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| http://dx.doi.org/10.3389/fpsyg.2015.00950 | DOI Listing |
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Department of Neurosurgery, Spine and Spinal Cord Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Lessons about physiological relevance learned from large-scale meta-analysis of co-expression networks in brain organoids.
PLoS Biol
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Maisonneuve-Rosemont Hospital Research Centre (CRHMR), Department of Medicine, University of Montreal, Montreal, Quebec, Canada.
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View Article and Find Full Text PDFA Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.
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January 2025
From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Alzheimer Centre Amsterdam, Neurology, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.
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Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Greece.
Bottom-up, data-driven, large-scale models provide a mechanistic understanding of neuronal functions. A new study in PLOS Biology builds a biologically realistic model of the rodent CA1 region that aims to become an accessible tool for the whole hippocampal community.
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Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges.
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