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The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.

The extensible neuroimaging archive toolkit (XNAT) is a common platform for storing and distributing neuroimaging data and is used by many key repositories of public neuroimaging data. Some examples include the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC, https://nitrc.org/), the ConnectomeDB for the Human Connectome Project (https://db.humanconnectome.org/), and XNAT Central (https://central.xnat.org/). We introduce Rxnat (https://github.com/adigherman/Rxnat), an open-source R package designed to interact with any XNAT-based repository. The program has similar capabilities with PyXNAT and XNATpy, which were developed for Python users. Rxnat was developed to address the increased popularity of R among neuroimaging researchers. The Rxnat package can query multiple XNAT repositories and download all or a specific subset of images for further processing. This provides a lingua franca for the large community of R analysts to interface with multiple XNAT-based publicly available neuroimaging repositories. The potential of Rxnat is illustrated using an example of neuroimaging data normalization from two neuroimaging repositories, NITRC and HCP.

Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.

Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access) In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Genes for complex disorders have proven hard to find using linkage analysis. The results rarely reach the desired level of significance and researchers often have failed to replicate positive findings. There is, however, a wealth of information from other scientific approaches which enables the formation of hypotheses on groups of genes or genomic regions likely to be enriched in disease loci. Examples include genes belonging to specific pathways or producing proteins interacting with known risk factors, genes that show altered expression levels in patients or even the group of top scoring locations in a linkage study. We show here that this hypothesis of enrichment for disease loci can be tested using genome-wide linkage data, provided that these data are independent from the data used to generate the hypothesis. Our method is based on the fact that non-parametric linkage analyses are expected to show increased scores at each one of the disease loci, although this increase might not rise above the noise of stochastic variation. By using a summary statistic and calculating its empirical significance, we show that enrichment hypotheses can be tested with power higher than the power of the linkage scan data to identify individual loci. Via simulated linkage scans for a number of different models, we gain insight in the interpretation of genome scan results and test the power of our proposed method. We present an application of the method to real data from a late-onset Alzheimer's disease linkage scan as a proof of principle.

We have previously reported strong linkage on chromosome 10q in pedigrees transmitting Alzheimer's disease through the mother, overlapping with many significant linkage reports including the largest reported study. Here, we report the most comprehensive fine mapping of this region to date. In a sample of 638 late-onset Alzheimer's disease (LOAD) cases and controls including 104 maternal LOAD cases, we genotyped 3,884 single nucleotide polymorphisms (SNPs) covering 15.2 Mb. We then used imputations and publicly available data to generate an extended dataset including 4,329 SNPs for 1,209 AD cases and 839 controls in the same region. Further, we screened eight genes in this region for rare alleles in 283 individuals by nucleotide sequencing, and we tested for possible monoallelic expression as it might underlie our maternal parent of origin linkage. We excluded the possibility of multiple rare coding risk variants for these genes and monoallelic expression when we could test for it. One SNP, rs10824310 in the PRKG1 gene, showed study-wide significant association without a parent of origin effect, but the effect size estimate is not of sufficient magnitude to explain the linkage, and no association is observed in an independent genome-wide association studies (GWAS) report. Further, no causative variants were identified though sequencing. Analysis of cases with maternal disease origin pointed to a few regions of interest that included the genes PRKG1 and PCDH15 and an intergenic interval of 200 Kb. It is likely that non-transcribed rare variants or other mechanisms involving these genomic regions underlie the observed linkage and parent of origin effect. Acquiring additional support and clarifying the mechanisms of such involvement is important for AD and other complex disorder genetics research.

The sequencing of the human genome has allowed us to observe globally and in detail the arrangement of genes along the chromosomes. There are multiple lines of evidence that this arrangement is not random, both in terms of intergenic distances and orientation of neighbouring genes. We have undertaken a systematic evaluation of the spatial distribution and orientation of known genes across the human genome. We used genome-level information, including phylogenetic conservation, single nucleotide polymorphism density and correlation of gene expression to assess the importance of this distribution. In addition to confirming and extending known properties of the genome, such as the significance of gene deserts and the importance of 'head to head' orientation of gene pairs in proximity, we provide significant new observations that include a smaller average size for intervals separating the 3' ends of neighbouring genes, a correlation of gene expression across tissues for genes as far as 100 kilobases apart and signatures of increasing positive selection with decreasing interval size surprisingly relaxing for intervals smaller than ~500 base pairs. Further, we provide extensive graphical representations of the genome-wide data to allow for observations and comparisons beyond what we address.

Complement activation plays a central role in autoimmune demyelination. To explore the possible effects of C5 on post-inflammatory tissue repair, we investigated the transcriptional profile induced by C5 in chronic experimental allergic encephalomyelitis (EAE) using oligonucleotide arrays. We used C5-deficient (C5-d) and C5-sufficient (C5-s) mice to compare the gene expression profile and we found that 390 genes were differentially regulated in C5-s mice as compared to C5-d mice during chronic EAE. Among them, a group of genes belonging to the family of insulin-like growth factor binding proteins (IGFBP) and transforming growth factor (TGF)-beta3 were found most significantly differentially regulated by C5. The dysregulation of these genes suggests that these proteins might be responsible for the gliosis and lack of remyelination seen in C5-d mice with chronic EAE.

Complement activation plays a central role in autoimmune demyelination. We have analyzed the role of complement C5 in EAE using C5-deficient (C5-d) and C5-sufficient (C5-s) mice and found that the absence of C5 resulted in fiber loss and extensive scarring. In this study, we investigated thetranscriptional profile induced by C5 in chronic EAE using oligonucleotide array.During chronic EAE (day 90, post-immunization), we found that 462 genes were differentially regulated in C5-s when compared with C5-d mice. The majority of these genes were related to cell growth and signal transduction; agroup of genes belonging to the insulin-like growth factors binding proteins family was also significantly regulated by C5. This category include insulin like growth factors (IGFBP) 3, 4 and 6, connective tissue growth factor (CTGF), WNT1 inducible signaling pathway protein 2, cysteine rich protein 61 and endothelial cell specific molecule-1. Using Real-time PCR we confirmed the down-regulation of these genes in C5-d mice in chronic EAE. When IGFBP3 and CTGF levels in chronic EAE were compared with those in control mice (day 0, post-immunization), significantly lower levels of mRNA expression were found only in C5-d mice. The down-regulation of genes belonging to the insulin-like growth factor binding family suggests that they might be responsible for the axonal loss and gliosis seen in C5-d during chronic EAE.