AAIC 22 Biomarkers Abstracts

Background: Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Method: Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (`SCD') and 18 subjects with mild cognitive impairment (`MCI'). Local activity was characterized by permutation entropy (PE). Network level interactions were studied using the inverted Joint Permutation Entropy (JPEinv), corrected for volume conduction. Entropy of the overall network across different temporal scales was quantified using a global permutation entropy measure. Result: The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta-band entropy. Betweengroup differences were widespread across brain regions. ROCanalysis of classification of MCI versus SCD subjects revealed that a linear regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power based models (76.9% [60.4-93.3%]). The global entropy measure is currently being optimized. Results will be available at a later stage. Conclusion: Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD and should be explored in larger studies.

Automated summary

This study investigates the potential of permutation entropy as a biomarker for Alzheimer's disease, finding that JPEinv performs better across different frequency bands compared to other models, showing promise as a potential biomarker for early-stage AD.