| 1. |
- Ferizi, U., et al.
(författare)
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Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison
- 2017
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Ingår i: NMR in Biomedicine. - : Wiley. - 0952-3480 .- 1099-1492. ; 30:9, s. Article no e3734 -
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Tidskriftsartikel (refereegranskat)abstract
- A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.
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| 2. |
- Nordin Fredrikson, Gunilla, et al.
(författare)
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Characterization of non-expressed C4 genes in a case of complete C4 deficiency: identification of a novel point mutation leading to a premature stop codon
- 1998
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Ingår i: Human Immunology. - 0198-8859. ; 59:11, s. 713-719
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Tidskriftsartikel (refereegranskat)abstract
- The genetic basis of complete C4 deficiency in a patient with SLE was investigated. Previous studies have demonstrated that this patient has two different major histocompatibility complex (MHC) haplotypes that each contain a major deletion and a non-expressed C4 gene. In the present study, non-expression of the C4 genes was explained by the finding of two distinct C4 gene mutations. A previously described two base pair insertion in exon 29 of the C4 gene was detected in the paternal MHC haplotype [HLA-A2, B40, SC00, DR6]. The maternal haplotype [HLA-A30, B18, F1C00, DR3] carried a C4 gene with a one base pair deletion in exon 20 generating a premature stop codon. This mutation was neither found in 10 individuals with known non-expressed C4 genes nor in 9 individuals homozygous for the complotype F1C30. The isotype and allotype specific regions of the patient's C4 genes were sequenced, and both contained C4A3a sequence. In conclusion, two different MHC haplotypes resembling the extended haplotypes [HLA-A2, B40, SC02, DR6] and [HLA-A30, B18, F1C30, DR3] both contained a non-expressed C4A gene that was due to either of two distinct mutations, demonstrating the heterogeneous genetic background of C4 deficiency.
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