Neural Dynamics of Truth and Deception: Insights from Brain States
Poster No:
951
Submission Type:
Abstract Submission
Authors:
Yulong Xia1, Weixiong Jiang1, Enbo Hu2, Gang Li1, Shuaiqi Li1, Lin Li1, Jinhua xu1, Shoujun Huang1, Xiaoping Ouyang3, Jing Yuan1
Institutions:
1College of Mathematical Medicine, Zhejiang Normal University, Jinhua, Zhejiang, 2School of Electronic Information, Hunan First Normal University, Changsha, Hunan, 3State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang
First Author:
Co-Author(s):
Xiaoping Ouyang
State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University
Hangzhou, Zhejiang
State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University
Hangzhou, Zhejiang
Introduction:
Deception, a complex human behavior, requires advanced cognitive functions and activates specific brain regions, notably the prefrontal and anterior parietal cortex (Hakun 2020; Gao 2022). Recent dynamic exploration of brain states has revealed rapid changes triggered by external stimuli and cognitive demands (McCormick 2020). Analyzing brain states provides insight into both temporal and spatial dimensions of brain dynamics (Medaglia 2018). However, the nuanced dynamics of brain states related to deception remain elusive. In this study, we initially investigated representative brain states associated with lie-, inverse-, and truth-telling. Subsequently, we examined their dynamic attributes and spatial patterns to understand the cognitive processes underlying deception.
Methods:
53 young men (20.20 ± 1.56 ys) participated in this study and their fMRI data were acquired using task block experiments (Jiang 2015). Each subject was instructed to engage in truth-, inverse-, or lie-telling for each task block. The preprocessed fMRI data were parcellated into 232 regions of interest (ROIs) (Luppi 2022). Brain state was defined as the profiles of BOLD signals across all ROIs at a single time point. Using each brain state as a node and the inverse of the Euclidean distance between two nodes as the weight of an edge, we constructed a large temporal network (7632*7632). To mitigate threshold-related biases, we generated four weighted sparse networks at sparsity of 10%, 20%, 30% and 40%. The Louvain community clustering algorithm, with λ ranging from 0.8 to 3 in increments of 0.1, was applied to each network 100 times, and a consensus algorithm was used to derive representative states (Medaglia 2018). Dynamic attributes of the states were quantified through fractional occupancy (FO) and average dwell time (DT). FO represents the fraction of time spent in each state over each task duration of each subject (48 TRs) (Meer et al. 2020). Repeated ANOVA across all tasks and paired comparisons were conducted to discern changes in FO and DT (P<0.05, FWE corrected), identifying two states with significant differences between lie-telling and truth-telling. Further analyses of these two states were performed at the levels of the whole-brain and network to unravel their spatial and functional implications.
Results:
The results unveiled six representative states when λ=1.6 and sparsity=20%, exhibiting similar states when λ ranged from 1.5 to 1.7 and sparsity varied from 10% to 30%. Two states exhibited significant difference in FO (F=7.47, P=7.98E-04; F=11.99, P=1.43E-05, respectively) and DT (F=4.11, P=0.0182; F=3.22, P=0.0425) (Fig. 1). In the Lie-prefer State, lie-telling showed a larger FO (P=6.59E-04) and DT (P=0.0041) compared to the true-telling, indicating a more frequent and prolonged presence. In contrast, in the Truth-prefer State, true-telling demonstrated higher FO and DT (Fig. 1). The spatial characteristics of Truth-prefer State revealed elevated BOLD signals in the somatomotor areas (Fig. 2A), while the Lie-prefer State exhibited heightened BOLD signals in the frontal and parietal cortex (Fig. 2B). Subnetwork analysis disclosed significant increases in the frontoparietal network (FPN) (P=3.40E-50) and default mode network (DMN) (P=7.98E-21) when comparing the Lie-prefer State with the Truth-prefer State, along with significant decreases in five networks (Fig. 2C) (P<4.70E-05) and no significance in the limbic network (LIM) (P=0.1371).
Conclusions:
This study revealed two brain states with different dynamic properties when lie-telling versus truth-telling. These states showed marked disparities in spatial patterns and network characteristics. The Lie-prefer State showed heightened cognitive involvement with increased activation in the FPN and DMN regions. These findings emphasize the difference of two key brain states during lie-related tasks, suggesting that their dynamic attributes may serve as biomarkers reflecting the varied cognitive involvement in such tasks.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Higher Cognitive Functions Other 1
Keywords:
Other - deception; brain states; temporal networks; clustering; dynamic attributes
1|2Indicates the priority used for review
Provide references using author date format
Hakun, J.G. (2020), 'fMRI investigation of the cognitive structure of the Concealed Information Test', Neuroscience and Crime, pp. 59-67.
Jiang, W. (2015), 'Decoding the processing of lying using functional connectivity MRI', Behavioral and Brain Functions, vol. 11, no. 1, pp. 1–11.
Luppi, A. I. (2022), 'A synergistic core for human brain evolution and cognition', Nature Neuroscience, vol. 25, no. 6, pp. 771-782.
McCormick, D.A. (2020), 'Neuromodulation of brain state and behavior', Annual Review of Neuroscience, vol. 43, pp. 391-415.
Medaglia, J.D. (2018), 'Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment', NeuroImage, vol. 166, pp. 293-306.
Meer, J.N. (2020), 'Movie viewing elicits rich and reliable brain state dynamics', Nature Communications, vol. 11, no. 1, pp. 5004.