Paper Info
Title
Clustering brain-network-connectivity states using kernel partial correlations
Abstract
In response to the demand on data-analytic tools that monitor time-varying connectivity patterns within brain networks, the present paper extends the framework of [Slavakis et al., SSP'16] to include kernel-based partial correlations as a tool for clustering dynamically evolving connectivity states of networks. Such an extension becomes feasible due to the argument which runs beneath also this work: network dynamics can be successfully captured if learning is performed in Rie-mannian manifolds. Sequences of kernel-based partial correlations, collected over time and across a network, are mapped to sequences of points in the Riemannian manifold of positive-(semi)definite matrices, and a sequence that corresponds to a specific connected state of the network forms a submanifold or cluster. Based on a very recently developed line of research, this work demonstrates that by exploiting Riemannian geometry in a specific way, the present clustering framework outperforms classical and state-of-the-art techniques on segmenting connectivity states, observed from both synthetic and real brain-network data.
Year
DOI
Venue
2016
10.1109/ACSSC.2016.7869039
2016 50th Asilomar Conference on Signals, Systems and Computers
Keywords
Field
DocType
Networks,fMRI,clustering,dynamic,Riemannian manifold,partial correlation,kernel
Kernel (linear algebra),Mathematical optimization,Network dynamics,Kernel embedding of distributions,Riemannian manifold,Computer science,Symmetric matrix,Riemannian geometry,Cluster analysis,Manifold
Conference
ISSN
ISBN
Citations 
1058-6393
978-1-5386-3955-9
0
PageRank 
References 
Authors
0.34
7
8
Name
Order
Citations
PageRank
Konstantinos Slavakis158340.76
Shiva Salsabilian200.34
David S. Wack3102.93
sarah feldt muldoon4174.85
Henry E. Baidoo-Williams5284.89
Jean Vettel6749.58
Matthew Cieslak7765.99
Scott T. Grafton843245.40