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].

Note: NeuroCAPs is most optimized for fMRI data preprocessed with fMRIPrep and assumes the data is BIDs compliant. Refer to NeuroCAPs’ BIDS Structure and Entities Documentation for additional information.

Citing

Smith, D. (2024). NeuroCAPs. Zenodo. https://doi.org/10.5281/zenodo.14940507

Usage

This package contains two main classes: TimeseriesExtractor for extracting the timeseries, and CAP for performing the CAPs analysis.

Main features for TimeseriesExtractor includes:

• Timeseries Extraction: Extract timeseries for resting-state or task data using Schaefer, AAL, or a lateralized Custom parcellation for spatial dimensionality reduction (TimeseriesExtractor.get_bold()).

• Parallel Processing: Use parallel processing to speed up timeseries extraction.

• Saving Timeseries: Save the nested dictionary containing timeseries as a pickle file (TimeseriesExtractor.timeseries_to_pickle()).

• Visualization: Visualize the timeseries at the region or node level of the parcellation (TimeseriesExtractor.visualize_bold()).

Main features for CAP includes:

• Grouping: Perform CAPs analysis for entire sample or groups of subject IDs

• Optimal Cluster Size Identification: Perform the Davies Bouldin, Silhouette, Elbow, or Variance Ratio criterions to identify the optimal cluster size and automatically save the optimal model as an attribute (CAP.get_caps()).

• Parallel Processing: Use parallel processing to speed up optimal cluster size identification.

• CAPs Visualization: Visualize the CAPs as outer products or heatmaps at either the region or node level of the parcellation (CAP.caps2plot()).

• Save CAPs as NifTIs: Convert the atlas used for parcellation to a statistical NifTI image (CAP.caps2niftis()).

• Surface Plot Visualization: Project CAPs onto a surface plot (CAP.caps2surf()).

• Correlation Matrix Creation: Create a correlation matrix from CAPs (CAP.caps2corr()).

• CAPs Metrics Calculation: Calculate several CAPs metrics as described in Liu et al., 2018 [1] and Yang et al., 2021 [2] (CAP.calculate_metrics()):

  • Temporal Fraction: The proportion of total volumes spent in a single CAP over all volumes in a run.

  • Persistence: The average time spent in a single CAP before transitioning to another CAP (average consecutive/uninterrupted time).

  • Counts: The total number of initiations of a specific CAP across an entire run. An initiation is defined as the first occurrence of a CAP.

  • Transition Frequency: The number of transitions between different CAPs across the entire run.

  • Transition Probability : The probability of transitioning from one CAP to another CAP (or the same CAP). This is calculated as (Number of transitions from A to B) / (Total transitions from A).

• Cosine Similarity Radar Plots: Create radar plots showing the cosine similarity between positive and negative activations of each CAP and each a-priori regions in a parcellation [3] [4] (CAP.caps2radar()).

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 tutorials for a more extensive demonstration of the features included in this package.

Dependencies

NeuroCAPs relies on several packages:

dependencies = [
   "numpy>=1.22.0",
   "pandas>=2.0.0",
   "joblib>=1.3.0",
   "matplotlib>=3.6.0",
   "seaborn>=0.11.0",
   "kneed>=0.8.0",
   "nibabel>=3.2.0",
   "nilearn>=0.10.1, !=0.10.3",
   "scikit-learn>=1.4.0",
   "scipy>=1.6.0",
   "brainspace>=0.1.16",
   "surfplot>=0.2.0",
   "neuromaps>=0.0.5",
   "pybids>=0.16.2; platform_system != 'Windows'",
   "plotly>=4.9",
   "nbformat>=4.2.0",
   "kaleido==0.1.0.post1; platform_system == 'Windows'",
   "kaleido; platform_system != 'Windows'",
   "setuptools; python_version>='3.12'",
   "typing_extensions; python_version<'3.11'",
   "vtk<9.4.0",
   "tqdm>=4.65.0"
   ]

Acknowledgements

Some foundational concepts in NeuroCAPs 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

[1] (1,2)

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

[3]

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

[4]

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

[5]

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