Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis

Understanding complex brain-behaviour relationships in psychiatric and neurological conditions is crucial for advancing clinical insights. This review explores the current landscape of network estimation methods in the context of functional MRI (fMRI) based network neuroscience, focusing on static undirected network analysis. We focused on papers published in a single year (2022) and characterised what we consider the fundamental building blocks of network analysis: sample size, network size, association type, edge inclusion strategy, edge weights, modelling level, and confounding factors. We found that the most common methods across all included studies (n = 191) were the use of pairwise correlations to estimate the associations between brain regions (79.6 %), estimation of weighted networks (95.3 %), and estimation of the network at the individual level (86.9 %). Importantly, a substantial number of studies lacked comprehensive reporting on their methodological choices, hindering the synthesis of research findings within the field. This review underscores the critical need for careful consideration and transparent reporting of fMRI network estimation methodologies to advance our understanding of complex brain-behaviour relationships. By facilitating the integration between network neuroscience and network psychometrics, we aim to significantly enhance our clinical understanding of these intricate connections.