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- Bruffaerts, R, et al.
(författare)
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Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72
- 2022
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 4:4, s. fcac182-
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Tidskriftsartikel (refereegranskat)abstract
- Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer’s disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.
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- Etminani, Kobra, 1984-, et al.
(författare)
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A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
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Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
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- Soliman, Amira, 1980-, et al.
(författare)
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Adopting transfer learning for neuroimaging : a comparative analysis with a custom 3D convolution neural network model
- 2022
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Ingår i: BMC Medical Informatics and Decision Making. - London : BioMed Central (BMC). - 1472-6947. ; 22, s. 1-15
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Tidskriftsartikel (refereegranskat)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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- Verstraeten, A., et al.
(författare)
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Effects of tree pollen on throughfall element fluxes in European forests
- 2023
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Ingår i: Biogeochemistry. - Göteborg : Springer. - 0168-2563 .- 1573-515X. ; 165:3, s. 311-325
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Tidskriftsartikel (refereegranskat)abstract
- The effects of tree pollen on precipitation chemistry are not fully understood and this can lead to misinterpretations of element deposition in European forests. We investigated the relationship between forest throughfall (TF) element fluxes and the Seasonal Pollen Integral (SPIn) using linear mixed-effects modelling (LME). TF was measured in 1990-2018 during the main pollen season (MPS, arbitrary two months) in 61 managed, mostly pure, even-aged Fagus, Quercus, Pinus, and Picea stands which are part of the ICP Forests Level II network. The SPIn for the dominant tree genus was observed at 56 aerobiological monitoring stations in nearby cities. The net contribution of pollen was estimated as the TF flux in the MPS minus the fluxes in the preceding and succeeding months. In stands of Fagus and Picea, two genera that do not form large amounts of flowers every year, TF fluxes of potassium (K+), ammonium-nitrogen (NH4+-N), dissolved organic carbon (DOC), and dissolved organic nitrogen (DON) showed a positive relationship with SPIn. However- for Fagus- a negative relationship was found between TF nitrate-nitrogen (NO3--N) fluxes and SPIn. For Quercus and Pinus, two genera producing many flowers each year, SPIn displayed limited variability and no clear association with TF element fluxes. Overall, pollen contributed on average 4.1-10.6% of the annual TF fluxes of K+ > DOC > DON > NH4+--N with the highest contribution in Quercus > Fagus > Pinus > Picea stands. Tree pollen appears to affect TF inorganic nitrogen fluxes both qualitatively and quantitatively, acting as a source of NH4+--N and a sink of NO3--N. Pollen appears to play a more complex role in nutrient cycling than previously thought.
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