# neurocaps
This is a Python package designed to perform Co-activation Patterns (CAPs) analyses. It utilizes k-means clustering to group timepoints (TRs) into brain states, applicable to both resting-state and task-based fMRI data. The package is compatible with data preprocessed using **fMRIPrep** and assumes your directory is BIDS-compliant, containing a derivatives folder with a pipeline folder (such as fMRIPrep) that holds the preprocessed BOLD data.
**Still in beta but stable.**
# Installation
**Note**: The `get_bold()` method in the `TimeseriesExtractor` class relies on pybids, which is only functional on POSIX operating systems and macOS. If you have a pickled timeseries dictionary in the correct nested form, you can use this package on Windows to visualize the BOLD timeseries, the `CAP` class, as well as the `merge_dicts()` and `standardize()` functions in the in the `neurcaps.analysis` submodule.
To install, use your preferred terminal:
**Installation using pip:**
```bash
pip install neurocaps
```
**From source (Development version):**
```bash
pip install git+https://github.com/donishadsmith/neurocaps.git
```
or
```bash
git clone https://github.com/donishadsmith/neurocaps/
cd neurocaps
pip install -e .
```
# Usage
**This package contains two main classes: `TimeseriesExtractor` for extracting the timeseries, and `CAP` for performing the CAPs analysis.**
**Note:** When extracting the timeseries, this package uses either the Schaefer atlas, the Automated Anatomical Labeling (AAL) atlas, or a custom parcellation where all nodes have a left and right version (bilateral nodes). The number of ROIs and networks for the Schaefer atlas can be adjusted with the parcel_approach parameter when initializing the TimeseriesExtractor class.
To modify it, you must use a nested dictionary, where the primary key is "Schaefer" and the sub-keys are "n_rois" and "yeo_networks". For example:
```python
parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7, "resolution_mm": 1}}
```
Similarly, the version of the AAL atlas can be modified using:
```python
parcel_approach = {"AAL": {"version": "SPM12"}}
```
If using a "Custom" parcellation approach, ensure each node in your dataset includes both left (lh) and right (rh) hemisphere versions (bilateral nodes).
Custom Key Structure:
- 'maps': Directory path containing necessary parcellation files. Ensure files are in a supported format (e.g., .nii for NIfTI files). For plotting purposes, this key is not required.
- 'nodes': list of all node labels used in your study, arranged in the exact order they correspond to indices in your parcellation files.
Each label should match the parcellation index it represents. For example, if the parcellation label "0" corresponds to the left hemisphere
visual cortex area 1, then "LH_Vis1" should occupy the 0th index in this list. This ensures that data extraction and analysis accurately reflect the anatomical regions intended. For timeseries extraction, this key is not required.
- 'regions': Dictionary defining major brain regions. Each region should list node indices under "lh" and "rh" to specify left and right hemisphere nodes. For timeseries extraction, this key is not required.
Example:
The provided example demonstrates setting up a custom parcellation containing nodes for the visual network (Vis) and hippocampus regions:
```Python
parcel_approach= {"Custom": {"maps": "/location/to/parcellation.nii.gz",
"nodes": ["LH_Vis1", "LH_Vis2", "LH_Hippocampus",
"RH_Vis1", "RH_Vis2", "RH_Hippocampus"],
"regions": {"Vis" : {"lh": [0,1],
"rh": [3,4]},
"Hippocampus": {"lh": [2],
"rh": [5]}}}}
```
**Main features for `TimeseriesExtractor` includes:**
- **Timeseries Extraction:** Extract timeseries for resting-state or task data, creating a nested dictionary containing the subject ID, run number, and associated timeseries. This serves as input for the `get_caps()` method in the `CAP` class.
- **Saving Timeseries:** Save the nested dictionary containing timeseries as a pickle file.
- **Visualization:** Visualize the timeseries of a Schaefer, AAL, or Custom parcellation node or region/network in a specific subject's run, with options to save the plots.
- **Parallel Processing:** Use parallel processing by specifying the number of CPU cores in the `n_cores` parameter in the `get_bold()` method. Testing on an HPC using a loop with TimeseriesExtractor.`get_bold()` to extract session 1 and 2 BOLD timeseries from 105 subjects from resting-state data (single run containing 360 volumes) and two task datasets (three runs containing 200 volumes each and two runs containing 200 volumes) reduced processing time from 5 hours 48 minutes to 1 hour 26 minutes (using 10 cores).
**Main features for `CAP` includes:**
- **Optimal Cluster Size Identification:** Perform the silhouette or elbow method to identify the optimal cluster size, saving the optimal model as an attribute.
- **Grouping:** Perform CAPs analysis independently on groups of subject IDs. K-means clustering, silhouette and elbow methods, and plotting are done for each group when specified.
- **CAP Visualization:** Visualize the CAPs as outer products or heatmaps, with options to use subplots to reduce the number of individual plots. You can save and use the plots. Refer to the docstring for the `caps2plot()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.
- **Surface Plot Visualization:** Convert the atlas used for parcellation to a stat map projected onto a surface plot. Refer to the docstring for the `caps2surf()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.
- **Correlation Matrix Creation:** Create a correlation matrix from CAPs. Refer to the docstring for the `caps2corr()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.
- **CAP Metrics Calculation:** Calculate CAP metrics as described in [Liu et al., 2018](https://doi.org/10.1016/j.neuroimage.2018.01.041)[^1] and [Yang et al., 2021](https://doi.org/10.1016/j.neuroimage.2021.118193)[^2]:
- *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 frequency of each CAP observed in a run.
- *Transition Frequency:* The number of switches between different CAPs across the entire run.
**Additionally, the `neurocaps.analysis` submodule contains two additional functions:**
- `merge_dicts`: Merge the subject_timeseries dictionaries for overlapping subjects across tasks to identify similar CAPs across different tasks. The merged dictionary can be saved as a pickle file.
- `standardize`: Standardizes each run independently for all subjects in the subject timeseries.
Please refer to [demo.ipynb](https://github.com/donishadsmith/neurocaps/blob/main/demo.ipynb) for a more extensive demonstration of the features included in this package.
Quick code example:
```python
# Examples use randomized data
from neurocaps.extraction import TimeseriesExtractor
from neurocaps.analysis import CAP
"""If an asterisk '*' is after a name, all confounds starting with the
term preceding the parameter will be used. in this case, all parameters
starting with cosine will be used."""
confounds = ["cosine*", "trans_x", "trans_x_derivative1", "trans_y",
"trans_y_derivative1", "trans_z","trans_z_derivative1",
"rot_x", "rot_x_derivative1", "rot_y", "rot_y_derivative1",
"rot_z","rot_z_derivative1"]
"""If use_confounds is True but no confound_names provided, there are hardcoded
confound names that will extract the data from the confound files outputted by fMRIPrep
`n_acompcor_separate` will use the first 'n' components derived from the separate
white-matter (WM) and cerebrospinal fluid (CSF). To use the acompcor components from the
combined mask, list them in the `confound_names` parameter"""
parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7, "resolution_mm": 2}}
extractor = TimeseriesExtractor(parcel_approach=parcel_approach, standardize="zscore_sample",
use_confounds=True, detrend=True, low_pass=0.15, high_pass=0.01,
confound_names=confounds, n_acompcor_separate=6)
bids_dir = "/path/to/bids/dir"
# If there are multiple pipelines in the derivatives folder, you can specify a specific pipeline
pipeline_name = "fmriprep-1.4.0"
# Resting State
# extractor.get_bold(bids_dir=bids_dir, task="rest", pipeline_name=pipeline_name)
# Task; use parallel processing with `n_cores`
extractor.get_bold(bids_dir=bids_dir, task="emo", condition="positive",
pipeline_name=pipeline_name, n_cores=10)
cap_analysis = CAP(parcel_approach=extractor.parcel_approach,
n_clusters=6)
cap_analysis.get_caps(subject_timeseries=extractor.subject_timeseries,
standardize = True)
# Visualize CAPs
cap_analysis.caps2plot(visual_scope="regions", plot_options="outer product",
task_title="- Positive Valence", ncol=3, sharey=True,
subplots=True)
cap_analysis.caps2plot(visual_scope="nodes", plot_options="outer product",
task_title="- Positive Valence", ncol=3,sharey=True,
subplots=True, xlabel_rotation=90, tight_layout=False,
hspace = 0.4)
```
**Plot Outputs:**
```python
# Get CAP metrics
outputs = cap_analysis.calculate_metrics(subject_timeseries=extractor.subject_timeseries, tr=2.0,
return_df=True, output_dir=output_dir,
metrics=["temporal fraction", "persistence"],
continuous_runs=True, file_name="All_Subjects_CAPs_metrics")
print(outputs["temporal fraction"])
```
**DataFrame Output:**
| Subject_ID | Group | Run | CAP-1 | CAP-2 | CAP-3 | CAP-4 | CAP-5 | CAP-6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | All Subjects | Continuous Runs | 0.14 | 0.17 | 0.14 | 0.2 | 0.15 | 0.19 |
| 2 | All Subjects | Continuous Runs | 0.17 | 0.17 | 0.16 | 0.16 | 0.15 | 0.19 |
| 3 | All Subjects | Continuous Runs | 0.15 | 0.2 | 0.14 | 0.18 | 0.17 | 0.17 |
| 4 | All Subjects | Continuous Runs | 0.17 | 0.21 | 0.18 | 0.17 | 0.1 | 0.16 |
| 5 | All Subjects | Continuous Runs | 0.14 | 0.19 | 0.14 | 0.16 | 0.2 | 0.18 |
| 6 | All Subjects | Continuous Runs | 0.16 | 0.21 | 0.16 | 0.18 | 0.16 | 0.13 |
| 7 | All Subjects | Continuous Runs | 0.16 | 0.16 | 0.17 | 0.15 | 0.19 | 0.17 |
| 8 | All Subjects | Continuous Runs | 0.17 | 0.21 | 0.13 | 0.14 | 0.17 | 0.18 |
| 9 | All Subjects | Continuous Runs | 0.18 | 0.1 | 0.17 | 0.18 | 0.16 | 0.2 |
| 10 | All Subjects | Continuous Runs | 0.14 | 0.19 | 0.14 | 0.17 | 0.19 | 0.16 |
```python
# Create surface plots of CAPs; there will be as many plots as CAPs
# If you experience coverage issues, usually smoothing helps to mitigate these issues
cap_analysis.caps2surf(fwhm=2)
```
**Plot Output:**
```python
# Create correlation matrix
cap_analysis.caps2corr(annot=True)
```
**Plot Output:**
# Testing
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](https://openfmri.org/dataset/ds000031/) and used in [Laumann et al., 2015](https://doi.org/10.1016/j.neuron.2015.06.037)[^4]. was also utilized. This data was obtained from the OpenfMRI database, accession number ds000031.
Modifications to the data included:
- Truncating the preprocessed BOLD data and confounds from 448 timepoints to 40 timepoints.
- Only including session 002 data.
- Adding a dataset_description.json file to the fmriprep folder.
- Excluding the nii.gz file in the root BIDS folder.
- Retaining only the mask, truncated preprocessed BOLD file, and truncated confounds file in the fmriprep folder.
- Slightly changing the naming style of the mask, preprocessed BOLD file, and confounds file in the fmriprep folder to conform with the naming conventions of modern fmriprep outputs.
- Testing with custom parcellations was done using the HCPex parcellation, an extension of the HCP (Human Connectome Project) parcellation, which adds 66 subcortical areas. This original atlas can be downloaded from.
Testing with custom parcellations was done with the HCPex parcellation, an extension of the HCP (Human Connectome Project) parcellation, which adds 66 subcortical areas [^5], [^6]. This original atlas can be downloaded from https://github.com/wayalan/HCPex.
# References
[^1]: 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]: 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
[^4]: 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
[^5]: Huang CC, Rolls ET, Feng J, Lin CP. An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas. Brain Struct Funct. 2022 Apr;227(3):763-778. Epub 2021 Nov 17. doi: 10.1007/s00429-021-02421-6
[^6]: Huang CC, Rolls ET, Hsu CH, Feng J, Lin CP. Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the "What" and "Where" Dual Stream Model. Cerebral Cortex. 2021 May 19;bhab113. doi: 10.1093/cercor/bhab113.
Raw data
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"description": "# neurocaps\r\nThis is a Python package designed to perform Co-activation Patterns (CAPs) analyses. It utilizes k-means clustering to group timepoints (TRs) into brain states, applicable to both resting-state and task-based fMRI data. The package is compatible with data preprocessed using **fMRIPrep** and assumes your directory is BIDS-compliant, containing a derivatives folder with a pipeline folder (such as fMRIPrep) that holds the preprocessed BOLD data.\r\n\r\n**Still in beta but stable.**\r\n\r\n# Installation\r\n\r\n**Note**: The `get_bold()` method in the `TimeseriesExtractor` class relies on pybids, which is only functional on POSIX operating systems and macOS. If you have a pickled timeseries dictionary in the correct nested form, you can use this package on Windows to visualize the BOLD timeseries, the `CAP` class, as well as the `merge_dicts()` and `standardize()` functions in the in the `neurcaps.analysis` submodule.\r\n\r\nTo install, use your preferred terminal:\r\n\r\n**Installation using pip:**\r\n\r\n```bash\r\n\r\npip install neurocaps\r\n\r\n```\r\n\r\n**From source (Development version):**\r\n\r\n```bash\r\npip install git+https://github.com/donishadsmith/neurocaps.git\r\n```\r\n\r\nor\r\n\r\n```bash\r\n\r\ngit clone https://github.com/donishadsmith/neurocaps/\r\ncd neurocaps\r\npip install -e .\r\n\r\n```\r\n\r\n# Usage\r\n**This package contains two main classes: `TimeseriesExtractor` for extracting the timeseries, and `CAP` for performing the CAPs analysis.**\r\n\r\n**Note:** When extracting the timeseries, this package uses either the Schaefer atlas, the Automated Anatomical Labeling (AAL) atlas, or a custom parcellation where all nodes have a left and right version (bilateral nodes). The number of ROIs and networks for the Schaefer atlas can be adjusted with the parcel_approach parameter when initializing the TimeseriesExtractor class.\r\n\r\nTo modify it, you must use a nested dictionary, where the primary key is \"Schaefer\" and the sub-keys are \"n_rois\" and \"yeo_networks\". For example:\r\n\r\n```python\r\nparcel_approach = {\"Schaefer\": {\"n_rois\": 100, \"yeo_networks\": 7, \"resolution_mm\": 1}}\r\n```\r\n\r\nSimilarly, the version of the AAL atlas can be modified using:\r\n\r\n```python\r\nparcel_approach = {\"AAL\": {\"version\": \"SPM12\"}}\r\n```\r\n\r\nIf using a \"Custom\" parcellation approach, ensure each node in your dataset includes both left (lh) and right (rh) hemisphere versions (bilateral nodes). \r\n\r\nCustom Key Structure:\r\n- 'maps': Directory path containing necessary parcellation files. Ensure files are in a supported format (e.g., .nii for NIfTI files). For plotting purposes, this key is not required.\r\n- 'nodes': list of all node labels used in your study, arranged in the exact order they correspond to indices in your parcellation files. \r\nEach label should match the parcellation index it represents. For example, if the parcellation label \"0\" corresponds to the left hemisphere \r\nvisual cortex area 1, then \"LH_Vis1\" should occupy the 0th index in this list. This ensures that data extraction and analysis accurately reflect the anatomical regions intended. For timeseries extraction, this key is not required.\r\n- 'regions': Dictionary defining major brain regions. Each region should list node indices under \"lh\" and \"rh\" to specify left and right hemisphere nodes. For timeseries extraction, this key is not required.\r\n \r\nExample:\r\nThe provided example demonstrates setting up a custom parcellation containing nodes for the visual network (Vis) and hippocampus regions:\r\n\r\n```Python\r\nparcel_approach= {\"Custom\": {\"maps\": \"/location/to/parcellation.nii.gz\",\r\n \"nodes\": [\"LH_Vis1\", \"LH_Vis2\", \"LH_Hippocampus\",\r\n \"RH_Vis1\", \"RH_Vis2\", \"RH_Hippocampus\"],\r\n \"regions\": {\"Vis\" : {\"lh\": [0,1],\r\n \"rh\": [3,4]},\r\n \"Hippocampus\": {\"lh\": [2],\r\n \"rh\": [5]}}}}\r\n ```\r\n\r\n**Main features for `TimeseriesExtractor` includes:**\r\n\r\n- **Timeseries Extraction:** Extract timeseries for resting-state or task data, creating a nested dictionary containing the subject ID, run number, and associated timeseries. This serves as input for the `get_caps()` method in the `CAP` class.\r\n- **Saving Timeseries:** Save the nested dictionary containing timeseries as a pickle file.\r\n- **Visualization:** Visualize the timeseries of a Schaefer, AAL, or Custom parcellation node or region/network in a specific subject's run, with options to save the plots.\r\n- **Parallel Processing:** Use parallel processing by specifying the number of CPU cores in the `n_cores` parameter in the `get_bold()` method. Testing on an HPC using a loop with TimeseriesExtractor.`get_bold()` to extract session 1 and 2 BOLD timeseries from 105 subjects from resting-state data (single run containing 360 volumes) and two task datasets (three runs containing 200 volumes each and two runs containing 200 volumes) reduced processing time from 5 hours 48 minutes to 1 hour 26 minutes (using 10 cores).\r\n\r\n**Main features for `CAP` includes:**\r\n- **Optimal Cluster Size Identification:** Perform the silhouette or elbow method to identify the optimal cluster size, saving the optimal model as an attribute.\r\n- **Grouping:** Perform CAPs analysis independently on groups of subject IDs. K-means clustering, silhouette and elbow methods, and plotting are done for each group when specified.\r\n- **CAP Visualization:** Visualize the CAPs as outer products or heatmaps, with options to use subplots to reduce the number of individual plots. You can save and use the plots. Refer to the docstring for the `caps2plot()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.\r\n- **Surface Plot Visualization:** Convert the atlas used for parcellation to a stat map projected onto a surface plot. Refer to the docstring for the `caps2surf()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.\r\n- **Correlation Matrix Creation:** Create a correlation matrix from CAPs. Refer to the docstring for the `caps2corr()` method in the `CAP` class for available **kwargs arguments and parameters to modify plots.\r\n- **CAP Metrics Calculation:** Calculate CAP metrics as described in [Liu et al., 2018](https://doi.org/10.1016/j.neuroimage.2018.01.041)[^1] and [Yang et al., 2021](https://doi.org/10.1016/j.neuroimage.2021.118193)[^2]:\r\n - *Temporal Fraction:* The proportion of total volumes spent in a single CAP over all volumes in a run.\r\n - *Persistence:* The average time spent in a single CAP before transitioning to another CAP (average consecutive/uninterrupted time).\r\n - *Counts:* The frequency of each CAP observed in a run.\r\n - *Transition Frequency:* The number of switches between different CAPs across the entire run.\r\n\r\n**Additionally, the `neurocaps.analysis` submodule contains two additional functions:**\r\n\r\n- `merge_dicts`: Merge the subject_timeseries dictionaries for overlapping subjects across tasks to identify similar CAPs across different tasks. The merged dictionary can be saved as a pickle file.\r\n- `standardize`: Standardizes each run independently for all subjects in the subject timeseries.\r\n\r\nPlease refer to [demo.ipynb](https://github.com/donishadsmith/neurocaps/blob/main/demo.ipynb) for a more extensive demonstration of the features included in this package.\r\n\r\nQuick code example:\r\n\r\n```python\r\n# Examples use randomized data\r\n\r\nfrom neurocaps.extraction import TimeseriesExtractor\r\nfrom neurocaps.analysis import CAP\r\n\r\n\"\"\"If an asterisk '*' is after a name, all confounds starting with the \r\nterm preceding the parameter will be used. in this case, all parameters \r\nstarting with cosine will be used.\"\"\"\r\nconfounds = [\"cosine*\", \"trans_x\", \"trans_x_derivative1\", \"trans_y\", \r\n \"trans_y_derivative1\", \"trans_z\",\"trans_z_derivative1\", \r\n \"rot_x\", \"rot_x_derivative1\", \"rot_y\", \"rot_y_derivative1\", \r\n \"rot_z\",\"rot_z_derivative1\"]\r\n\r\n\"\"\"If use_confounds is True but no confound_names provided, there are hardcoded \r\nconfound names that will extract the data from the confound files outputted by fMRIPrep\r\n`n_acompcor_separate` will use the first 'n' components derived from the separate \r\nwhite-matter (WM) and cerebrospinal fluid (CSF). To use the acompcor components from the \r\ncombined mask, list them in the `confound_names` parameter\"\"\"\r\nparcel_approach = {\"Schaefer\": {\"n_rois\": 100, \"yeo_networks\": 7, \"resolution_mm\": 2}}\r\n\r\nextractor = TimeseriesExtractor(parcel_approach=parcel_approach, standardize=\"zscore_sample\",\r\n use_confounds=True, detrend=True, low_pass=0.15, high_pass=0.01, \r\n confound_names=confounds, n_acompcor_separate=6)\r\n\r\nbids_dir = \"/path/to/bids/dir\"\r\n\r\n# If there are multiple pipelines in the derivatives folder, you can specify a specific pipeline\r\npipeline_name = \"fmriprep-1.4.0\"\r\n\r\n# Resting State\r\n# extractor.get_bold(bids_dir=bids_dir, task=\"rest\", pipeline_name=pipeline_name)\r\n\r\n# Task; use parallel processing with `n_cores`\r\nextractor.get_bold(bids_dir=bids_dir, task=\"emo\", condition=\"positive\", \r\n pipeline_name=pipeline_name, n_cores=10)\r\n\r\ncap_analysis = CAP(parcel_approach=extractor.parcel_approach,\r\n n_clusters=6)\r\n\r\ncap_analysis.get_caps(subject_timeseries=extractor.subject_timeseries, \r\n standardize = True)\r\n\r\n# Visualize CAPs\r\ncap_analysis.caps2plot(visual_scope=\"regions\", plot_options=\"outer product\", \r\n task_title=\"- Positive Valence\", ncol=3, sharey=True, \r\n subplots=True)\r\n\r\ncap_analysis.caps2plot(visual_scope=\"nodes\", plot_options=\"outer product\", \r\n task_title=\"- Positive Valence\", ncol=3,sharey=True, \r\n subplots=True, xlabel_rotation=90, tight_layout=False, \r\n hspace = 0.4)\r\n\r\n```\r\n**Plot Outputs:**\r\n\r\n\r\n\r\n```python\r\n\r\n# Get CAP metrics\r\noutputs = cap_analysis.calculate_metrics(subject_timeseries=extractor.subject_timeseries, tr=2.0, \r\n return_df=True, output_dir=output_dir,\r\n metrics=[\"temporal fraction\", \"persistence\"],\r\n continuous_runs=True, file_name=\"All_Subjects_CAPs_metrics\")\r\n\r\nprint(outputs[\"temporal fraction\"])\r\n```\r\n**DataFrame Output:**\r\n| Subject_ID | Group | Run | CAP-1 | CAP-2 | CAP-3 | CAP-4 | CAP-5 | CAP-6 |\r\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\r\n| 1 | All Subjects | Continuous Runs | 0.14 | 0.17 | 0.14 | 0.2 | 0.15 | 0.19 |\r\n| 2 | All Subjects | Continuous Runs | 0.17 | 0.17 | 0.16 | 0.16 | 0.15 | 0.19 |\r\n| 3 | All Subjects | Continuous Runs | 0.15 | 0.2 | 0.14 | 0.18 | 0.17 | 0.17 |\r\n| 4 | All Subjects | Continuous Runs | 0.17 | 0.21 | 0.18 | 0.17 | 0.1 | 0.16 |\r\n| 5 | All Subjects | Continuous Runs | 0.14 | 0.19 | 0.14 | 0.16 | 0.2 | 0.18 |\r\n| 6 | All Subjects | Continuous Runs | 0.16 | 0.21 | 0.16 | 0.18 | 0.16 | 0.13 |\r\n| 7 | All Subjects | Continuous Runs | 0.16 | 0.16 | 0.17 | 0.15 | 0.19 | 0.17 |\r\n| 8 | All Subjects | Continuous Runs | 0.17 | 0.21 | 0.13 | 0.14 | 0.17 | 0.18 |\r\n| 9 | All Subjects | Continuous Runs | 0.18 | 0.1 | 0.17 | 0.18 | 0.16 | 0.2 |\r\n| 10 | All Subjects | Continuous Runs | 0.14 | 0.19 | 0.14 | 0.17 | 0.19 | 0.16 |\r\n\r\n```python\r\n# Create surface plots of CAPs; there will be as many plots as CAPs\r\n# If you experience coverage issues, usually smoothing helps to mitigate these issues\r\ncap_analysis.caps2surf(fwhm=2)\r\n```\r\n**Plot Output:**\r\n\r\n\r\n```python\r\n# Create correlation matrix\r\ncap_analysis.caps2corr(annot=True)\r\n```\r\n**Plot Output:**\r\n\r\n\r\n# Testing \r\nThis 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](https://openfmri.org/dataset/ds000031/) and used in [Laumann et al., 2015](https://doi.org/10.1016/j.neuron.2015.06.037)[^4]. was also utilized. This data was obtained from the OpenfMRI database, accession number ds000031. \r\n\r\nModifications to the data included:\r\n\r\n- Truncating the preprocessed BOLD data and confounds from 448 timepoints to 40 timepoints.\r\n- Only including session 002 data.\r\n- Adding a dataset_description.json file to the fmriprep folder.\r\n- Excluding the nii.gz file in the root BIDS folder.\r\n- Retaining only the mask, truncated preprocessed BOLD file, and truncated confounds file in the fmriprep folder.\r\n- Slightly changing the naming style of the mask, preprocessed BOLD file, and confounds file in the fmriprep folder to conform with the naming conventions of modern fmriprep outputs.\r\n- Testing with custom parcellations was done using the HCPex parcellation, an extension of the HCP (Human Connectome Project) parcellation, which adds 66 subcortical areas. This original atlas can be downloaded from.\r\n\r\nTesting with custom parcellations was done with the HCPex parcellation, an extension of the HCP (Human Connectome Project) parcellation, which adds 66 subcortical areas [^5], [^6]. This original atlas can be downloaded from https://github.com/wayalan/HCPex.\r\n\r\n# References\r\n[^1]: Liu, X., Zhang, N., Chang, C., & Duyn, J. H. (2018). Co-activation patterns in resting-state fMRI signals. NeuroImage, 180, 485\u2013494. https://doi.org/10.1016/j.neuroimage.2018.01.041\r\n\r\n[^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\r\n\r\n[^3]: Kupis, L., Romero, C., Dirks, B., Hoang, S., Parlad\u00e9, 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\r\n\r\n[^4]: 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\u2013670. https://doi.org/10.1016/j.neuron.2015.06.037\r\n\r\n[^5]: Huang CC, Rolls ET, Feng J, Lin CP. An extended Human Connectome Project multimodal parcellation atlas of the human cortex and subcortical areas. Brain Struct Funct. 2022 Apr;227(3):763-778. Epub 2021 Nov 17. doi: 10.1007/s00429-021-02421-6\r\n\r\n[^6]: Huang CC, Rolls ET, Hsu CH, Feng J, Lin CP. Extensive Cortical Connectivity of the Human Hippocampal Memory System: Beyond the \"What\" and \"Where\" Dual Stream Model. Cerebral Cortex. 2021 May 19;bhab113. doi: 10.1093/cercor/bhab113.\r\n",
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