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

Functional Integration Functional Segregation Effective ConnectivityFunctional Connectivity

Attention V1 V5 An Example

Set source target stimuli source target Two Interpretations Context-sensitive connectivityModulation of stimulus-specific responses

How it works: Interactions V1 X Attention

How it works: GLM V1 Att V1XAtt z = -9 mm

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

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

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

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

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: , 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: , 1997 –Christian Ruff’s ppt “Experimental Design” Tutorial:

Structural Equation Modelling (SEM) Christos Pliatsikas

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

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 AB (causes)

This approach offers a move from correlational analysis (inherently bi- directional) to uni-directional connections (‘paths’) which imply causality a1a2 = a 21 a1a3 = a 21 x a 32 a1a4 = a 21 x a 32 x a 43 a2a3 = a 32 x a 23 a2a4 = a 32 x a 43 a3a4 = a 43 a2 a1a4 a3 a 21 a 23 a 32 a 43

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

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.

Steps in SEM 1.Select regions of interest 2.Build a model about how the regions are connected to each other 3.See what patterns of covariance the model predicts 4.Compare them to the observed patterns 5.“Goodness of fit” model: difference between predicted and observed patterns

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

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

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_STOR E/talks/fletcher03fun.pps PPI%20&%20SEM.ppt

Thank you!