EEG‐based information transfer paths during motion discrimination: Detectable differences between normal cognition and MCI

Existing work on brain connectivity analysis was mainly focused on functional connectivity rather than effective connectivity, making it a challenge to fully understand the dynamic interactions and causal relationships within the brain network. As an effort to address this problem, in this study, we applied the new causality model, causalized convergent cross mapping (cCCM), to electroencephalography data under motion discrimination tasks and aimed to capture the differences in brain effective connectivity between mild cognitive impairment (MCI) patients and those with normal cognition (NC). It was shown that cCCM can capture the inter-region information transfer which may not be captured by Pearson correlation. That is, region pairs with low functional connectivity may still exhibit strong effective connectivity. Our results indicated that those with NC exhibit stronger effective connectivity than MCI individuals across certain region pairs, and the number of such region pairs (where those with NC shows more active information transfer) increases as the load imposed upon the brain increases. Concurrently, those with MCI activate many more information transfer paths than NC individuals during the motion detection task. This may reflect the compensatory mechanism of the brain communication network for weakened links under cognitive impairment.