Dynamic connectivity regression: Determining state-related changes in brain connectivity

By Ivor Cribben, Ragnheidur Haraldsdottir, Lauren Y. Atlas, Tor D. Wager, Martin A. Lindquist. NeuroImage, 2012

300 citations recorded by the source corpus.

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N=26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method’s ability to observe how the networks between different brain regions changed with subjects’ emotional state.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3a9413a07422f022094bafc6a5637a6c9aaa3d5a

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Citations in the corpus

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, background): “, 2011), cognitive and affective states (Cribben et al., 2012; Ekman et al., 2012; Eryilmaz et al., 2011; Richiardi et al., 2011; Shirer et al., 2012) and also spontaneously (Britz et al.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, unknown): “Recent analyses have shown that it is possible to identify, in a data-drivenmanner, significant change-points in brain functional network architecture (Cribben et al., 2012), as well as dynamic reconfigurations of brain network topology that are relevant for understanding individual differences in b”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “…and include using topological network descriptions as features in a clustering analysis (e.g., modularity or community membership (Bassett et al., 2011; Jones et al., 2012; Kinnison et al., 2012)), or formal models to detect change points in connectivity, as introduced by Cribben et al. (2012).”
Tracking whole-brain connectivity dynamics in the resting state. (2014, unknown): “Stable connectivity patterns and change points must be learned from the data directly, and formal models for this process are just being developed (Cribben et al. 2012).”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “Indeed, othermodels have been used to simulate the dynamics of the correlation time series such as vector autoregressive (VAR) models (Cribben et al., 2012; Monti et al., 2014), deterministic oscillations (Leonardi et al.”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, background): “Evaluation of changes across variety of window lengths performed using separate windows (Cribben et al., 2012) and comparisons with time-frequency approaches which do not require windowing at all (Yaesoubi et al., 2015) will be interesting to examine in future work.”