Exploring Alzheimer's Disease and Mild Cognitive Impairment Using Symbolic Information Approaches
Identifying early alterations associated with Alzheimer's disease (AD) is crucial for improving diagnosis and intervention strategies. Mild cognitive impairment (MCI) is considered a prodromal stage of AD, although not all individuals with MCI progress to dementia. Distinguishing between progressive and stable forms of MCI remains a major challenge in the search for reliable biomarkers of disease development. Here, we analyze magnetoencephalography (MEG) recordings from 80 participants distributed among four groups: AD patients, MCI patients who later progressed to AD, stable MCI patients, and healthy controls. To characterize the underlying brain dynamics, we employ the Bandt–Pompe symbolic methodology and compute information-theoretic quantifiers, including Shannon permutation entropy and statistical complexity. By mapping the signals onto the complexity–entropy plane, we investigate whether distinct dynamical signatures emerge across the different clinical groups. The results contribute to a better understanding of MEG signal dynamics and highlight the potential of entropy-complexity analysis as a tool for the early detection of AD.
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