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
U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Front Neurol. 2022 Apr 14;13:855125. doi: 10.3389/fneur.2022.855125. eCollection 2022.
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 , a national consortium dedicated to identifying disease and subject-specific 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 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 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.
神经影像学研究往往缺乏可重复性，而可重复性是科学方法的主要特征之一。多中心合作项目增加了样本量并限制了方法的灵活性，从而为提高统计效力和可推广的结果奠定了基础。然而，多中心合作项目本质上受到临床和临床前MRI扫描仪的硬件、软件以及脉冲和序列设计异质性的限制，并且缺乏采集协议、数据分析和数据共享的基准。我们提出了一个总体愿景，该愿景促成了一个全国性联盟的组建，该联盟致力于识别各种神经和神经精神疾病的疾病及受试者特异性神经影像学生物标志物。这一宏伟目标需要努力提高先进MRI数据的诊断和预后能力。为此，23家意大利住院与护理科学研究所（IRCCS）齐聚一堂，这些研究所在神经学和神经影像学领域拥有技术和临床专长。每个IRCCS都配备了用于临床或临床前研究的高场或超高场MRI扫描仪（即≥3T），或者在MRI数据分析和基础设施方面拥有既定的专业知识。该网络的行动是在几个工作包（WP）中确定的。一个临床工作包（WP1）为主要神经疾病定义了最低标准临床定性MRI评估的指南。两个神经影像技术工作包（WP2和WP3，分别针对临床和临床前扫描仪）建立了体模质量控制以及用于研究健康人类参与者和野生型小鼠大脑的先进统一定量MRI协议。在公平原则下，还实施了一个基于网络的电子基础设施，用于跨站点存储和共享数据（WP4）。最后，RIN将所有这些努力转化为对患有痴呆症的患者和动物模型进行大规模多模态数据收集（即案例研究）。通过高质量和高度一致的采集协议跨机构和病理学获取数据、优化分析流程和数据共享程序，RIN可以最大限度地提高公共投资在研究和临床实践中的影响力。
