Dynamic causal modelling of evoked potentials: a reproducibility study

Abstract

Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.

Fig. 1

Model specification. The sources comprising the network are connected with forward (dark grey), backward (grey) or lateral (light grey) connections as shown. A1: primary auditory cortex, STG: superior temporal gyrus, IFG: inferior temporal gyrus. Three different models were tested within the same architecture (a–c), allowing for learning-related changes in forward F, backward B and forward and backward FB connections, respectively. The broken lines indicate the connections we allowed to change. Sources of activity, modelled as dipoles (estimated posterior moments and locations), are superimposed in an MRI of a standard brain in MNI space (d).

Fig. 2

Grand mean ERPs, i.e., averaged over all subjects. (a) ERP responses to the standard and deviant tones overlaid on a whole scalp map of 128 EEG electrodes. (b) ERP responses to the standard and deviant tones at channel C21 (fronto-central). (c) MMN, the difference wave obtained by subtracting grand-average ERP to standards from ERP to deviants, at channel C21. (d) grand mean MMN response averaged across subjects and over the time window of [100, 200] ms interpolated for a 3D scalp topography.

Fig. 3

Bayesian model selection among DCMs for the three models, F, B and FB, expressed relative to a DCM in which no connections were allowed to change (null model). The graphs show the free energy approximation to the log-evidence. (a) Log-evidence for models F, B and FB for each subject (relative to the null model). The diamond attributed to each subject identifies the best model on the basis of the subject's highest log-evidence. (b) Log-evidence at the group level, i.e., pooled over subjects, for the three models.

Fig. 4

DCM results for a single subject [subject 9] (FB model). (a) Reconstructed responses for each source and changes in coupling adjacent to the connections during oddball processing relative to standards. The mismatch response is expressed in nearly every source. (b) Predicted (solid) and observed (broken) responses in measurement space, which result from a projection of the scalp data onto their first three spatial modes.

Fig. 5

Coupling gains and their conditional probability estimated over subjects for each connection in the network for models F (a) and FB (b). There are widespread learning-related changes in all connections, expressed as modulations of coupling for deviants relative to standards.