An automatic analysis framework for FDOPA PET neuroimaging

and the FDOPA PET imaging working group:

doi:10.1177/0271678X231168687

An automatic analysis framework for FDOPA PET neuroimaging

and the FDOPA PET imaging working group:

Neuroimaging Department
King's College Hospital
Department of Psychosis Studies
MRC London Institute of Medical Sciences and Imperial College London Institute of Clinical Sciences
Institute of Clinical Sciences (ICS)
Faculty of Medicine (Faculty Division Ahus)
Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
University of Padua
Department of Neuroscience, University of Padua and Neuroscience Center (PNC), University of Padua, Padua, Italy.
Psychiatric Imaging Group
Department of Psychosis Studies, King's College, London, UK; Psychiatric Imaging Group MRC London Institute of Medical Sciences, Hammersmith Hospital, London, UK; Institute of Clinical Sciences, Faculty of Medicine, Imperial College, Hammersmith Hospital, London, UK. Electronic address: [email protected].
Institute for Clinical Sciences, Medical Research Council Clinical Sciences Centre, London W12 0NN, United Kingdom.
University of Padova, Padova, Italy, Imperial College, King's College NHLI, Imperial College, Chelsea and Westminster Hospital
South London and Maudsley NHS Foundation Trust, London, UK.
Compass Pathways Ltd
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK; Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Bari, Italy.
Department of Basic Medical Sciences
Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
Seoul National University
Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ
Child and Adolescent Psychiatry Division, Department of Child and Adolescent Health, Geneva University Hospital, Geneva, Switzerland.
Psychology & Neurosicences
Division of Imaging Sciences and Biomedical Engineering
Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Early Intervention Psychosis Clinical Academic Group, South London and Maudsley NHS Trust, London, United Kingdom.
Bethlem Royal & Maudsley Hosp, South London & Maudsley NHS Fdn Trust
Genomic Medicine, Manchester Academic Health Sciences Centre, Institute of Human Development, Manchester University, Central Manchester University Hospitals NHS Foundation Trust, Manchester, England.

Research output: Contribution to journal › Article › peer-review

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Abstract

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King’s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Ki^cer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

Original language	English
Pages (from-to)	1285-1300
Number of pages	16
Journal	Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Volume	43
Issue number	8
Early online date	7 Apr 2023
DOIs	https://doi.org/10.1177/0271678X231168687
Publication status	Published - Aug 2023

Keywords

big data repository
data analysis pipeline
dopamine synthesis
FDOPA PET
neuroimaging biomarker

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10.1177/0271678X231168687Licence: CC BY

Nordio et al 2023Licence: CC BY

Cite this

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title = "An automatic analysis framework for FDOPA PET neuroimaging",

abstract = "In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King{\textquoteright}s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.",

keywords = "big data repository, data analysis pipeline, dopamine synthesis, FDOPA PET, neuroimaging biomarker",

author = "{and the FDOPA PET imaging working group:} and Giovanna Nordio and Rubaida Easmin and Alessio Giacomel and Ottavia Dipasquale and Daniel Martins and Steven Williams and Federico Turkheimer and Oliver Howes and Mattia Veronese and Sameer Jauhar and Maria Rogdaki and Robert McCutcheon and Stephen Kaar and Luke Vano and Grazia Rutigliano and Ilinca Angelescu and Faith Borgan and Enrico D'Ambrosio and Tarik Dahoun and Euitae Kim and Seoyoung Kim and Micheal Bloomfield and Alice Egerton and Arsime Demjaha and Ilaria Bonoldi and Chiara Nosarti and James Maccabe and Philip McGuire and Julian Matthews and Talbot, {Peter S}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Wellcome Trust Digital Award (no. 215747/Z/19/Z) and supported by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King{\textquoteright}s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. MV is supported by MIUR, Italian Ministry for Education, under the initiatives “Departments of Excellence” (Law 232/2016) and by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King{\textquoteright}s College London. Publisher Copyright: {\textcopyright} The Author(s) 2023.",

year = "2023",

month = aug,

doi = "10.1177/0271678X231168687",

language = "English",

volume = "43",

pages = "1285--1300",

journal = "Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism",

issn = "0271-678X",

publisher = "Nature Publishing Group",

number = "8",

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T1 - An automatic analysis framework for FDOPA PET neuroimaging

AU - and the FDOPA PET imaging working group:

AU - Nordio, Giovanna

AU - Easmin, Rubaida

AU - Giacomel, Alessio

AU - Dipasquale, Ottavia

AU - Martins, Daniel

AU - Williams, Steven

AU - Turkheimer, Federico

AU - Howes, Oliver

AU - Veronese, Mattia

AU - Jauhar, Sameer

AU - Rogdaki, Maria

AU - McCutcheon, Robert

AU - Kaar, Stephen

AU - Vano, Luke

AU - Rutigliano, Grazia

AU - Angelescu, Ilinca

AU - Borgan, Faith

AU - D'Ambrosio, Enrico

AU - Dahoun, Tarik

AU - Kim, Euitae

AU - Kim, Seoyoung

AU - Bloomfield, Micheal

AU - Egerton, Alice

AU - Demjaha, Arsime

AU - Bonoldi, Ilaria

AU - Nosarti, Chiara

AU - Maccabe, James

AU - McGuire, Philip

AU - Matthews, Julian

AU - Talbot, Peter S

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Wellcome Trust Digital Award (no. 215747/Z/19/Z) and supported by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. MV is supported by MIUR, Italian Ministry for Education, under the initiatives “Departments of Excellence” (Law 232/2016) and by the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King’s College London. Publisher Copyright: © The Author(s) 2023.

PY - 2023/8

Y1 - 2023/8

N2 - In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King’s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

AB - In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King’s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

KW - big data repository

KW - data analysis pipeline

KW - dopamine synthesis

KW - FDOPA PET

KW - neuroimaging biomarker

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JF - Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism

IS - 8

ER -

An automatic analysis framework for FDOPA PET neuroimaging

Abstract

Keywords

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