Differentiating neurodegenerative diseases based on EEG complexity

Abstract

Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and α-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 β applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on β. N = 230 patients with a diagnosis of tauopathy or α-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent β was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on β values was performed to identify independent subgroups. Data-driven clustering based on β differentiated tauopathies (overall lower β values) from α-synucleinopathies (higher β 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 β values were found between tauopathies and α-synucleinopathies. Hence, β is proposed as a possible biomarker of differential diagnosis and neuronal connectivity.

Keywords: Degenerative diseases; Quantitative EEG; Spectrum power-law decay exponent; Tauopathies; α-Synucleinopathies.

Fig. 1

Non-hierarchical clustering of neurodegeneration based on EEG power-law exponent β. (N = 230; 2 Groups by pseudo-F). LEFT: Cluster A: N = 77. RIGHT: Cluster B: N = 153. Cluster A presented overall sig. higher β values, including more α-synucleinopathies with impaired cognition (PDMCI, PDD). Cluster B presented overall sig. lower β values, including more tauopathies with impaired cognition (PSP, CBD, AD).

Fig. 2

Topoplot showing differences in topographical distribution of power-law exponent β based on selected sites of recordings between Cluster A and Cluster B as well as in principal included α-synucleinopathies (for Cluster A) and tauopathies (for Cluster B). Range colours based on β values from lower (blue) to higher (yellow).

Fig. 3

Heatmap of correlation coefficients matrix showing differences in functional connectivity among frontal sites of recording between identified clusters (LEFT: Cluster A, N = 77; RIGHT: Cluster B, N = 153) based on β values. Range colours based on correlation r.