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Predictive Modeling Approach for Investigating the Association between Brain Morphometry and Subject Attributes
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Kyunk-Ah Sohn | - |
| dc.contributor.author | AYCHEH HABTAMU MINASSIE | - |
| dc.date.accessioned | 2019-04-01T16:42:17Z | - |
| dc.date.available | 2019-04-01T16:42:17Z | - |
| dc.date.issued | 2019-02 | - |
| dc.identifier.other | 28460 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/15204 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :컴퓨터공학과,2019. 2 | - |
| dc.description.tableofcontents | 1. Introduction 1 1.1. Anatomy of the Human Brain 1 1.1.1. Lobes of the Brain 3 1.1.2. Brain Morphometry 5 1.1.2.1. Image Preprocessing 7 1.1.2.2. Voxel Based Morphometry 10 1.1.2.3. Surface Based Morphometry 11 1.1.2.4. Brain ROI 12 1.1.2.5. Measuring Cortical Thickness 13 1.2. Motivation of the Study 15 1.3. Problem Statement 17 1.4. Contribution 18 1.5. Layout of the Dissertation 19 2. Materials and Data 21 2.1. Study Participants 21 2.2. Ethics 22 2.3. Image Acquisition and Preprocessing 23 3. Biological Brain Age Prediction Modeling Approach 25 3.1. Overview 25 3.2. Related Works 26 3.3. Methodology 30 3.3.1. Filtering Outliers 30 3.3.2. Regression Models 34 3.3.2.1. Sparse Group Lasso 35 3.3.2.2. Gaussian Process Regression 39 3.3.2.3. Deep Neural Network 41 3.3.2.4. Stacked Autoencoder 44 3.3.2.5. Cross Validation 45 3.4. Results and Discussion 47 3.4.1. Effect of Outliers 47 3.4.2. Performance of Hybrid Methods 48 3.4.3. Consistency of SGL 51 3.4.4. Analysis of the Proposed Model 51 3.4.5. Analysis of Brain Features Significantly Contributing to Age Estimation 54 4. Association between Education and Elderly Cortical Thickness 56 4.1. Overview 56 4.2. Prior Works 57 4.3. Modeling Approach 60 4.3.1. Characteristics of Education Data 60 4.3.2. Statistical Analysis 62 4.3.3. Classification Models 63 4.3.3.1. Sparse Group LASSO 65 4.3.3.2. Partial Least Square 65 4.3.3.3. Deep Learning 68 4.3.3.4. Support Vector Machine 69 4.3.3.5. Performance metrics of Classification Models 72 4.4. Results and Discussion 73 4.4.1. Evaluation results 73 4.4.2. Evaluation Results on Selected Features 75 4.4.3. Discussion 77 5. Conclusion and Future Works 80 References 84 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Predictive Modeling Approach for Investigating the Association between Brain Morphometry and Subject Attributes | - |
| dc.title.alternative | HABTAMU MINASSIE AYCHEH | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | HABTAMU MINASSIE AYCHEH | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2019. 2 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 905164 | - |
| dc.identifier.uci | I804:41038-000000028460 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000028460 | - |
| dc.description.alternativeAbstract | In the domain of computational neuroscience, scientists are making continuous efforts to understand healthy brain structure which helps to identify and explain damages and brain diseases disrupting its functions. Noticeably, magnetic resonance image (MRI) technology and brain morphometry methods opened noble opportunities for further study of biological brain structure in neuroscience using machine learning techniques. Among these, the association between cortex structural change and cognitive ageing are getting more focus in current researches. There is a great demand to realize why cognitive functioning decline differently in normal ageing. In addition, many research works indicated that a cognitive reserve can be improved by lifestyle factors such as education, job satisfaction and leisure activities at older ages and these factors are useful to reduce risk of brain pathology like Alzheimer disease. In this regard, the importance of education to delay cortical thinning due to normal ageing recently taking researchers’ attention in the domain. In other words, there is an interest of realizing the hypothesis stating that the mean cortical thickness of an individual might have different rate of change at older ages with respect to individual’s further educational attainment. In light with these, the main objective of this dissertation is to analyze predictive modeling approaches that can be used to assess the associations between demographic attributes of a subject and its brain cortical structure. The study focuses on the investigation of the two demographic attributes chronological age and educational level and their effect on brain cortex structure. We design robust features selection method that identifies the most important explanatory features among the macrostructure of cortex region interests (ROIs) and confounding factors of demographic properties. The proposed model is tested using 2,911 cognitively normal subjects (age 45-91 years) which are collected at a single medical center and acquired their brain magnetic resonance images. All images were acquired using the same scanner with the same protocol. Accordingly, first the modeling of brain age prediction from cortical thickness data gathered from large cohort brain images is presented. This study proposes to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain’s anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent state of the art methods. In the second study, the framework of investigating the association between education and elderly cortical thickness is presented. The study is tested by further sub setting the dataset representation into three situations based on the levels of education measured in years of study. Among this, we obtained an optimal accuracy of 90% by using Deep Learning model on the most important predicting variables selected using Sparse Group LASSO when the sub dataset is represented containing unschooled (0 years of study) and post graduate levels of participants. | - |
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- Graduate School of Ajou University > Department of Computer Engineering > 4. Theses(Ph.D)
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