Brain Topology Disruption in Early-Onset Dementia: Review of Current Findings and the Need for Network Resilience Focused Models.

INTRODUCTION

Early-Onset Dementia (EOD), including Frontotemporal Dementia (FTD), behavioral variant FTD (bvFTD), and Early-Onset Alzheimer's Disease (EOAD), presents significant diagnostic and therapeutic challenges due to heterogeneous clinical features and rapid progression. EOD involves distinct patterns of brain network disruption, measurable through graph-theoretical analysis.

METHODS

We reviewed 23 studies applying graph theory to electroencephalography (EEG), functional MRI (fMRI), diffusion tensor imaging (DTI), and fluorodeoxyglucose positron emission tomography (FDG-PET) in EOD populations. Metrics included global and local efficiency, small-world properties, modularity, hub connectivity, and rich-club organization.

RESULTS

EOD demonstrates widespread topological disruption, including reduced global and local efficiency, hub vulnerability, and modular fragmentation. Subtype-specific patterns include compensatory efficiency increases and parietal-to-frontal hub shifts in FTD; disruption of hub connectivity and modular integrity within the salience network in bvFTD; and pronounced deterioration of the default-mode network in EOAD. Small-world properties are generally preserved in early stages, reflecting initial compensatory reorganization that precedes system-wide collapse.

CONCLUSION

Graph-theoretical analysis reveals characteristic topological disruptions in EOD. However, most studies rely on static measures, limiting insight into dynamic network vulnerability. Incorporating network resilience based computational models, longitudinal designs, and standardized analytical pipelines could clarify network failure mechanisms, uncover latent fragilities, and guide targeted interventions.