The alternated brain states in resting state after moral decisions
Poster No:
636
Submission Type:
Abstract Submission
Authors:
Eric Wang1,2, Xinyi Xu2, Ruyi Jiang2, Wu Haiyan2
Institutions:
1The Chinese University of Hong Kong, Hong Kong, 2Centre for Cognitive and Brain Sciences, University of Macau, Macao
First Author:
Eric Wang
The Chinese University of Hong Kong|Centre for Cognitive and Brain Sciences, University of Macau
Hong Kong|Macao
The Chinese University of Hong Kong|Centre for Cognitive and Brain Sciences, University of Macau
Hong Kong|Macao
Co-Author(s):
Introduction:
Dishonesty, a prevalent transgression of moral norms, is closely linked to the emotional aftermath stemming from heightened arousal and aversion to guilt. Studies have shown that instances of dishonesty may induce feelings of anxiety or guilt, leading to evident "after-effects" that impact subsequent behavior and neural activity (Kouchaki et al., 2016). In this work, we attempt to understand the impact of moral decisions on the brain states through a model-based method called Hidden Markov Model (HMM). This method overcomes the limitations that fail to capture time-varying changes in brain activity (Hindriks et al., 2016). It serves as a valuable tool to capture dynamic neural processes that are represented as discrete brain states. Since rs-fMRI displays intricate spatiotemporal dynamics, which can be influenced by previous tasks (Waites et al., 2005), applying HMM on rs-fMRI provides a unique opportunity to fully exploit the richness of the rs-fMRI data. Specifically, it allows us to capture how resting-state brain dynamics may shift due to prior moral decisions.
Methods:
The task used in this study was part of a series of experiments spanning seven sessions (see details in Xu et al., 2024). As shown in Figure1A, during the task, participants acted as information senders, passing food preference information to a fictitious receiver while considering the reward amount.
HMM analysis was performed on two sessions of rs-fMRI data before and after the task (Vidaurre et al., 2018). The observed time series here was the BOLD signal from 33 parcels in whole-brain parcellation of both cortical and sub-cortical. We performed exploratory analysis using leave-one-out cross-validation Calinski–Harabasz scores to determine the optimal HMM model with K ranging from 2 to 10. We found that the score peaked when K = 10. Therefore, we decided to use 10-state solutions in the following analysis (Figure1B).
To quantify the functional relevance of the inferred hidden states, we performed forward association decoding P(Activation | State) to map the spatial expression of each state onto 16 Neurosynth topics, which encompass a variety of brain processes applicable to our task.
HMM analysis was performed on two sessions of rs-fMRI data before and after the task (Vidaurre et al., 2018). The observed time series here was the BOLD signal from 33 parcels in whole-brain parcellation of both cortical and sub-cortical. We performed exploratory analysis using leave-one-out cross-validation Calinski–Harabasz scores to determine the optimal HMM model with K ranging from 2 to 10. We found that the score peaked when K = 10. Therefore, we decided to use 10-state solutions in the following analysis (Figure1B).
To quantify the functional relevance of the inferred hidden states, we performed forward association decoding P(Activation | State) to map the spatial expression of each state onto 16 Neurosynth topics, which encompass a variety of brain processes applicable to our task.
·(A) Experiment procedure: Participants received 2 scans between a information sending task (B)10 HMM states, pairwise covariance (top) and relative weighting (bottom) of the 33 parcellations
Results:
Figure 2A displays the Neurosynth result where each brain state shows distinct functional signatures. For example, State 4 is characterized by a strong association with emotion, while State 10 is primarily associated with conflict, flexibility and attention control. We next compared the differences in HMM states in post-task sessions and pre-task sessions. As shown in 2B, relative to the pre-task, we found out that the post-task was characterised by significantly higher occupancy in state 10 (P< 0.05), and significantly lower occupancy in state 4 (P< 0.05). To further understand this observation, we asked whether there is a relationship between state 10 and the behaviour results. Shown in 2C, the total lie rates were positively correlated with the mean interval time of state 10 before the task (rho = 0.4697, P < 0.05), and negatively correlated after the task (rho = -0.4613, P < 0.05). This indicates that, before the task, fewer visits were associated with higher lie rates, whereas after the task, more frequent visits were linked to higher lie rates. Additionally, by calculating the cumulative probability of transitions from other states to state 10, we also found that the higher the probability of other states transitioning to state 10 after the task, the higher the participants' total lie rates (rho = 0.4788, P < 0.05).
·(A)Neurosynth Topic maps (B)Changes in fractional occupancy in Pre and Post-Task in each state (C) Left and right: Pre and Post-Task S10 and Lie Rate,Middel: Transition Probability to S10 and Lie Rate
Conclusions:
In conclusion, by using a ten-state HMM derived from rs-fMRI, we reveal that dishonest behaviour requires heightened engagement with networks related to attentional control, likely due to the increased need for managing internal conflicts and overriding default honest impulses to pursue dishonest rewards. These findings highlight the adaptive neural mechanisms underlying moral dilemmas and the sustained impact of dishonesty on brain state organisation.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Social Neuroscience Other
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Emotions
FUNCTIONAL MRI
Modeling
Social Interactions
Other - Hidden Markov Model
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
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Provide references using APA citation style.
Kouchaki, M., & Gino, F. (2016). Memories of unethical actions become obfuscated over time. Proceedings of the National Academy of Sciences of the United States of America, 113(22), 6166–6171. https://doi.org/10.1073/pnas.1523586113
Vidaurre D, Abeysuriya R, Becker R, Quinn AJ, Alfaro-Almagro F, Smith SM, Woolrich MW. Discovering dynamic brain networks from big data in rest and task. Neuroimage. 2018 Oct 15;180(Pt B):646-656. doi: 10.1016/j.neuroimage.2017.06.077. Epub 2017 Jun 29. PMID: 28669905; PMCID: PMC6138951.
Waites, A. B., Stanislavsky, A., Abbott, D. F., & Jackson, G. D. (2005). Effect of prior cognitive state on resting state networks measured with functional connectivity. Human brain mapping, 24(1), 59–68. https://doi.org/10.1002/hbm.20069
Xu, X. J., Mobbs, D., & Wu, H. (2024). Unethical amnesia brain: Memory and metacognitive distortion induced by dishonesty. BioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.03.03.583239