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Browsing by Author "Zaghlool, Shaza B."

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    Dynamic Causal Modeling Across Network Topologies
    Zaghlool, Shaza B. (Virginia Tech, 2014-04-03)
    Dynamic Causal Modeling (DCM) uses dynamical systems to represent the high-level neural processing strategy for a given cognitive task. The logical network topology of the model is specified by a combination of prior knowledge and statistical analysis of the neuro-imaging signals. Parameters of this a-priori model are then estimated and competing models are compared to determine the most likely model given experimental data. Inter-subject analysis using DCM is complicated by differences in model topology, which can vary across subjects due to errors in the first-level statistical analysis of fMRI data or variations in cognitive processing. This requires considerable judgment on the part of the experimenter to decide on the validity of assumptions used in the modeling and statistical analysis; in particular, the dropping of subjects with insufficient activity in a region of the model and ignoring activation not included in the model. This manual data filtering is required so that the fMRI model's network size is consistent across subjects. This thesis proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various data set sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.
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    Mendelian inheritance of trimodal CpG methylation sites suggests distal cis-acting genetic effects
    Zaghlool, Shaza B.; Al-Shafai, Mashael; Al Muftah, Wadha A.; Kumar, Pankaj; Gieger, Christian; Waldenberger, Melanie; Falchi, Mario; Suhre, Karsten (2016-11-22)
    Background Environmentally influenced phenotypes, such as obesity and insulin resistance, can be transmitted over multiple generations. Epigenetic modifications, such as methylation of DNA cytosine-guanine (CpG) pairs, may be carriers of inherited information. At the population level, the methylation state of such “heritable” CpG sites is expected to follow a trimodal distribution, and their mode of inheritance should be Mendelian. Methods Using the Illumina Infinium 450 K DNA methylation array, we determined DNA CpG-methylation in blood cells from a family cohort 123 individuals of Arab ethnicity, including 18 elementary father-mother-child trios, we asked whether Mendelian inheritance of CpG methylation is observed, and most importantly, whether it is independent of any genetic signals. Using 40× whole genome sequencing, we therefore excluded all CpG sites with possibly confounding genetic variants (SNP) within the binding regions of the Illumina probes. Results We identified a total of 955 CpG sites that displayed a trimodal distribution and confirmed trimodality in a study of 1805 unrelated Caucasians. Of 955 CpG sites, 99.9% observed a strict Mendelian pattern of inheritance and had no SNP within +/−110 nucleotides of the CpG site by design. However, in 97% of these cases a distal cis-acting SNP within a +/−1 Mbp window was found that explained the observed CpG distribution, excluding the hypothesis of epigenetic inheritance for these clear-cut trimodal sites. Using power analysis, we showed that in 46% of all cases, the closest CpG-associated SNP was located more than 1000 bp from the CpG site. Conclusions Our findings suggest that CpG methylation is maintained over larger genomic distances. Furthermore, nearly half of the SNPs associated with these trimodal sites were also associated with the expression of nearby genes (P = 4.08 × 10−6), implying a regulatory effect of these trimodal CpG sites.
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    Missing data estimation in fMRI dynamic causal modeling
    Zaghlool, Shaza B.; Wyatt, Christopher Lee (Frontiers, 2014-07-04)
    Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.