Harmonic decomposition of structural and functional connectomes in Alzheimer's disease

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder and the leading cause of dementia in older adults, accounting for 60–70% of cases. It is characterized by a gradual decline in memory and cognitive functions, eventually impairing basic daily activities. Although there is no cure, current treatments can only slow symptom progression or provide support to patients and families. With an aging global population, Alzheimer’s represents a major public health challenge, ranking among the top ten causes of death worldwide according to WHO (2019). Despite decades of research, the causes and progression mechanisms remain poorly understood. Genetic, molecular, and environmental factors play a significant role, but their interactions are still under investigation. In this context, neuroimaging has made notable advances, improving our understanding of brain connectivity networks at both structural and functional levels—an essential step toward better diagnostic and therapeutic strategies. Computational neuroscience has emerged as a key interdisciplinary field for modeling and simulating brain dynamics through mathematical tools. A central concept is the connectome, which represents the complete map of neural connections, studied from two perspectives: structural connectivity (SC) and functional connectivity (FC). Analyzing these networks helps reveal how information flows through the brain and how neurological diseases such as Alzheimer’s disrupt this flow. This project explores how Alzheimer’s affects brain dynamics and connectivity by applying harmonic decomposition methods inspired by Connectome Harmonics (Atasoy et al., 2016, 2017), Functional Harmonics (Glomb et al., 2021), and the HADES framework (Vohryzek et al., 2024). The goal is to analyze patient data from the ADNI database to identify dependencies between SC and FC and their evolution throughout disease progression. Objectives:

Apply harmonic decomposition methods to analyze brain connectivity in three groups: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer’s disease patients (AD). Investigate the relationship between SC and FC and how Alzheimer’s alters normal brain dynamics. Compare brain network organization between healthy individuals and Alzheimer’s patients.

Hypothesis: Harmonic decomposition will significantly differentiate brain activity patterns among HC, MCI, and AD groups, revealing alterations in structural and functional networks that correlate with disease progression.

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