stimulus-modulated connectivity between distributed brain regions or networks
to run: python wb-ppi.py --help
description
Whole-brain psychophysiological interactions (PPIs) measure stimulus- or task-modulated connectivity between all brain region pairs defined by an atlas parcellation. Each multiple regression model includes the target time series as the dependent variable, and as independent variables the a) convolved stimulus or psychological factor b) seed time series or physiological factor and c) product of a x b. Regression coefficients corresponding to c are saved to wb-ppi/sub-{subject}/sub-{subject}_task-{task}_atlas-{atlas}_strat-{strategy}_ppi.npy. These numpy matrices can be combined across runs and subjects (see example/) to build t-statistic matrices, while controlling the false discovery rate. Results are saved to wb-ppi/group/atlas-{atlas}_strat-{strategy}_report.html, where atlas refers to one of the available parcellations and strategy references to the confound regression strategy used to denoise the post-processed fMRI data.
references
Di, Xin, and Bharat B. Biswal. "Toward task connectomics: examining whole-brain task modulated connectivity in different task domains." Cerebral Cortex 29.4 (2019): 1572-1583.
Gerchen, Martin Fungisai, David Bernal‐Casas, and Peter Kirsch. "Analyzing task‐dependent brain network changes by whole‐brain psychophysiological interactions: A comparison to conventional analysis." Human brain mapping 35.10 (2014): 5071-5082.
O’Reilly, Jill X., et al. "Tools of the trade: psychophysiological interactions and functional connectivity." Social cognitive and affective neuroscience 7.5 (2012): 604-609.