Normalized linear variance decay dimension density and its application of dynamical complexity detection in physiological (fMRI) time series

2011 ◽  
Vol 375 (17) ◽  
pp. 1789-1795 ◽  
Author(s):  
Xiaoping Xie ◽  
Xiaohu Zhao ◽  
Youtong Fang ◽  
Zhitong Cao ◽  
Guoguang He
Keyword(s):  
Time Series ◽  
Dynamical Complexity ◽  
Fmri Time Series ◽  
Variance Decay

Coherence between fMRI time-series distinguishes two spatial working memory networks

NeuroImage ◽  
2005 ◽  
Vol 26 (1) ◽  
pp. 177-183 ◽  
Author(s):  
Clayton E. Curtis ◽  
Felice T. Sun ◽  
Lee M. Miller ◽  
Mark D'Esposito
Keyword(s):  
Working Memory ◽  
Time Series ◽  
Spatial Working Memory ◽  
Fmri Time Series

scholarly journals Bayesian fMRI time series analysis with spatial priors

NeuroImage ◽  
2005 ◽  
Vol 24 (2) ◽  
pp. 350-362 ◽  
Author(s):  
William D. Penny ◽  
Nelson J. Trujillo-Barreto ◽  
Karl J. Friston
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Fmri Time Series

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

SPATIO-TEMPORAL CORRELATIONS FROM fMRI TIME SERIES BASED ON THE NN-ARx MODEL

2010 ◽  
Vol 09 (04) ◽  
pp. 381-406 ◽  
Author(s):  
J. BOSCH-BAYARD ◽  
J. RIERA-DIAZ ◽  
R. BISCAY-LIRIO ◽  
K. F. K. WONG ◽  
A. GALKA ◽  
...  
Keyword(s):  
Time Series ◽  
Arx Model ◽  
Temporal Correlations ◽  
Fmri Time Series ◽  
Spatio Temporal
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