@article {838,
	title = {Detecting functional connectivity change points for single-subject fMRI data.},
	journal = {Front Comput Neurosci},
	volume = {7},
	year = {2013},
	month = {2013},
	pages = {143},
	abstract = {<p>Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.</p>},
	issn = {1662-5188},
	doi = {10.3389/fncom.2013.00143},
	author = {Cribben, Ivor and Wager, Tor D and Lindquist, Martin A}
}
@article {cribben2012dynamic,
	title = {Dynamic connectivity regression: Determining state-related changes in brain connectivity},
	journal = {NeuroImage},
	year = {2012},
	publisher = {Elsevier},
	author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A}
}
@article {842,
	title = {Dynamic connectivity regression: determining state-related changes in brain connectivity.},
	journal = {Neuroimage},
	volume = {61},
	year = {2012},
	month = {2012 Jul 16},
	pages = {907-20},
	abstract = {<p>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{\textquoteright}s ability to observe how the networks between different brain regions changed with subjects{\textquoteright} emotional state.</p>},
	keywords = {Algorithms, Brain, brain mapping, Computer Simulation, Emotions, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2012.03.070},
	author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A}
}
