š¤ AI Summary
Causal interactions in brain networks exhibit inherent nonlinearity and time-varying dynamics, which conventional linear Granger causality methods fail to capture effectively. To address this limitation, we propose TAR4Cāa novel framework that integrates threshold autoregressive (TAR) modeling into the Granger causality paradigm for joint modeling and interpretable inference of directional, nonlinear, and time-varying causal relationships in brain networks. Evaluated on multichannel EEG data recorded during motor execution and motor imagery tasks, TAR4C robustly identifies cross-subject consistent, task-discriminative causal connectivity patterns: both conditions rely on primary motor cortex-driven causality, yet motor imagery lacks the sensorimotor feedback-mediated regulatory pathways observed during motor execution. TAR4C thus establishes a new paradigm for dynamic, mechanistically interpretable causal analysis of functional brain networks.
š Abstract
One fundamental challenge of data-driven analysis in neuroscience is modeling causal interactions and exploring the connectivity between nodes in a brain network. Various statistical methods, using different perspectives and data modalities, have been developed to understand the causal structures in brain dynamics. This study introduces a novel statistical approach, TAR4C, to dissect causal interactions in multichannel EEG recordings. TAR4C uses the threshold autoregressive (TAR) model to describe causal interactions between nodes in a brain network from two perspectives. The first tests whether one node controls the dynamics of another. The controlling node, named the threshold variable, implies its causative role since it operates as a switching mechanism governing the instantaneous transitions between autoregressive structures. This concept is known as threshold non-linearity. Once verified between a node pair, the next step in TAR modeling is assessing the causal node's predictive ability on the other's activity, representing causal interactions in autoregressive terms, a concept underlying Granger (G) causality. TAR4C can discover non-linear, time-dependent causal interactions while maintaining the G-causality framework. The approach's efficacy is demonstrated through EEG data from a motor execution/imagery experiment. By comparing causal interactions during motor execution and imagery, TAR4C reveals key similarities and differences in brain connectivity across subjects.