Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.

Recordings from resting-state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of "ground truth" has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas.

Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI.

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity.

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation.

A continuous entropy rate estimator for spike trains using a K-means-based context tree.

Entropy rate quantifies the change of information of a stochastic process (Cover & Thomas, 2006). For decades, the temporal dynamics of spike trains generated by neurons has been studied as a stochastic process (Barbieri, Quirk, Frank, Wilson, & Brown, 2001; Brown, Frank, Tang, Quirk, & Wilson, 1998; Kass & Ventura, 2001; Metzner, Koch, Wessel, & Gabbiani, 1998; Zhang, Ginzburg, McNaughton, & Sejnowski, 1998). We propose here to estimate the entropy rate of a spike train from an inhomogeneous hidden Markov model of the spike intervals.