
parcellation="Schaefer200TianS2",
collection="CognitiveFunctions"
)
display(f"{neurosynth_tab.shape[0]} genes x {neurosynth_tab.shape[1]} parcels", neurosynth_tab.head(5))
INFO | 16/06/25 09:25:25 | nispace: Loading neurosynth maps.
INFO | 16/06/25 09:25:25 | nispace: Applying collection filter from: /Users/llotter/nispace-data/reference/neurosynth/collection-CognitiveFunctions.collect.
INFO | 16/06/25 09:25:25 | nispace: Loading and inner-merging data parcellated with 'Schaefer200' and 'TianS2'
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
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
'101 genes x 232 parcels'
| hemi-L_div-Vis_lab-1 | hemi-L_div-Vis_lab-2 | hemi-L_div-Vis_lab-3 | hemi-L_div-Vis_lab-4 | hemi-L_div-Vis_lab-5 | hemi-L_div-Vis_lab-6 | hemi-L_div-Vis_lab-7 | hemi-L_div-Vis_lab-8 | hemi-L_div-Vis_lab-9 | hemi-L_div-Vis_lab-10 | ... | hemi-R_lab-THA+VA | hemi-R_lab-THA+DA | hemi-R_lab-NAc+shell | hemi-R_lab-NAc+core | hemi-R_lab-pGP | hemi-R_lab-aGP | hemi-R_lab-aPUT | hemi-R_lab-pPUT | hemi-R_lab-aCAU | hemi-R_lab-pCAU | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| set | map | |||||||||||||||||||||
| Perception | perception | 1.133071 | 3.301463 | 4.446642 | 0.212208 | 1.815583 | -1.475900 | 0.358515 | 5.493434 | 2.475737 | -0.202547 | ... | 0.100313 | 0.311329 | -2.674099 | -1.880081 | -0.173204 | 0.542946 | 0.142748 | 0.280095 | -1.960971 | -1.473937 |
| visual | 5.890478 | 7.901569 | 8.199807 | 5.368539 | 6.184002 | 1.743208 | 7.003493 | 6.539099 | 7.760546 | 5.723816 | ... | -1.944004 | -0.140336 | -5.345172 | -4.911377 | -2.240981 | -3.005180 | -3.477686 | -2.754033 | -3.630711 | -2.689753 | |
| visuospatial | 1.006021 | 1.249790 | 1.218041 | 1.212403 | -0.096277 | 0.073850 | 0.927427 | 1.782185 | 2.118819 | 0.272614 | ... | -1.269447 | -0.437938 | -2.301355 | -0.998670 | -1.118729 | -1.852602 | -1.699228 | -0.953515 | -1.389019 | -0.730410 | |
| auditory | -0.883764 | -1.091530 | -0.854216 | -0.117754 | -0.325813 | -0.861369 | 0.680713 | 0.190991 | 0.163705 | 0.262778 | ... | -0.832790 | -1.889987 | -4.012762 | -3.527764 | 0.048870 | -0.347030 | -1.386799 | -0.001288 | -2.472988 | -1.429422 | |
| olfactory | -1.140938 | -1.159713 | -1.762673 | -0.487174 | -1.150570 | -0.774892 | -0.316994 | -1.440115 | -1.305542 | -0.840673 | ... | 0.083408 | -0.104390 | 0.946097 | -0.309021 | 2.983119 | 2.371118 | 1.089475 | 1.433120 | -0.586631 | -0.358755 |
5 rows × 232 columns
Features of cortex topology ("cortexfeatures")#
The
cortexfeatures dataset is a collection of features of cortical topology maps from neuromaps.The maps are only available in surface spaces, but parcellated data can of course easily be brought together with MNI input volumes.
Collections:
"MEG": MEG power maps published in Shafiei et al., 2022 (https://doi.org/10.1371/journal.pbio.3001735)"Metabolism": Metabolism maps published in Vaishnavi et al., 2010 (https://doi.org/10.1073/pnas.1010459107)"CortexOrganisation": Different maps of cortex structure (thickness, t1/t2), organisation (sensory-association axis), and development (expansion, species homology)
Note that for two expansion maps, there’s only right hemisphere data available.
[14]:
# Get all genes for a given parcellation
cortexfeatures_maps = fetch_reference(
"cortexfeatures",
space="fsLR",
)
print("Number of cortexfeatures maps:", len(cortexfeatures_maps))
print("First map path:", cortexfeatures_maps[0])
INFO | 16/06/25 09:25:25 | nispace: Loading cortexfeatures maps.
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)
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
Number of cortexfeatures maps: 23
First map path: (PosixPath('/Users/llotter/nispace-data/reference/cortexfeatures/map/feature-thickness_pub-hcps1200/feature-thickness_pub-hcps1200_space-fsLR_hemi-L.surf.gii.gz'), PosixPath('/Users/llotter/nispace-data/reference/cortexfeatures/map/feature-thickness_pub-hcps1200/feature-thickness_pub-hcps1200_space-fsLR_hemi-R.surf.gii.gz'))
Collections will subset and sort terms; cortex parcellations are available.
[15]:
# Get Shafiei et al. MEG Power maps in
cortexfeatures_tab = fetch_reference(
"cortexfeatures",
space="fsLR",
collection="MEG",
parcellation="HCP"
)
display(f"{cortexfeatures_tab.shape[0]} maps", cortexfeatures_tab.head(5))
INFO | 16/06/25 09:25:25 | nispace: Loading cortexfeatures maps.
INFO | 16/06/25 09:25:25 | nispace: Applying collection filter from: /Users/llotter/nispace-data/reference/cortexfeatures/collection-MEG.collect.
INFO | 16/06/25 09:25:25 | nispace: Loading data parcellated with 'Glasser'
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)
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
'7 maps'
| hemi-L_lab-V1 | hemi-L_lab-MST | hemi-L_lab-V6 | hemi-L_lab-V2 | hemi-L_lab-V3 | hemi-L_lab-V4 | hemi-L_lab-V8 | hemi-L_lab-4 | hemi-L_lab-3b | hemi-L_lab-FEF | ... | hemi-R_lab-p47r | hemi-R_lab-TGv | hemi-R_lab-MBelt | hemi-R_lab-LBelt | hemi-R_lab-A4 | hemi-R_lab-STSva | hemi-R_lab-TE1m | hemi-R_lab-PI | hemi-R_lab-a32pr | hemi-R_lab-p24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| feature-megpoweralpha_pub-shafiei2022 | 0.387189 | 0.371029 | 0.419728 | 0.393263 | 0.397562 | 0.379381 | 0.373574 | 0.280895 | 0.307106 | 0.243195 | ... | NaN | 0.258090 | 0.326856 | 0.335782 | 0.318433 | 0.289083 | 0.309614 | 0.279493 | 0.146504 | 0.194861 |
| feature-megpowerbeta_pub-shafiei2022 | 0.081581 | 0.084571 | 0.092286 | 0.083078 | 0.085123 | 0.084859 | 0.082015 | 0.123797 | 0.125518 | 0.117502 | ... | NaN | 0.082419 | 0.088494 | 0.091363 | 0.092477 | 0.083383 | 0.082821 | 0.085638 | 0.070595 | 0.095166 |
| feature-megpowerdelta_pub-shafiei2022 | 0.287946 | 0.285411 | 0.253132 | 0.283037 | 0.278647 | 0.290103 | 0.295168 | 0.291391 | 0.277593 | 0.314561 | ... | NaN | 0.372782 | 0.320389 | 0.310155 | 0.318652 | 0.354477 | 0.337920 | 0.358141 | 0.247408 | 0.349247 |
| feature-megpowergamma1_pub-shafiei2022 | 0.016545 | 0.016488 | 0.016378 | 0.016892 | 0.017763 | 0.018468 | 0.018105 | 0.023799 | 0.022541 | 0.023665 | ... | NaN | 0.029115 | 0.017564 | 0.015964 | 0.020731 | 0.021669 | 0.021285 | 0.022166 | 0.016908 | 0.023213 |
| feature-megpowergamma2_pub-shafiei2022 | 0.009168 | 0.009479 | 0.007795 | 0.009303 | 0.009853 | 0.010531 | 0.010682 | 0.009163 | 0.008926 | 0.009566 | ... | NaN | 0.022181 | 0.009675 | 0.008285 | 0.013160 | 0.014524 | 0.014891 | 0.014345 | 0.007601 | 0.010786 |
5 rows × 360 columns
BigBrain features ("bigbrain")#
The bigbrain dataset is adopted from the BigBrainWarp toolbox (https://bigbrainwarp.readthedocs.io) by Paquola et al. See the automatically printed info below for more details.
Collections:
"CorticalLayers": Different maps of cortical layer thickness (1-6) published in Wagstyl et al., 2020 (https://doi.org/10.1371/journal.pbio.3000678)"DifferentiationGradients": Gradient maps of cortical differentiation included with the BigBrainWarp toolbox"feature-histogradient1/2": first two eigenvectors of cytoarchitectural differentiation derived from BigBrain"feature-microgradient1/2": first two eigenvector of microstructural differentiation derived from quantitative in-vivo T1 imaging"feature-funcgradient1/2/3": first three eigenvectors of functional differentiation derived from rs-fMRI
Note that for two expansion maps, there’s only right hemisphere data available.
[20]:
# Get all genes for a given parcellation
bigbrain_maps = fetch_reference(
"bigbrain",
space="fsaverage",
)
print("Number of bigbrain maps:", len(bigbrain_maps))
print("First map path:", bigbrain_maps[0])
INFO | 16/06/25 09:27:25 | nispace: Loading bigbrain maps.
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 licensed under the CC BY-NC-SA 4.0 license.
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)
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
Number of bigbrain maps: 13
First map path: (PosixPath('/Users/llotter/nispace-data/reference/bigbrain/map/feature-layer1_pub-wagstyl2020/feature-layer1_pub-wagstyl2020_space-fsaverage_hemi-L.shape.gii.gz'), PosixPath('/Users/llotter/nispace-data/reference/bigbrain/map/feature-layer1_pub-wagstyl2020/feature-layer1_pub-wagstyl2020_space-fsaverage_hemi-R.shape.gii.gz'))
Collections will subset and sort terms; cortex parcellations are available.
[21]:
# Get cortical layer thickness maps
bigbrain_tab = fetch_reference(
"bigbrain",
space="fsaverage",
collection="DifferentiationGradients",
parcellation="Schaefer200"
)
display(f"{bigbrain_tab.shape[0]} maps", bigbrain_tab.head(5))
INFO | 16/06/25 09:27:27 | nispace: Loading bigbrain maps.
INFO | 16/06/25 09:27:27 | nispace: Applying collection filter from: /Users/llotter/nispace-data/reference/bigbrain/collection-DifferentiationGradients.collect.
INFO | 16/06/25 09:27:27 | nispace: Loading data parcellated with 'Schaefer200'
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 licensed under the CC BY-NC-SA 4.0 license.
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)
To ensure reproducibility, note the NiSpace commit/version: 9887eac15f2dba714de4f9344cfeb39cf7420e2b
'7 maps'
| hemi-L_div-Vis_lab-1 | hemi-L_div-Vis_lab-2 | hemi-L_div-Vis_lab-3 | hemi-L_div-Vis_lab-4 | hemi-L_div-Vis_lab-5 | hemi-L_div-Vis_lab-6 | hemi-L_div-Vis_lab-7 | hemi-L_div-Vis_lab-8 | hemi-L_div-Vis_lab-9 | hemi-L_div-Vis_lab-10 | ... | hemi-R_div-Default_lab-PFCm+1 | hemi-R_div-Default_lab-PFCm+2 | hemi-R_div-Default_lab-PFCm+3 | hemi-R_div-Default_lab-PFCm+4 | hemi-R_div-Default_lab-PFCm+5 | hemi-R_div-Default_lab-PFCm+6 | hemi-R_div-Default_lab-PFCm+7 | hemi-R_div-Default_lab-PCC+1 | hemi-R_div-Default_lab-PCC+2 | hemi-R_div-Default_lab-PCC+3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| map | |||||||||||||||||||||
| feature-histogradient1_pub-paquola2021 | 0.396202 | 0.598884 | 0.326065 | 0.631844 | 0.274718 | 0.481230 | 0.041881 | 0.352943 | 0.509418 | 0.240793 | ... | -0.238716 | -0.425919 | -0.108872 | -0.357142 | -0.243042 | -0.394422 | -0.391514 | 0.327984 | 0.008510 | 0.138938 |
| feature-histogradient2_pub-paquola2021 | -0.199851 | 0.133102 | 0.092499 | 0.015694 | 0.222189 | -0.317501 | 0.379477 | 0.182478 | 0.270283 | 0.231073 | ... | -0.307540 | -0.130802 | -0.186044 | -0.189851 | -0.181120 | -0.053847 | 0.068960 | -0.031519 | 0.095256 | 0.026301 |
| feature-microgradient1_pub-paquola2021 | -0.008298 | 0.007619 | -0.068237 | 0.059946 | 0.018820 | 0.031213 | 0.108157 | -0.011606 | 0.062248 | 0.108594 | ... | -0.076054 | -0.093718 | -0.101298 | -0.087163 | -0.080423 | -0.063367 | -0.039383 | -0.011067 | -0.056551 | -0.012604 |
| feature-microgradient2_pub-paquola2021 | 0.008824 | -0.013949 | -0.016407 | -0.009944 | -0.018447 | 0.003640 | -0.020313 | -0.007329 | -0.017412 | -0.012929 | ... | -0.007570 | -0.019199 | -0.014719 | -0.011837 | -0.007193 | -0.006115 | 0.004094 | 0.010782 | -0.001166 | 0.008716 |
| feature-funcgradient1_pub-paquola2021 | 0.198735 | 0.210743 | 0.245741 | 0.247181 | 0.129999 | 0.051108 | 0.193364 | 0.222374 | 0.209818 | 0.218404 | ... | -0.768585 | -0.536115 | -0.232727 | -0.611842 | -0.513837 | -0.249603 | -0.264178 | -0.164339 | -0.458201 | -0.039034 |
5 rows × 200 columns
Fetch integrated parcellations#
We have some volumetric and surface parcellations integrated. These can be loaded via nispace.datasets.fetch_parcellation().
The first argument should be a string with the parcellation’s name. The other arguments, space, n_parcels, resolution, and hemi are used to find the matching parcellation. If no parcellation matching all criteria is found, an error message with relevant info will be returned.
If a parcellation was found, the function will return the path to the parcellation (hemispheres) and a list of labels. See API reference for detailed info.
Currently available:
"Schaefer{100 | 200 | 400}"(MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR)"TianS{1 | 2 | 3}"(MNI152NLin6Asym, MNI152NLin2009cAsym)"Glasser"(fsaverage, fsLR)"Destrieux"(MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR)"DesikanKilliany"(MNI152NLin6Asym, MNI152NLin2009cAsym, fsaverage, fsLR)"DesikanKillianyTourville"(MNI152NLin6Asym, MNI152NLin2009cAsym)
Cortex and subcortex parcellations can be combined on-the-fly. This is very easily achieved by passing two parcellation names as one string, i.e.:
"DesikanKillianyAseg": Combination of Desikan-Killiany and Aseg parcellation (MNI152NLin6Asym, MNI152NLin2009cAsym)"Schaefer200TianS1": Combination of Schaefer200 and TianS1 parcellation (MNI152NLin6Asym, MNI152NLin2009cAsym)
Only cortex-subcortex combinations are allowed.
Some parcellations have “alias” names. This just means that different names will refer to the same parcellation.
"DesikanKilliany":"Desikan""DesikanKillianyTourville":"DKT""Glasser":"HCP","MMP""TianS{1 | 2 | 3}":"MelbourneS{1 | 2 | 3}"
The parcellation name strings are the same as those that can be passed to the parcellation argument of the fetch_reference function above. Calling cortex-subcortex combinations will result in joined dataframes
[16]:
from nispace.datasets import fetch_parcellation
# for plotting
from nispace.datasets import fetch_template, parcellation_lib
from nilearn.plotting import plot_roi, plot_surf_roi
import matplotlib.pyplot as plt
for parc_name in parcellation_lib:
print("Parcellation:", parc_name)
if "alias" in parcellation_lib[parc_name]:
print("Alias for:", parcellation_lib[parc_name]["alias"])
continue
parc, labels, space = fetch_parcellation(
parc_name,
return_space=True,
return_loaded=True
)
print("Type:", type(parc))
print("Space:", space)
print("First 5 labels:", labels[:5])
# volumetric
if "mni152" in space.lower():
plot_roi(parc, title=parc_name)
# surface -> plot only left hemisphere
else:
plot_surf_roi(fetch_template("fsaverage", hemi="L"), roi_map=parc[0].agg_data())
plt.show()
Parcellation: Schaefer100
INFO | 15/06/25 15:57:21 | nispace: Loading cortex parcellation 'Schaefer100' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_div-Vis_lab-1', 'hemi-L_div-Vis_lab-2', 'hemi-L_div-Vis_lab-3', 'hemi-L_div-Vis_lab-4', 'hemi-L_div-Vis_lab-5']
Parcellation: Schaefer200
INFO | 15/06/25 15:57:22 | nispace: Loading cortex parcellation 'Schaefer200' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_div-Vis_lab-1', 'hemi-L_div-Vis_lab-2', 'hemi-L_div-Vis_lab-3', 'hemi-L_div-Vis_lab-4', 'hemi-L_div-Vis_lab-5']
Parcellation: Schaefer400
INFO | 15/06/25 15:57:23 | nispace: Loading cortex parcellation 'Schaefer400' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_div-Vis_lab-1', 'hemi-L_div-Vis_lab-2', 'hemi-L_div-Vis_lab-3', 'hemi-L_div-Vis_lab-4', 'hemi-L_div-Vis_lab-5']
Parcellation: TianS1
INFO | 15/06/25 15:57:24 | nispace: Loading subcortex parcellation 'TianS1' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-HIP', 'hemi-L_lab-AMY', 'hemi-L_lab-pTHA', 'hemi-L_lab-aTHA', 'hemi-L_lab-NAc']
Parcellation: MelbourneS1
Alias for: TianS1
Parcellation: TianS2
INFO | 15/06/25 15:57:25 | nispace: Loading subcortex parcellation 'TianS2' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-aHIP', 'hemi-L_lab-pHIP', 'hemi-L_lab-lAMY', 'hemi-L_lab-mAMY', 'hemi-L_lab-THA+DP']
Parcellation: MelbourneS2
Alias for: TianS2
Parcellation: TianS3
INFO | 15/06/25 15:57:26 | nispace: Loading subcortex parcellation 'TianS3' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-HIP+head+m', 'hemi-L_lab-HIP+head+l', 'hemi-L_lab-HIP+body', 'hemi-L_lab-HIP+tail', 'hemi-L_lab-THA+VPm']
Parcellation: MelbourneS3
Alias for: TianS3
Parcellation: Glasser
INFO | 15/06/25 15:57:26 | nispace: Loading cortex parcellation 'Glasser' in 'fsaverage' space.
Type: <class 'tuple'>
Space: fsaverage
First 5 labels: ['hemi-L_lab-V1', 'hemi-L_lab-MST', 'hemi-L_lab-V6', 'hemi-L_lab-V2', 'hemi-L_lab-V3']
INFO | 15/06/25 15:57:26 | nispace: Loading fsaverage 'pial' template in '41k' resolution.
Parcellation: HCP
Alias for: Glasser
Parcellation: MMP
Alias for: Glasser
Parcellation: DesikanKilliany
INFO | 15/06/25 15:57:29 | nispace: Loading cortex parcellation 'DesikanKilliany' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-bankssts', 'hemi-L_lab-caudalanteriorcingulate', 'hemi-L_lab-caudalmiddlefrontal', 'hemi-L_lab-cuneus', 'hemi-L_lab-entorhinal']
Parcellation: Desikan
Alias for: DesikanKilliany
Parcellation: DesikanKillianyTourville
INFO | 15/06/25 15:57:30 | nispace: Loading cortex parcellation 'DesikanKillianyTourville' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-caudalanteriorcingulate', 'hemi-L_lab-caudalmiddlefrontal', 'hemi-L_lab-cuneus', 'hemi-L_lab-entorhinal', 'hemi-L_lab-fusiform']
Parcellation: DKT
Alias for: DesikanKillianyTourville
Parcellation: Destrieux
INFO | 15/06/25 15:57:31 | nispace: Loading cortex parcellation 'Destrieux' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-G+and+S+frontomargin', 'hemi-L_lab-G+and+S+occipital+inf', 'hemi-L_lab-G+and+S+paracentral', 'hemi-L_lab-G+and+S+subcentral', 'hemi-L_lab-G+and+S+transv+frontopol']
Parcellation: Aseg
INFO | 15/06/25 15:57:31 | nispace: Loading subcortex parcellation 'Aseg' in 'MNI152NLin2009cAsym' space.
Type: <class 'nibabel.nifti1.Nifti1Image'>
Space: MNI152NLin2009cAsym
First 5 labels: ['hemi-L_lab-Thalamus', 'hemi-L_lab-Caudate', 'hemi-L_lab-Putamen', 'hemi-L_lab-Pallidum', 'hemi-L_lab-Hippocampus']
Fetch example datasets#
We include a few example datasets for evaluation and exploration purposes. Available:
"happy"(CAVE: DUMMY DATA): n = 100 fake subjects in two groups. The first 50 are “happy” subjects (sub-001H - sub-050H), generated from CB1 and MU-opioid receptor PET maps. The second half are “neutral”/control subjects, each generated from a mixture of other PET maps. For analyses, they can be treated as two independent groups or two scans of the same subject."abide": fALFF resting-state fMRI data from the ABIDE-I dataset, fetched from nilearn and parcellated. When fetching this, you will receive an associated table with subject information."enigma": ENIGMA cortical thickness case-control results. Valued are Cohen’s d per parcel.
To fetch an example dataset, use the fetch_example function. Currently, only parcellated data is available, so you have to specify the parcellation. For ENIGMA, there’s only the "DesikanKilliany" parcellation, some more for the other two datasets.
[17]:
from nispace.datasets import fetch_example
print("Happy data")
tab_example = fetch_example("happy", "Glasser")
display(tab_example.head(5))
print("ABIDE data")
tab_example, info_example = fetch_example("abide", "Schaefer200TianS1")
display(tab_example.head(5))
display(info_example.head(5))
print("ENIGMA data")
tab_example = fetch_example("enigma", "DesikanKilliany")
display(tab_example)
Happy data
INFO | 15/06/25 15:57:32 | nispace: Loading example dataset: 'happy', parcellated with: Glasser.
| hemi-L_lab-V1 | hemi-L_lab-MST | hemi-L_lab-V6 | hemi-L_lab-V2 | hemi-L_lab-V3 | hemi-L_lab-V4 | hemi-L_lab-V8 | hemi-L_lab-4 | hemi-L_lab-3b | hemi-L_lab-FEF | ... | hemi-R_lab-p47r | hemi-R_lab-TGv | hemi-R_lab-MBelt | hemi-R_lab-LBelt | hemi-R_lab-A4 | hemi-R_lab-STSva | hemi-R_lab-TE1m | hemi-R_lab-PI | hemi-R_lab-a32pr | hemi-R_lab-p24 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sub-001H | 0.336429 | 0.471550 | 0.455314 | 0.362316 | 0.333990 | 0.381957 | 0.437665 | 0.381304 | 0.490977 | 0.555933 | ... | 0.630642 | 0.595949 | 0.530645 | 0.497731 | 0.552863 | 0.592239 | 0.550500 | 0.571144 | 0.641664 | 0.672440 |
| sub-002H | 0.360431 | 0.499942 | 0.481051 | 0.387166 | 0.359117 | 0.410283 | 0.473021 | 0.399784 | 0.514548 | 0.580300 | ... | 0.664077 | 0.637451 | 0.564991 | 0.525941 | 0.584053 | 0.631199 | 0.586171 | 0.615562 | 0.682176 | 0.712676 |
| sub-003H | 0.227936 | 0.356384 | 0.332664 | 0.250524 | 0.230877 | 0.266542 | 0.314204 | 0.278995 | 0.355711 | 0.430758 | ... | 0.498984 | 0.464809 | 0.407976 | 0.377520 | 0.422245 | 0.465237 | 0.435265 | 0.438843 | 0.510712 | 0.526831 |
| sub-004H | 0.334931 | 0.482154 | 0.453596 | 0.360782 | 0.336447 | 0.387239 | 0.450365 | 0.386952 | 0.491461 | 0.565563 | ... | 0.646925 | 0.624795 | 0.543489 | 0.504370 | 0.563390 | 0.616762 | 0.568944 | 0.602902 | 0.679163 | 0.708075 |
| sub-005H | 0.310893 | 0.466165 | 0.429950 | 0.336922 | 0.315447 | 0.364915 | 0.427671 | 0.376783 | 0.473257 | 0.553516 | ... | 0.632794 | 0.607021 | 0.524912 | 0.486204 | 0.545657 | 0.600055 | 0.551947 | 0.586539 | 0.671353 | 0.698421 |
5 rows × 360 columns
ABIDE data
INFO | 15/06/25 15:57:32 | nispace: Loading example dataset: 'abide', parcellated with: Schaefer200TianS1.
INFO | 15/06/25 15:57:32 | nispace: Returning parcellated and associated subject data.
| hemi-L_div-Vis_lab-1 | hemi-L_div-Vis_lab-2 | hemi-L_div-Vis_lab-3 | hemi-L_div-Vis_lab-4 | hemi-L_div-Vis_lab-5 | hemi-L_div-Vis_lab-6 | hemi-L_div-Vis_lab-7 | hemi-L_div-Vis_lab-8 | hemi-L_div-Vis_lab-9 | hemi-L_div-Vis_lab-10 | ... | hemi-L_lab-PUT | hemi-L_lab-CAU | hemi-R_lab-HIP | hemi-R_lab-AMY | hemi-R_lab-pTHA | hemi-R_lab-aTHA | hemi-R_lab-NAc | hemi-R_lab-GP | hemi-R_lab-PUT | hemi-R_lab-CAU | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| subject | |||||||||||||||||||||
| 50003 | 1530.1179 | 1677.4820 | 1607.0056 | 1508.5145 | 1187.09780 | 1510.6517 | 1534.3547 | 1513.21020 | 1557.09070 | 1394.90080 | ... | 1288.88000 | 1246.14330 | 1061.72600 | 1057.3959 | 1185.1282 | 1366.7485 | 1374.32760 | 1028.86320 | 1220.36430 | 1366.8842 |
| 50004 | 1077.5151 | 1094.0139 | 1033.4298 | 1036.3722 | 686.27716 | 1194.0590 | 917.2903 | 1004.15094 | 813.08685 | 1002.22266 | ... | 1011.58075 | 921.38135 | 932.35223 | 891.8384 | 961.8646 | 942.3631 | 1018.32367 | 854.32776 | 956.10596 | 943.5990 |
| 50005 | 1287.0707 | 1186.0944 | 1024.3271 | 1388.4406 | 814.70250 | 1461.1589 | 1083.4020 | 1166.30380 | 946.10547 | 1254.68530 | ... | 1273.04880 | 1111.30180 | 1052.04750 | 1133.1693 | 1140.8767 | 1122.5364 | 1142.63590 | 993.38480 | 1309.49410 | 1070.8911 |
| 50006 | 1301.8971 | 1094.0586 | 991.6907 | 1503.6521 | 751.03217 | 1558.8677 | 1003.2750 | 1206.68430 | 955.63060 | 1779.52200 | ... | 1111.70310 | 1024.06250 | 1161.70690 | 1230.7429 | 1103.4869 | 1058.5040 | 929.65420 | 929.08440 | 1046.40090 | 1028.6166 |
| 50007 | 1503.5083 | 1181.2339 | 1113.0653 | 1403.4066 | 838.12440 | 1385.6886 | 1114.9324 | 1288.69320 | 946.09924 | 1222.88730 | ... | 1196.67370 | 1202.98320 | 1167.58180 | 1088.9086 | 1052.7567 | 1034.6626 | 1227.07100 | 994.04517 | 1185.00150 | 1214.7137 |
5 rows × 216 columns
| site | site_num | dx | dx_num | dsm_iv_tr | age | sex | sex_num | qc_rater_1 | qc_func_rater_2 | qc_func_rater_3 | adi_r_social_total_a | adi_r_verbal_total_bv | adi_rrb_total_c | ados_total | srs_raw_total | scq_total | aq_total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| subject | ||||||||||||||||||
| 50003 | PITT | 9 | ASD | 1 | 1.0 | 24.45 | M | 1 | OK | OK | OK | 27.0 | 22.0 | 5.0 | 13.0 | NaN | NaN | NaN |
| 50004 | PITT | 9 | ASD | 1 | 1.0 | 19.09 | M | 1 | OK | OK | OK | 19.0 | 12.0 | 5.0 | 18.0 | NaN | NaN | NaN |
| 50005 | PITT | 9 | ASD | 1 | 1.0 | 13.73 | F | 2 | OK | maybe | OK | 23.0 | 19.0 | 3.0 | 12.0 | NaN | NaN | NaN |
| 50006 | PITT | 9 | ASD | 1 | 1.0 | 13.37 | M | 1 | OK | maybe | OK | 13.0 | 10.0 | 4.0 | 12.0 | NaN | NaN | NaN |
| 50007 | PITT | 9 | ASD | 1 | 1.0 | 17.78 | M | 1 | OK | maybe | OK | 21.0 | 14.0 | 9.0 | 17.0 | NaN | NaN | NaN |
ENIGMA data
INFO | 15/06/25 15:57:32 | nispace: Loading example dataset: 'enigma', parcellated with: DesikanKilliany.
| L_bankssts | L_caudalanteriorcingulate | L_caudalmiddlefrontal | L_cuneus | L_entorhinal | L_fusiform | L_inferiorparietal | L_inferiortemporal | L_isthmuscingulate | L_lateraloccipital | ... | R_rostralanteriorcingulate | R_rostralmiddlefrontal | R_superiorfrontal | R_superiorparietal | R_superiortemporal | R_supramarginal | R_frontalpole | R_temporalpole | R_transversetemporal | R_insula | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MDD | -0.058 | -0.042 | -0.014 | 0.047 | -0.041 | -0.117 | -0.063 | -0.049 | -0.104 | -0.023 | ... | -0.098 | -0.038 | -0.078 | 0.032 | -0.031 | -0.053 | -0.062 | 0.013 | -0.051 | -0.115 |
| PTSD | -0.100 | -0.100 | -0.120 | -0.070 | 0.050 | -0.060 | -0.140 | -0.070 | -0.020 | -0.150 | ... | 0.010 | -0.100 | -0.120 | -0.120 | -0.140 | -0.150 | -0.100 | -0.020 | -0.050 | -0.110 |
| AN | -0.738 | -0.065 | -0.760 | -0.663 | 0.060 | -0.538 | -0.895 | -0.537 | -0.620 | -0.747 | ... | -0.003 | -0.507 | -0.722 | -0.925 | -0.522 | -0.756 | -0.332 | -0.055 | -0.258 | -0.339 |
| ADHD | 0.000 | -0.040 | -0.050 | 0.020 | -0.080 | -0.100 | 0.010 | -0.030 | 0.030 | 0.030 | ... | -0.010 | 0.000 | 0.000 | 0.010 | 0.000 | -0.020 | 0.010 | -0.120 | 0.010 | -0.050 |
| ASD | 0.000 | 0.020 | 0.050 | 0.060 | -0.150 | NaN | 0.010 | -0.050 | 0.020 | -0.010 | ... | 0.090 | 0.220 | 0.200 | -0.040 | -0.050 | -0.080 | 0.090 | -0.150 | -0.130 | -0.100 |
| OCD | -0.060 | 0.003 | -0.090 | -0.042 | -0.062 | -0.109 | -0.140 | -0.087 | -0.068 | -0.074 | ... | 0.005 | -0.091 | -0.038 | -0.047 | 0.014 | 0.003 | 0.021 | 0.015 | -0.024 | -0.065 |
| BD | -0.207 | -0.095 | -0.266 | -0.056 | -0.036 | -0.288 | -0.265 | -0.250 | -0.132 | -0.156 | ... | -0.087 | -0.264 | -0.256 | -0.158 | -0.194 | -0.184 | -0.102 | -0.059 | -0.109 | -0.168 |
| SCZ | -0.352 | -0.119 | -0.363 | -0.203 | -0.203 | -0.491 | -0.362 | -0.449 | -0.309 | -0.331 | ... | -0.120 | -0.313 | -0.397 | -0.219 | -0.438 | -0.386 | -0.207 | -0.236 | -0.262 | -0.406 |
| 22q11.2 | -0.030 | -0.210 | 0.510 | 0.520 | 0.160 | 0.190 | 0.250 | 0.280 | 0.300 | 0.220 | ... | 0.160 | 0.800 | 0.530 | 0.300 | -0.320 | 0.710 | 0.140 | 0.010 | 0.060 | 0.630 |
| Epilepsy - all | -0.092 | 0.039 | -0.319 | -0.157 | -0.264 | -0.187 | -0.197 | -0.085 | -0.049 | -0.190 | ... | 0.093 | -0.197 | -0.269 | -0.313 | -0.123 | -0.223 | -0.109 | -0.181 | -0.182 | -0.022 |
| Epilepsy - temporal | -0.077 | 0.087 | -0.202 | -0.091 | -0.188 | -0.108 | -0.127 | -0.038 | 0.004 | -0.126 | ... | -0.026 | -0.046 | -0.100 | -0.134 | 0.042 | -0.071 | 0.039 | -0.276 | -0.072 | -0.030 |
| Epilepsy - generalized | -0.100 | 0.181 | -0.403 | -0.190 | -0.445 | -0.359 | -0.242 | -0.207 | -0.021 | -0.311 | ... | 0.200 | -0.242 | -0.365 | -0.474 | -0.165 | -0.303 | -0.100 | -0.033 | -0.161 | 0.069 |
| PD | -0.104 | 0.043 | -0.135 | -0.112 | -0.096 | -0.198 | -0.188 | -0.193 | -0.177 | -0.166 | ... | -0.010 | -0.047 | -0.110 | -0.174 | -0.077 | -0.153 | -0.094 | -0.096 | -0.068 | -0.077 |
13 rows × 68 columns