1 / 22

Introduction to connectivity: Psychophysiological Interactions

This comprehensive guide introduces psychophysiological interactions, functional segregation, integration, and effective connectivity in neuroimaging studies, with a focus on the modulatory effects of attention on brain connectivity. Learn about Structural Equation Modeling (SEM) as a tool to assess causal relationships between brain regions. Explore different modeling approaches and the steps involved in analyzing connectivity patterns under varying conditions. Acknowledgements include key studies and resources for further reading.

nburns
Download Presentation

Introduction to connectivity: Psychophysiological Interactions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related