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Search: WFRF:(Vandenberghe Rik) > Natural sciences

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1.
  • Shi, Liu, et al. (author)
  • Replication study of plasma proteins relating to Alzheimer's pathology.
  • 2021
  • In: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279 .- 1552-5260. ; 17:9, s. 1452-1464
  • Journal article (peer-reviewed)abstract
    • This study sought to discover and replicate plasma proteomic biomarkers relating to Alzheimer's disease (AD) including both the "ATN" (amyloid/tau/neurodegeneration) diagnostic framework and clinical diagnosis.Plasma proteins from 972 subjects (372 controls, 409 mild cognitive impairment [MCI], and 191 AD) were measured using both SOMAscan and targeted assays, including 4001 and 25 proteins, respectively.Protein co-expression network analysis of SOMAscan data revealed the relation between proteins and "N" varied across different neurodegeneration markers, indicating that the ATN variants are not interchangeable. Using hub proteins, age, and apolipoprotein E ε4 genotype discriminated AD from controls with an area under the curve (AUC) of 0.81 and MCI convertors from non-convertors with an AUC of 0.74. Targeted assays replicated the relation of four proteins with the ATN framework and clinical diagnosis.Our study suggests that blood proteins can predict the presence of AD pathology as measured in the ATN framework as well as clinical diagnosis.
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2.
  • Soliman, Amira, 1980-, et al. (author)
  • Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model
  • 2022
  • In: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 22, s. 1-15
  • Journal article (peer-reviewed)abstract
    • Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones. © 2022, The Author(s).
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  • Result 1-2 of 2
Type of publication
journal article (2)
Type of content
peer-reviewed (2)
Author/Editor
Vandenberghe, Rik (2)
Frisoni, Giovanni B. (2)
Blennow, Kaj, 1958 (1)
Zetterberg, Henrik, ... (1)
Pilotto, Andrea (1)
Padovani, Alessandro (1)
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Aarsland, Dag (1)
Lemstra, Afina W. (1)
Tsolaki, Magda (1)
Kettunen, Petronella (1)
Etminani, Kobra, 198 ... (1)
Scheltens, Philip (1)
Teunissen, Charlotte ... (1)
Barkhof, Frederik (1)
Ashton, Nicholas J. (1)
Martínez-Lage, Pablo (1)
Lleó, Alberto (1)
Rami, Lorena (1)
Engelborghs, Sebasti ... (1)
Molinuevo, José L (1)
Davidsson, Anette (1)
Ochoa-Figueroa, Migu ... (1)
Hye, Abdul (1)
Nevado-Holgado, Alej ... (1)
Lovestone, Simon (1)
Visser, Pieter Jelle (1)
Nicastro, Nicolas (1)
Garibotto, Valentina (1)
Wallin, Anders (1)
Freund-Levi, Yvonne, ... (1)
Bauckneht, Matteo (1)
Chincarini, Andrea (1)
Brendel, Matthias (1)
Rominger, Axel (1)
Bruffaerts, Rose (1)
Kramberger, Milica G ... (1)
Trost, Maja (1)
Camacho, Valle (1)
Nobili, Flavio (1)
Morbelli, Silvia (1)
Lill, Christina M (1)
Bertram, Lars (1)
Sleegers, Kristel (1)
Ressner, Marcus (1)
Frölich, Lutz (1)
Bos, Isabelle (1)
Vos, Stephanie J. B. (1)
Johannsen, Peter (1)
Byttner, Stefan, 197 ... (1)
Andreasson, Ulf (1)
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University
Karolinska Institutet (2)
University of Gothenburg (1)
Halmstad University (1)
Örebro University (1)
Linköping University (1)
Language
English (2)
Research subject (UKÄ/SCB)
Natural sciences (2)Undo refine
Medical and Health Sciences (2)

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