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Introduction to connectivity: Psychophysiological Interactions

Roland Benoit MfD 2007/8. Introduction to connectivity: Psychophysiological Interactions. Functional Segregation. Functional Integration. Functional Connectivity. Effective Connectivity. Attention. V1. V5. An Example. Set. stimuli. source. source. target. target.

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Introduction to connectivity: Psychophysiological Interactions

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  1. Roland Benoit MfD 2007/8 Introduction to connectivity: Psychophysiological Interactions

  2. Functional Segregation Functional Integration Functional Connectivity Effective Connectivity

  3. Attention V1 V5 An Example

  4. Set stimuli source source target target Two Interpretations Context-sensitive connectivity Modulation of stimulus-specific responses

  5. How it works: Interactions V1 X Attention

  6. How it works: GLM 0 0 1 z = -9 mm V1 Att V1XAtt

  7. How it works: Deconvolution y = V1*b1 + Att*b2 + (V1xAtt)*b3 + e c = [0 0 1] (HRF V1) X (HRF  Att) ≠ HRF (V1 X Att) • Deconvolve physiological regressor (V1) • Calculate interaction term (V1xAtt) • Convolve interaction term

  8. How it is done: PPI & SPM5 • Estimate GLM • Extract time series at Region of Interest

  9. How it is done: PPI & SPM5 3. Deconvolve, Calculate Interaction, Reconvolve

  10. How it is done: PPI & SPM5 3. Estimate new GLM

  11. Acknowledgements • Data from • C. Buchel and K. Friston. Modulation of connectivity in visual pathways by attention: Cortical interactions evaluated with structural equation modelling and fMRI, Cerebral Cortex, 7: 768-778, 1997 • Figures from • K.J. Friston, C. Buchel, G.R. Fink, J. Morris, E. Rolls, and R. Dolan. Psychophysiological and modulatory interactions in Neuroimaging. NeuroImage, 6:218-229, 1997 • Christian Ruff’s ppt “Experimental Design” • Tutorial: http://www.fil.ion.ucl.ac.uk/spm/data/

  12. Structural Equation Modelling (SEM) Christos Pliatsikas

  13. Differences from PPI • Better in identifying causal relationships • Based on regression analysis, estimated simultaneously as an interlocked system of relationships • Looks at covariances in activity between different brain areas • Combines these data with anatomical models of brain areas connections • Connectivity can be compared over time or across conditions

  14. SEM comprises a set of regions and a set of directed connections • These connections are presumed to represent causal relationships • A priori assumption of causality, without inference from the data A B (causes)

  15. a32 a2 a3 a23 a21 a43 a1 a4 This approach offers a move from correlational analysis (inherently bi-directional) to uni-directional connections (‘paths’) which imply causality a1a2 = a21 a1a3 = a21 x a32 a1a4 = a21 x a32 x a43 a2a3 = a32 x a23 a2a4 = a32 x a43 a3a4 = a43

  16. For SEM we need… • An anatomical model, consisting of specified regions and interconnections • A functional model, through a correlation matrix that generates the path strengths

  17. Particular connection strengths in an SEM presuppose a set of instantaneous correlations among regions • Connection strengths can be set to minimise discrepancy between the observed and the implied correlations.

  18. Steps in SEM • Select regions of interest • Build a model about how the regions are connected to each other • See what patterns of covariance the model predicts • Compare them to the observed patterns • “Goodness of fit” model: difference between predicted and observed patterns

  19. Different model approaches • We look at how effective connectivity is affected by a variable (eg attention) • We observe patterns of covariance under 2 conditions (attention vs non attention) • 2 models applied to the data: • Null model: estimates of the free parameters are constrained to be the same for both groups • Alternative model: estimates of the free parameters are allowed to differ between groups • We check at “goodness of fit” of both models • The model that has better fit determines whether connectivity is different across the 2 conditions

  20. SEM: pros and cons • Looks at influence of several brain areas simultaneously-more complete model • Based on assumptions backed by neuroanatomy • Lack of temporal information • Causality is predetermined, and this might overlook several aspects of neural activity

  21. Further reading… • Jezzard et al (eds)(2001): Functional MRI. An introduction to methods • Penny et al (2004): Modelling functional Integration • www-bmu.psychiatry.cam.ac.uk/PUBLICATION_STORE/talks/fletcher03fun.pps • http://www.fil.ion.ucl.ac.uk/~mgray/Presentations/PPI%20&%20SEM.ppt

  22. Thank you!

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