NeuroCAPs: Neuroimaging Co-Activation Patterns

NeuroCAPs (Neuroimaging Co-Activation Patterns) is a Python package for performing Co-Activation Patterns (CAPs) analyses on resting-state or task-based fMRI data. CAPs identifies recurring brain states by applying k-means clustering on BOLD timeseries data ¹.

Installation

Requires Python 3.10-3.14.

Standard Installation

pip install neurocaps

Windows Users: Enable long paths and use:

pip install neurocaps[windows]

Development Version

git clone --depth 1 https://github.com/donishadsmith/neurocaps/
cd neurocaps
pip install -e .

# For windows
# pip install -e .[windows]

# Clone with submodules to include test data ~140 MB
git submodule update --init

Docker

A Docker image is available with demos and headless VTK display configured:

# Pull image
docker pull donishadsmith/neurocaps && docker tag donishadsmith/neurocaps neurocaps

# Run interactive bash
docker run -it neurocaps

# Run Jupyter Notebook
docker run -it -p 9999:9999 neurocaps notebook

Features

NeuroCAPs is built around two main classes (TimeseriesExtractor and CAP) and includes several features to perform the complete CAPs workflow from postprocessing to visualizations. Notable features includes:

Component	Key Features
Timeseries Extraction (`TimeseriesExtractor`)	supports Schaefer, AAL, and deterministic custom parcellations performs nuisance regression and motion scrubbing reports quality control based on framewise displacement Important: Optimized for BIDS-compliant data preprocessed with fMRIPrep and assumes data is BIDs compliant. Refer to NeuroCAPs' BIDS Structure and Entities Documentation for additional information.
CAPs Analysis (`CAP`)	performs k-means clustering finds the optimal number of clusters (silhouette, elbow, variance ratio, Davies-Bouldin) computes temporal dynamic metrics (temporal fraction, persistence, counts, transition frequency and probabilities) ² ³ converts CAPs to NIfTI images creates visualizations (heatmaps, outer products, surface plots, correlation matrices, cosine similarity radar plots ⁴ ⁵).
Standalone Functions	plots transition matrices merges timeseries data across tasks or sessions ⁶ generates and fetches custom parcellation approaches

Full details for every function and parameter are available in the API Documentation.

Quick Start

The following code demonstrates basic usage of NeuroCAPs (with simulated data) to perform CAPs analysis. A version of this example using real data from OpenNeuro is available on the readthedocs. Additional tutorials and interactive demonstrations are also available.

Extract timeseries data

import numpy as np
from neurocaps.extraction import TimeseriesExtractor
from neurocaps.utils import simulate_bids_dataset

# Set seed
np.random.seed(0)

# Generate a BIDS directory with fMRIPrep derivatives
bids_root = simulate_bids_dataset(n_subs=3, n_runs=1, n_volumes=100, task_name="rest")

# Using Schaefer, one of the default parcellation approaches
parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7}}

# List of fMRIPrep-derived confounds for nuisance regression
acompcor_names = [f"a_comp_cor_0{i}" for i in range(5)]
confound_names = ["cosine*", "trans*", "rot*", *acompcor_names]

# Initialize extractor with signal cleaning parameters
extractor = TimeseriesExtractor(
    space="MNI152NLin2009cAsym",
    parcel_approach=parcel_approach,
    confound_names=confound_names,
    standardize=False,
    # Run discarded if more than 30% of volumes exceed FD threshold
    fd_threshold={"threshold": 0.90, "outlier_percentage": 0.30},
)

# Extract preprocessed BOLD data
extractor.get_bold(bids_dir=bids_root, task="rest", tr=2, n_cores=1, verbose=False)

# Check QC information
qc_df = extractor.report_qc()
print(qc_df)

Quality Control Dataframe.

Use k-means clustering to identify the optimal number of CAPs from the data using a heuristic

from neurocaps.analysis import CAP
from neurocaps.utils import PlotDefaults

# Initialize CAP class
cap_analysis = CAP(parcel_approach=extractor.parcel_approach, groups=None)

plot_kwargs = {**PlotDefaults.get_caps(), "figsize": (4, 3), "step": 2}

# Find optimal CAPs (2-20) using silhouette method; results are stored
cap_analysis.get_caps(
    subject_timeseries=extractor.subject_timeseries,
    n_clusters=range(2, 21),
    standardize=True,
    cluster_selection_method="silhouette",
    max_iter=500,
    n_init=10,
    show_figs=True,
    **plot_kwargs,
)

Compute temporal dynamic metrics for downstream statistical analyses

# Calculate temporal fraction of each CAP
metric_dict = cap_analysis.calculate_metrics(
    extractor.subject_timeseries, metrics=["temporal_fraction"]
)
print(metric_dict["temporal_fraction"])

Temporal Fraction Dataframe.

Note that CAP-1 is the dominant brain state across subjects (highest frequency).

Visualize CAPs

# Create surface and radar plots for each CAP
surface_kwargs = {**PlotDefaults.caps2surf(), "layout": "row", "size": (500, 100)}

radar_kwargs = {**PlotDefaults.caps2radar(), "height": 400, "width": 485}
radar_kwargs["radialaxis"] = {"range": [0, 0.4], "tickvals": [0.1, "", "", 0.4]}
radar_kwargs["legend"] = {"yanchor": "top", "y": 0.75, "x": 1.15}

cap_analysis.caps2surf(**surface_kwargs).caps2radar(**radar_kwargs)

Radar plots show network alignment (measured by cosine similarity): "High Amplitude" represents alignment to activations (> 0), "Low Amplitude" represents alignment to deactivations (< 0).

Each CAP can be characterized using either maximum alignment (CAP-1: Vis+/SomMot-; CAP-2: SomMot+/Vis-) or predominant alignment ("High Amplitude" − "Low Amplitude"; CAP-1: SalVentAttn+/SomMot-; CAP-2: SomMot+/SalVentAttn-).

import pandas as pd

for cap_name in cap_analysis.caps["All Subjects"]:
    df = pd.DataFrame(cap_analysis.cosine_similarity["All Subjects"][cap_name])
    df["Net"] = df["High Amplitude"] - df["Low Amplitude"]
    df["Regions"] = cap_analysis.cosine_similarity["All Subjects"]["Regions"]
    print(f"{cap_name}:", "\n", df, "\n")

CAP-1:

CAP-2:

Note: For information about logging, refer to NeuroCAPs' Logging Guide.

Citing

If you would like to cite NeuroCAPs, you can use:

   Smith, D., (2025). NeuroCAPs: A Python Package for Performing Co-Activation Patterns Analyses on Resting-State and Task-Based fMRI Data. Journal of Open Source Software, 10(112), 8196, https://doi.org/10.21105/joss.08196

Reporting Issues

Bug reports, feature requests, and documentation enhancements can be reported using the templates offered when creating a new issue in the issue tracker.

Contributing

Please refer the contributing guidelines on how to contribute to NeuroCAPs.

Acknowledgements

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, specifically:

mtorabi59's pydfc, a toolbox that allows comparisons among several popular dynamic functionality methods.
62442katieb's IDConn, a pipeline for assessing individual differences in resting-state or task-based functional connectivity.

References

Liu, X., Chang, C., & Duyn, J. H. (2013). Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Frontiers in Systems Neuroscience, 7. https://doi.org/10.3389/fnsys.2013.00101 ↩
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 ↩
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 ↩

Metadata

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