Ordinal patterns for connectivity networks in brain disease diagnosis

Abstract

Brain connectivity networks have been widely used for diagnosis of brain-related diseases,e.g.,Alzheimer’s disease (AD),mild cognitive impairment (MCI),and attention deficit hyperactivity disorder (ADHD). Although several network descriptors have been designed for representing brain connectivity networks,most of them not only ignore the important weight information of edges,but also cannot capture the modular local structures of brain connectivity networks by only focusing on individual brain regions. In this paper,we propose a new network descriptor (called ordinal pattern) for brain connectivity networks,and apply it for brain disease diagnosis. Specifically,we first define ordinal patterns that contain sequences of weighted edges based on a functional connectivity network. A frequent ordinal pattern mining algorithm is then developed to identify those frequent ordinal patterns in a brain connectivity network set. We further perform discriminative ordinal pattern selection,followed by a SVM classification process. Experimental results on both the ADNI and the ADHD-200 data sets demonstrate that the proposed method achieves significant improvement compared with state-of-the-art brain connectivity network based methods.