EEG-based information transfer paths during motion discrimination: Detectable differences between normal cognition
Alina Renli1,2,3, Boxin Sun2, Ming Gu2
1Department of Neuroscience, Michigan State University, East Lansing, Michigan, USA.
Background:
This study explores how cognitive impairment can affect the effective connectivity of the brain network under task stimuli.
Method:
Our research focuses on task-based electroencephalography (64-channel), in which participants were asked to perform a motion direction discrimination task. The current dataset includes 56 consensus-diagnosed, community-dwelling American seniors (ages 60-90 years, 28 normal cognition [NC]; 28 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We evaluate the effective connectivity across all the possible region of interest (ROI) pairs using causalized convergent cross mapping and identify possible discrepancies between seniors with NC and MCI.
Results:
Individuals with MCI exhibit weaker links than those with NC in some region pairs but concurrently activate more information transfer paths in other region pairs, especially in the frontal and temporal areas.
Conclusion:
Our results demonstrate the compensatory mechanism of the brain communication network for weakened links in critical regions under cognitive impairment.
Highlights:
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.
