Graph signal processing to identify biomarkers in brain networks in dementia

Abstract

Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Graph theory has been the standard tool to provide biomarkers in imaging connectomics showing the Alzheimer’s disease (AD). We propose a novel concept - graph signal processing - to analyze the evolution of disease graphs leading from mild cognitive impairment (MCI) to AD and derive frequency-based biomarkers representative for this disease. We show that high oscillations derived from the graph Fourier decomposition can provide important discriminatory information. To quantify the qualitative intuition of high oscillations, we use two concepts from signal theory: (1) zero crossings and (2) total variations. We apply these concepts on functional and structural brain connectivity networks for control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects. Our results applied to functional brain networks suggest that graph signal processing can accurately describe the frequencies of brain networks, and explain how AD is associated with low frequency and localized averaging confirmed by clinical results.