Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
Eur J Neurosci. 2023 Jun;57(12):2017-2039. doi: 10.1111/ejn.15854. Epub 2022 Dec 14.
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
神经信息学是一个专注于软件开发的研究领域,这些软件工具能够识别、分析、建模、组织和共享多尺度神经科学数据。随着大数据现象的出现,神经信息学在过去二十年中得到了迅猛发展,其特点是所谓的“3V”(即体量、速度和种类繁多),这使得神经科学家能够通过临床、基因组和放射技术的技术改进,更快、更廉价地获取和处理数据。这种情况导致了“数据泛滥”,因为神经科学家现在可以在几天内收集到比十年前一年还要多的研究数据。为了解决这一现象,一些专注于神经影像学的神经信息学平台已经出现,这些平台由国家或跨国机构资助,目标如下:(i)开发用于归档和组织分析数据的工具(XNAT、REDCap 和 LabKey);(ii)开发从还原方法演变为多维模型的数据驱动模型(RIN、IVN、HBD、EuroPOND、E-DADS 和 GAAIN BRAIN);(iii)开发电子基础设施,以提供足够的计算能力和存储资源(neuGRID、HBP-EBRAINS、LONI 和 CONP)。尽管目前情况仍然分散,但在国家和国际层面都在尝试引入高标准的开放、可查找、可访问、可互操作和可重复使用(FAIR)的神经科学技术。