TY - GEN
T1 - fMRI activation network analysis using Bose-Einstein entropy
AU - Wang, Jianjia
AU - Wilson, Richard C.
AU - Hancock, Edwin R.
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - In this paper, we present a novel method for characterizing networks using the entropy associated with bosonic particles in thermal equilibrium with a heat-bath. According to this analogy, the normalized Laplacian plays the role of Hamiltonian operator, and the associated energy states are populated according to Bose-Einstein statistics. This model is subject to thermal agitation by the heat reservoir. The physics of the system can be captured by using a partition function defined over the normalized Laplacian eigenvalues. Various global thermodynamic characterizations of the network including its entropy and energy then can be computed from the derivative of corresponding partition function with respect to temperature. We explore whether the resulting entropy can be used to construct an effective information theoretic graph-kernel for the purposes of classifying different types of graph or network structure. To this end, we construct a Jensen-Shannon kernel using the Bose-Einstein entropy for a sample of networks, and then apply kernel principle components analysis (kPCA) to map graphs into low dimensional feature space. We apply the resulting method to classify fMRI activation networks from patients with suspected Alzheimer disease.
AB - In this paper, we present a novel method for characterizing networks using the entropy associated with bosonic particles in thermal equilibrium with a heat-bath. According to this analogy, the normalized Laplacian plays the role of Hamiltonian operator, and the associated energy states are populated according to Bose-Einstein statistics. This model is subject to thermal agitation by the heat reservoir. The physics of the system can be captured by using a partition function defined over the normalized Laplacian eigenvalues. Various global thermodynamic characterizations of the network including its entropy and energy then can be computed from the derivative of corresponding partition function with respect to temperature. We explore whether the resulting entropy can be used to construct an effective information theoretic graph-kernel for the purposes of classifying different types of graph or network structure. To this end, we construct a Jensen-Shannon kernel using the Bose-Einstein entropy for a sample of networks, and then apply kernel principle components analysis (kPCA) to map graphs into low dimensional feature space. We apply the resulting method to classify fMRI activation networks from patients with suspected Alzheimer disease.
KW - Bose-Einstein statistics
KW - Jensen-Shannon divergence
KW - Network entropy
UR - http://www.scopus.com/inward/record.url?scp=84996799521&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-49055-7_20
DO - 10.1007/978-3-319-49055-7_20
M3 - Conference Proceeding
AN - SCOPUS:84996799521
SN - 9783319490540
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 218
EP - 228
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop S+SSPR 2016, Proceedings
A2 - Biggio, Battista
A2 - Wilson, Richard
A2 - Loog, Marco
A2 - Escolano, Francisco
A2 - Robles-Kelly, Antonio
PB - Springer Verlag
T2 - Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2016
Y2 - 29 November 2016 through 2 December 2016
ER -