Hypothesis Testing For Network Data in Functional Neuroimaging

Understand

In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain.

• One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies.
• Networks, however, are not Euclidean objects, and hence classical methods do not directly apply.
• We address this challenge by drawing on concepts and techniques from geometry, and high-dimensional statistical inference.