Browsing by Subject "Variability"

Now showing 1 - 4 of 4
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    Assessing the inter- and intra-animal variability of in vivo OsteoProbe skeletal measures in untreated dogs
    (Elsevier, 2016-12) McNerny, Erin M.B.; Organ, Jason M.; Wallace, Joseph M.; Newman, Christopher L.; Brown, Drew M.; Allen, Matthew R.; Department of Anatomy and Cell Biology, School of Medicine
    The OsteoProbe is a second-generation reference point indentation (RPI) device without a reference probe that is designed to simplify RPI testing for clinical use. Successful clinical implementation of the OsteoProbe would benefit from a better understanding of how its output, bone material strength index (BMSi), relates to the material properties of bone and under what conditions it reliably correlates with fracture risk. Large animal models have the potential to help fill this knowledge gap, as cadaveric studies are retrospective and limited by incomplete patient histories (including the potential use of bone matrix altering drugs such as bisphosphonates). The goal of this study was to assess the intra and inter-animal variability of OsteoProbe measures in untreated beagle dogs (n = 12), and to evaluate this variability in comparison to traditional mechanical testing. OsteoProbe measurements were performed in vivo on the left tibia of each dog and repeated 6 months later on the day of sacrifice. Within-animal variation of BMSi (CV of 5–10 indents) averaged 8.9 and 9.0% at the first and second timepoints, respectively. In contrast, inter-animal variation of BMSi increased from 5.3% to 9.1%. The group variation of BMSi was on par with that of traditional 3-point mechanical testing; inter-animal variation was 10% for ultimate force, 13% for stiffness, and 12% for total work as measured on the femur. There was no significant change in mean BMSi after 6 months, but the individual change with time across the 12 dogs was highly variable, ranging from − 12.4% to + 21.7% (mean 1.6%, SD 10.6%). No significant correlations were found between in vivo tibia BMSi and femur mechanical properties measured by ex vivo 3-pt bending, but this may be a limitation of sample size or the tests being performed on different bones. No relationship was found between BMSi and tissue mineral density, but a strong positive correlation was found between BMSi and tibia cortical thickness (ρ = 0.706, p = 0.010). This report shows that while the OsteoProbe device has inter-individual variability quite similar to that of traditional mechanical testing, the longitudinal changes show high levels of heterogeneity across subjects. We further highlight the need for standardization in post-testing data processing and further study of the relationships between OsteoProbe and traditional mechanical testing.
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    Automatic classification of white regions in liver biopsies by supervised machine learning
    (Elsevier, 2014-04) Vanderbeck, Scott; Bockhorst, Joseph; Komorowski, Richard; Kleiner, David E.; Gawrieh, Samer; Medicine, School of Medicine
    Automated assessment of histological features of non-alcoholic fatty liver disease (NAFLD) may reduce human variability and provide continuous rather than semiquantitative measurement of these features. As part of a larger effort, we perform automatic classification of steatosis, the cardinal feature of NAFLD, and other regions that manifest as white in images of hematoxylin and eosin-stained liver biopsy sections. These regions include macrosteatosis, central veins, portal veins, portal arteries, sinusoids and bile ducts. Digital images of hematoxylin and eosin-stained slides of 47 liver biopsies from patients with normal liver histology (n = 20) and NAFLD (n = 27) were obtained at 20× magnification. The images were analyzed using supervised machine learning classifiers created from annotations provided by two expert pathologists. The classification algorithm performs with 89% overall accuracy. It identified macrosteatosis, bile ducts, portal veins and sinusoids with high precision and recall (≥ 82%). Identification of central veins and portal arteries was less robust but still good. The accuracy of the classifier in identifying macrosteatosis is the best reported. The accurate automated identification of macrosteatosis achieved with this algorithm has useful clinical and research-related applications. The accurate detection of liver microscopic anatomical landmarks may facilitate important subsequent tasks, such as localization of other histological lesions according to liver microscopic anatomy.
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    Characteristics and variability of structural networks derived from diffusion tensor imaging
    (Elsevier, 2012) Cheng, Hu; Wang, Yang; Sheng, Jinhua; Kronenberger, William G.; Mathews, Vincent P.; Hummer, Tom A.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
    Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.
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    Distinct Patterns of Impaired Cognitive Control Among Boys and Girls with ADHD Across Development
    (Springer, 2021) DeRonda, Alyssa; Zhao, Yi; Seymour, Karen E.; Mostofsky, Stewart H.; Rosch, Keri S.; Biostatistics, School of Public Health
    This study examined whether girls and boys with ADHD show similar impairments in cognitive control from childhood into adolescence and the developmental relationship between cognitive control and ADHD symptoms. Participants include 8-17-year-old children with ADHD (n = 353, 104 girls) and typically developing (TD) controls (n = 241, 86 girls) with longitudinal data obtained from n = 137. Participants completed two go/no-go (GNG) tasks that varied in working memory demand. Linear mixed-effects models were applied to compare age-related changes in cognitive control for each GNG task among girls and boys with ADHD and TD controls and in relation to ADHD symptoms. Boys with ADHD showed impaired response inhibition and increased response variability across tasks. In contrast, girls with ADHD showed impaired response inhibition only with greater working memory demands whereas they displayed increased response variability regardless of working memory demands. Analysis of age-related change revealed that deficits in cognitive control under minimal working memory demands increase with age among girls with ADHD and decrease with age among boys with ADHD. In contrast, deficits in cognitive control with greater working memory demands decrease with age among both boys and girls with ADHD compared to TD peers. Among children with ADHD poor response inhibition during childhood predicted inattentive symptoms in adolescence and was associated with less age-related improvement in inattentive symptoms. These findings suggest that girls and boys with ADHD show differential impairment in cognitive control across development and response inhibition in childhood may be an important predictor of ADHD symptoms in adolescence.