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@inproceedings{Giacomel:2023,
author = {Giacomel, A and Martins, D and Nordio, G and Easmine, R and Howes, O and Williams, SCR and Turkheimer, F and De, Groot M and Dipasquale, O and Veronese, M},
title = {Assessing Multisite PET Neuroimaging Harmonisation for Normative Modelling},
year = {2023}
}

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TY  - CPAPER
AB  - Although normative modelling has been recently widely adopted by the neuroimaging community to estimate deviation of cohorts or single subjects, from a reference population's trajectory, it has never been applied on molecular neuroimaging datasets. This is because the typical sample size of molecular imaging datasets of single studies is not adequate for a reliable estimation of population models. Here, we pooled scans from different datasets and compared the performance of common neuroimaging harmonisation techniques when used on molecular neuroimaging data, by measuring the effect of the harmonisation on the deviation scores of the normative model. As harmonisation methods we employed a 3D Gaussian filter and a Bayesian scale and shift estimation method (Combat). By statistically testing the parameters of the deviation-score distribution and inspecting the explained variance map of the model, we selected Combat, trading-off between the two parameters' performance, as the most suitable way to harmonise multi-site/multi-scanner molecular neuroimaging datasets.
AU  - Giacomel,A
AU  - Martins,D
AU  - Nordio,G
AU  - Easmine,R
AU  - Howes,O
AU  - Williams,SCR
AU  - Turkheimer,F
AU  - De,Groot M
AU  - Dipasquale,O
AU  - Veronese,M
PY  - 2023///
TI  - Assessing Multisite PET Neuroimaging Harmonisation for Normative Modelling
ER  -

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