Differentiating neurodegenerative diseases based on EEG complexity

Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and alpha-synucleinopathies were considered. The neurodegenerative processes and relative differential diagnosis were addressed through a qEEG non-linear analytic method. Study aims were to test accuracy of the power law exponent beta applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on beta. N = 230 patients with a diagnosis of tauopathy or alpha-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent beta was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on beta values was performed to identify independent subgroups. Data-driven clustering based on beta differentiated tauopathies (overall lower beta values) from alpha-synucleinopathies (higher beta values) with high sensitivity and specificity. Tauopathies also presented lower values in the correlation coefficients matrix among frontal sites of recording. In conclusion, significant differences in beta values were found between tauopathies and alpha-synucleinopathies. Hence, beta is proposed as a possible biomarker of differential diagnosis and neuronal connectivity.