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Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.
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Trends Plant Sci
September 2025
Crop and Soils Sciences, University of Georgia, Athens, GA 30602, USA; Institute of Plant Breeding and Genetics and Genomics, University of Georgia, Athens, GA 30602, USA.
Synthetic biology holds great potential to transform agriculture, yet its progress is constrained by the complexity of multigenomic, multitrait, and multi-environment data. Desirable traits often arise from complex gene networks acting across diverse conditions, making them difficult to predict and optimize manually. In the past decade, artificial intelligence (AI) has supported this process, but its large data needs and poor integration limit its role to pattern recognition rather than explanatory trait design.
View Article and Find Full Text PDFJ Neurotrauma
September 2025
Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA.
Mean apparent propagator MRI (MAP-MRI) quantifies subtle alterations in tissue microstructure noninvasively and provides a more nuanced and comprehensive assessment of tissue architectural and structural integrity compared with other diffusion MRI techniques. We investigate the sensitivity of MAP-MRI-derived quantitative imaging biomarkers to detect previously unseen microstructural damage in patients with mild traumatic brain injuries (mTBI), whose clinical scans otherwise appeared normal. We developed and validated an MAP-MRI data processing pipeline for analyzing diffusion-weighted images for use in healthy controls and mTBI patients whose longitudinal scans were obtained from the GE/NFL/mTBI MRI database.
View Article and Find Full Text PDFCell Rep Methods
September 2025
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland. Electronic address:
In cancer research, multiplexed imaging allows detailed characterization of the tumor microenvironment (TME) and its link to patient prognosis. The integrated immunoprofiling of large adaptive cancer patient cohorts (IMMUcan) consortium collects multi-modal imaging data from thousands of patients with cancer to perform broad molecular and cellular spatial profiling. Here, we describe and compare two workflows for multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) developed within IMMUcan to enable the generation of standardized data for cancer tissue analysis.
View Article and Find Full Text PDFJMIR Cancer
September 2025
iCARE Secure Data Environment & Digital Collaboration Space, NIHR Imperial Biomedical Research Centre, London, United Kingdom.
Background: Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
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