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There is increasing evidence to suggest functional connectivity networks arenon-stationary. This has lead to the development of novel methodologies withwhich to accurately estimate time-varying functional connectivity networks.Many of these methods provide unprecedented temporal granularity by estimatinga functional connectivity network at each point in time; resulting inhigh-dimensional output which can be studied in a variety of ways. One possiblemethod is to employ graph embedding algorithms. Such algorithms effectively mapestimated networks from high-dimensional spaces down to a low dimensionalvector space; thus facilitating visualization, interpretation andclassification. In this work, the dynamic properties of functional connectivityare studied using working memory task data from the Human Connectome Project. Arecently proposed method is employed to estimate dynamic functionalconnectivity networks. The results are subsequently analyzed using two graphembedding methods based on linear projections. These methods are shown toprovide informative embeddings that can be directly interpreted as functionalconnectivity networks.

Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data