Investigating the Multidimensional Structure of Apathy in Cognitive Decline: An Exploratory Network Analysis of the Apathy Evaluation Scale

Objective: Apathy is a multidimensional syndrome present in neurodegenerative disorders such as Alzheimer's disease and mild cognitive impairment, distinct from depression. It requires targeted assessment tools able to explore and measure it as a complex system. This study aimed to explore the latent structure of apathy in individuals with cognitive decline using Exploratory Graph Analysis (EGA) applied to the Apathy Evaluation Scale - Clinician version (AES-C), and to compare this structure with traditional factorial models. Methods: A total of 214 individuals, including patients with mild cognitive impairment (MCI), Alzheimer's disease (AD), and healthy controls, completed the AES-C and a standardized neuropsychological battery. EGA was used to estimate the network-based factor structure of the AES-C. Confirmatory factor analysis compared the EGA-derived model with both the original structure proposed by Marin et al. and a model derived through exploratory factor analysis (EFA). Results: EGA revealed a four-factor structure: interest and motivation, autonomy, novelty, and social apathy. This solution demonstrated better fit, parsimony, and conceptual clarity than both the original and EFA-derived models. The EGA model showed good internal consistency, convergent and discriminant validity, and performed consistently across MCI and AD subgroups. Conclusions: EGA provided a clearer understanding of apathy by identifying specific, clinically relevant, and objective dimensions that differ in disease progression. This precise characterization facilitates targeted and timely interventions and has the potential to initiate the development of new test batteries fully validated through network methods, allowing more complex analysis of apathy symptoms.