neurocaps is a Python package for performing Co-activation Patterns (CAPs) analyses on resting-state or task-based fMRI data (resting-state & task-based). CAPs identifies recurring brain states through k-means clustering of BOLD timeseries data 1.
neurocaps is most optimized for fMRI data preprocessed with fMRIPrep and assumes a BIDs compliant directory such as the example directory structures below:
Basic BIDS directory:
bids_root/
├── dataset_description.json
├── sub-<subject_label>/
│ └── func/
│ └── *task-*_events.tsv
├── derivatives/
│ └── fmriprep-<version_label>/
│ ├── dataset_description.json
│ └── sub-<subject_label>/
│ └── func/
│ ├── *confounds_timeseries.tsv
│ ├── *brain_mask.nii.gz
│ └── *preproc_bold.nii.gz
BIDS directory with session-level organization:
bids_root/
├── dataset_description.json
├── sub-<subject_label>/
│ └── ses-<session_label>/
│ └── func/
│ └── *task-*_events.tsv
├── derivatives/
│ └── fmriprep-<version_label>/
│ ├── dataset_description.json
│ └── sub-<subject_label>/
│ └── ses-<session_label>/
│ └── func/
│ ├── *confounds_timeseries.tsv
│ ├── *brain_mask.nii.gz
│ └── *preproc_bold.nii.gz
Note: Only the preprocessed BOLD file is required. Additional files such as the confounds tsv (needed for denoising), mask, and task timing tsv file (needed for filtering a specific task condition) depend on the specific analyses. The "dataset_description.json" is required in both the bids root and pipeline directories for querying with pybids
To install neurocaps, follow the instructions below using your preferred terminal.
pip install neurocaps
Windows Users
To avoid installation errors related to long paths not being enabled, pybids will not be installed by default. Refer to official Microsoft documentation to enable long paths.
To include pybids in your installation, use:
pip install neurocaps[windows]
Alternatively, you can install pybids separately:
pip install pybids
To install the latest development version from the source, there are two options:
pip install git+https://github.com/donishadsmith/neurocaps.git
git clone https://github.com/donishadsmith/neurocaps/
cd neurocaps
pip install -e .
Windows Users
To include pybids when installing the development version on Windows, use:
git clone https://github.com/donishadsmith/neurocaps/
cd neurocaps
pip install -e .[windows]
Note, documentation of each function can be found in the API section of the documentation homepage.
This package contains two main classes: TimeseriesExtractor for extracting the timeseries, and CAP for performing the CAPs analysis.
Main features for TimeseriesExtractor includes:
Main features for CAP includes:
Additionally, the neurocaps.analysis submodule contains additional functions:
merge_dicts: Merge the subject_timeseries dictionaries for overlapping subjects across tasks to identify similar CAPs across different tasks 5. The merged dictionary can be saved as a pickle file.standardize: Standardizes each run independently for all subjects in the subject timeseries.change_dtype: Changes the dtype of all subjects in the subject timeseries to help with memory usage.transition_matrix: Uses the "transition_probability" output from CAP.calculate_metrics to generate and visualize the averaged transition probability matrix for all groups from the analysis.Please refer to the demos or the tutorials on the documentation website for a more extensive demonstration of the features included in this package.
Demonstration:
Use dataset from OpenNeuro 6:
# Download Sample Dataset from OpenNeuro, requires the openneuro-py package
# [Dataset] doi: doi:10.18112/openneuro.ds005381.v1.0.0
import os
from openneuro import download
demo_dir = "neurocaps_demo"
os.makedirs(demo_dir)
# Include the run-1 and run-2 data from two tasks for two subjects
include = [
"dataset_description.json",
"sub-0004/ses-2/func/*run-[12]*events*",
"sub-0006/ses-2/func/*run-[12]*events*",
"derivatives/fmriprep/sub-0004/fmriprep/sub-0004/ses-2/func/*run-[12]*confounds_timeseries*",
"derivatives/fmriprep/sub-0004/fmriprep/sub-0004/ses-2/func/*run-[12]_space-MNI152NLin*preproc_bold*",
"derivatives/fmriprep/sub-0004/fmriprep/sub-0004/ses-2/func/*run-[12]_space-MNI152NLin*brain_mask*",
"derivatives/fmriprep/sub-0006/fmriprep/sub-0006/ses-2/func/*run-[12]*confounds_timeseries*",
"derivatives/fmriprep/sub-0006/fmriprep/sub-0006/ses-2/func/*run-[12]_space-MNI152NLin*preproc_bold*",
"derivatives/fmriprep/sub-0006/fmriprep/sub-0006/ses-2/func/*run-[12]_space-MNI152NLin*brain_mask*",
]
download(dataset="ds005381", include=include, target_dir=demo_dir, verify_hash=False)
# Create a "dataset_description" file for the pipeline folder if needed
import json
desc = {
"Name": "fMRIPrep - fMRI PREProcessing workflow",
"BIDSVersion": "1.0.0",
"DatasetType": "derivative",
"GeneratedBy": [{"Name": "fMRIPrep", "Version": "20.2.0", "CodeURL": "https://github.com/nipreps/fmriprep"}],
}
with open("neurocaps_demo/derivatives/fmriprep/dataset_description.json", "w", encoding="utf-8") as f:
json.dump(desc, f)
from neurocaps.extraction import TimeseriesExtractor
from neurocaps.analysis import CAP
# Set specific confounds for nuisance regression
confounds = ["cosine*", "trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]
# Set parcellation
parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7, "resolution_mm": 2}}
# Initialize TimeseriesExtractor
extractor = TimeseriesExtractor(
space="MNI152NLin6Asym",
parcel_approach=parcel_approach,
standardize="zscore_sample",
use_confounds=True,
detrend=True,
low_pass=0.1,
high_pass=None,
n_acompcor_separate=2, # 2 acompcor from WM and CSF masks = 4 total
confound_names=confounds,
fd_threshold={"threshold": 0.35, "outlier_percentage": 0.20, "n_before": 2, "n_after": 1, "use_sample_mask": True},
)
# Extract timeseries for subjects in the BIDS directory; Subject 0006 run-1 will be flagged and skipped
extractor.get_bold(
bids_dir="neurocaps_demo",
pipeline_name="fmriprep", # Can specify if multiple pipelines exists in derivatives directory
task="DET",
condition="late", # Can extract a specific condition if events.tsv is available
condition_tr_shift=1,
session="2",
n_cores=None,
verbose=True,
).timeseries_to_pickle("neurocaps_demo/derivatives", "timeseries.pkl")
Output:
2024-12-31 19:06:58,729 neurocaps._utils.extraction.check_confound_names [INFO] Confound regressors to be used if available: cosine*, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z.
2024-12-31 19:06:58,913 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 1] Preparing for Timeseries Extraction using [FILE: sub-0004_ses-2_task-DET_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz].
2024-12-31 19:06:58,926 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 1] The following confounds will be used for nuisance regression: cosine00, cosine01, cosine02, cosine03, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z, a_comp_cor_00, a_comp_cor_01, a_comp_cor_33, a_comp_cor_34.
2024-12-31 19:07:10,165 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 1] Nuisance regression completed; extracting [CONDITION: late].
2024-12-31 19:07:10,205 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 2] Preparing for Timeseries Extraction using [FILE: sub-0004_ses-2_task-DET_run-2_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz].
2024-12-31 19:07:10,219 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 2] The following confounds will be used for nuisance regression: cosine00, cosine01, cosine02, cosine03, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z, a_comp_cor_00, a_comp_cor_01, a_comp_cor_100, a_comp_cor_101.
2024-12-31 19:07:21,070 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0004 | SESSION: 2 | TASK: DET | RUN: 2] Nuisance regression completed; extracting [CONDITION: late].
2024-12-31 19:07:21,107 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0006 | SESSION: 2 | TASK: DET | RUN: 1] Preparing for Timeseries Extraction using [FILE: sub-0006_ses-2_task-DET_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz].
2024-12-31 19:07:21,120 neurocaps._utils.extraction.extract_timeseries [WARNING] [SUBJECT: 0006 | SESSION: 2 | TASK: DET | RUN: 1] Timeseries Extraction Skipped: Run flagged due to more than 20.0% of the volumes exceeding the framewise displacement threshold of 0.35. Percentage of volumes exceeding the threshold limit is 23.636363636363637% for [CONDITION: late].
2024-12-31 19:07:21,121 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0006 | SESSION: 2 | TASK: DET | RUN: 2] Preparing for Timeseries Extraction using [FILE: sub-0006_ses-2_task-DET_run-2_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz].
2024-12-31 19:07:21,135 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0006 | SESSION: 2 | TASK: DET | RUN: 2] The following confounds will be used for nuisance regression: cosine00, cosine01, cosine02, cosine03, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z, a_comp_cor_00, a_comp_cor_01, a_comp_cor_24, a_comp_cor_25.
2024-12-31 19:07:31,746 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 0006 | SESSION: 2 | TASK: DET | RUN: 2] Nuisance regression completed; extracting [CONDITION: late].
# Initialize CAP class
cap_analysis = CAP(parcel_approach=extractor.parcel_approach)
# Pkl files can also be used as input for `subject_timeseries`; only 2 clusters for simplicity
cap_analysis.get_caps(subject_timeseries=extractor.subject_timeseries, n_clusters=2, standardize=True)
# `sharey` only applicable to outer product plots
kwargs = {
"sharey": True,
"ncol": 3,
"subplots": True,
"cmap": "coolwarm",
"xticklabels_size": 10,
"yticklabels_size": 10,
"xlabel_rotation": 90,
"cbarlabels_size": 10,
}
# Outer Product
cap_analysis.caps2plot(visual_scope="regions", plot_options=["outer_product"], suffix_title="DET Task - late", **kwargs)
# Heatmap
kwargs["xlabel_rotation"] = 0
cap_analysis.caps2plot(visual_scope="regions", plot_options=["heatmap"], suffix_title="DET Task - late", **kwargs)
Plot Outputs:
# Get CAP metrics
outputs = cap_analysis.calculate_metrics(
subject_timeseries=extractor.subject_timeseries,
tr=2.0, # TR to convert persistence to time units
return_df=True,
metrics=["temporal_fraction", "persistence"],
continuous_runs=True,
)
# Subject 0006 only has run-2 data since run-1 was flagged during timeseries extraction
print(outputs["temporal_fraction"])
DataFrame Output:
| Subject_ID | Group | Run | CAP-1 | CAP-2 |
|---|---|---|---|---|
| 0004 | All Subjects | run-continuous | 0.193182 | 0.806818 |
| 0006 | All Subjects | run-2 | 0.121951 | 0.878049 |
# Create surface plots
kwargs = {
"cmap": "cold_hot",
"layout": "row",
"size": (500, 200),
"zoom": 1,
"cbar_kws": {"location": "bottom"},
"color_range": (-1, 1),
}
cap_analysis.caps2surf(**kwargs)
Plot Outputs:
# Create Pearson correlation matrix
kwargs = {"annot": True, "cmap": "viridis", "xticklabels_size": 10, "yticklabels_size": 10, "cbarlabels_size": 10}
cap_analysis.caps2corr(**kwargs)
Plot Output:
# Create radar plots showing cosine similarity between region/networks and caps
radialaxis = {
"showline": True,
"linewidth": 2,
"linecolor": "rgba(0, 0, 0, 0.25)",
"gridcolor": "rgba(0, 0, 0, 0.25)",
"ticks": "outside",
"tickfont": {"size": 14, "color": "black"},
"range": [0, 0.6],
"tickvals": [0.1, "", "", 0.4, "", "", 0.6],
}
legend = {
"yanchor": "top",
"y": 0.99,
"x": 0.99,
"title_font_family": "Times New Roman",
"font": {"size": 12, "color": "black"},
}
colors = {"High Amplitude": "black", "Low Amplitude": "orange"}
kwargs = {
"radialaxis": radialaxis,
"fill": "toself",
"legend": legend,
"color_discrete_map": colors,
"height": 400,
"width": 600,
}
cap_analysis.caps2radar(**kwargs)
Plot Outputs:
# Get transition probabilities for all participants in a dataframe, then convert to an averaged matrix
from neurocaps.analysis import transition_matrix
# Optimal cluster sizes are saved automatically
cap_analysis.get_caps(
subject_timeseries=extractor.subject_timeseries,
cluster_selection_method="silhouette",
standardize=True,
show_figs=True,
n_clusters=range(2, 6),
)
outputs = cap_analysis.calculate_metrics(
subject_timeseries=extractor.subject_timeseries,
return_df=True,
metrics=["transition_probability"],
continuous_runs=True,
)
print(outputs["transition_probability"]["All Subjects"])
kwargs = {
"cmap": "Blues",
"fmt": ".3f",
"annot": True,
"vmin": 0,
"vmax": 1,
"xticklabels_size": 10,
"yticklabels_size": 10,
"cbarlabels_size": 10,
}
trans_outputs = transition_matrix(
trans_dict=outputs["transition_probability"], show_figs=True, return_df=True, **kwargs
)
print(trans_outputs["All Subjects"])
Outputs:
2024-12-31 16:51:03,148 neurocaps.analysis.cap [INFO] [GROUP: All Subjects | METHOD: silhouette] Optimal cluster size is 2.
| Subject_ID | Group | Run | 1.1 | 1.2 | 2.1 | 2.2 |
|---|---|---|---|---|---|---|
| 0004 | All Subjects | run-continuous | 0.470588 | 0.529412 | 0.114286 | 0.885714 |
| 0006 | All Subjects | run-2 | 0.600000 | 0.400000 | 0.057143 | 0.942857 |
| From/To | CAP-1 | CAP-2 |
|---|---|---|
| CAP-1 | 0.535294 | 0.464706 |
| CAP-2 | 0.085714 | 0.914286 |
This package was tested using a closed dataset as well as a modified version of a single-subject open dataset to test the TimeseriesExtractor function on GitHub Actions. The open dataset provided by Laumann & Poldrack and used in Laumann et al., 20157. was also utilized. This data was obtained from the OpenfMRI database, accession number ds000031.
Modifications to the data included:
Testing with custom parcellations was done with the HCPex parcellation, an extension of the HCP (Human Connectome Project) parcellation, which adds 66 subcortical areas 8, 9. This original atlas can be downloaded from https://github.com/wayalan/HCPex.
Please refer the contributing guidelines on how to contribute to neurocaps.
Neurocaps relies on several popular data processing, machine learning, neuroimaging, and visualization packages.
Additionally, some foundational concepts in this package take inspiration from features or design patterns implemented in other neuroimaging Python packages, specically:
Liu, X., Zhang, N., Chang, C., & Duyn, J. H. (2018). Co-activation patterns in resting-state fMRI signals. NeuroImage, 180, 485–494. https://doi.org/10.1016/j.neuroimage.2018.01.041 ↩ ↩2
Yang, H., Zhang, H., Di, X., Wang, S., Meng, C., Tian, L., & Biswal, B. (2021). Reproducible coactivation patterns of functional brain networks reveal the aberrant dynamic state transition in schizophrenia. NeuroImage, 237, 118193. https://doi.org/10.1016/j.neuroimage.2021.118193 ↩
Zhang, R., Yan, W., Manza, P., Shokri-Kojori, E., Demiral, S. B., Schwandt, M., Vines, L., Sotelo, D., Tomasi, D., Giddens, N. T., Wang, G., Diazgranados, N., Momenan, R., & Volkow, N. D. (2023). Disrupted brain state dynamics in opioid and alcohol use disorder: attenuation by nicotine use. Neuropsychopharmacology, 49(5), 876–884. https://doi.org/10.1038/s41386-023-01750-w ↩
Ingwersen, T., Mayer, C., Petersen, M., Frey, B. M., Fiehler, J., Hanning, U., Kühn, S., Gallinat, J., Twerenbold, R., Gerloff, C., Cheng, B., Thomalla, G., & Schlemm, E. (2024). Functional MRI brain state occupancy in the presence of cerebral small vessel disease — A pre-registered replication analysis of the Hamburg City Health Study. Imaging Neuroscience, 2, 1–17. https://doi.org/10.1162/imag_a_00122 ↩
Kupis, L., Romero, C., Dirks, B., Hoang, S., Parladé, M. V., Beaumont, A. L., Cardona, S. M., Alessandri, M., Chang, C., Nomi, J. S., & Uddin, L. Q. (2020). Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. NeuroImage: Clinical, 28, 102396. https://doi.org/10.1016/j.nicl.2020.102396 ↩
Hyunwoo Gu and Joonwon Lee and Sungje Kim and Jaeseob Lim and Hyang-Jung Lee and Heeseung Lee and Minjin Choe and Dong-Gyu Yoo and Jun Hwan (Joshua) Ryu and Sukbin Lim and Sang-Hun Lee (2024). Discrimination-Estimation Task. OpenNeuro. [Dataset] doi: https://doi.org/10.18112/openneuro.ds005381.v1.0.0 ↩
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U., Schlaggar, B. L., Mumford, J. A., Poldrack, R. A., & Petersen, S. E. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87(3), 657–670. https://doi.org/10.1016/j.neuron.2015.06.037 ↩
Huang, CC., Rolls, E.T., Feng, J. et al. An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas. Brain Struct Funct 227, 763–778 (2022). https://doi.org/10.1007/s00429-021-02421-6 ↩
Huang, C.-C., Rolls, E. T., Hsu, C.-C. H., Feng, J., & Lin, C.-P. (2021). Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the “What” and “Where” Dual Stream Model. Cerebral Cortex, 31(10), 4652–4669. https://doi.org/10.1093/cercor/bhab113 ↩
FAQs
Co-activation Patterns (CAPs) Python package
We found that neurocaps demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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