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Nichols, T.

Publications and source records attributed to Nichols, T..

23 records · Page 2Linked to original sources

Estimating relative CWD susceptibility and disease progression in farmed whitetail deer with rare PRNP alleles

Chronic wasting disease is a prion disease affecting both free-ranging and farmed cervids in North America and Scandinavia. A range of cervid species have been found to be susceptible, each with variations in the gene for the normal prion protein, PRNP, reportedly influencing both disease susceptibility and progression in the respective hosts. Despite the finding of several different PRNP alleles in whitetail deer, the majority of past research has focused on two of the more common alleles identified - the 96G and 96S alleles. In the present study, we evaluate both infection status and disease stage in nearly 2100 farmed deer depopulated in the United States and Canada, including 714 CWD-positive deer and correlate our findings with PRNP genotype, including the more rare 95H, 116G, and 226K alleles. We found significant differences in either likelihood of being found infected or disease stage (and in many cases both) at the time of depopulation in all genotypes present, relative to the most common 96GG genotype. Despite high prevalence in many of the herds examined, infection was not found in several of the reported genotypes. These findings suggest that additional research is necessary to more properly define the role that these genotypes may play in managing CWD in both farmed and free-ranging whitetail deer, with consideration for factors including relative fitness levels, incubation periods, and the kinetics of shedding in animals with these rare genotypes.

microbiology↗

Spatial Confidence Sets for Raw Effect Size Images

The mass-univariate approach for functional magnetic resonance imagery (fMRI) analysis remains a widely used and fundamental statistical tool within neuroimaging. However, this method suffers from at least two fundamental limitations: First, with sample sizes growing to 4, 5 or even 6 digits, the entire approach is undermined by the null hypothesis fallacy, i.e. with sufficient sample size, there is high enough statistical power to reject the null hypothesis everywhere, making it difficult if not impossible to localize effects of interest. Second, with any sample size, when cluster-size inference is used a significant p-value only indicates that a cluster is larger than chance, and no notion of spatial uncertainty is provided. Therefore, no perception of confidence is available to express the size or location of a cluster that could be expected with repeated sampling from the population.\n\nIn this work, we address these issues by extending on a method proposed by Sommerfeld, Sain, and Schwartzman (2018) to develop spatial Confidence Sets (CSs) on clusters found in thresholded raw effect size maps. While hypothesis testing indicates where the null, i.e. a raw effect size of zero, can be rejected, the CSs give statements on the locations where raw effect sizes exceed, and fall short of, a non-zero threshold, providing both an upper and lower CS.\n\nWhile the method can be applied to any parameter in a mass-univariate General Linear Model, we motivate the method in the context of BOLD fMRI contrast maps for inference on percentage BOLD change raw effects. We propose several theoretical and practical implementation advancements to the original method in order to deliver an improved performance in small-sample settings. We validate the method with 3D Monte Carlo simulations that resemble fMRI data. Finally, we compute CSs for the Human Connectome Project working memory task contrast images, illustrating the brain regions that show a reliable %BOLD change for a given %BOLD threshold.

neuroscience↗

Beyond Bonferroni Revisited: Concerns over inflated false positives in conservation genetics, genetics, and neuroscience

In 2006, Narum published a paper in Conservation Genetics that was motivated by the stringent nature of the Bonferroni approach for family wise error correction. That work suggested that the approach of Benjamini and Yekutieli in 2001 provided adequate correction and was more biologically relevant. However, there are crucial differences between the original Benjamini and Yekutieli procedure and that described by Narum. After carefully reviewing both papers, we believe that the Narum procedure is both different than the Benjamini and Yekutieli procedure and does not adequately control for family wise error. We provide an overview of approaches for FWE correction as well as evidence for the faulty implementation of the Benjamini and Yekutieli procedure by Narum using the equations from the respective papers, data from both papers, and the results of simulation.

genetics↗

Large-scale neuroimaging and genetic study reveals genetic architecture of brain white matter microstructure

Microstructural changes of white matter (WM) tracts are known to be associated with various neuropsychiatric disorders/diseases. Heritability of structural changes of WM tracts has been examined using diffusion tensor imaging (DTI) in family-based studies for different age groups. The availability of genetic and DTI data from recent large population-based studies offers opportunity to further improve our understanding of genetic contributions. Here, we analyzed the genetic architecture of WM tracts using DTI and single-nucleotide polymorphism (SNP) data of unrelated individuals in the UK Biobank (n [~] 8000). The DTI parameters were generated using the ENIGMA-DTI pipeline. We found that DTI parameters are substantially heritable on most WM tracts. We observed a highly polygenic or omnigenic architecture of genetic influence across the genome as well as the enrichment of SNPs in active chromatin regions. Our bivariate analyses showed strong genetic correlations for several pairs of WM tracts as well as pairs of DTI parameters. We performed voxel-based analysis to illustrate the pattern of genetic effects on selected parts of the tract-based spatial statistics skeleton. Comparing the estimates from the UK Biobank to those from small population-based studies, we illustrated that sufficiently large sample size is essential for genetic architecture discovery in imaging genetics. We confirmed this finding with a simulation study.

genetics↗

Exploring the Impact of Analysis Software on Task fMRI Results

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this methodological plurality comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.\n\nIn this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

neuroscience↗