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On The Causal Network Of Face-selective Regions In Human Brain During Movie Watching

arXiv 2501.02333 ↗First posted 4 Jan 2025q-bio.NC cs.LG eess.IVcited by 0

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Abstract (arXiv)

Understanding the causal interactions in some brain tasks, such as face processing, remains a challenging and ambiguous process for researchers. In this study, we address this issue by employing a novel causal discovery method -Directed Acyclic Graphs via M-matrices for Acyclicity (DAGMA)- to investigate the causal structure of the brain's face-selective network and gain deeper insights into its mechanism. Using fMRI data of natural movie stimuli, we extract causal network of face-selective regions and analyze how frames containing faces influence this network. Specifically, our findings reveal that the presence of faces in the stimuli, causally affects the number of identified connections within the network. Additionally, our results highlight the crucial role of subcortical regions in satisfying causal sufficiency, emphasizing it's importance in causal studies of brain. This study provides a new perspective on understanding the causal architecture of the face-selective network of the brain, motivating further research on neural causality.

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References · 8

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  3. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 140
  4. 2022From Statistical to Causal Learningcited by 40
  5. 2021Can Information Flows Suggest Targets for Interventions in Neural Circuits?cited by 6
  6. 2023Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessmentcited by 4
  7. 2021Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Studycited by 2
  8. 2024Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networkscited by 1

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