ArticleFrontiers in neuroinformatics2018
Pipeline for Analyzing Lesions After Stroke (PALS).
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis.Journal of the American Heart Association · 2022Pooled it
- ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.Translational psychiatry · 2020Pooled it
- Anatomical and Functional Characterization in Children With Unilateral Cerebral Palsy: An Atlas-Based Analysis.Neurorehabilitation and neural repair · 2020Trial
- Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke.Neurology · 2023Observational
- The elusive metric of lesion load.Brain structure & function · 2023Review
- Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies.Scientific reports · 2022Article
- Corticospinal Tract Lesion Load Originating From Both Ventral Premotor and Primary Motor Cortices Are Associated With Post-stroke Motor Severity.Neurorehabilitation and neural repair · 2022Article
- The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.Human brain mapping · 2022Review
- Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center.Frontiers in neuroscience · 2021Article
- A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data.Human brain mapping · 2019Article
- Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients.Frontiers in human neuroscience · 2019Article
- Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review.Frontiers in neurology · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (
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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.