The Semi-constrained Network-Based Statistic (scNBS): Integrating Local and Global Information for Brain Network Inference

Abstract

Functional connectomics has become a popular topic over the last two decades. Researchers often conduct inference at the level of groups of edges, or “components", with various versions of the Network-Based Statistic (NBS) to tackle the problem of multiple comparisons and to improve statistical power. Existing NBS methods pool information at one of two scales: within the local neighborhood as estimated from the data or within predefined large-scale brain networks. As such, these methods do not yet account for both local and network-level interactions that may have clinical significance. In this paper, we introduce the “Semi-constrained Network-Based Statistic" or scNBS, a novel method that uses a data-driven selection procedure to pool individual edges bounded by predefined large-scale networks. We also provide a comprehensive statistical pipeline for inference at a large-scale network-level. Through benchmarking studies using both synthetic and empirical data, we demonstrate the increased power and validity of scNBS as compared to traditional approaches. We also demonstrate that scNBS results are consistent for repeated measurements, meaning it is robust. Finally, we highlight the importance of methods designed to achieve a balance between focal and broad-scale levels of inference, thus enabling researchers to more accurately capture the spatial extent of effects that emerge across the functional connectome.