Background. Small-world network consists of networks with local specialization and global integration. Our objective is to detect small-world properties alteration based on cortical thickness in mild cognitive impairment (MCI) including stables and converters, and early Alzheimer's disease (AD) compared to controls. Methods. MRI scans of 13 controls, 10？MCI, and 10 with early AD were retrospectively analyzed; 11？MCI converters, 11？MCI stables, and 10 controls from the ADNI website were also included. Results. There were significantly decreased local efficiencies in patients with MCI and AD compared to controls; and MCI patients showed increased global efficiency compared to AD and controls. The MCI converters experience the worst local efficiency during the converting period to AD; the stables, however, have highest local and global efficiency. Conclusions. The abnormal cortical thickness-based small-world properties in MCI and AD as well as the distinct patterns between two MCI subtypes suggest that small-world network analysis has the potential to better differentiate different stages of early dementia. 1. Introduction Brain network is an important concept in the study of brain mechanisms underlying disrupted anatomical and functional brain connectivity in many neurological diseases. It describes the human brain as a large, interacting, and complex network characterized by highly coordinated, efficient, and integrated neuronal activities among specialized and widely distributed cerebral cortices [1]. Brain small-world network is a relatively new concept, characterized by a class of regional hubs with short communication length and high clustering coefficient based on quantitative imaging values such as fMRI or EEG/MEG time series, diffusion indices, and volumetric and cortical thickness measurements [2–5]. Based on graph theory, the topological properties of small-world network include many short-distance neighboring connections and some long-distance connections that are composed of numerous nodes (vertices) related to one another by edges (connections) [6]. A few advantages of brain small-world topologies have been proposed and described [1, 7]. These include maximizing the complexity and plasticity of function while minimizing cost (i.e., less neuronal utilization); optimal synchronization of neural activity among different brain regions via central hubs; and most importantly, protecting the brain from random failure by redundant densely neighbored connections and targeted attacks under disease conditions with high resilience by high centrality and
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