Network discovery with DCM.
Bottom Line: The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks.The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions.We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.
Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. email@example.comShow MeSH
Related in: MedlinePlus
Mentions: As for the simulated data of the previous section, we inverted a DCM with full connectivity using the first 256 volumes of the time-series. Because we did not know the level of observation noise in these data, we reduced the prior expectation of its log-precision to four; otherwise, the analyses of simulated and empirical data were identical. A summary of the conditional expectations of hidden states generating regional activity are shown in Fig. 10 (upper right). The solid lines are time-dependent means and the grey regions are 90% confidence intervals (i.e., confidence tubes). These states comprise, for each region, neuronal activity, vasodilatory signal, normalised flow, volume and deoxyhemoglobin content, where the last three are log-states. These hidden states provide the predicted responses in the upper left panel for each region and the associated prediction errors (red dotted lines). The same data are plotted in the lower panels for the first four minutes of data acquisition, with hidden neuronal states on the left and hemodynamic states on the right (where log-states are plotted as states). These results are presented to show that inferred neuronal activity in the visual region (highlighted in blue) follows visual stimulation (grey filled areas — high for attention and low for no attention). This confirms that model inversion has effectively deconvolved neuronal activity from hemodynamic signals; and that this deconvolution is veridical, in relation to known experimental manipulations. Recall that the model was not informed of these manipulations but can still recover evoked responses. The associated hemodynamic states of all regions are shown on the lower right (blue highlights blood flow in the visual region). It can be seen that changes in blood flow are in the order of 10%, which is in the physiologically plausible range.
Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. firstname.lastname@example.org