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Network discovery with DCM.

Friston KJ, Li B, Daunizeau J, Stephan KE - Neuroimage (2010)

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.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. k.firston@fil.ion.ucl.ac.uk

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Related in: MedlinePlus

Conditional estimates of hidden states: A summary of the conditional expectations (means) of the hidden states generating observed regional data is shown on the 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. The last three are log-states. These hidden states provide the predicted responses (conditional expectation) in the upper left for each region and associated prediction errors (red dotted lines), in relation to the observed data. The same data are plotted in the lower panels for about the first four minutes of data acquisition. These results show that the inferred neuronal activity in the visual region (highlighted in blue) follows visual stimulation (grey filled areas — high for attention and low for no attention). The resulting hemodynamic changes are shown as conditional means on the lower right (blue highlights blood flow in the visual region). In this figure log-states have been plotted as states (with a normalised steady-state value of one).
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f0050: Conditional estimates of hidden states: A summary of the conditional expectations (means) of the hidden states generating observed regional data is shown on the 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. The last three are log-states. These hidden states provide the predicted responses (conditional expectation) in the upper left for each region and associated prediction errors (red dotted lines), in relation to the observed data. The same data are plotted in the lower panels for about the first four minutes of data acquisition. These results show that the inferred neuronal activity in the visual region (highlighted in blue) follows visual stimulation (grey filled areas — high for attention and low for no attention). The resulting hemodynamic changes are shown as conditional means on the lower right (blue highlights blood flow in the visual region). In this figure log-states have been plotted as states (with a normalised steady-state value of one).

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.


Network discovery with DCM.

Friston KJ, Li B, Daunizeau J, Stephan KE - Neuroimage (2010)

Conditional estimates of hidden states: A summary of the conditional expectations (means) of the hidden states generating observed regional data is shown on the 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. The last three are log-states. These hidden states provide the predicted responses (conditional expectation) in the upper left for each region and associated prediction errors (red dotted lines), in relation to the observed data. The same data are plotted in the lower panels for about the first four minutes of data acquisition. These results show that the inferred neuronal activity in the visual region (highlighted in blue) follows visual stimulation (grey filled areas — high for attention and low for no attention). The resulting hemodynamic changes are shown as conditional means on the lower right (blue highlights blood flow in the visual region). In this figure log-states have been plotted as states (with a normalised steady-state value of one).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094760&req=5

f0050: Conditional estimates of hidden states: A summary of the conditional expectations (means) of the hidden states generating observed regional data is shown on the 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. The last three are log-states. These hidden states provide the predicted responses (conditional expectation) in the upper left for each region and associated prediction errors (red dotted lines), in relation to the observed data. The same data are plotted in the lower panels for about the first four minutes of data acquisition. These results show that the inferred neuronal activity in the visual region (highlighted in blue) follows visual stimulation (grey filled areas — high for attention and low for no attention). The resulting hemodynamic changes are shown as conditional means on the lower right (blue highlights blood flow in the visual region). In this figure log-states have been plotted as states (with a normalised steady-state value of one).
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.

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.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. k.firston@fil.ion.ucl.ac.uk

Show MeSH
Related in: MedlinePlus