Multi-subject analyses with dynamic causal modeling.

NeuroImage
Christian Herbert KasessChristian Windischberger

Abstract

Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects' parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects' posterior densities according to Bayes' theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and...Continue Reading

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Oct 19, 2011·Proceedings of the National Academy of Sciences of the United States of America·Todd A HareAntonio Rangel
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Feb 24, 2021·Cognitive Science·Andrea StoccoLauren K Graham
Oct 5, 2021·Brain Structure & Function·Otto MuzikVaibhav A Diwadkar

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