ThickNet Features for Prognostic Applications in Dementia

Principal Investigator: Mirza Faisal Beg
Student: Pradeep Reddy Raamana

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.

For more details, please refer to our publication:

Raamana, P. R., Wang, L., Beg, M. F., 2013. Thickness network (thicknet) features for the detection of prodromal AD. In: Proceedings of MICCAI Machine Learning in Medical Imaging workshop. Springer-Verlag LNCS 8184, pp. 115–123.