28,863 research outputs found
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Recent studies on analyzing dynamic brain connectivity rely on sliding-window
analysis or time-varying coefficient models which are unable to capture both
smooth and abrupt changes simultaneously. Emerging evidence suggests
state-related changes in brain connectivity where dependence structure
alternates between a finite number of latent states or regimes. Another
challenge is inference of full-brain networks with large number of nodes. We
employ a Markov-switching dynamic factor model in which the state-driven
time-varying connectivity regimes of high-dimensional fMRI data are
characterized by lower-dimensional common latent factors, following a
regime-switching process. It enables a reliable, data-adaptive estimation of
change-points of connectivity regimes and the massive dependencies associated
with each regime. We consider the switching VAR to quantity the dynamic
effective connectivity. We propose a three-step estimation procedure: (1)
extracting the factors using principal component analysis (PCA) and (2)
identifying dynamic connectivity states using the factor-based switching vector
autoregressive (VAR) models in a state-space formulation using Kalman filter
and expectation-maximization (EM) algorithm, and (3) constructing the
high-dimensional connectivity metrics for each state based on subspace
estimates. Simulation results show that our proposed estimator outperforms the
K-means clustering of time-windowed coefficients, providing more accurate
estimation of regime dynamics and connectivity metrics in high-dimensional
settings. Applications to analyzing resting-state fMRI data identify dynamic
changes in brain states during rest, and reveal distinct directed connectivity
patterns and modular organization in resting-state networks across different
states.Comment: 21 page
Optimal map of the modular structure of complex networks
Modular structure is pervasive in many complex networks of interactions
observed in natural, social and technological sciences. Its study sheds light
on the relation between the structure and function of complex systems.
Generally speaking, modules are islands of highly connected nodes separated by
a relatively small number of links. Every module can have contributions of
links from any node in the network. The challenge is to disentangle these
contributions to understand how the modular structure is built. The main
problem is that the analysis of a certain partition into modules involves, in
principle, as many data as number of modules times number of nodes. To confront
this challenge, here we first define the contribution matrix, the mathematical
object containing all the information about the partition of interest, and
after, we use a Truncated Singular Value Decomposition to extract the best
representation of this matrix in a plane. The analysis of this projection allow
us to scrutinize the skeleton of the modular structure, revealing the structure
of individual modules and their interrelations.Comment: 21 pages, 10 figure
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