Multi-subject analyses with dynamic causal modeling
Open accessAbstract
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 PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.
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BPA has disadvantages: posterior covariances can make posterior estimates behave counterintuitively, even when they are mathematically sensible.
“However, BPA also has some disadvantages (Kasess et al., 2010). One potential problem is that the posterior covariances can make the posterior estimates behave in a counterintuitive way, even when they are mathematically perfectly sensible.”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kasess et al., 2010
Other fixed-effects methods do not suffer from the particular problem of posterior covariances making estimates counterintuitive, although they have other restrictions.
“Other fixed-effects methods do not suffer from this particular problem, although they have other restrictions; for details see Kasess et al. (2010).”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kasess et al. (2010)
For data with high signal-to-noise ratio, in the presence of pronounced posterior covariances, the Bayesian average can deviate substantially from the mean of the maximum a posteriori (MAP) estimates across subjects.
“This can become particularly severe for data with a high signal-to-noise ratio where, in the presence of pronounced posterior covariances, the Bayesian average can deviate substantially from the mean of the maximum a posteriori (MAP) estimates across subjects.”
From Ten simple rules for dynamic causal modeling (2010) · cites this paper as Kasess et al., 2010
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- Authors
- Kasess, C. H., Stephan, K. E., Weissenbacher, A., Pezawas, L., Moser, E., & Windischberger, C.
- Affiliations
- Medical University of Vienna (AT)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Windischberger, Christian
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-02
- Volume
- 49
- Issue
- 4
- Pages
- 3065-3074
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2837922
- OpenAlex
- W1995008852
- Cited by
- 70
- References
- 53
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 2 · 2021: 7 · 2020: 6 · 2019: 5 · 2018: 3 · 2017: 1 · 2016: 7 · 2015: 2 · 2014: 10 · 2013: 5 · 2012: 8
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (056750)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- dynamic causal modeling, time averaging, population heterogeneity, signal-to-noise ratio, parameter interdependence, neural network model
- MeSH terms
- Animals, Brain, Brain Mapping, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Causality
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