TY  - JOUR
AU  - Su, Wynn
AU  - Mishra, Satyam
AU  - Bijalwan, Anchit
PY  - 2025
DA  - 2025/06/24
TI  - Exploring the Temporal Dynamics and Causal Interactions Between the Amygdala and vmPFC: A Multidimensional Approach Using Time-Lagged Correlation, Granger Causality, and Entropy Analysis
JO  - Biomedical Informatics and Smart Healthcare
T2  - Biomedical Informatics and Smart Healthcare
JF  - Biomedical Informatics and Smart Healthcare
VL  - 1
IS  - 1
SP  - 27
EP  - 34
DO  - 10.62762/BISH.2025.346171
UR  - https://www.icck.org/article/abs/BISH.2025.346171
KW  - amygdala-vmPFC connectivity
KW  - time-lagged correlation
KW  - granger causality
KW  - EEG signal analysis
KW  - entropy metrics
KW  - neural dynamics
AB  - Understanding the dynamic interaction between the amygdala and the ventromedial prefrontal cortex (vmPFC) is essential for unraveling the neural mechanisms underlying emotion regulation. This study introduces a multidimensional analytical framework that integrates functional connectivity, time-lagged correlation, Granger causality analysis (GCA), and entropy-based complexity metrics to explore the amygdala–vmPFC relationship during emotionally aversive tasks using intracranial EEG (iEEG) data. Our findings reveal a significant negative functional correlation (r = -0.009, p < 0.05) between the amygdala and vmPFC, indicating an inhibitory relationship. Time-lagged correlation analysis further uncovers a temporal delay (~30 time points), suggesting that amygdala activity precedes vmPFC modulation. Entropy analysis shows that the vmPFC exhibits higher signal complexity (H = 4.26) than the amygdala (H = 3.98), consistent with its role in higher-order emotional regulation. However, GCA results yielded no statistically significant directional influence, highlighting the non-linear and multifaceted nature of these interactions. This study is among the first to combine entropy, time-lag, and causality metrics for analyzing vmPFC–amygdala dynamics in human iEEG. Our integrated framework offers deeper insights into brain region interplay and lays the groundwork for future research in affective neuroscience and neuroeconomics.
SN  - 3068-5524
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Su2025Exploring,
  author = {Wynn Su and Satyam Mishra and Anchit Bijalwan},
  title = {Exploring the Temporal Dynamics and Causal Interactions Between the Amygdala and vmPFC: A Multidimensional Approach Using Time-Lagged Correlation, Granger Causality, and Entropy Analysis},
  journal = {Biomedical Informatics and Smart Healthcare},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {27-34},
  doi = {10.62762/BISH.2025.346171},
  url = {https://www.icck.org/article/abs/BISH.2025.346171},
  abstract = {Understanding the dynamic interaction between the amygdala and the ventromedial prefrontal cortex (vmPFC) is essential for unraveling the neural mechanisms underlying emotion regulation. This study introduces a multidimensional analytical framework that integrates functional connectivity, time-lagged correlation, Granger causality analysis (GCA), and entropy-based complexity metrics to explore the amygdala–vmPFC relationship during emotionally aversive tasks using intracranial EEG (iEEG) data. Our findings reveal a significant negative functional correlation (r = -0.009, p < 0.05) between the amygdala and vmPFC, indicating an inhibitory relationship. Time-lagged correlation analysis further uncovers a temporal delay (~30 time points), suggesting that amygdala activity precedes vmPFC modulation. Entropy analysis shows that the vmPFC exhibits higher signal complexity (H = 4.26) than the amygdala (H = 3.98), consistent with its role in higher-order emotional regulation. However, GCA results yielded no statistically significant directional influence, highlighting the non-linear and multifaceted nature of these interactions. This study is among the first to combine entropy, time-lag, and causality metrics for analyzing vmPFC–amygdala dynamics in human iEEG. Our integrated framework offers deeper insights into brain region interplay and lays the groundwork for future research in affective neuroscience and neuroeconomics.},
  keywords = {amygdala-vmPFC connectivity, time-lagged correlation, granger causality, EEG signal analysis, entropy metrics, neural dynamics},
  issn = {3068-5524},
  publisher = {Institute of Central Computation and Knowledge}
}