ArticleFrontiers in neuroscience2021
Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Characteristics of cohort data management systems (CDMS): a scoping review.BMC medical research methodology · 2025Article
- An open-source repository-based tool for quality control of imaging protocol compliance: demonstration in a multicentre MRI study.The British journal of radiology · 2025Article
- Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers.Molecular psychiatry · 2025Article
- Image analysis research in neuroradiology: bridging clinical and technical domains.Neuroradiology · 2025Review
- Sex/gender effects of glial reactivity on preclinical Alzheimer's disease pathology.Molecular psychiatry · 2025Article
- How to establish and maintain a multimodal animal research dataset using DataLad.Scientific data · 2023Article
- Article
- The art and science of using quality control to understand and improve fMRI data.Frontiers in neuroscience · 2023Article
- The role of artificial intelligence in paediatric neuroradiology.Pediatric radiology · 2022Review
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery.Scientific data · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer's disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.