Open Access

Lukas Roell, Stephan Wunderlich, David Roell, Florian Raabe, Elias Wagner, Zhuanghua Shi, Andrea Schmitt, Peter Falkai, Sophia Stoecklein, Daniel Keeser

• 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 analyzedBackground 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 functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline. Conclusion Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.…