Abstract
In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple clustering method is a powerful analytical tool, which identifies clustering patterns of subjects depending on their FC in specific brain areas. However, when one applies an existing multiple clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i.e., vectorizing a correlation matrix. Such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple clustering method based on Wishart mixture models, which preserves the correlation matrix structure without vectorization. The uniqueness of this method is that the multiple clustering of subjects is based on particular networks of nodes (or regions of interest, ROIs), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI sub-network. The key assumption of the method is independence among sub-networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.
Highlights
In neuroscience, the functional magnetic resonance imaging is a vital tool to non-invasively access brain activity
For functional magnetic resonance imaging (fMRI) data, we aim to demonstrate the workability of the proposed method in a real situation
We consider the application to two types of datasets released in the Human Connectome Project (HCP) [49]
Summary
The functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. FC is promising as potential biomarkers for various psychiatric disorders [6, 7]. It is recently suggested that the clustering of patients based on FC may reveal important subtypes of psychiatric disorders [8, 9, 10, 11]. Despite its usefulness, high dimensionality of FC data hinders an effective application of conventional clustering methods. This is mainly because in addition to the well-known problem of ‘curse of dimensionality’ [12] for high-dimensional datasets, there are specific problems for cluster analysis. There may exist the underlying multiple cluster solutions of subjects, which are characterized by different combinations of features
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