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0.0 2.0 1.0 3.18 MB

MATLAB 0.33% Jupyter Notebook 90.31% Python 8.99% Shell 0.37%

event_detection's Introduction

event_detection

Perform event detection as in Betzel et al (2021): Individualized event structure drives individual differences in whole-brain functional connectivity

what is here

  • Example parcellated resting fMRI BOLD time series from here.
  • System labels for each brain region.
  • Analysis script.

what does the script do?

  1. Reads in the parcel time series data, generates edge time series, and calculates the root sum squared (RSS) amplitude at each frame.
  2. Uses a circular-shift null model to generate null time series and repeats the procedure from Step 1, resulting in a null distribution of RSS values.
  3. Identifies statistically significant frames (observed RSS > than that of null) and partitions time series into segments of temporally contiguous supra-threshold frames.
  4. Identifies peak RSS within each segment and extracts pattern of activity (parcel time series) and edge time series (co-fluctuation) at that instant.
  5. Makes a couple figures.

If you use this code, please cite: R Betzel, S Cutts, S Greenwell, O Sporns (2021). Individualized event structure drives individual differences in whole-brain functional connectivity. bioRxiv link to paper

event_detection's People

Contributors

eurunuela avatar vinferrer avatar rbetzel avatar

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