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Dynamic and Individualized Small-Scale Brain Functional Organization
Dynamic and Individualized Small-Scale Brain Functional Organization
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211150913
- ISBN
- 9798383565988
- DDC
- 616
- 저자명
- Luo, Wenjing.
- 서명/저자
- Dynamic and Individualized Small-Scale Brain Functional Organization
- 발행사항
- [Sl] : Yale University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 129 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Constable, R. Todd.
- 학위논문주기
- Thesis (Ph.D.)--Yale University, 2024.
- 초록/해제
- 요약Since functional MRI (fMRI) was first introduced as a non-invasive neuroimaging method for imaging brain function, it has been widely used for brain mapping wherein activities in various brain regions are associated with functions in both healthy and patient populations. To reduce noise in fMRI signals and ensure interpretability, the smallest spatial units in fMRI images, the voxels, tend to be grouped into nodes, and the nodes are grouped into networks, usually by applying an atlas defining the nodes. Despite that various parcellation methods have been proposed based on different imaging modalities and criteria, in almost all previous studies, a single fixed atlas (or atlases) is (are) used for analysis across all subject groups and task-induced brain states. However, recent work has shown that the topography of the nodes differs between subjects and reconfigures as the brain executes different functions. In this thesis, I revealed empirical evidence of the flexibility in small-scale brain network organization across subjects and task-induced states, demonstrated its significant impact on brain network studies in the field, and proposed a potential approach to identify task-specific functional networks. In the first chapter, I provide an overview of brain parcellation and brain-behavior modeling and discuss the contribution of this thesis to the field. In the second chapter, I demonstrate that the changes in functional connectivity within nodes are predictive of task-induced brain states and traits of interest. The systematic changes in within-node, small-scale functional organization are not controlled for when fixed atlases are applied. These local changes are the empirical basis of the flexible node definition. In the third chapter, I demonstrate that if flexible node topography under different task-induced brain states and between different subject groups was taken into account by applying individualized atlases instead of fixed atlases, the results of widely used brain network analysis could change significantly. Thus, conclusions of between-node network analysis can be misleading if functional node reconfiguration is ignored. In the fourth chapter, I propose a data-driven approach to identify brain networks predictive of specific cognitive measures of interest that do not rely on pre-defined functional atlases. Using this approach, I identify nodes that are involved in multiple cognitive functions but have different topography for each function, suggesting that functional units reconfigure under different cognitive demands. Overall, the work presented here reveals the empirical evidence of flexible local functional organization in the brain, emphasizes its impact on brain network research, and provides a potential brain-behavior modeling framework to incorporate flexibility. It also lays a theoretical and empirical foundation for future research capturing and analyzing flexible and dynamic brain functional units.
- 일반주제명
- Neurosciences
- 일반주제명
- Biomedical engineering
- 일반주제명
- Medical imaging
- 일반주제명
- Cognitive psychology
- 키워드
- Brain network
- 키워드
- Functional MRI
- 기타저자
- Yale University Biomedical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383565988
■035 ▼a(MiAaPQ)AAI30694575
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a616
■1001 ▼aLuo, Wenjing.
■24510▼aDynamic and Individualized Small-Scale Brain Functional Organization
■260 ▼a[Sl]▼bYale University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a129 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Constable, R. Todd.
■5021 ▼aThesis (Ph.D.)--Yale University, 2024.
■520 ▼aSince functional MRI (fMRI) was first introduced as a non-invasive neuroimaging method for imaging brain function, it has been widely used for brain mapping wherein activities in various brain regions are associated with functions in both healthy and patient populations. To reduce noise in fMRI signals and ensure interpretability, the smallest spatial units in fMRI images, the voxels, tend to be grouped into nodes, and the nodes are grouped into networks, usually by applying an atlas defining the nodes. Despite that various parcellation methods have been proposed based on different imaging modalities and criteria, in almost all previous studies, a single fixed atlas (or atlases) is (are) used for analysis across all subject groups and task-induced brain states. However, recent work has shown that the topography of the nodes differs between subjects and reconfigures as the brain executes different functions. In this thesis, I revealed empirical evidence of the flexibility in small-scale brain network organization across subjects and task-induced states, demonstrated its significant impact on brain network studies in the field, and proposed a potential approach to identify task-specific functional networks. In the first chapter, I provide an overview of brain parcellation and brain-behavior modeling and discuss the contribution of this thesis to the field. In the second chapter, I demonstrate that the changes in functional connectivity within nodes are predictive of task-induced brain states and traits of interest. The systematic changes in within-node, small-scale functional organization are not controlled for when fixed atlases are applied. These local changes are the empirical basis of the flexible node definition. In the third chapter, I demonstrate that if flexible node topography under different task-induced brain states and between different subject groups was taken into account by applying individualized atlases instead of fixed atlases, the results of widely used brain network analysis could change significantly. Thus, conclusions of between-node network analysis can be misleading if functional node reconfiguration is ignored. In the fourth chapter, I propose a data-driven approach to identify brain networks predictive of specific cognitive measures of interest that do not rely on pre-defined functional atlases. Using this approach, I identify nodes that are involved in multiple cognitive functions but have different topography for each function, suggesting that functional units reconfigure under different cognitive demands. Overall, the work presented here reveals the empirical evidence of flexible local functional organization in the brain, emphasizes its impact on brain network research, and provides a potential brain-behavior modeling framework to incorporate flexibility. It also lays a theoretical and empirical foundation for future research capturing and analyzing flexible and dynamic brain functional units.
■590 ▼aSchool code: 0265.
■650 4▼aNeurosciences
■650 4▼aBiomedical engineering
■650 4▼aMedical imaging
■650 4▼aCognitive psychology
■653 ▼aBrain network
■653 ▼aBrain parcellation
■653 ▼aFunctional MRI
■653 ▼aFunctional connectivity
■653 ▼aCognitive measures
■690 ▼a0317
■690 ▼a0541
■690 ▼a0574
■690 ▼a0633
■71020▼aYale University▼bBiomedical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0265
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160123▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
