Functional Connectome of Brain White Matter

脑白质功能连接组

• 批准号: 10735976
• 负责人: ZHAOHUA DING
• 金额: $ 48.83万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735976
• 关键词: 3-Dimensional; Address; Age; Algorithms; Alzheimer' s Disease; Atlases; Behavior; Behavioral; Brain; Cognition; Cognitive; Communication; Data; Data Set; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Exclusion; Fiber; Foundations; Functional Imaging; Functional Magnetic Resonance Imaging; Gender; Goals; Human; Image; Investigation; Light; Measurement; Measures; Methodology; Methods; Modeling; Multiple Sclerosis; Parkinson Disease; Pathologic; Pathology; Pattern; Reproducibility; Research; Resolution; Rest; Schizophrenia; Signal Transduction; Standardization; Stimulus; Stroke; Structure; Surface; Testing; Time; Variant; Work; behavior measurement; blood oxygen level dependent; cognitive function; connectome; density; digital; gray matter; hemodynamics; imaging study; independent component analysis; innovation; neural; response; tractography; white matter

ABSTRACT / SUMMARY The goals of this research are to extend our discoveries of how blood oxygenation level dependent (BOLD) signals in white matter (WM), detected using functional magnetic resonance imaging (fMRI), are related to neural activity in gray matter (GM), and to implement new analyses that properly incorporate WM signals into models of brain function derived from imaging data. For the past three decades, nearly all analyses of brain fMRI data have ignored WM signals and usually have removed them as nuisance regressors. However, that view has changed in light of more recent evidence that WM BOLD signals represent potentially important and heretofore overlooked indicators of neural activity that are intimately related to how cortical regions communicate, and so should be incorporated into complete assessments of functional connectivity. We have recently shown that BOLD signals are robustly detectable in WM when appropriate analyses are used, that the hemodynamic response function in WM is different from GM, and that WM tracts show reproducible patterns of apparent connectivity which may be summarized in Functional Connectivity Matrices (FCMs), obtained by analyzing resting state correlations between segmented WM and GM parcellations. Furthermore, distinct, reproducible networks of WM emerge from data-driven analyses in similar manner to cortical circuits. In this proposal we aim to develop new analyses and apply them to large numbers of publicly available data. We aim (1) to quantify the functional relationships between WM fibers and GM circuits at a finer scale and in greater detail. We will extend the concept of FCMs to three dimensions to derive those WM tracts that show synchronous time courses with pairs of GM regions that themselves are identified from a matrix of GM-GM connectivity; (2) to use data-driven, model-free independent component analyses to identify WM and GM functional networks and quantify the correlations between them; and (3) to construct a suite of detailed and quantitative atlases characterizing functional connectivity and network topology in WM, and establish their relationships with behavioral and cognitive measures. Templates and digital atlases provide a way to spatially normalize data to common spaces, and measure normal and abnormal variations quantitatively. Extending and applying the methodology from structural and diffusion MRI fields to create atlases of WM functional data will enable reproducible quantification, normalization, and interpretation of our results. Each analysis will also examine the influence of gender and age on WM functional metrics. Impact: BOLD signals in WM reflect neural activity that is related to cortical brain function, so analyses of the functional engagement of WM are essential to properly model brain networks. This research would demonstrate how WM and GM activities are related, and how to integrate them to obtain a more complete model of brain organization. The results will lay a firm foundation for exploiting functional connectivity in white matter associated with pathological or other changes across a spectrum of disorders and conditions.

ABSTRACT / SUMMARY The goals of this research are to extend our discoveries of how blood oxygenation level dependent (BOLD) signals in white matter (WM), detected using functional magnetic resonance imaging (fMRI), are related to neural activity in gray matter (GM), and to implement new analyses that properly incorporate WM signals into models of brain function derived from imaging data. For the past three decades, nearly all analyses of brain fMRI data have ignored WM signals and usually have removed them as nuisance regressors. However, that view has changed in light of more recent evidence that WM BOLD signals represent potentially important and heretofore overlooked indicators of neural activity that are intimately related to how cortical regions communicate, and so should be incorporated into complete assessments of functional connectivity. We have recently shown that BOLD signals are robustly detectable in WM when appropriate analyses are used, that the hemodynamic response function in WM is different from GM, and that WM tracts show reproducible patterns of apparent connectivity which may be summarized in Functional Connectivity Matrices (FCMs), obtained by analyzing resting state correlations between segmented WM and GM parcellations. Furthermore, distinct, reproducible networks of WM emerge from data-driven analyses in similar manner to cortical circuits. In this proposal we aim to develop new analyses and apply them to large numbers of publicly available data. We aim (1) to quantify the functional relationships between WM fibers and GM circuits at a finer scale and in greater detail. We will extend the concept of FCMs to three dimensions to derive those WM tracts that show synchronous time courses with pairs of GM regions that themselves are identified from a matrix of GM-GM connectivity; (2) to use data-driven, model-free independent component analyses to identify WM and GM functional networks and quantify the correlations between them; and (3) to construct a suite of detailed and quantitative atlases characterizing functional connectivity and network topology in WM, and establish their relationships with behavioral and cognitive measures. Templates and digital atlases provide a way to spatially normalize data to common spaces, and measure normal and abnormal variations quantitatively. Extending and applying the methodology from structural and diffusion MRI fields to create atlases of WM functional data will enable reproducible quantification, normalization, and interpretation of our results. Each analysis will also examine the influence of gender and age on WM functional metrics. Impact: BOLD signals in WM reflect neural activity that is related to cortical brain function, so analyses of the functional engagement of WM are essential to properly model brain networks. This research would demonstrate how WM and GM activities are related, and how to integrate them to obtain a more complete model of brain organization. The results will lay a firm foundation for exploiting functional connectivity in white matter associated with pathological or other changes across a spectrum of disorders and conditions.