Scholarlink

<p>Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain’s temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states—termed <i>truth-preferred</i> and <i>lie-preferred</i>—exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neural dynamics of deception: insights from fMRI studies of brain states

  • 期刊论文

  • 发表日期：

    2025年2月20日

查看全文
  • Weixiong Jiang,
  • Lin Li,
  • Yulong Xia,
  • Sajid Farooq,
  • Gang Li,
  • Shuaiqi Li,
  • Jinhua Xu,
  • Sailing He,
  • Xiangyu Wu,
  • Shoujun Huang,
  • Jing Yuan,
  • Dexing Kong

摘要

Deception is a complex behavior that requires greater cognitive effort than truth-telling, with brain states dynamically adapting to external stimuli and cognitive demands. Investigating these brain states provides valuable insights into the brain’s temporal and spatial dynamics. In this study, we designed an experiment paradigm to efficiently simulate lying and constructed a temporal network of brain states. We applied the Louvain community clustering algorithm to identify characteristic brain states associated with lie-telling, inverse-telling, and truth-telling. Our analysis revealed six representative brain states with unique spatial characteristics. Notably, two distinct states—termed truth-preferred and lie-preferred—exhibited significant differences in fractional occupancy and average dwelling time. The truth-preferred state showed higher occupancy and dwelling time during truth-telling, while the lie-preferred state demonstrated these characteristics during lie-telling. Using the average z-score BOLD signals of these two states, we applied generalized linear models with elastic net regularization, achieving a classification accuracy of 88.46%, with a sensitivity of 92.31% and a specificity of 84.62% in distinguishing deception from truth-telling. These findings revealed representative brain states for lie-telling, inverse-telling, and truth-telling, highlighting two states specifically associated with truthful and deceptive behaviors. The spatial characteristics and dynamic attributes of these brain states indicate their potential as biomarkers of cognitive engagement in deception.