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Item Association of structural brain imaging markers with alcoholism incorporating structural connectivity information: a regularized statistical approach(Office of the Vice Chancellor for Research, 2016-04-08) Karas, Marta; Dzemidzic, Mario; Goñi, Joaquin; Kareken, David A.; Harezlak, JaroslawAbstract: Brain imaging studies collect multiple imaging data types, but most analyses are done for each modality separately. Statistical methods that simultaneously utilize and combine multiple data types can instead provide a more holistic view of brain function. Here we model associations between alcohol abuse phenotypes and imaging data while incorporating prior scientific knowledge. Specifically, we utilize cortical thickness and integrated rectified mean curvature measures obtained by FreeSurfer software [1] to predict the alcoholism-related phenotypes while incorporating prior information from the structural connectivity between cortical regions. The sample consisted of 148 young (21-35 years) social-to-heavy drinking male subjects from several alcoholism risk studies [2,3,4]. Structural connectivity model [5] was used to estimate the density of connections between 66 cortical regions based on Desikan-Killiany atlas [6]. We employed a functional linear model with a penalty operator to quantify the relative contributions of imaging markers obtained from high resolution structural MRI (cortical thickness and curvature) as predictors of drinking frequency and risk-relevant personality traits, while co-varying for age. Model parameters were estimated by a unified approach directly incorporating structural connectivity information into the estimation by exploiting the joint eigenproperties of the predictors and the penalty operator [7]. We found that the best predictive imaging markers of the Alcohol Use Disorders Identification Test (AUDIT) score were the average thickness of left frontal pole (-), right transverse temporal gyrus (+), left inferior parietal lobule (+), right supramarginal gyrus (-), right rostral middle frontal gyrus (+), right precentral gyrus (+), left superior parietal lobule (-), left lateral orbitofrontal cortex (+), left rostral middle frontal gyrus (+), left postcentral gyrus (+) and left supramarginal gyrus (-), where (+) denotes positive and (-) negative association. In summary, the use of structural connectivity information allowed the incorporation of different modalities in associating cortical measures and alcoholism risk.Item Dimension-agnostic and granularity-based spatially variable gene identification using BSP(Springer Nature, 2023-11-14) Wang, Juexin; Li, Jinpu; Kramer, Skyler T.; Su, Li; Chang, Yuzhou; Xu, Chunhui; Eadon, Michael T.; Kiryluk, Krzysztof; Ma, Qin; Xu, Dong; BioHealth Informatics, School of Informatics and ComputingIdentifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.Item Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue(MDPI, 2020-04-17) Guo, Yan; Ning, Xia; Mathé, Ewy; Wang, Kai; Li, Lang; Zhang, Chi; Zhao, Zhongming; Medical and Molecular Genetics, School of MedicineThe International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.Item Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism(Springer Nature, 2021-06-09) Matthews, Harold S.; Palmer, Richard L.; Baynam, Gareth S.; Quarrell, Oliver W.; Klein, Ophir D.; Spritz, Richard A.; Hennekam, Raoul C.; Walsh, Susan; Shriver, Mark; Weinberg, Seth M.; Hallgrimsson, Benedikt; Hammond, Peter; Penington, Anthony J.; Peeters, Hilde; Claes, Peter D.; Biology, School of ScienceCraniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype–phenotype relations.Item Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level(Frontiers, 2019-06-21) Kudela, Maria A.; Dzemidzic, Mario; Oberlin, Brandon G.; Lin, Zikai; Goñi, Joaquín; Kareken, David A.; Harezlak, Jaroslaw; Neurology, IU School of MedicineDynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.Item Test–Retest Reliability of Computerized Neurocognitive Testing in Youth Ice Hockey Players(Oxford University Press, 2016-06) Womble, Melissa N.; Reynolds, Erin; Schatz, Philip; Shah, Kishan M.; Kontos, Anthony P.; Orthopaedic Surgery, School of MedicineComputerized neurocognitive tests are frequently used to assess pediatric sport-related concussions; however, only 1 study has focused on the test–retest reliability of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) in high school athletes and age influences have largely been ignored. Therefore, the purpose was to investigate the test–retest reliability of ImPACT and underlying age influences in a pediatric population. Two hundred (169 men and 31 women) youth ice hockey players completed ImPACT before/after a 6-month season. Reliability was assessed using Pearson correlation coefficients, intraclass correlation coefficients (ICCs), and regression-based methods (RBz). ICCs for the sample ranged from .48 to .75 (single)/.65 to .86 (average). In general, the older athletes (15–18: Single/Average ICCs = .35–.75/.52–.86) demonstrated greater reliability across composites than the younger athletes (11–14: Single/Average ICCs = .54–.63/.70–.77). Although there was variation in athletes' performance across two test administrations, RBz revealed that only a small percentage of athletes performed beyond 80%, 90%, and 95% confidence intervals. Statistical metrics demonstrated reliability coefficients for ImPACT composites in a pediatric sample similar to previous studies, and also revealed important age-related influences.