{
"cells": [
{
"cell_type": "markdown",
"id": "809917e0",
"metadata": {},
"source": [
"# NiSpace workflows\n",
"\n",
"The notebooks so far have built up the NiSpace pipeline step by step — `fit()`, `transform_y()`, `clean_y()`, `colocalize()`, `permute()`, `plot()`. That level of control is great when you need flexibility. But for standard analyses, NiSpace provides **workflow functions** that run the complete pipeline in a single call.\n",
"\n",
"Three workflows are available:\n",
"\n",
"| Function | Question answered | Null model |\n",
"|----------|-------------------|------------|\n",
"| `colocalization()` | Does this map/ Do these maps colocalize with reference maps? | Surrogate map permutation |\n",
"| `group_colocalization()` | Do brain alterations in this group colocalize with reference maps? | Group label permutation |\n",
"| `xsea()` | Does this map/ Do these maps colocalize with predefined sets of reference maps? | Surrogate map permutation |\n",
"| `group_xsea()` | Do brain alterations in this group colocalize with predefined sets of reference maps? | Group label permutation |\n",
"\n",
"All workflows return a fitted `NiSpace` object. Use the `get_*` methods to retrieve results:\n",
"\n",
"```python\n",
"nsp = colocalization(...)\n",
"colocs = nsp.get_colocalizations()\n",
"p_values = nsp.get_p_values()\n",
"q_values = nsp.get_p_values(mc_method=\"meff\") # or any other mc_method\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d279ee5b",
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"execution": {
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"source": [
"import tqdm.notebook\n",
"tqdm.notebook.tqdm = tqdm.tqdm\n",
"\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "5d01c728",
"metadata": {},
"source": [
"## `colocalization()` — single map\n",
"\n",
"The simplest workflow: you have one group-level brain map and want to test whether they colocalize with a set of reference maps."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "927e9865",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.workflows: Loading integrated pet dataset as X data.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.datasets: Loading pet maps.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.datasets: Loading integrated collection 'UniqueTracers' for dataset 'pet'.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.datasets: Filtering maps by collection.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.datasets: Loading data parcellated with 'Schaefer200Parcels7Networks'\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The NiSpace \"PET\" dataset is based on openly available nuclear imaging maps largely accessed via neuromaps \n",
"(https://neuromaps-main.readthedocs.io/). If requested in the varying original spaces and resolutions (termed \"MNI152\", \n",
"\"fsaverageOriginal\", or \"fsLROriginal\"), the maps are downloaded directly from the source and cached locally. If, as is highly \n",
"recommended, the maps are requested in a defined space (\"MNI152NLin2009cAsym\", \"MNI152NLin6Asym\", \"fsaverage\", or \"fsLR\"), \n",
"they are downloaded from the NiSpace-data GitHub repo (find them in `~HOME/nispace-data/reference/pet/map`). \n",
"The NiSpace-hosted MNI maps were directly registered to 2mm MNI152NLin6Asym space, and transformed to 2mm MNI152NLin2009cAsym \n",
"with a pre-estimated MNI-to-MNI transformation using SynthMorph v4 (https://martinos.org/malte/synthmorph/). The resulting maps \n",
"were masked with a liberal grey matter mask generated from the Harvard-Oxford atlas and scaled from 1e-6 to 1. The scaling was \n",
"transferred from MNI to surface maps if both were available for the same source (e.g., maps from Beliveau et al.).\n",
"The accompanying metadata table contains detailed information about tracers, source samples, original publications and data \n",
"sources, as well as the publication licenses. Every map should be cited when used. The responsibility for this lies with the user! \n",
"We additionally ask to cite:\n",
"- Markello et al., 2022 (https://doi.org/10.1038/s41592-022-01625-w)\n",
"- Hansen et al., 2022 (https://doi.org/10.1038/s41593-022-01186-3)\n",
"- Dukart et al., 2021 (https://doi.org/10.1002/hbm.25244)\n",
"- Hoffmann et al., 2024 (https://doi.org/10.1162/imag_a_00197; if NiSpace-processed maps are used)\n",
"To ensure reproducibility, note the NiSpace commit/version: 0.0.1b5.dev68+g20be93445.d20260520\n",
"\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Processed in fsaverage space, use volumetric maps only for subcortex!\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: SPECT, not PET!\n",
"- target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018 Source: Garciagomez2018 free https://doi.org/10.33588/imagendiagnostica.901.2\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016 Source: Wey2016 CC0 1.0 https://doi.org/10.1126/scitranslmed.aaf7551\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Unlike other tracers, [18F]GE-179 binds to open (active) NMDA receptors!\n",
"- target-rCPS_tracer-leucine_n-42_dx-hc_pub-smith2023 Source: Smith2023 CC0 1.0 https://doi.org/10.18112/openneuro.ds004654.v1.0.1, https://doi.org/10.18112/openneuro.ds004730.v1.0.0, https://doi.org/10.18112/openneuro.ds004731.v1.0.0, https://doi.org/10.18112/openneuro.ds004733.v1.0.0, https://doi.org/10.1016/j.nbd.2020.104978, https://doi.org/10.1177/0271678X221090997, https://doi.org/10.1038/jcbfm.2009.7, https://doi.org/10.1093/sleep/zsy088, https://doi.org/10.1177/0271678X221121873\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"\n",
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.api: *** NiSpace.fit() - Data extraction and preparation. ***\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.core.parcellation: Building multi-space Parcellation for 'Schaefer200Parcels7Networks' from library.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.core.parcellation: Available spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage, fsLR\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.core.parcellation: Parcellation 'Schaefer200Parcels7Networks': validation passed.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:10 | nispace.core.parcellation: Lazy-loading parcellation image for space 'MNI152NLin2009cAsym'.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.core.parcellation: Parcellation 'Schaefer200Parcels7Networks': active space set to 'MNI152NLin2009cAsym'.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.api: Checking input data for 'x' (should be, e.g., PET data):\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING | 01/06/26 15:06:11 | nispace.io: Parcellated data contains nan values!\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.api: Got 'x' data for 29 x 200 parcels.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.api: Checking input data for 'y' (should be, e.g., subject data):\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.io: Input type: dict, assuming (img_name, img) pairs for imaging data.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.io: Background (bg) handling: ignoring bg: True (bg value: ['auto', 0.0]); dropping bg parcels: False\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:11 | nispace.io: Parcellating imaging data.\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Parcellating (4 proc): 0%| | 0/1 [00:00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nispace.workflows import colocalization\n",
"from nispace.io import load_img\n",
"\n",
"pain_map = load_img(\"neuroquery/pain.nii.gz\")\n",
"\n",
"nsp = colocalization(\n",
" y={\"Pain\": pain_map}, # dict key becomes the map label; can also be just the image\n",
" x=\"pet\", # string shortcut: automatically fetches the PET reference dataset\n",
" x_collection=\"UniqueTracers\", # dataset collection to use for the PET reference dataset, optional\n",
" parcellation=\"Schaefer200\", # parcellation to use for the analysis\n",
" colocalization_method=\"spearman\", # colocalization method, default is \"spearman\"\n",
" permute_kwargs={\"p_tails\": \"upper\"}, # directional test\n",
" mc_method=\"meff\", # multiple comparison correction method, default is \"meff\" (effective number of tests)\n",
" n_perm=1000, # number of null runs, increase to >=10000 for final analyses\n",
" seed=42, # random seed for reproducibility\n",
" n_proc=4, # number of processes to use for parallelization, default is 1; set to -1 to use all available cores\n",
" return_nispace_only=True # return only the NiSpace object, will be default in future versions\n",
")"
]
},
{
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"execution_count": 3,
"id": "5a5629b5",
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],
"source": [
"# the nsp_object is a fully fitted NiSpace object — you can use all methods on it\n",
"nsp.plot(sort_by=\"coloc\")"
]
},
{
"cell_type": "markdown",
"id": "eb554d63",
"metadata": {},
"source": [
"## `colocalization()` — multiple maps\n",
"\n",
"You have **multiple** brain maps and want to test whether they colocalize with a set of reference maps. \n",
"Here, we have an important parameter, `pooled_p`, that determines if p values are calculated for the average of colocalizations acoss your input maps or if one p value is calculated for each of the input maps.\n",
"\n",
"Examples:\n",
"- `pooled_p=True`: Multiple alterations maps from the same disorder (e.g., subject-level or group-level). \"Do these 5 meta-analytic maps comparing depression to controls colocalize consistently with X?\" \n",
"- `pooled_p=False`: \"How does each of these ENGIMA maps for different disorders correlate spatially with nuclear imaging maps?\""
]
},
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"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Loading enigmathick maps.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Loading data parcellated with 'DesikanKilliany'\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ENIGMA: 22 disorder maps x 68 parcels\n",
"Disorders: ['dx-mdd_age-adult_pub-schmaal2017', 'dx-mdd_age-adolescent_pub-schmaal2017', 'dx-adhd_age-allages_pub-hoogman2019', 'dx-adhd_age-adult_pub-hoogman2019'] ...\n",
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.workflows: Loading integrated pet dataset as X data.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Loading pet maps.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Loading integrated collection 'UniqueTracers' for dataset 'pet'.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Filtering maps by collection.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.datasets: Loading data parcellated with 'DesikanKilliany'\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The NiSpace \"PET\" dataset is based on openly available nuclear imaging maps largely accessed via neuromaps \n",
"(https://neuromaps-main.readthedocs.io/). If requested in the varying original spaces and resolutions (termed \"MNI152\", \n",
"\"fsaverageOriginal\", or \"fsLROriginal\"), the maps are downloaded directly from the source and cached locally. If, as is highly \n",
"recommended, the maps are requested in a defined space (\"MNI152NLin2009cAsym\", \"MNI152NLin6Asym\", \"fsaverage\", or \"fsLR\"), \n",
"they are downloaded from the NiSpace-data GitHub repo (find them in `~HOME/nispace-data/reference/pet/map`). \n",
"The NiSpace-hosted MNI maps were directly registered to 2mm MNI152NLin6Asym space, and transformed to 2mm MNI152NLin2009cAsym \n",
"with a pre-estimated MNI-to-MNI transformation using SynthMorph v4 (https://martinos.org/malte/synthmorph/). The resulting maps \n",
"were masked with a liberal grey matter mask generated from the Harvard-Oxford atlas and scaled from 1e-6 to 1. The scaling was \n",
"transferred from MNI to surface maps if both were available for the same source (e.g., maps from Beliveau et al.).\n",
"The accompanying metadata table contains detailed information about tracers, source samples, original publications and data \n",
"sources, as well as the publication licenses. Every map should be cited when used. The responsibility for this lies with the user! \n",
"We additionally ask to cite:\n",
"- Markello et al., 2022 (https://doi.org/10.1038/s41592-022-01625-w)\n",
"- Hansen et al., 2022 (https://doi.org/10.1038/s41593-022-01186-3)\n",
"- Dukart et al., 2021 (https://doi.org/10.1002/hbm.25244)\n",
"- Hoffmann et al., 2024 (https://doi.org/10.1162/imag_a_00197; if NiSpace-processed maps are used)\n",
"To ensure reproducibility, note the NiSpace commit/version: 0.0.1b5.dev68+g20be93445.d20260520\n",
"\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Processed in fsaverage space, use volumetric maps only for subcortex!\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: SPECT, not PET!\n",
"- target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018 Source: Garciagomez2018 free https://doi.org/10.33588/imagendiagnostica.901.2\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016 Source: Wey2016 CC0 1.0 https://doi.org/10.1126/scitranslmed.aaf7551\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Unlike other tracers, [18F]GE-179 binds to open (active) NMDA receptors!\n",
"- target-rCPS_tracer-leucine_n-42_dx-hc_pub-smith2023 Source: Smith2023 CC0 1.0 https://doi.org/10.18112/openneuro.ds004654.v1.0.1, https://doi.org/10.18112/openneuro.ds004730.v1.0.0, https://doi.org/10.18112/openneuro.ds004731.v1.0.0, https://doi.org/10.18112/openneuro.ds004733.v1.0.0, https://doi.org/10.1016/j.nbd.2020.104978, https://doi.org/10.1177/0271678X221090997, https://doi.org/10.1038/jcbfm.2009.7, https://doi.org/10.1093/sleep/zsy088, https://doi.org/10.1177/0271678X221121873\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"\n",
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.core.parcellation: Building multi-space Parcellation for 'DesikanKilliany' from library.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.core.parcellation: Available spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage, fsLR\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.core.parcellation: Parcellation 'DesikanKilliany': validation passed.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING | 01/06/26 15:06:22 | nispace.io: Parcellated data contains nan values!\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:22 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING | 01/06/26 15:06:22 | nispace.io: Parcellated data contains nan values!\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Colocalizing (spearman, 4 proc): 0%| | 0/22 [00:00"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"pooled_p='mean' → p-values shape: (1, 29)\n",
" 1 pooled row × 29 receptor columns\n"
]
}
],
"source": [
"from nispace.datasets import fetch_reference\n",
"\n",
"# ENIGMA cortical thickness effect sizes — 22 psychiatric disorders, DesikanKilliany parcellation\n",
"# This is a natural multi-map case: we have separate effect size maps per disorder\n",
"enigma = fetch_reference(\"enigmathick\", parcellation=\"DesikanKilliany\", print_references=False)\n",
"print(f\"ENIGMA: {enigma.shape[0]} disorder maps x {enigma.shape[1]} parcels\")\n",
"print(\"Disorders:\", list(enigma.index[:4]), \"...\")\n",
"\n",
"# --- pooled_p=False (default): one p-value per disorder × receptor pair ---\n",
"# Use case: \"how does each disorder's cortical thinning relate to each receptor?\"\n",
"nsp_multi = colocalization(\n",
" y=enigma,\n",
" x=\"pet\",\n",
" x_collection=\"UniqueTracers\",\n",
" parcellation=\"DesikanKilliany\",\n",
" colocalization_method=\"spearman\",\n",
" pooled_p=False, # one p per (Y map, X map) pair — default\n",
" mc_method=\"meff\",\n",
" n_perm=1000, # increase to >=10000 for final analyses\n",
" seed=42, n_proc=4,\n",
" plot=False, # not correctly implemented currently\n",
" return_nispace_only=True\n",
")\n",
"p_per_map = nsp_multi.get_p_values()\n",
"print(f\"\\npooled_p=False → p-values shape: {p_per_map.shape}\")\n",
"print(f\" {enigma.shape[0]} disorder rows × {p_per_map.shape[1]} receptor columns\")\n",
"\n",
"# --- pooled_p=\"mean\": one pooled p-value per X map across all Y maps ---\n",
"# Use case: \"do these disorders collectively and consistently colocalize with each receptor?\"\n",
"nsp_pooled = colocalization(\n",
" y=enigma,\n",
" x=\"pet\",\n",
" x_collection=\"UniqueTracers\",\n",
" parcellation=\"DesikanKilliany\",\n",
" colocalization_method=\"spearman\",\n",
" pooled_p=\"mean\", # average colocs across Y maps → one p per X map\n",
" mc_method=\"meff\",\n",
" n_perm=1000,\n",
" seed=42, n_proc=4,\n",
" plot=True,\n",
" return_nispace_only=True\n",
")\n",
"p_pooled = nsp_pooled.get_p_values()\n",
"print(f\"\\npooled_p='mean' → p-values shape: {p_pooled.shape}\")\n",
"print(f\" 1 pooled row × {p_pooled.shape[1]} receptor columns\")"
]
},
{
"cell_type": "markdown",
"id": "806b91c9",
"metadata": {},
"source": [
"## `group_colocalization()` — patient vs. control\n",
"\n",
"The group comparison workflow handles the full pipeline for individual subject data: covariate regression, effect size computation, colocalization, and group label permutation.\n",
"\n",
"The `design` argument is a DataFrame with one row per subject. The column `\"groups\"` is required; any other columns are treated as covariates to regress out. A `\"site\"` column triggers ComBat harmonization if `combat=True`.\n",
"\n",
"We use the anorexia nervosa example dataset here.\n",
"\n",
"> **Note:** This dataset is **simulated** and not intended for scientific use — it exists purely for demonstration purposes.\n",
"\n",
"This workflow employs group label permutation to generate null maps (as compared to spatial null models). This is a very elegant approach for group comparisons in spatial correlation analyses because general spatial patterns will naturally be conserved in the permuted alteration images, without relying on a null model's ability to capture said spatial patterns."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "52a3024c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-01T13:06:25.058187Z",
"iopub.status.busy": "2026-06-01T13:06:25.058092Z",
"iopub.status.idle": "2026-06-01T13:06:28.571439Z",
"shell.execute_reply": "2026-06-01T13:06:28.571030Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.datasets: Loading example dataset: 'anorexianervosa', parcellated with: Schaefer200Parcels7Networks.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.workflows: Loading integrated pet dataset as X data.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.datasets: Loading pet maps.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.datasets: Loading integrated collection 'UniqueTracers' for dataset 'pet'.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.datasets: Filtering maps by collection.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.datasets: Loading data parcellated with 'Schaefer200Parcels7Networks'\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The NiSpace \"PET\" dataset is based on openly available nuclear imaging maps largely accessed via neuromaps \n",
"(https://neuromaps-main.readthedocs.io/). If requested in the varying original spaces and resolutions (termed \"MNI152\", \n",
"\"fsaverageOriginal\", or \"fsLROriginal\"), the maps are downloaded directly from the source and cached locally. If, as is highly \n",
"recommended, the maps are requested in a defined space (\"MNI152NLin2009cAsym\", \"MNI152NLin6Asym\", \"fsaverage\", or \"fsLR\"), \n",
"they are downloaded from the NiSpace-data GitHub repo (find them in `~HOME/nispace-data/reference/pet/map`). \n",
"The NiSpace-hosted MNI maps were directly registered to 2mm MNI152NLin6Asym space, and transformed to 2mm MNI152NLin2009cAsym \n",
"with a pre-estimated MNI-to-MNI transformation using SynthMorph v4 (https://martinos.org/malte/synthmorph/). The resulting maps \n",
"were masked with a liberal grey matter mask generated from the Harvard-Oxford atlas and scaled from 1e-6 to 1. The scaling was \n",
"transferred from MNI to surface maps if both were available for the same source (e.g., maps from Beliveau et al.).\n",
"The accompanying metadata table contains detailed information about tracers, source samples, original publications and data \n",
"sources, as well as the publication licenses. Every map should be cited when used. The responsibility for this lies with the user! \n",
"We additionally ask to cite:\n",
"- Markello et al., 2022 (https://doi.org/10.1038/s41592-022-01625-w)\n",
"- Hansen et al., 2022 (https://doi.org/10.1038/s41593-022-01186-3)\n",
"- Dukart et al., 2021 (https://doi.org/10.1002/hbm.25244)\n",
"- Hoffmann et al., 2024 (https://doi.org/10.1162/imag_a_00197; if NiSpace-processed maps are used)\n",
"To ensure reproducibility, note the NiSpace commit/version: 0.0.1b5.dev68+g20be93445.d20260520\n",
"\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Processed in fsaverage space, use volumetric maps only for subcortex!\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: SPECT, not PET!\n",
"- target-FDOPA_tracer-fluorodopa_n-12_dx-hc_pub-garciagomez2018 Source: Garciagomez2018 free https://doi.org/10.33588/imagendiagnostica.901.2\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- target-HDAC_tracer-martinostat_n-8_dx-hc_pub-wey2016 Source: Wey2016 CC0 1.0 https://doi.org/10.1126/scitranslmed.aaf7551\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
" CAVE: Unlike other tracers, [18F]GE-179 binds to open (active) NMDA receptors!\n",
"- target-rCPS_tracer-leucine_n-42_dx-hc_pub-smith2023 Source: Smith2023 CC0 1.0 https://doi.org/10.18112/openneuro.ds004654.v1.0.1, https://doi.org/10.18112/openneuro.ds004730.v1.0.0, https://doi.org/10.18112/openneuro.ds004731.v1.0.0, https://doi.org/10.18112/openneuro.ds004733.v1.0.0, https://doi.org/10.1016/j.nbd.2020.104978, https://doi.org/10.1177/0271678X221090997, https://doi.org/10.1038/jcbfm.2009.7, https://doi.org/10.1093/sleep/zsy088, https://doi.org/10.1177/0271678X221121873\n",
"- 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\n",
"- 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\n",
"- 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\n",
"- 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\n",
"\n",
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.parcellation: Building multi-space Parcellation for 'Schaefer200Parcels7Networks' from library.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.parcellation: Available spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage, fsLR\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.parcellation: Parcellation 'Schaefer200Parcels7Networks': validation passed.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING | 01/06/26 15:06:25 | nispace.io: Parcellated data contains nan values!\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.workflows: DataFrame provided for design. Expecting 'groups' and, if paired==True, 'subjects' columns.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.workflows: Design matrix of shape (100, 3). Assuming 100 subjects/maps.\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.clean_y: Detected categorical covariates: none; continuous: ['age', 'sex'].\u001b[0m\n"
]
},
{
"data": {
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l0r2dv/76a7nPA8l1fNmIB7rytB2g9KHuV199hfvuu0+fgW5B3GJ5oMjISPTu3RvVq1fHkSNHMHfuXBQWFmLHjh3lHmsj113X2e1kbA8++CA++ugjZGdnw2w2IzExEZMnT2ZUGuIWi0gAn2MRCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhuVmLl+CgIyNYbmJrKwsvPbaa0hOTkbz5s0xd+5ch5equ6MdO3YAgMuv6PUEDMtN9OnTB2vXrkW/fv0QEhKC8ePHu34tu5to6dKl9mv6eXnxz+hq/I24gdWrVyMtLQ1ffPEFHn/8cZw+fRr9+/dHlSpVsG3bNvvlCNxJeHg4tm7dim3btgGA/R+By2fIefqZcgzLDfzyyy9o164dgoODMWXKFOTn5+O5554DABw7dgyvvfaaw6XB9KRKr0WJtm3bolmzZli4cCGAPy8Qc/kaFFLvoGIUPLvdDcTExGDx4sXIycnBtGnTMGvWLAQHBwMA/ve//8FsNv/lZc1upqKiIvj5+eH06dPo3r07Zs+ejfnz5yMwMBC5ubnIy8vD/v378cEHH7h0daRbDc9udwNFRUXo3r070tPTERISYr/2xNatW5GUlIQvv/wSrVu3hs1m0/1NDnr16oXvvvsOQOlWKTMzEyEhIfZLw9lsNjzyyCMYMGCArnPqjWHpKD8/337RyxUrVuDtt9/G2bNnUa1aNRQUFCAnJwdNmjTBggULoJTS/eHViRMnMH36dMTHx6N27dr2K/d++eWX+OGHHxASEmK/WpanY1g62bt3L1q1aoXp06ejW7duMJvN2Lt3L5YsWYLc3FycOHECQ4YMQfPmzREQEICSkhK32vt2OfTTp0+jZcuWGDt2LLp37+7wNU/GsHSyZ88etGjRAlarFS1atMC4cePQtm1bvce6pry8PPz666/Izs5GZGQk7r33XvvXhgwZgnXr1uGHH34o96KrHsmlazqRJt5//33VqlUrNXz4cDVt2jT1j3/8Q3l5ealHH31U7d271/7OKpf/V29nzpxR7du3V1WrVlUdO3ZUJpNJvf322/avnzhxQlWuXFkdPHhQxyndC8PSQVRUlJo3b57985KSEvXRRx+pihUrqujoaIe3uHEH//znP1W7du3U+fPn1dq1a1VwcLDatm2bUqr0bZ2Ki4vV2rVrdZ7SvTCsm+y3335T8fHx9ot82mw2+4VOBwwYoOrUqaNCQ0NVUlJSmffI0sORI0dUXFycysjIUEopdf/999vf8+rkyZNqxIgRav369XqO6Jbc59mwhwgPD4evry9SU1MBlJ4OdPmJfocOHdCpUyd8+eWXSE9Px8aNG/UcFUDpeYCBgYHw8vLCqlWr8Ouvv2LMmDEAgEuXLmHTpk1uf06jHhjWTRYUFITU1FT89ttv6Ny5Mz7//HMApWdYTJ8+HTk5OWjVqhXq1KnjcE13vcTGxqJx48b46quv8MILL2DEiBH2N61buXIljh07Zt8bSH/iQYeb5NKlS8jOzkZWVhbq16+PkSNH4uuvv8aoUaPsJ97abDZ89NFHAEqPcem9h23p0qXo0KEDunbtiieeeALnz59H7dq1sXnzZuzZswcTJ07EpEmTdJ3RXXF3+03St29fbNu2DQcOHEBCQgKGDh2KiIgIWK1WnDx5EsHBwWjTpg0iIyPxxhtv4PXXX0dOTo5u844ePRorV67Em2++iQceeABHjx7F4MGDsWHDBkRFRSEwMBA9e/bEiy++qNuMbk3vJ3meYObMmap69epq9erVymq1KpPJpLy8vNR9992nVqxYoS5duqSUKt07uGzZMtW+fXuHNxa/2Y4dO6YCAwPVd999Z1+2cuVK9c4776iUlBT1448/qtzc3DLvLkN/YljCiouLVfXq1dWSJUuUUkq9/PLLqlWrVmrPnj2qXr16KjQ0VLVr187+NjEXL15U6enpOk6s1L///W/1wAMPKKVK32JpwoQJKjQ0VMXGxqo6deqoPXv2KKUUw/oL3HkhLC0tDc2aNUNSUhLy8vKwePFivPTSS4iPj0ePHj3QqFEjxMXFISwsDEop+Pv7o379+rrOHB0djZycHFitVowYMQJpaWlYtGgRDh8+jODgYGzZsgUAXxryV7jzQlitWrXQo0cPhIaG4ssvv0RUVJQ9nKZNm+LcuXP2HQAlJSW6n70OAMnJyZg6dSqqVauG8PBwfPjhh2jUqBGA0mtyuNM5i+6KYQkLCwuzv/F4dHQ0MjMzcejQIRQUFGDSpEmoWbOm/Q/VHaICgOrVq2Pnzp3YvXs3oqOjERMTg/Pnz2P69Ok4d+6cU28N6+kY1k1Uv3591K9fH23atEFsbCzMZjO++eYbAHC7s9ctFgsSExMBlL5eLDU1FfPmzcOcOXN0nswYuLtdB9988w3OnTuHpk2bIiYmxi1ewPhXCgsLsXXrVuTl5aFr1656j2MIDItIgPs89iC6hTAsIgEMi0gAwyISwLCIBDAsjRUWFmLs2LEoLCzUexSnGG1eo+Dudo3l5+fDYrHAarXarxnozow2r1Fwi0UkgGERCfDIcwVLSkqQlZWFkJAQzV/6kJ+f7/C/7k5yXqUUzp49i6ioKLc6D/Jm8MjnWMeOHUNMTIzeY3iMzMxMREdH6z3GTWXoLdbs2bPx+uuvIzs7Gw0bNsTMmTNx9913/+33hYSEAABqLRwK70Cz9Jiaiei5T+8RXFKMImzCV/bftycxbFiffPIJnnvuOaSmpqJZs2aYPn06kpOTsW/fPlSpUuUvv/fywz/vQLOhwvIx+eo9gmv+/2MhT3ylsWEf+E6bNg19+/ZFnz59kJCQgNTUVAQGBuLdd9/VezQiY4Z16dIlbN++HUlJSfZlXl5eSEpKsl+P4UqFhYXIz893+CCSZMiwTpw4AZvNhvDwcIfl4eHhyM7OLnP7lJQUWCwW+wd3XJA0Q4blqlGjRsFqtdo/MjMz9R6JbnGG3Hlx2223wdvbu8yVYnNychAREVHm9mazGWazcXZSkPEZMiw/Pz80adIEGzZsQOfOnQGUHvTdsGEDBg0a5PT9FOy3wMvfX2hK8mSGDAsAnnvuOfTq1Qt33XUX7r77bkyfPh3nz59Hnz599B6NyLhhPfLII8jLy8Orr76K7OxsNGrUCGvWrCmzQ4NID4YNCwAGDRrk0kM/opvFI/YKEt1sDItIAMMiEsCwiAQwLCIBDItIgKF3t9+o6MZZ8AkyzqlOa7N26j2CS/LPlqDCHXpPoQ9usYgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLg0QeI/X2K4evDf1tIe/yrIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSIBHHyDOWnU7vM3GuXZ78oxGeo/gkmJVBOCQ3mPoglssIgEMi0gAwyISwLCIBDAsIgEMi0gAwyISwLCIBHj0AeKCioC3cY4P8xLTBsItFpEAhkUkgGERCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAjz7zArXPAoFFek/htOSoRnqP4BK+NJ+INMWwiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIgEcfIL503gyvErPeYziNL803Dm6xiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIAMMiEuDRB4h9zMXw8i/Wewy6BXGLRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAo8+8KMn1B/z99R7Dabx2u3Fwi0UkgGERCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAjz5AXKtBJnyCjHPt9q947XbD4BaLSADDIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAR59gLjQ5gObzTi/Ar6C2Di4xSISwLCIBDAsIgEMi0gAwyISwLCIBDAsIgEMi0iAcY6OCsg6ZYFXgXEuMR2Lo3qPQE7iFotIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIgEefedGgahZ8g/z0HsNpp/UegJzGLRaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCPPoA8eEzleB9yThv7l0Rp/QegZzELRaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCPPoA8emDFeDlb5xrt/+ctVPvEVySf7YEFe7Qewp9cItFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJMCjz7wIibPCO7BA7zGclhzVSO8RXFKsigAc0nsMXXCLRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCPPkB87mCYoV6aX0XvAchp3GIRCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkwKMPENdsfBQ+QcZ5c++veO12w+AWi0gAwyISwLCIBDAsIgEMi0gAwyISwLCIBDAsIgEefYD4YHZleAUa5xXEyfc30nsEl/AS00SkKYZFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAI8+86LE5gUUG+fflrV8ab5huN1fVUpKCpo2bYqQkBBUqVIFnTt3xr59+xxuU1BQgIEDB6JSpUoIDg5G165dkZOTo9PERGW5XVgbN27EwIED8dNPP2HdunUoKirCAw88gPPnz9tvM3z4cHzxxRdYtmwZNm7ciKysLHTp0kXHqYkcud1DwTVr1jh8/t5776FKlSrYvn07WrduDavVioULF2Lp0qVo06YNAGDRokWoU6cOfvrpJzRv3lyPsYkcuN0W62pWqxUAULFiRQDA9u3bUVRUhKSkJPtt4uPjUa1aNWzZsqXc+ygsLER+fr7DB5Ektw6rpKQEw4YNQ8uWLVGvXj0AQHZ2Nvz8/BAWFuZw2/DwcGRnZ5d7PykpKbBYLPaPmJgY6dHJw7l1WAMHDsQvv/yCjz/++IbuZ9SoUbBarfaPzMxMjSYkKp/bPce6bNCgQVi9ejX++9//Ijo62r48IiICly5dwpkzZxy2Wjk5OYiIiCj3vsxmM8xm41zxlozP7bZYSikMGjQIK1aswLfffou4uDiHrzdp0gS+vr7YsGGDfdm+fftw9OhRJCYm3uxxicrldlusgQMHYunSpfj8888REhJif95ksVgQEBAAi8WCp59+Gs899xwqVqyI0NBQDB48GImJia7vEcz3BYp8BX4KGclRjfQewSWe/NJ8twtr7ty5AID77rvPYfmiRYvQu3dvAMBbb70FLy8vdO3aFYWFhUhOTsacOXNu8qRE1+Z2YSml/vY2/v7+mD17NmbPnn0TJiJynds9xyK6FTAsIgEMi0gAwyISwLCIBDAsIgFut7v9ZkpsvA++QX56j+G09/kKYsPgFotIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIgEefefHTb3HwCvTXewzn3f5fvScgJ3GLRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCPPkBcUuADmDz6V0BCuMUiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIgGcfHfVWpR8GwTf3Ng5usYgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLAsIgEePSZF1XCrfAOKtB7DLoFcYtFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAI8+QFxY7APvIuP8CtZm7dR7BJfkny1BhTv0nkIf3GIRCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkwDhHRwWYTAomE6/dLoXXbiciTTEsIgEMi0gAwyISwLCIBDAsIgEMi0gAwyISwAPEBjpAzFcQGwe3WEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCPPrMi/41NiIg2Di/Ar403zi4xSISwLCIBDAsIgEMi0gAwyISwLCIBDAsIgEMi0iAcY6OCpi8uSO8Avz1HsNph7MW6D2CS/jSfCLSFMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBHn2AGDZT6QeRxrjFIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLg0WdeeBV4wctA/7bw2u3GYZy/KiIDYVhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAow8QV7kjDz5BZr3HoFsQt1hEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJ8OgDxNEhVvgG+ek9htM+ztqp9wgu4Zt7E5GmGBaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQnw6APEab/HwCvQX+8xnJbcspHeI7iEl5gmIk0xLCIBDItIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAR49JkXOGkGzhvn2u1r+dJ8w+AWi0gAwyISwLCIBDAsIgEMi0gAwyISwLCIBDAsIgGefYDYYJKjGuk9gkv40nwi0hTDIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAR59gDiydi58gvgKYil8BTERaYphEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJMCjz7zw9iqBt1eJ3mPQLcjtt1ivvfYaTCYThg0bZl9WUFCAgQMHolKlSggODkbXrl2Rk5Oj35BEV3HrsH7++WfMmzcPDRo0cFg+fPhwfPHFF1i2bBk2btyIrKwsdOnSRacpicpy27DOnTuHxx57DAsWLECFChXsy61WKxYuXIhp06ahTZs2aNKkCRYtWoTNmzfjp59+0nFioj+5bVgDBw7EQw89hKSkJIfl27dvR1FRkcPy+Ph4VKtWDVu2bCn3vgoLC5Gfn+/wQSTJLXdefPzxx0hLS8PPP/9c5mvZ2dnw8/NDWFiYw/Lw8HBkZ2eXe38pKSkYN26cxKhE5XK7LVZmZiaGDh2KDz/8EP7+/prc56hRo2C1Wu0fmZmZmtwv0bW4XVjbt29Hbm4u7rzzTvj4+MDHxwcbN27EjBkz4OPjg/DwcFy6dAlnzpxx+L6cnBxERESUe59msxmhoaEOH0SS3O6hYNu2bZGRkeGwrE+fPoiPj8eLL76ImJgY+Pr6YsOGDejatSsAYN++fTh69CgSExP1GJmoDLcLKyQkBPXq1XNYFhQUhEqVKtmXP/3003juuedQsWJFhIaGYvDgwUhMTETz5s1dWldBkS+8i3w1m10ar91uHG4XljPeeusteHl5oWvXrigsLERycjLmzJmj91hEdiallNJ7iJstPz8fFosFd346HN4GupiMpf1BvUdwSbEqwvf4HFar1eOe17rdzguiWwHDIhLAsIgEMCwiAQyLSADDIhJgyONYWsn7vSK8ArQ5H/Fm+CnrU71HcAmv3U5EmmJYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkwKMPEAdEnod3YLHeYziNryA2Dm6xiAQwLCIBDItIAMMiEsCwiASIhXX1lWqJPIkmYU2ZMgWffPKJ/fPu3bujUqVKqFq1Knbt2qXFKogMRZOwUlNTERMTAwBYt24d1q1bh6+//hrt2rXDiBEjtFgFkaFocoA4OzvbHtbq1avRvXt3PPDAA4iNjUWzZs20WAWRoWgSVoUKFZCZmYmYmBisWbMGEydOBAAopWCz2bRYhYjiYhNUsXH236zN2qn3CC7x5JfmaxJWly5d8Oijj6JWrVo4efIk2rVrBwDYsWMHatasqcUqiAxFk7DeeustxMbGIjMzE1OnTkVwcDAA4I8//sCAAQO0WAWRoWgS1pYtWzBs2DD4+Dje3eDBg7F582YtVkFkKJo8wbj//vtx6tSpMsutVivuv/9+LVZBZCiahKWUgslkKrP85MmTCAoK0mIVRIZyQw8Fu3TpAgAwmUzo3bs3zOY/32vKZrMhPT0dLVq0uLEJiQzohsKyWCwASrdYISEhCAgIsH/Nz88PzZs3R9++fW9sQiIDuqGwFi1aBACIjY3FiBEjEBgYqMlQREanyV7BJ598EsePH0etWrUclh84cAC+vr6IjY3VYjWa8/ZW8PYu0XsMp/Gl+cahyc6L3r17l7tbfevWrejdu7cWqyAyFE3C2rFjB1q2bFlmefPmzbFz504tVkFkKJqEZTKZcPbs2TLLrVarW58rSCRFk7Bat26NlJQUh4hsNhtSUlLQqlUrLVZBZCia7LyYMmUKWrdujdq1a+Oee+4BAPzwww/Iz8/Ht99+q8UqiAxFky1WQkIC0tPT0b17d+Tm5uLs2bN48sknsXfvXtSrV0+LVRAZimZXwo2KisLkyZO1ujsiQ9PsVX4//PADHn/8cbRo0QLHjx8HACxZsgSbNm3SahVEhqHJFmv58uV44okn8NhjjyEtLQ2FhYUASvcKTp48GV999ZUWq9FcdNgZ+ASZ//6GbkLpPQA5TZMt1sSJE5GamooFCxbA19fXvrxly5ZIS0vTYhVEhqJJWPv27UPr1q3LLLdYLLy+IHkkTcKKiIjAwYMHyyzftGkTqlevrsUqiAxFk7D69u2LoUOHYuvWrTCZTMjKysKHH36IF154Af3799diFUSGosnOi5deegklJSVo27YtLly4gNatW8NsNuOFF17A4MGDtVgFkaGYlFKa7Wy6dOkSDh48iHPnziEhIcF+tSZ3k5+fD4vFgvtX9zPWXsE2x/UewSXFqgjf43NYrVaEhobqPc5Npelbpfr5+SEhIUHLuyQypOsOq0uXLnjvvfcQGhpqv/bFtQQHB6Nu3bro16+f/eX8RLey6w7LYrHYr8z0d7EUFhYiNTUVP/74I1atWnW9qyQyjOsO6/L1Lq7+72vZvXs3mjZter2rE3HsTBi8LxnnOVY0jPUcy5Np/o4AmZmZyMzMLLO8du3avCoueQxNwiouLsbo0aNhsVgQGxuL2NhYWCwWvPLKKygqKgIAeHt7o2HDhlqsjsjtabJXcPDgwfjss88wdepUJCYmAii9nvvYsWNx8uRJzJ07V4vVEBmGJmEtXboUH3/8sf3tewCgQYMGiImJQc+ePRkWeRxNHgqazeZyrx0YFxcHPz8/LVZBZCiahDVo0CBMmDDB/josoHQX+6RJkzBo0CAtVkFkKDd0gPhK69evR3R0tH0Hxa5du3Dp0iW0bdv2xiYkMqAbOkB8pa5duzp8fvnNvok8kSYHiI2qYtAF+ATxgqKkPeO8ZTyRgWiyuz0uLq7cd3S87NAhz3zHCfJcmoQ1bNgwh8+LioqwY8cOrFmzBiNGjNBiFUSGoklYQ4cOLXf57Nmz8b///U+LVRAZiuhzrHbt2mH58uWSqyByS6Jhffrpp6hYsaLkKojckiYPBRs3buyw80IphezsbOTl5WHOnDlarILIUDQJq3Pnzg6fe3l5oXLlyrjvvvsQHx+vxSqIDEWTsMaMGaPF3dx0edZgeBX56z2G02L1HoCcpslzrLS0NGRkZNg///zzz9G5c2f8+9//xqVLl7RYBZGhaBLWs88+i/379wMoPRj8yCOPIDAwEMuWLcPIkSO1WAWRoWgS1v79+9GoUSMAwLJly3Dvvfdi6dKleO+997i7nTySJmEppVBSUgKg9OUj7du3B1B6hvuJEye0WAWRoWgS1l133YWJEydiyZIl2LhxIx566CEAwOHDhxEeHq7FKogMRZOwpk+fjrS0NAwaNAgvv/wyatasCaD0AHGLFi20WAWRoWiyu71BgwYOewUve/311+Ht7a3FKogMRdM3Rbiav79xjhERaUk0LHfXPOZ3+AUb5ypSC7N26j2CS/LPlqDCHXpPoQ++gphIAMMiEnBDYd1zzz1444037GddEFGpGwqrb9++2LJlC5o0aYI6dergxRdfxI8//ggN332VyJBuKKwnn3wSy5cvx4kTJ/Dmm2/izJkz6NatGyIiIvDUU09h5cqVuHjxolazEhmGZtdub9++PebNm4esrCysWrUKkZGRGD16NCpVqoQOHTrgxx9/1GJVRIYgsvOiWbNmmDRpEjIyMpCRkYG2bdvijz/+kFgVkVsSP45Vo0YNDB8+XHo1RG6Fu9uJBHj0mRcR/vkw+/vqPYbTkqMa6T2CS4pVEQDPvAoyt1hEAkTCstls2LlzJ06fPi1x90RuT5Owhg0bhoULFwIojeree+/FnXfeiZiYGHz//fdarILIUDQJ69NPP7W/k+MXX3yBw4cPY+/evRg+fDhefvllLVZBZCiahHXixAlEREQAAL766it069YNd9xxB5566qlyXwBJdKvTJKzw8HDs3r0bNpsNa9aswT/+8Q8AwIULF/gKYvJImuxu79OnD7p3747IyEiYTCYkJSUBALZu3cpLTJNH0iSssWPHol69esjMzES3bt1gNpsBAN7e3njppZe0WAWRoZiUB77GIz8/HxaLBdFvj4NXgHGuy3G44wK9R3BJ6UvzD8FqtSI0NFTvcW4qzY5jbdiwAR06dECNGjVQo0YNdOjQAevXr9fq7okMRZOw5syZgwcffBAhISEYOnQohg4ditDQULRv3x6zZ8/WYhVEhqLJc6zJkyfjrbfewqBBg+zLhgwZgpYtW2Ly5MkYOHCgFqshMgxNtlhnzpzBgw8+WGb5Aw88AKvVqsUqiAxFk7AefvhhrFixoszyzz//HB06dNBiFUSGoslDwYSEBEyaNAnff/89EhMTAQA//fQTfvzxRzz//POYMWOG/bZDhgzRYpVEbk2T3e1xcXHOrcxkwqFD+r8+h7vbbw5P3t2uyRbr8OHDWtwN0S3Do19BXLXaSfgEmfUew2l8BbFxaBbWsWPHsGrVKhw9erTMG3pPmzZNq9UQGYImYW3YsAEPP/wwqlevjr1796JevXr4/fffoZTCnXfeqcUqiAxFk93to0aNwgsvvICMjAz4+/tj+fLlyMzMxL333otu3bppsQoiQ9EkrD179uDJJ58EAPj4+ODixYsIDg7G+PHjMWXKFC1WQWQomoQVFBRkf14VGRmJ3377zf61EydOaLEKIkPR5DlW8+bNsWnTJtSpUwft27fH888/j4yMDHz22Wdo3ry5FqsgMhRNwpo2bRrOnTsHABg3bhzOnTuHTz75BLVq1eIeQfJImoRVvXp1+38HBQUhNTVVi7slMizNDxCfO3cOJSUlDss87XQWIs1OaRo0aBC+//57FBQU2JcrpWAymWCz2bRYjeaKbN4osRnnKlIBeg9ATtMkrMcffxxKKbz77rsIDw+HyWTS4m6JDEuTsHbt2oXt27ejdu3aWtwdkeFpchyradOmyMzM1OKuiG4Jmmyx3nnnHfTr1w/Hjx9HvXr14Ovr+J5TDRo00GI1RIahSVh5eXn47bff0KdPH/syk8nk9jsviKRoEtZTTz2Fxo0b46OPPuLOCyJoFNaRI0ewatUq1KxZU4u7IzI8TXZetGnTBrt27dLirohuCZpssTp27Ijhw4cjIyMD9evXL7Pz4uGHH9ZiNZorLPaBd5Fxrk6wNmun3iO4pPRiMnpPoQ9N/qr69esHABg/fnyZr3HnBXkiTcK6+txAIk+n2buNENGfGBaRAIZFJOCGwsrKytJqDqJbyg2FVbduXSxdulSrWYhuGTcU1qRJk/Dss8+iW7duOHXqlFYzERneDe1uHzBgANq1a4enn34aCQkJWLBgATp27KjVbOIsARfhE8hDBaS9Gz6OFRcXh2+//RazZs1Cly5dUKdOHfj4ON5tWlraja6GyFA0Own3s88+Q4UKFdCpU6cyYRF5mhsuYMGCBXj++eeRlJSEX3/9FZUrV9ZiLiJDu6GwHnzwQWzbtg2zZs2yX7udiG4wLJvNhvT0dERHR2s1D9Et4YbCWrdunVZzEN1SeEoTkQCGRSSAYREJ8OgDTi/Ffo2gEONcuz05qpHeI7ikWBUBOKT3GLrgFotIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBHn2AePiu7vAO9Nd7DKdVQ4beI5CTuMUiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIgEcfIH6mziYEBBvnVzAwK1PvEVziyW/uzS0WkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAoxzdFTArO8fgFeAcV5BPLDrPL1HICdxi0UkwC3DOn78OB5//HFUqlQJAQEBqF+/Pv73v//Zv66UwquvvorIyEgEBAQgKSkJBw4c0HFiIkduF9bp06fRsmVL+Pr64uuvv8bu3bvx5ptvokKFCvbbTJ06FTNmzEBqaiq2bt2KoKAgJCcno6CgQMfJif7kds+xpkyZgpiYGCxatMi+LC4uzv7fSilMnz4dr7zyCjp16gQAeP/99xEeHo6VK1eiR48eZe6zsLAQhYWF9s/z8/MFfwIiN9xirVq1CnfddRe6deuGKlWqoHHjxliwYIH964cPH0Z2djaSkpLsyywWC5o1a4YtW7aUe58pKSmwWCz2j5iYGPGfgzyb24V16NAhzJ07F7Vq1cLatWvRv39/DBkyBIsXLwYAZGdnAwDCw8Mdvi88PNz+tauNGjUKVqvV/pGZaayXX5DxuN1DwZKSEtx1112YPHkyAKBx48b45ZdfkJqail69el3XfZrNZpjNZi3HJPpLbrfFioyMREJCgsOyOnXq4OjRowCAiIgIAEBOTo7DbXJycuxfI9Kb24XVsmVL7Nu3z2HZ/v37cfvttwMo3ZERERGBDRs22L+en5+PrVu3IjEx8abOSnQtbvdQcPjw4WjRogUmT56M7t27Y9u2bZg/fz7mz58PADCZTBg2bBgmTpyIWrVqIS4uDqNHj0ZUVBQ6d+7s0rqiaubBJ4gPEUl7bhdW06ZNsWLFCowaNQrjx49HXFwcpk+fjscee8x+m5EjR+L8+fN45plncObMGbRq1Qpr1qyBv79xTk+iW5tJKaX0HuJmy8/PL91Fv2KIobZY/62/Qu8RXFJ6MZlDsFqtCA0N1Xucm8rtnmMR3QoYFpEAhkUkgGERCWBYRAIYFpEAtzuOdTNVDbbCN8hP7zGclhzVSO8RXFKsigAc0nsMXXCLRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQDPPkAccAbmQF+9x3DaSb0HIKdxi0UkgGERCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkwKPPvAj0ugSzl3EuBLw2a7feI7ik9Eq4ek+hD26xiAQwLCIBDItIAMMiEsCwiAQwLCIBDItIAMMiEuDRB4jNXkXw5z8tJIB/VkQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQnw6APEa/9IgE+QWe8xnPZt/SC9R3AJ39ybiDTFsIgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSIBHHyAusnmjxOat9xhOC9B7AHIat1hEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAjz6zIv/i9kF/2Dj/ArWIlTvEchJ3GIRCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkwDhHRwWsy42Hz3njXLt9XdYXeo/gkvyzJahwh95T6INbLCIBDItIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAR49AHiQ8cqwyvAX+8xnJbctpHeI7iEb+5NRJpiWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQnw6DMvvLxL4OVTovcYdAviFotIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBHn2A2MfPBi8/m95j0C2IWywiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLAsIgEePQB4uTqe2AO9tV7DKe9mbVT7xFcwjf3JiJNMSwiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLg0QeIcwpD4evjp/cYTkuOaqT3CC7hm3sTkaYYFpEAhkUkgGERCWBYRAIYFpEAhkUkgGERCWBYRAI8+syLWkG5hnpp/mYY5ywRT8ctFpEAhkUkgGERCWBYRAIYFpEAhkUkgGERCWBYRAI8+gDx+j9qwzvIrPcYTgvFb3qPQE7iFotIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAQwLCIBHn2A+PbQ0/ANMs6rck/qPQA5jVssIgEMi0gAwyISwLCIBDAsIgEMi0gAwyISwLCIBDAsIgEefeZFbOBJmIOMc+32pVkZeo/gkvyzJahwh95T6INbLCIBDItIAMMiEsCwiAQwLCIBDItIAMMiEsCwiAR49AHiLXlx8LlgnGu3Jzfy1nsElxSrIgCH9B5DF9xiEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJMCjDxDXq/AH/IKN8wrifXoPQE5zuy2WzWbD6NGjERcXh4CAANSoUQMTJkyAUsp+G6UUXn31VURGRiIgIABJSUk4cOCAjlMTOXK7sKZMmYK5c+di1qxZ2LNnD6ZMmYKpU6di5syZ9ttMnToVM2bMQGpqKrZu3YqgoCAkJyejoKBAx8mJ/uR2DwU3b96MTp064aGHHgIAxMbG4qOPPsK2bdsAlG6tpk+fjldeeQWdOnUCALz//vsIDw/HypUr0aNHD91mJ7rM7bZYLVq0wIYNG7B//34AwK5du7Bp0ya0a9cOAHD48GFkZ2cjKSnJ/j0WiwXNmjXDli1byr3PwsJC5OfnO3wQSXK7LdZLL72E/Px8xMfHw9vbGzabDZMmTcJjjz0GAMjOzgYAhIeHO3xfeHi4/WtXS0lJwbhx42QHJ7qC222x/vOf/+DDDz/E0qVLkZaWhsWLF+ONN97A4sWLr/s+R40aBavVav/IzMzUcGKistxuizVixAi89NJL9udK9evXx5EjR5CSkoJevXohIiICAJCTk4PIyEj79+Xk5KBRo0bl3qfZbIbZbJzXXZHxud0W68KFC/DychzL29sbJSUlAIC4uDhERERgw4YN9q/n5+dj69atSExMvKmzEl2L222xOnbsiEmTJqFatWqoW7cuduzYgWnTpuGpp54CAJhMJgwbNgwTJ05ErVq1EBcXh9GjRyMqKgqdO3d2aV2bsqrDO9A4W7JdWR/pPYJLPPkS024X1syZMzF69GgMGDAAubm5iIqKwrPPPotXX33VfpuRI0fi/PnzeOaZZ3DmzBm0atUKa9asgb+/v46TE/3JpK48pcFD5Ofnw2KxoM5HI421xbrbiFusQ7BarQgNDdV7nJvK7Z5jEd0KGBaRAIZFJIBhEQlgWEQCGBaRAIZFJMDtDhDfTMHmQvgY6JhyclQjvUdwCa/dTkSaYlhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAow8QG83arJ16j+AST35pPrdYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkgGERCfDoA8TZeRZ4nTPQS4jJMLjFIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAQyLSADDIhLg0WdetK/zK/yCffUew2m8drtxcItFJIBhEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAI8+QPxzbjV4nzfrPYbTfsr6VO8RXMJrtxORphgWkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJ8OgDxBHB+fAN8tN7DKfxFcTGwS0WkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCPPvMiJvAM/IKMc+326Vk79R7BJXxpPhFpimERCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAjz5AXKJMKFEmvcdwGl+abxzcYhEJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAow8Qh/legNnXOK8gXpv1q94juISvICYiTTEsIgEMi0gAwyISwLCIBDAsIgEMi0gAwyIS4NEHiNNOx8DnklnvMZxX2VgHiD0Zt1hEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSSAYREJYFhEAjz6zIszBQHw9jbQmRdkGNxiEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJMCjDxBXCLgI3wCb3mM4jW/ubRzcYhEJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAow8Q7/s1Gl4B/nqP4bRa+EPvEchJ3GIRCWBYRAIYFpEAhkUkgGERCWBYRAIYFpEAhkUkgGERCfDoMy+UuQTKXKL3GHQL4haLSADDIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAR59gBg+qvSDSGPcYhEJYFhEAhgWkQCGRSSAYREJYFhEAhgWkQCGRSTAow8Q+/oXwSvAW+8xnLY2a6feI7gk/2wJKtyh9xT64BaLSADDIhLAsIgEMCwiAQyLSADDIhLAsIgEMCwiAR59gLhN9QPwC/bVewynJUc10nsElxSrIgCH9B5DF9xiEQlgWEQCGBaRAIZFJIBhEQlgWEQCGBaRAIZFJIBhEQnw6DMvvvu9JrwC/fUew2n7st7XewSX8KX5RKQphkUkgGERCWBYRAIYFpEAhkUkgGERCfDI41hKlb7vcMnFQp0ncU3+2RK9R3BJ/rnSeS//vj2JSXngT33s2DHExMToPYbHyMzMRHR0tN5j3FQeGVZJSQmysrIQEhICk8mk9zi3LKUUzp49i6ioKHh5edazDo8Mi0iaZ/0zQnSTMCwiAQyLSADDIhLAsIgEMCwiAQyLSMD/A0IgGKrjcudDAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.clean_y: Protecting 1 variable(s) during regression: ['groups_HC'].\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mINFO | 01/06/26 15:06:25 | nispace.core.clean_y: Regressing 2 between covariate(s) from Y.\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Regressing 2 between covariate(s) from Y, protecting 1 variable(s) (4 proc): 0%| | 0/200 [00:00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nispace.workflows import group_colocalization\n",
"from nispace.datasets import fetch_example\n",
"\n",
"# load the anorexia nervosa example data\n",
"an_data = fetch_example(\"anorexianervosa\", parcellation=\"Schaefer200\")\n",
"\n",
"# extract group labels\n",
"groups = an_data.index.str.extract(r'(AN|HC)$')[0].values\n",
"\n",
"# generate synthetic covariates\n",
"rng = np.random.default_rng(42)\n",
"age = np.concatenate([rng.normal(25, 5, 50), rng.normal(30, 5, 50)])\n",
"sex = rng.integers(0, 2, 100)\n",
"\n",
"# design matrix: groups column is mandatory, others are covariates\n",
"design = pd.DataFrame({\n",
" \"groups\": groups,\n",
" \"age\": age,\n",
" \"sex\": sex\n",
"}, index=an_data.index)\n",
"\n",
"nsp_gc = group_colocalization(\n",
" y=an_data,\n",
" design=design,\n",
" x=\"pet\",\n",
" x_collection=\"UniqueTracers\",\n",
" parcellation=\"Schaefer200\",\n",
" comparison_method=\"hedges(a,b)\",\n",
" colocalization_method=\"spearman\",\n",
" n_perm=1000,\n",
" seed=42,\n",
" n_proc=4,\n",
" return_nispace_only=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "18a94177",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-01T13:06:28.572981Z",
"iopub.status.busy": "2026-06-01T13:06:28.572862Z",
"iopub.status.idle": "2026-06-01T13:06:28.578871Z",
"shell.execute_reply": "2026-06-01T13:06:28.578547Z"
}
},
"outputs": [
{
"data": {
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" \n",
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" 0.048022 | \n",
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\n",
" \n",
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" \n",
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" 0.008 | \n",
" 0.093917 | \n",
"
\n",
" \n",
" | target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018 | \n",
" -0.152391 | \n",
" 0.026 | \n",
" 0.276367 | \n",
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