SPARK

SPARK is a Python implementation for identifying resting-state functional networks from fMRI data using sparse dictionary learning and K-hubness analysis.

Repository Structure

network-analysis/
└── SPARK/
    ├── install_spark.sh
    ├── run_spark.sh
    ├── run_spark_slurm.sh
    ├── requirements.txt
    ├── requirements-dev.txt
    ├── pipeline_steps1_6.py
    ├── step1_load_data.py
    ├── step2_estimate_scale.py
    ├── step3_bootstrap.py
    ├── step4_dictionary.py
    ├── step5_clustering.py
    ├── step6_kmap_atoms.py
    └── utils.py

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Installation

Clone the repository

git clone https://github.com/multifunkim/network-analysis.git
cd network-analysis/SPARK

Standard installation

bash install_spark.sh

Developer installation

bash install_spark.sh --developer

Developer mode additionally registers the SPARK Python Jupyter kernel.

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Running SPARK

Local workstation

Edit the configuration section at the beginning of

run_spark.sh

Then execute

bash run_spark.sh

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SLURM clusters

Edit the configuration section at the beginning of

run_spark_slurm.sh

including your SLURM parameters if needed (account, time, cpus, memory, etc.).

Submit the job

sbatch run_spark_slurm.sh

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Configuration

Before running SPARK, update the following variables inside the run_spark.sh:

input_dir="/path/to/input"
mask_path="/path/to/mask.nii.gz"
output_base="/path/to/output"
suffix="_processed"

Input directory

Directory containing the preprocessed fMRI files.

Example

sub-HC043_ses-01_processed.nii.gz
sub-HC044_ses-01_processed.nii.gz
...

Mask

Gray matter mask used during SPARK.

Output directory

SPARK automatically creates one output folder for each subject.

File suffix

SPARK searches for all files ending with

*_processed.nii
*_processed.nii.gz

For HCP datasets, for example,

suffix="_rfMRI_smoothed_k8"

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Automatic Processing

The launcher automatically

• searches all matching fMRI files
• extracts subject identifiers
• creates output directories
• skips completed subjects
• prevents duplicate processing using lock files
• removes lock files after successful completion

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Output

For each subject SPARK generates

Subject/

    step1.log/
    step2.log/
    step3.log/
    step4.log/
    step5.log/

    KMAP_Subject/

        k_hubness_Subject.nii.gz

        atom_001.nii.gz
        atom_002.nii.gz
        ...

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Citation