| 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. |
- van Doormaal, Perry T. C., et al.
(författare)
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The role of de novo mutations in the development of amyotrophic lateral sclerosis
- 2017
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Ingår i: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 38:11, s. 1534-1541
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Tidskriftsartikel (refereegranskat)abstract
- The genetic basis combined with the sporadic occurrence of amyotrophic lateral sclerosis (ALS) suggests a role of de novo mutations in disease pathogenesis. Previous studies provided some evidence for this hypothesis; however, results were conflicting: no genes with recurrent occurring de novo mutations were identified and different pathways were postulated. In this study, we analyzed whole-exome data from 82 new patient-parents trios and combined it with the datasets of all previously published ALS trios (173 trios in total). The per patient de novo rate was not higher than expected based on the general population (P = 0.40). We showed that these mutations are not part of the previously postulated pathways, and gene-gene interaction analysis found no enrichment of interacting genes in this group (P = 0.57). Also, we were able to show that the de novo mutations in ALS patients are located in genes already prone for de novo mutations (P < 1 x 10(-15)). Although the individual effect of rare de novo mutations in specific genes could not be assessed, our results indicate that, in contrast to previous hypothesis, de novo mutations in general do not impose a major burden on ALS risk.
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