Andrew Reid PhD

In our new preprint, we describe a novel methodology for (1) identifying the most probable "core" tract trajectory for two arbitrary brain regions, and (2) estimating tract-specific anisotropy (TSA) at all points along this trajectory. We describe the outcomes of regressing this TSA metric against participants' age and sex. Our hope is that this new method can serve as a complement to the popular TBSS approach, where researchers desire to investigate effects specific to a pre-established set of ROIs.

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.

The term "connectome" was introduced in a seminal 2005 PNAS article, as a sort of analogy to the genome. However, unlike genomics, the methods available to study human connectomics remain poorly defined and difficult to interpret. In particular, the use of diffusion-weighted imaging approaches to estimate physical connectivity is fraught with inherent limitations, which are often overlooked in the quest to publish "connectivity" findings. Here, I provide a brief commentary on these issues, and highlight a number of ways neuroscience can proceed in light of them.

Our article proposing a threshold-free method for comparing seed-based connectivity estimates was recently accepted to Brain Structure & Function. We compared two structural covariance approaches (cortical thickness and voxel-based morphometry), and two functional ones (resting-state functional MRI and meta-analytic connectivity mapping, or MACM).