Thickness NETwork (ThickNet) features for the detection of prodromal AD

Abstract

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between two nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, thickness network (ThickNet) features are computed using nodal degree, betweenness and clustering coefficient measures. Fusing them with multiple kernel learning, we demonstrate their potential for the detection of prodromal AD. © 2013 Springer International Publishing.