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• W2912123280 abstract "Event Abstract Back to Event Effective connectivity modulations of win-and loss feedback: A dynamic causal modeling study of the human connectome gambling task. Frederik Van De Steen1*, Ruth M. Krebs2 and Daniele Marinazzo1 1 Ghent University, Data Analysis, Belgium 2 Ghent University, Experimental psychology, Belgium The main goal of this study was to investigate changes in effective connectivity associated with reward and punishment. More specifically, changes in connectivity between the ventral striatum (VS), anterior insula (aI), anterior cingulate cortex (ACC) and occipital cortex (OCC) that are related to win- and loss- feedback were studied. Here, fMRI data from the human connectome project [1] was used for our study purposes. Data from 369 unrelated subjects performing a gambling task was analyzed. In short, participants played a card game where they had to guess whether the upcoming card would be higher or less than 5 (range was between 1 and 9). After the gamble, feedback was provided indicating a reward, punishment or neutral trial. The minimally preprocessed data was used and extra spatially smoothed with a 5-mm FWHM Gaussian kernel. The images were then entered in a first level general linear model (GLM) and summary statistic images of the first level GLM were entered in a second level GLM. The following two contrasts were used to identify the relevant brain regions at the group level: [Win – Neut] AND [Loss-Neut] (i.e. conjunction), and [Win-neut]. Based on the group level results, time-series of VS, aI, ACC and OCC were extracted for every subject and used in further dynamic causal modeling (DCM, [2]) analysis. We specified a fully connected model (i.e. all nodes are reciprocally connected) where the win and loss events were allowed to modulate all connections. The driving input consisted of all feedback events (win, loss and neutral events) and entered the DCM's via OCC. The fully connected model was estimated for every subject and then used in the recently proposed parametric empirical Bayesian (PEB, [3]) framework for estimating DCM parameters at the group level. Finally, we used Bayesian model reduction to obtain the best 255 nested models. Since there was no clear winning model, Bayesian model averaging (BMA) of the 256 model (full + 255 nested models) parameters was performed . For both the win and loss feedback, the connection from OCC to VS and from VS to ACC significantly increased. For the aI to OCC connection, we see a significant increase in win feedback but a significant decrease for loss feedback. In addition the connection from VS to aI increased for win feedback. Overall, the VS appears as a key region in conveying loss and win information across the network. Acknowledgements This research was supported by the Fund for Scientific Research-Flanders (FWO-V), Grant FWO16/ASP_H/255. References 1. Van Essen, D. et al. The WU-Minn Human Connectome Project: An overview. NeuroImage, 2013, 80: 62–79. 2. Friston, Karl J., Lee Harrison, and Will Penny. Dynamic causal modelling. Neuroimage , 2003, 19(4): 1273-1302. 3. Friston, Karl J., et al. Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage Keywords: dynamic causal modeling, Reward, connectivity, Feedback, Losses Conference: 12th National Congress of the Belgian Society for Neuroscience, Gent, Belgium, 22 May - 22 May, 2017. Presentation Type: Poster Presentation Topic: Cognition and Behavior Citation: Van De Steen F, Krebs RM and Marinazzo D (2019). Effective connectivity modulations of win-and loss feedback: A dynamic causal modeling study of the human connectome gambling task.. Front. Neurosci. Conference Abstract: 12th National Congress of the Belgian Society for Neuroscience. doi: 10.3389/conf.fnins.2017.94.00064 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 24 Apr 2017; Published Online: 25 Jan 2019. * Correspondence: Mr. Frederik Van De Steen, Ghent University, Data Analysis, Ghent, 9000, Belgium, frederik.vandesteen@ugent.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Frederik Van De Steen Ruth M Krebs Daniele Marinazzo Google Frederik Van De Steen Ruth M Krebs Daniele Marinazzo Google Scholar Frederik Van De Steen Ruth M Krebs Daniele Marinazzo PubMed Frederik Van De Steen Ruth M Krebs Daniele Marinazzo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
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• W2912123280 title "Effective connectivity modulations of win-and loss feedback: A dynamic causal modeling study of the human connectome gambling task." @default.
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