Effective connectivity during the prospect of reward and task-difficulty: A dynamic causal modelling study.
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1
Ghent University, Data analysis, Belgium
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2
Ghent University, Experimental psychology, Belgium
The aim of this work is to study how different brain areas interact during reward and attention. More specifically, preparatory processing during reward and attentional demands is investigated. Dynamic causal modelling (DCM) for event-related responses is used to analyse electroencephalography (EEG, David et al., 2006) data where participants performed a cued visual discrimination task. The cue indicated whether the upcoming task was rewarded or not and whether the target would be difficult or easy. Event related potentials analysis of the cue period showed a clear enhancement of the P2, P3 and contingent negative variation (CNV) for reward cues. Only the late part of the CNV was affected by cues signalling difficult targets (Schevernels et al, 2014). In order to explain these effects in terms of dynamical interactions in the brain, different DCM's are fitted to the data. Here, interactions between bilateral lingual gyrus, bilateral lateral prefontal cortex and bilateral motor areas will be studied. In addition, the anterior cingulate cortex (ACC) will be included in some DCM's. Bayesian model comparison will be used to 1) determine whether the inclusion of ACC provides the best trade-off between model fit and model complexity and 2) identify which of the connections between the different brain areas are modulated by the task (Stephan et al., 2009). Finally, a post-hoc analysis of the DCM's will be performed in order to link the DCM parameters to the temporal ERP effects. This will be done by comparing models showing little or no predicted effects with models showing the largest predicted effects.
Acknowledgements
This work is supported by a pre-doctoral fellowship by the Flemish Research Foundation (FWO).
References
David O., Kiebel S. J., Harrison L.M., Mattout J., Kilner J.M., Friston K.J.,
(2006). Dynamic causal modeling of evoked responses in EEG
and MEG. Neuroimage, 30:1255–1272.
Schevernels H., Krebs R.M., Santens P., Woldorff M.G., Boehler C.N., (2014). Task preparation
processes related to reward prediction precede those related to task-difficulty expectation.
Neuroimage, 84:639–647.
Stephan K.E., Penny W.D., Daunizeau J., Moran R.J., Friston K.J., (2009). Bayesian model selection for group studies. NeuroImage 46, 1004 – 1017.
Keywords:
dynamic causal modelling,
Attention,
EEG,
effective connectivity,
Reward
Conference:
Second Belgian Neuroinformatics Congress, Leuven, Belgium, 4 Dec - 4 Dec, 2015.
Presentation Type:
Poster Presentation
Topic:
Methods and Modeling
Citation:
Van De Steen
F,
Krebs
RM and
Marinazzo
D
(2015). Effective connectivity during the prospect of reward and task-difficulty: A dynamic causal modelling study..
Front. Neuroinform.
Conference Abstract:
Second Belgian Neuroinformatics Congress.
doi: 10.3389/conf.fninf.2015.19.00036
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Received:
05 Nov 2015;
Published Online:
17 Nov 2015.
*
Correspondence:
Mr. Frederik Van De Steen, Ghent University, Data analysis, Ghent, Oost-Vlaanderen, 9000, Belgium, frederik.vandesteen@ugent.be