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author Nigri, Anna
Ferraro, Stefania
Gandini Wheeler-Kingshott, Claudia A. M.
Tosetti, Michela
Redolfi, Alberto
Forloni, Gianluigi
D'Angelo, Egidio
Aquino, Domenico
Biagi, Laura
Bosco, Paolo
Carne, Irene
De Francesco, Silvia
Demichelis, Greta
Gianeri, Ruben
Lagana, Maria Marcella
Micotti, Edoardo
Napolitano, Antonio
Palesi, Fulvia
Pirastru, Alice
Savini, Giovanni
Alberici, Elisa
Amato, Carmelo
Arrigoni, Filippo
Baglio, Francesca
Bozzali, Marco
Castellano, Antonella
Cavaliere, Carlo
Contarino, Valeria Elisa
Ferrazzi, Giulio
Gaudino, Simona
Marino, Silvia
Manzo, Vittorio
Pavone, Luigi
Politi, Letterio S.
Roccatagliata, Luca
Rognone, Elisa
Rossi, Andrea
Tonon, Caterina
Lodi, Raffaele
Tagliavini, Fabrizio
Bruzzone, Maria Grazia
author_facet Nigri, Anna
Ferraro, Stefania
Gandini Wheeler-Kingshott, Claudia A. M.
Tosetti, Michela
Redolfi, Alberto
Forloni, Gianluigi
D'Angelo, Egidio
Aquino, Domenico
Biagi, Laura
Bosco, Paolo
Carne, Irene
De Francesco, Silvia
Demichelis, Greta
Gianeri, Ruben
Lagana, Maria Marcella
Micotti, Edoardo
Napolitano, Antonio
Palesi, Fulvia
Pirastru, Alice
Savini, Giovanni
Alberici, Elisa
Amato, Carmelo
Arrigoni, Filippo
Baglio, Francesca
Bozzali, Marco
Castellano, Antonella
Cavaliere, Carlo
Contarino, Valeria Elisa
Ferrazzi, Giulio
Gaudino, Simona
Marino, Silvia
Manzo, Vittorio
Pavone, Luigi
Politi, Letterio S.
Roccatagliata, Luca
Rognone, Elisa
Rossi, Andrea
Tonon, Caterina
Lodi, Raffaele
Tagliavini, Fabrizio
Bruzzone, Maria Grazia
author_sort Nigri, Anna
collection PubMed
description Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.
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spelling pubmed-90478712022-04-29 Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network Nigri, Anna Ferraro, Stefania Gandini Wheeler-Kingshott, Claudia A. M. Tosetti, Michela Redolfi, Alberto Forloni, Gianluigi D'Angelo, Egidio Aquino, Domenico Biagi, Laura Bosco, Paolo Carne, Irene De Francesco, Silvia Demichelis, Greta Gianeri, Ruben Lagana, Maria Marcella Micotti, Edoardo Napolitano, Antonio Palesi, Fulvia Pirastru, Alice Savini, Giovanni Alberici, Elisa Amato, Carmelo Arrigoni, Filippo Baglio, Francesca Bozzali, Marco Castellano, Antonella Cavaliere, Carlo Contarino, Valeria Elisa Ferrazzi, Giulio Gaudino, Simona Marino, Silvia Manzo, Vittorio Pavone, Luigi Politi, Letterio S. Roccatagliata, Luca Rognone, Elisa Rossi, Andrea Tonon, Caterina Lodi, Raffaele Tagliavini, Fabrizio Bruzzone, Maria Grazia Front Neurol Neurology Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures. Frontiers Media S.A. 2022-04-14 /pmc/articles/PMC9047871/ /pubmed/35493836 http://dx.doi.org/10.3389/fneur.2022.855125 Text en Copyright © 2022 Nigri, Ferraro, Gandini Wheeler-Kingshott, Tosetti, Redolfi, Forloni, D'Angelo, Aquino, Biagi, Bosco, Carne, De Francesco, Demichelis, Gianeri, Lagana, Micotti, Napolitano, Palesi, Pirastru, Savini, Alberici, Amato, Arrigoni, Baglio, Bozzali, Castellano, Cavaliere, Contarino, Ferrazzi, Gaudino, Marino, Manzo, Pavone, Politi, Roccatagliata, Rognone, Rossi, Tonon, Lodi, Tagliavini, Bruzzone and The RIN–Neuroimaging. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Nigri, Anna
Ferraro, Stefania
Gandini Wheeler-Kingshott, Claudia A. M.
Tosetti, Michela
Redolfi, Alberto
Forloni, Gianluigi
D'Angelo, Egidio
Aquino, Domenico
Biagi, Laura
Bosco, Paolo
Carne, Irene
De Francesco, Silvia
Demichelis, Greta
Gianeri, Ruben
Lagana, Maria Marcella
Micotti, Edoardo
Napolitano, Antonio
Palesi, Fulvia
Pirastru, Alice
Savini, Giovanni
Alberici, Elisa
Amato, Carmelo
Arrigoni, Filippo
Baglio, Francesca
Bozzali, Marco
Castellano, Antonella
Cavaliere, Carlo
Contarino, Valeria Elisa
Ferrazzi, Giulio
Gaudino, Simona
Marino, Silvia
Manzo, Vittorio
Pavone, Luigi
Politi, Letterio S.
Roccatagliata, Luca
Rognone, Elisa
Rossi, Andrea
Tonon, Caterina
Lodi, Raffaele
Tagliavini, Fabrizio
Bruzzone, Maria Grazia
Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title_full Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title_fullStr Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title_full_unstemmed Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title_short Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network
title_sort quantitative mri harmonization to maximize clinical impact: the rin–neuroimaging network
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047871/
https://www.ncbi.nlm.nih.gov/pubmed/35493836
http://dx.doi.org/10.3389/fneur.2022.855125
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