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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.

  • 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

Requirements

  • SParsity-based Analysis of Reliable K-hubness (SPARK)

  • SPM8 or 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.