Borrelli, P; Savini, G; Cavaliere, C; Palesi, F; Grazia Bruzzone, M; Aquino, D; Biagi, L; ... the RIN – Neuroimaging Network; + view all Borrelli, P; Savini, G; Cavaliere, C; Palesi, F; Grazia Bruzzone, M; Aquino, D; Biagi, L; Bosco, P; Carne, I; Ferraro, S; Giulietti, G; Napolitano, A; Nigri, A; Pavone, L; Pirastru, A; Redolfi, A; Tagliavini, F; Tosetti, M; Salvatore, M; Gandini Wheeler-Kingshott, CAM; Aiello, M; the RIN – Neuroimaging Network; - view fewer (2023) Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Physica Medica , 112 , Article 102610. 10.1016/j.ejmp.2023.102610.

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Abstract

Purpose: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. / Methods: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A “traveling brains” dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. / Results: The results show an inter-center and inter-subject variability of <10%, except for “clustering coefficient” (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners’ hardware. / Conclusions: The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

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