Andrew Reid PhD

In this post, I focus on the logic underlying statistical inference based on fMRI research designs. This consists of (1) modelling the hemodynamic response; (2) "first-level" within-subject analysis of time series; (3) "second-level" population inferences drawn from a random sample of participants; and (4) dealing with familywise error. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In neuroscience, the conversation around the term "functional connectivity" can be confusing, largely due to the implicit notion that associations can map directly onto physical connections. In our recent Nature Neuroscience perspective piece, we propose the redefinition of this term as a causal inference, in order to refocus the conversation around how we investigate brain connectivity, and interpret the results of such investigations.

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.