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You searched for +publisher:"Vanderbilt University" +contributor:("Kang, Hakmook"). Showing records 1 – 2 of 2 total matches.

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Vanderbilt University

1. -1150-117X. Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data.

Degree: MS, Biostatistics, 2020, Vanderbilt University

FMRI data can help identify areas of the brain that are activated due to external stimuli. Comparing activated brain regions in participants with and without a disease can help researches understand more about how certain brain activation patterns relate to that disease. To analyze fMRI data, there are steps that need to be taken in order to reduce artifacts and to standardize regions of the brain across subjects in order to do group analysis. In many fMRI data analysis studies, a general linear regression model is fit in order to do hypothesis testing at each voxel. With thousands of voxels, it is necessary to control for multiple comparisons. Random field theory (RFT) and methods controlling the false discovery rate (FDR) are most commonly employed methods. However, applying RFT or FDR on thousands of p-values may inflate the Type II error rate at each voxel, which hinders scientifically meaningful findings. In this thesis, traditional approaches to analyzing fMRI data were compared to an approach using the likelihood paradigm by using multi-subject data. In the likelihood paradigm approach, the family-wise error rate stays small as the number of comparisons increases, an advantage over the traditional approaches. We found that the likelihood paradigm approach was very conservative, as there were no voxels found to be active. This results in false positive and false negative rates all equal to 0. In the simulation, similar results were found. Adding a larger effect size in the simulation did change the results more to what we expect: false negative rates are not close to 1, and false positive rates hover around 0. In the data analysis and simulation study, similar trends were seen for the RFT and FDR methods. A The small amount of activation in the likelihood paradigm approach can possibly be attributed to our defined alternatives used in the likelihood ratio. Advisors/Committee Members: Kang, Hakmook (advisor).

Subjects/Keywords: fMRI data analysis; Likelihood Paradigm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-1150-117X. (2020). Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data. (Masters Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15965

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Chicago Manual of Style (16th Edition):

-1150-117X. “Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data.” 2020. Masters Thesis, Vanderbilt University. Accessed April 13, 2021. http://hdl.handle.net/1803/15965.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-1150-117X. “Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data.” 2020. Web. 13 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1150-117X. Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data. [Internet] [Masters thesis]. Vanderbilt University; 2020. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1803/15965.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1150-117X. Likelihood Paradigm on Multiple Subjects with Task-Induced fMRI Data. [Masters Thesis]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/15965

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Vanderbilt University

2. -3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.

Degree: PhD, Biostatistics, 2020, Vanderbilt University

When conducting analyses on high dimensional data, one could face statistical difficulties due to large dimensionality and the noisy nature of the data. In this dissertation, we specifically look into potential complexities one might encounter when analyzing electronic medical record (EMR) and functional magnetic resonance imaging (fMRI) data. Phenome-Wide Association study (PheWAS) is a newly proposed method that scans through phenotypes (Phecodes) with a specific genotype of interest using logistic regression. Since the clinical diagnoses in EMR are often inaccurate which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classifying controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased estimates. However, this method could be inefficient and the accuracy of the lists cannot be guaranteed. We propose to estimate relative risk (RR) instead. With simulation and real data application, we show that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to extend PheWAS to larger-scale phenotypes which preserve more disease-related clinical information than Phecodes. The main purpose of task-induced fMRI is to measure neuronal activities related to specific task. fMRI data usually require several preprocessing steps before analysis. Among all, spatial smoothing is a necessary step known to increase signal-to-noise ratios but the choice of degree of smoothing is often arbitrary. One critical statistical issue in fMRI analysis is the balance between Type I and II error rates. We first demonstrate the influence of the degree of smoothing and experimental factors on the trade-off between Type I and II error rates. Next, we propose to use second-generation p-values (SGPV) as an inference tool instead of the traditional p-values for hypothesis testing. By allowing the interval null hypothesis, we have shown that SGPV is able to alleviate the critical statistical issue by controlling Type I error rate more steadily while obtaining enough power. Advisors/Committee Members: Johnson, Robert (advisor), Kang, Hakmook (advisor).

Subjects/Keywords: EMR; PheWAS; fMRI; Statistical analysis; study design; Type I error rate; Type II error rate; p-value; Multiple comparison; Second-generation p-values; SGPV; Interval null; Hypothesis testing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-3660-4570. (2020). Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15924

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-3660-4570. “Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.” 2020. Doctoral Dissertation, Vanderbilt University. Accessed April 13, 2021. http://hdl.handle.net/1803/15924.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-3660-4570. “Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data.” 2020. Web. 13 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2020. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/1803/15924.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3660-4570. Coping With Complexities in High Dimensional Data: PheWAS in EMR and Statistical Inference in fMRI Data. [Doctoral Dissertation]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/15924

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

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