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LICENSE (1068, 2022-07-06)
SPARK_HDI.m (2210, 2022-07-06)
SPARK_fMRI_pupillometry.m (5458, 2022-07-06)

# arousal-rsfMRIpupil-hub * `SPARK_fMRI_pupillometry.m` performs a four-step analysis: STEP 1. Pupillometry processing STEP 2. State stratification of fMRI data using pupillometry STEP 3. Bootstrap resampling of state-stratified fMRI data STEP 4. Sparse dictionary learning of resampled data * Other scripts to implement the remainings of the SPARK analysis, such as the parallel implementation of the sparse dictionary learning, spatial K-means clustering, background noise removal, and k-hubness estimation, can be found and adapted from [SPARK](https://github.com/multifunkim/spark-matlab). * `SPARK_HDI.m` computes the hub disruption index (HDI) to compare k-hubness estimated from fMRI data in two arousal states, e.g., high and low arousal. For further questions please raise an issue [here](https://github.com/Kangjoo/Arousal_RSfMRI_Hub/issues) ------------ # Requirements * SParsity-based Analysis of Reliable K-hubness ([SPARK](https://github.com/multifunkim/spark-matlab)) * [SPM8](https://www.fil.ion.ucl.ac.uk/spm/software/spm8/) or [SPM12](https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) is required to process pupillometry data. ------------ # Citation If you use this library for your publications, please cite it as: Kangjoo Lee, Corey Horien, David O’Connor, Bronwen Garand-Sheridan, Fuyuze Tokoglu, Dustin Scheinost, Evelyn M.R. Lake, R. Todd Constable, “Arousal impacts distributed hubs modulating the integration of brain functional connectivity”, Neuroimage (2022), [Link](https://doi.org/10.1016/j.neuroimage.2022.1193***).

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