Novel histogram-weighted cortical thickness networks and a multi-scale analysis of predictive power in Alzheimer's disease

摘要

Network analysis based on anatomical covariance (cortical thickness) has been gaining increasing popularity in the last decade. However, there has not been a systematic study of the impact of nodal sizes and edge definitions on predictive performance among various network studies. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this study, we present a histogram-based approach to construct weighted networks that enables a comparison across vastly different methods of network analysis. We design several weighted networks based on cortical thickness and perform a robust evaluation and comparison of their predictive power. We present several interesting insights obtained.