Poster No:
897
Submission Type:
Abstract Submission
Authors:
Miroslaw Wyczesany1, Thomas Kroker2, Anna Lesniewska1, Maimu Rehbein2, Kati Roesmann3, Ida Wessing2, Markus Junghöfer2
Institutions:
1Jagiellonian University, Krakow, PL, 2University of Münster, Münster, DE, 3University of Siegen, Siegen, DE
First Author:
Co-Author(s):
Introduction:
In our previous study, we were able to show that cognitive biases that are associated with decision-making (the framing effect or the gambler's fallacy) can be reduced by the offline transcranial magnetic stimulation of the ventromedial prefrontal cortex (vmPFC) in a gambling task (Kroker et al. 2022). To get better insight into the role of this structure in rational decision-making, we carried out an effective connectivity analysis of MEG responses to feedback that informs participants of gains or losses. To manipulate the activity of the vmPFC, we performed off-line transcranial magnetic stimulation of this area.
Methods:
37 subjects (age 19-29 yrs, mean 23.4; 17 women, 20 men). Each of them took part in two counterbalanced offline TMS sessions, held on different days (excitatory EX or inhibitory IN. A gambling task with a choice of either holding or losing a known sum of money or gambling for a potential higher gain was described in (Kroker et al. 2023). We analyzed 1.5-second epochs of brain responses to feedback information that marked WIN or LOSE trials. MEG signals were collected using a 275 whole-head sensor system (CTF Systems; first-order axial gradiometers). Preprocessing was based on the procedure in (Mantini et al. 2011) and further extended in (Spadone et al. 2021) using the Atlantis Connectivity Toolbox (https://atlantis.psychologia.uj.edu.pl). The signal was filtered (2–48 Hz) and screened for bad channels and artifacts. Then ICA was run, followed by automatic classification of brain and non-brain components. Brain components were then localized with the Minimum Norm Method using their weight matrices. The preselected ROIs were as follows: perigenual vmPFC (pgVM; 0 37 -13), lateral orbitofrontal cortex (L/R latOFC'; -33 32 -19 / 34 32 -19), dorsolateral PFC (L/R DL; -39 34 37 / 32 50 26), dorsal anterior cingulate (dACC'; 4 24 34), anterior insula (L/R aIns; 36 20 0 / -34 20 2), temporal pole (L/R TmpPole; -45 6 -45 / 45 16 -45). ROI signals were reconstructed a sum of IC signals in the ROI locations subjected to PCA to yield a scalar value. The resulting signals were corrected for source leakage (Colclough et al. 2015). Finally, the directional connectivity was estimated in the beta and gamma bands using the non-normalized directed transfer function in the theta, alpha and beta bands (Kaminski, Blinowska, 1991)).
Results:
The main effect of session revealed a massive increase of network activity (EX>IN) of the vmPFC, OFC, DL and aIns regions. The vmPFC mostly increased its inflow, while the DL areas their outflows. Also, heightened outflows from bilateral aIns towards bilateral TmpPole were visible. The main effects of outcome was mainly seen as an increased signaling from the pgVM to the ROFC and the RTmpPole in response to LOSS feedback. Interaction effects included increased flow from the pgVM to the RaIns after the EX stimulation for GAIN feedback. Another interaction observed was a strengthened connectivity from both pgVM and RDL towards LDL after the EX stimulation in response to LOSS cues.
Conclusions:
The observed effects accompany behavioral changes seen as improved rationality of choices after pgVM excitatory stimulation. The impact of stimulation on the configuration of outflows and inflows indicates the cascade of information flow between involved nodes when processing decision feedback. Two functionally separate networks can be distinguished based on connectivity data, the first one including vmPFC, OFC, and DL areas, and the second one including bilateral aIns and TmpPole regions. The observed interaction of stimulation and feedback information can be considered a neural substrate of increased sensitivity to loss signals after pgVM excitation, which may guide more efficient preparation of behavioral plans important for decision-making.
Brain Stimulation:
Non-invasive Magnetic/TMS
Emotion, Motivation and Social Neuroscience:
Reward and Punishment
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
EEG/MEG Modeling and Analysis
Keywords:
ADULTS
Cortex
MEG
Source Localization
Transcranial Magnetic Stimulation (TMS)
Other - effective connectivity
1|2Indicates the priority used for review
Provide references using author date format
Colclough GL, Brookes MJ, Smith SM, Woolrich MW. (2015). 'A symmetric multivariate leakage correction for MEG connectomes'. NeuroImage. vol. 117, pp. 439–448
Kamiński MJ, Blinowska KJ. (1991). 'A new method of the description of the information flow in the brain structures'. Biological Cybernetics. vol. 65(, no. 3, pp. 203–210.
Kroker T, Wyczesany M, Rehbein MA, Roesmann K, Wessing I, Junghöfer M. (2022). 'Noninvasive stimulation of the ventromedial prefrontal cortex modulates rationality of human decision-making'. Scientific Reports. vol. 12, no 1, pp. 20213.
Kroker T, Wyczesany M, Rehbein MA, Roesmann K, Wessing I, Wiegand A, et al. (2023). 'Excitatory stimulation of the ventromedial prefrontal cortex reduces cognitive gambling biases via improved feedback learning'. Scientific Reports. vol. 13, no 1, pp. 17984.
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Spadone S, Wyczesany M, Della Penna S, Corbetta M, Capotosto P. 'Directed flow of beta band communication during reorienting of attention within the dorsal attention network'. Brain connectivity. vol. 11, no. 9, pp. 717–72