Browsing by Subject "Brain disorder"

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    Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort
    (Oxford University Press, 2019-07) Du, Lei; Liu, Kefei; Zhu, Lei; Yao, Xiaohui; Risacher, Shannon L.; Guo, Lei; Saykin, Andrew J.; Shen, Li; Radiology & Imaging Sciences, IU School of Medicine
    Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online.
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    Lessons from a BACE1 inhibitor trial: off-site but not off base
    (Elsevier, 2014-10) Lahiri, Debomoy K.; Maloney, Bryan; Long, Justin M.; Greig, Nigel H.; Department of Medical & Molecular Genetics, IU School of Medicine
    Alzheimer's disease (AD) is characterized by formation of neuritic plaque primarily composed of a small filamentous protein called amyloid-β peptide (Aβ). The rate-limiting step in the production of Aβ is the processing of Aβ precursor protein (APP) by β-site APP-cleaving enzyme (BACE1). Hence, BACE1 activity plausibly plays a rate-limiting role in the generation of potentially toxic Aβ within brain and the development of AD, thereby making it an interesting drug target. A phase II trial of the promising LY2886721 inhibitor of BACE1 was suspended in June 2013 by Eli Lilly and Co., due to possible liver toxicity. This outcome was apparently a surprise to the study's team, particularly since BACE1 knockout mice and mice treated with the drug did not show such liver toxicity. Lilly proposed that the problem was not due to LY2886721 anti-BACE1 activity. We offer an alternative hypothesis, whereby anti-BACE1 activity may induce apparent hepatotoxicity through inhibiting BACE1's processing of β-galactoside α-2,6-sialyltransferase I (STGal6 I). In knockout mice, paralogues, such as BACE2 or cathepsin D, could partially compensate. Furthermore, the short duration of animal studies and short lifespan of study animals could mask effects that would require several decades to accumulate in humans. Inhibition of hepatic BACE1 activity in middle-aged humans would produce effects not detectable in mice. We present a testable model to explain the off-target effects of LY2886721 and highlight more broadly that so-called off-target drug effects might actually represent off-site effects that are not necessarily off-target. Consideration of this concept in forthcoming drug design, screening, and testing programs may prevent such failures in the future.