  Statistical techniques are needed to analyse data structures with complex
dependencies such that clinically useful information can be extracted.
Individual-specific networks, which capture dependencies in complex biological
systems, are often summarized by graph-theoretical features. These features,
which lend themselves to outcome modelling, can be subject to high variability
due to arbitrary decisions in network inference and noise. Correlation-based
adjacency matrices often need to be sparsified before meaningful
graph-theoretical features can be extracted, requiring the data analysts to
determine an optimal threshold.. To address this issue, we propose to
incorporate a flexible weighting function over the full range of possible
thresholds to capture the variability of graph-theoretical features over the
threshold domain. The potential of this approach, which extends concepts from
functional data analysis to a graph-theoretical setting, is explored in a
plasmode simulation study using real functional magnetic resonance imaging
(fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed
initiative. The simulations show that our modelling approach yields accurate
estimates of the functional form of the weight function, improves inference
efficiency, and achieves a comparable or reduced root mean square prediction
error compared to competitor modelling approaches. This assertion holds true in
settings where both complex functional forms underlie the outcome-generating
process and a universal threshold value is employed. We demonstrate the
practical utility of our approach by using resting-state fMRI data to predict
biological age in children. Our study establishes the flexible modelling
approach as a statistically principled, serious competitor to ad-hoc methods
with superior performance.
