Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([<sup>11</sup>C]-(+)-PHNO and [<sup>18</sup>F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [<sup>18</sup>F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77-0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

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Publication Details

Journal
Molecular Psychiatry
Published
2025-02-28
DOI
https://doi.org/10.1038/s41380-025-02938-w
Citations
4
Primary Topic
Schizophrenia research and treatment
Type
article
Field-Weighted Citation Impact
5.88

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article

Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging

Oliver Howes, J Donocik, Julian C. Matthews, Ilaria Bonoldi et al.
4 citations
Molecular Psychiatry
Schizophrenia research and treatment
5.88
article

Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging

Oliver Howes, J Donocik, Julian C. Matthews, Ilaria Bonoldi, Enrico D’Ambrosio, Alessio Giacomel, Rubaida Easmin, Euitae Kim, Faith Borgan, Maria Rogdaki, Daniel Martins, Stephen J. Kaar, Mattia Veronese, Micheal Bloomfield, Giovanna Nordio, Marius de Groot, Steven Williams, Alice Egerton, Chiara Nosarti, Arsime Demjaha, Pierluigi Selvaggi, Matthew M. Nour, Luke James Vano, Ottavia Dipasquale, Grazia Rutigliano, Tarik Dahoun, James Hunter MacCabe, Robert Ali McCutcheon, Federico Turkheimer, Peter S. Talbot, Philip K. McGuire, Ilinca Angelescu, Seoyoung Kim
article en
4 citations

Abstract

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([<sup>11</sup>C]-(+)-PHNO and [<sup>18</sup>F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [<sup>18</sup>F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77-0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

Molecular PsychiatryVol. 30(8)
University of Geneva (CH), University of Padua (IT), Seoul National University (KR), Greater Manchester Mental Health NHS Foundation Trust (GB), Wellcome Centre for Human Neuroimaging (GB), King's College London (GB), South London and Maudsley NHS Foundation Trust (GB), Manchester Academic Health Science Centre (GB), Age UK (GB), Hammersmith Hospital (GB), Seoul National University Bundang Hospital (KR), University of Manchester (GB), University of Oxford (GB), University Hospital of Geneva (CH), MRC London Institute of Medical Sciences (GB), University College London (GB), Imperial College London (GB), University of Bari Aldo Moro (IT)
South London and Maudsley NHS Foundation Trust, Wellcome Trust, National Institute for Health and Care Research, Department of Health and Social Care, King's College London, NIHR Maudsley Biomedical Research Centre
Good health and well-being
Openalex Percentile: Top 4%
Schizophrenia research and treatment
5.88
DOIOpen Access PDFOpenAlex
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