BCN Neuroimaging Centre

University of Groningen

The Netherlands

PPI

Friston (1997)

Gitelman (2003)

BCN Neuroimaging Centre

University of Groningen

The Netherlands

Basic fMRI refreshments

BCN Neuroimaging Centre

University of Groningen

The Netherlands

Friston et al

1997

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Friston 1997

Introduction Aim:

define PPI Address interpretation

Basic idea: Correlation between areas changes as context

changes.

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efficasy and contributions

Functional specialization Functional integration

Functional connectivity (correlation)

Effective connectivity (taking into account full model)

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iGikki β eβGxx

Effective connectivityefficacy and contributions

Test on : H0: ik=0i.e., test correlation between regions

Note if more regions, towards effective connectivity.

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interactions

Imagine 1 task (gr), two conditions (ga)

iGariari eβGggggx ][

Note gr and ga are mean corrected

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Physiological interaction

Imagine 2 areas (gr, and ga)

iGariari eβGggggx ][

ga

grBRAIN

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Physiological interaction

Example in paper: gr=PP ga=V1 Responding area: V5

Note this is interaction and not only due to PP, PP activity is a confound

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Non linear models

SKIP

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Psychophysiological interaction

xk : source region (V1)

gp : task (-1 or +1 label)

iGpkipki eβGgxgxx ][

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xk : V1

gp : task (attention)

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fit

V1 V5 V1 V5

attention No attention

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Once more be aware

V1

V5

V1

V5

?

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Summary

Psychophysiological interaction Predict activity in area B by area A as a function of

context PPIeffective connectivity PPI=contribution (c.f. correlation)

Note on interpretations. Connection AB influenced by task Influence TaskB is modified by activity in A No guarantee that connections are direct.

BCN Neuroimaging Centre

University of Groningen

The Netherlands

Gitelman et al

2003

(where Friston went “wrong”)

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Aim

Show importance of deconvolution

How to deconvolve properly

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Introduction

Don’t analyze interactions on raw BOLD signal. (using SEM PPI etc)

“veridical models of neuronal interactions require the neural signal or at least a well-constrained approximation to it. “

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A simulation (see examples)

Time shift (0-8 s)

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Convolved with exp decay & hrf

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Deconvolve A&B

Interaction

Reconvolve

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Conclusion

Interaction with the convolved signal

Interaction at neural representation +

convolution

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Noise effect

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Noise effect

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conclusion

Noise has more effect on HRF interactions

Deconvolution reduces noise

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Real dataER

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Conclusion

There is an effect for event related designs.

Not so strong as simulations.

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Real data

Block

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Conclusion

Effect on BLOCK design data is not dramatic.

In short: Calculating interactions at neural representation pays

especially for ER designs. Friston was wrong, but not that far off because of block

design in his experiment.

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(i.e. BOLD signal)

Hxy

tt xhy

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)())((

)())((

AAA

BABABA

PxHHxHPHPy

xxHHxHxyy

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How to obtain xA from yA

NOTE112 columnsBasis set

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How to obtain xA from yA

εHXβy A

X has too many columnsover determined matrixnot one unique solution

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Solution

Biased estimation. (bayesian stat.)

I start to get lost…..

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What I do understand

High frequencies are a problem in deconvolution. Convolution is low pass filter. high frequency

information is losthigh frequency estimates are unstable/unreliable.

High frequencies were also the most troubling in interactions based on BOLD signal (cf ER & BLOCK designs)

High frequencies are regularized using bayesian stat.