Time-Reversal Enhanced Dynamic Causality Distribution Learning and Its Application in Identifying Dynamic ECNs in MCI Patients.

Objective: Dynamic causal influences between brain regions are crucial for understanding the temporal variation and fluctuation of the interaction in human brain. However, recent causal discovery approaches often focus on fixed causality under directed acyclic graph constraints, and do not infer the dynamic and fluctuating nature of causality, which commonly exists in the brain.

Methods: We propose a causality learning framework with evolving distribution for non-stationary and non-linear systems. Based on this framework, a time-reversal enhanced dynamic causality distribution learning (TRDCDL) model is constructed, which integrates spatio-temporal information to identify evolving distributional sparse interactions in data.

Results: TRDCDL is validated in two synthetic models, which show the accuracy in learning both linear and non-linear causality within synthetic data. We further apply TRDCDL to the Alzheimer's Disease Neuroimaging Initiative dataset and infer dynamic effective connectivity networks (dECNs) among two stages of mild cognitive impairment (MCI).

Conclusion: The results reveal significant differences in dECNs between brain regions across the these stages, indicating that dECNs can serve as reliable neuromarkers for distinguishing different stages of MCI.

Significance: Significant reductions in dynamic causal influences within the default mode network and bilateral limbic network, along with few increased connectivity, reflect neurodegeneration and changing patterns of dECNs as MCI progresses.