TY - GEN
T1 - Multi-kernel change detection for dynamic functional connectivity graphs
AU - Karanikolas, Georgios Vasileios
AU - Sporns, Olaf
AU - Giannakis, Georgios B
PY - 2018/4/10
Y1 - 2018/4/10
N2 - Dynamic functional connectivity (dFC) analyses of fMRI time-courses are typically performed using sliding-window based schemes. Such approaches not only inherently confine analysis to a single time-scale, but also do not generally lend themselves to accurate change-time estimates of the dynamically evolving graph topology. Change point detection methods on the other hand, offer the potential to overcome both limitations. However, the approaches employed so far in the dFC context are limited to detecting changes in linear relationships among time-courses corresponding to distinct regions of the brain. The present work puts forth a novel multi-kernel change point detection approach with the goal of capturing changes in the generally nonlinear relationships among time-courses, and thus in the topologies of the corresponding dynamically evolving FC graphs. The approach is tested on dynamic causal model (DCM) based synthetic resting-state fMRI data.
AB - Dynamic functional connectivity (dFC) analyses of fMRI time-courses are typically performed using sliding-window based schemes. Such approaches not only inherently confine analysis to a single time-scale, but also do not generally lend themselves to accurate change-time estimates of the dynamically evolving graph topology. Change point detection methods on the other hand, offer the potential to overcome both limitations. However, the approaches employed so far in the dFC context are limited to detecting changes in linear relationships among time-courses corresponding to distinct regions of the brain. The present work puts forth a novel multi-kernel change point detection approach with the goal of capturing changes in the generally nonlinear relationships among time-courses, and thus in the topologies of the corresponding dynamically evolving FC graphs. The approach is tested on dynamic causal model (DCM) based synthetic resting-state fMRI data.
KW - change detection
KW - fMRI
KW - kernel-based regression
KW - multiple kernel learning
UR - http://www.scopus.com/inward/record.url?scp=85050978397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050978397&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2017.8335618
DO - 10.1109/ACSSC.2017.8335618
M3 - Conference contribution
AN - SCOPUS:85050978397
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 1555
EP - 1559
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -