A review of heterogeneous data mining for brain disorders
It addresses the challenge of integrating complex, high-dimensional data for brain disorder research, but is incremental as it reviews existing methods rather than proposing new ones.
This paper reviews data mining methods for analyzing brain disorders, focusing on integrating heterogeneous data like tensor neuroimaging and brain connectivity networks to improve disease investigation and therapeutic interventions.
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.