Browsing by Subject "Brain function"

Now showing 1 - 3 of 3
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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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    Maternal deprivation induces alterations in cognitive and cortical function in adulthood
    (Nature Publishing Group, 2018-03-27) Janetsian-Fritz, Sarine S.; Timme, Nicholas M.; Timm, Maureen M.; McCane, Aqilah M.; Baucum, Anthony J., II; O’ Donnell, Brian F.; Lapish, Christopher C.; Psychology, School of Science
    Early life trauma is a risk factor for a number of neuropsychiatric disorders, including schizophrenia (SZ). The current study assessed how an early life traumatic event, maternal deprivation (MD), alters cognition and brain function in rodents. Rats were maternally deprived in the early postnatal period and then recognition memory (RM) was tested in adulthood using the novel object recognition task. The expression of catechol-o-methyl transferase (COMT) and glutamic acid decarboxylase (GAD67) were quantified in the medial prefrontal cortex (mPFC), ventral striatum, and temporal cortex (TC). In addition, depth EEG recordings were obtained from the mPFC, vertex, and TC during a paired-click paradigm to assess the effects of MD on sensory gating. MD animals exhibited impaired RM, lower expression of COMT in the mPFC and TC, and lower expression of GAD67 in the TC. Increased bioelectric noise was observed at each recording site of MD animals. MD animals also exhibited altered information theoretic measures of stimulus encoding. These data indicate that a neurodevelopmental perturbation yields persistent alterations in cognition and brain function, and are consistent with human studies that identified relationships between allelic differences in COMT and GAD67 and bioelectric noise. These changes evoked by MD also lead to alterations in shared information between cognitive and primary sensory processing areas, which provides insight into how early life trauma confers a risk for neurodevelopmental disorders, such as SZ, later in life.
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    Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm
    (Springer Nature, 2017) Liu, Kefei; Yao, Xiaohui; Yan, Jingwen; Chasioti, Danai; Risacher, Shannon; Nho, Kwangsik; Saykin, Andrew; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there.