Metrics for estimating individual-level changes in functional brain connectivity and their correspondence with...

With the rise of precision medicine and large neuroimaging datasets, measuring brain changes on an individual level becomes more possible and more important. However, functional connectivity has some mathematical and conceptual quirks that make estimating individual-level changes more complicated than, for example, estimating structural brain changes. Here, we compared six different change scores, i.e., metrics for quantifying change in functional connectivity, using a large sample with two time points, roughly 2 years apart, of low-motion data from the ABCD Study ( $\text{N=2,719}$ , ages 9-13 years). First, we compared distributions and potential interpretations of each metric. Then, we assessed how well different metrics captured topology change estimates by comparing them to individual-level changes in clustering coefficient, betweenness centrality, and network strength. As multivariate metrics may be more reliable than single connections and researchers often interpret connectivity changes in topological terms, this offers additional insight into the implications of change score choice. Overall, our analyses revealed widely different distributions between change scores that conferred vastly different results and interpretations of functional connectivity changes between time points. Thus, we provide recommendations for each change score and its optimal (or suboptimal) use cases, depending on the population, study design, and research question.