Differentiating neurodegenerative diseases based on EEG complexity

• Published in: Scientific reports 2024-10, Vol.14 (1), p.24365-10, Article 24365
• Main Authors: Mostile, Giovanni, Terranova, Roberta, Carlentini, Giulia, Contrafatto, Federico, Terravecchia, Claudio, Donzuso, Giulia, Sciacca, Giorgia, Cicero, Calogero Edoardo, Luca, Antonina, Nicoletti, Alessandra, Zappia, Mario
• Format: Article
• Language: English
• Subjects: 692/53/2421; 692/617/375; Aged; Aged, 80 and over; Alzheimer's disease; Biomarkers; Cognition; Cognition & reasoning; Correlation coefficient; Degenerative diseases; Diagnosis, Differential; Differential diagnosis; Disease; EEG; Electroencephalography; Electroencephalography - methods; Female; Fractals; Humanities and Social Sciences; Humans; Male; Middle Aged; multidisciplinary; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neurodegenerative Diseases - diagnosis; Neurodegenerative Diseases - physiopathology; Neuropsychology; Quantitative EEG; Science; Science (multidisciplinary); Spectrum power-law decay exponent; Synucleinopathies - diagnosis; Synucleinopathies - physiopathology; Tau protein; Tauopathies; Tauopathies - diagnosis; Tauopathies - physiopathology; α-Synucleinopathies
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-74035-x

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.