How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics

作者：Lukas Roell, Stephan Wunderlich, David Roell, Florian J. Raabe, Elias Wagner, Zhuanghua Shi, Andrea Schmitt, Peter Falkai, Sophia Stoecklein, Daniel Keeser · 发表于：NeuroImage · 年份：2025 · DOI：10.1016/j.neuroimage.2025.121195 · 被引用次数：5 · 研究领域：Functional Brain Connectivity Studies、MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications

BACKGROUND: Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series. OBJECTIVE: Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research. METHODS: We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion. RESULTS: First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the fu...