Psychophysiological Interaction

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Psychophysiological interaction (PPI) is a brain connectivity analysis method for functional brain imaging data, mainly functional magnetic resonance imaging (fMRI). It estimates context-dependent changes in effective connectivity (coupling) between brain regions.[1][2] Thus, PPI analysis identifies brain regions whose activity depends on an interaction between psychological context (the task) and physiological state (the time course of brain activity) of the seed region.[3]

History and development[]

The PPI method was proposed by Friston and colleagues in 1997.[1] The model includes an interaction term between a psychological variable (task design) and physiological variable (the time series of a brain region). If the interaction term can explain the brain activation of another brain region after taking into account the main effects of the psychological and physiological variables, then it implies a task-dependent connectivity between the two brain regions.

The PPI method is mainly applied to blood-oxygen-level dependent (BOLD) signals measured by functional MRI (fMRI). The hemodynamic response is slow compared with fast neuronal activities. Therefore, Gitelman and colleagues proposed to first deconvolve the BOLD time series of the seed with hemodynamic response function, so that the resulting "neuronal level" signals could match with the timing of the task design (the psychological variable).[4] Before the multiplication, both the psychological and physiological variables should be centered.[5]

If an fMRI experiment is designed with multiple conditions, then there will be multiple psychological variables included in the model. In this case, a modeling strategy named generalized PPI was proposed.[6]

Traditionally, the PPI analysis was implemented using a seed-based strategy. That is, the PPI term is defined using a pre-defined seed, and a voxel-wise analysis is performed to identify regions in the whole brain that showed task modulated connectivity with the seed region. The PPI method can also be applied to every pair of regions in the brain, so that the whole-brain task modulated connectivity, i.e. task connectome, can be mapped.[7]

Implementations[]

Major fMRI data analysis software, including SPM, FSL, and AFNI, all have modules for PPI analysis. There is also Generalized PPI Toolbox, which is a MATLAB based toolbox dedicated for PPI analysis.

See also[]

References[]

  1. ^ a b Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ (October 1997). "Psychophysiological and modulatory interactions in neuroimaging". NeuroImage. 6 (3): 218–29. CiteSeerX 10.1.1.8.1112. doi:10.1006/nimg.1997.0291. PMID 9344826.
  2. ^ Friston KJ (January 2011). "Functional and effective connectivity: a review". Brain Connectivity. 1 (1): 13–36. CiteSeerX 10.1.1.222.9471. doi:10.1089/brain.2011.0008. PMID 22432952.
  3. ^ O'Reilly JX, Woolrich MW, Behrens TE, Smith SM, Johansen-Berg H (June 2012). "Tools of the trade: psychophysiological interactions and functional connectivity". Social Cognitive and Affective Neuroscience. 7 (5): 604–9. doi:10.1093/scan/nss055. PMC 3375893. PMID 22569188.
  4. ^ Gitelman, Darren; Penny, William; Ashburner, John; Friston, Karl (2003). "Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution". NeuroImage. 19 (1): 200–207. doi:10.1016/S1053-8119(03)00058-2.
  5. ^ Di X, Reynolds RC, Biswal BB (April 2017). "Imperfect (de)convolution may introduce spurious psychophysiological interactions and how to avoid it". Human Brain Mapping. 38 (4): 1723–1740. doi:10.1002/hbm.23413. PMID 28105655.
  6. ^ McLaren DG, Ries ML, Xu G, Johnson SC (July 2012). "A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches". NeuroImage. 61 (4): 1277–86. doi:10.1016/j.neuroimage.2012.03.068. PMC 3376181. PMID 22484411.
  7. ^ Di X, Biswal BB (April 2019). "Toward Task Connectomics: Examining Whole-Brain Task Modulated Connectivity in Different Task Domains". Cerebral Cortex. 29 (4): 1572–1583. doi:10.1093/cercor/bhy055. PMC 7302740. PMID 29931116.


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