Andrew Reid PhD

This is the first of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology. In this post, I will try to provide an intuitive explanation of (1) the Pearson correlation coefficient, (2) confounding, and (3) how partial correlations can be used to address confounding.

Functional connectivity is a term originally coined to describe statistical dependence relationships between time series. But should such a relationship really be called connectivity? Functional correlations can easily arise from networks in the complete absence of physical connectivity (i.e., the classical axon/synapse projection we know from neurobiology). In this post I elaborate on recent conversations I've had regarding the use of correlations or partial correlations to infer the presence of connections, and their use in constructing graphs for topological analyses.