Construct validation of a DCM for resting state fMRI
By Adeel Razi, Joshua Kahan, Geraint E. Rees, Karl J. Friston. NeuroImage, 2015
301 citations recorded by the source corpus.
Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems — known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for resting state fMRI – as measured in terms of their complex cross spectral density – referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden states. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/e52ec01b318327d092486484b8ac8ae0f97534fe
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Michael Breakspear
- Karl J. Friston
- Olaf Sporns
- Andrew Zalesky
- Mohamed L. Seghier
- Adeel Razi
- Cathy J. Price
- Luca Cocchi
Citations in the corpus
- A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI (2019, background): “By fitting data features in the frequency domain, estimation is significantly quicker, more efficient, and more sensitive to group differences (Razi et al., 2015).”
- Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “accommodates measurements effects including spatial or temporal filtering and additive noise (Razi et al., 2015).”