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

TY - JOUR

T1 - Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data

AU - Monti, Ricardo Pio

AU - Lorenz, Romy

AU - Hellyer, Peter

AU - Leech, Robert

AU - Anagnostopoulos, Christoforos

AU - Montana, Giovanni

N1 - 4 pages, 1 figure, 5th International Workshop on Pattern Recognition in Neuroimaging, Stanford University, 2015

PY - 2015/6/17

Y1 - 2015/6/17

N2 - There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estimate time-varying functional connectivity networks. Many of these methods provide unprecedented temporal granularity by estimating a functional connectivity network at each point in time; resulting in high-dimensional output which can be studied in a variety of ways. One possible method is to employ graph embedding algorithms. Such algorithms effectively map estimated networks from high-dimensional spaces down to a low dimensional vector space; thus facilitating visualization, interpretation and classification. In this work, the dynamic properties of functional connectivity are studied using working memory task data from the Human Connectome Project. A recently proposed method is employed to estimate dynamic functional connectivity networks. The results are subsequently analyzed using two graph embedding methods based on linear projections. These methods are shown to provide informative embeddings that can be directly interpreted as functional connectivity networks.

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

KW - stat.AP

KW - q-bio.NC

M3 - Article

JO - arXiv

JF - arXiv

ER -