Browsing by Subject "medical disorders"

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    Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM
    (Institute of Electrical and Electronics Engineers, 2014-08) Cong, Shan; Rizkalla, Maher; Du, Eliza Y.; West, John; Risacher, Shannon; Saykin, Andrew J.; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Department of Medicine, IU School of Medicine
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.
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    Comparison of Multi-Sample Variant Calling Methods for Whole Genome Sequencing
    (Institute of Electrical and Electronics Engineers, 2014-10) Nho, Kwangsik; West, John D.; Li, Huian; Henschel, Robert; Bharthur, Apoorva; Tavares, Michel C.; Saykin, Andrew J.; Department of Medicine, IU School of Medicine
    Rapid advancement of next-generation sequencing (NGS) technologies has facilitated the search for genetic susceptibility factors that influence disease risk in the field of human genetics. In particular whole genome sequencing (WGS) has been used to obtain the most comprehensive genetic variation of an individual and perform detailed evaluation of all genetic variation. To this end, sophisticated methods to accurately call high-quality variants and genotypes simultaneously on a cohort of individuals from raw sequence data are required. On chromosome 22 of 818 WGS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which is the largest WGS related to a single disease, we compared two multi-sample variant calling methods for the detection of single nucleotide variants (SNVs) and short insertions and deletions (indels) in WGS: (1) reduce the analysis-ready reads (BAM) file to a manageable size by keeping only essential information for variant calling ("REDUCE") and (2) call variants individually on each sample and then perform a joint genotyping analysis of the variant files produced for all samples in a cohort ("JOINT"). JOINT identified 515,210 SNVs and 60,042 indels, while REDUCE identified 358,303 SNVs and 52,855 indels. JOINT identified many more SNVs and indels compared to REDUCE. Both methods had concordance rate of 99.60% for SNVs and 99.06% for indels. For SNVs, evaluation with HumanOmni 2.5M genotyping arrays revealed a concordance rate of 99.68% for JOINT and 99.50% for REDUCE. REDUCE needed more computational time and memory compared to JOINT. Our findings indicate that the multi-sample variant calling method using the JOINT process is a promising strategy for the variant detection, which should facilitate our understanding of the underlying pathogenesis of human diseases.