结构连接和功能连接的融合及其在孤独症中的应用
文献类型：学位论文
| 作者 | 邓昭宇 |
| 答辩日期 | 2023-06 |
| 文献子类 | 硕士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 严超赣 |
| 关键词 | 孤独症谱系障碍 弥散张量成像 静息态功能磁共振成像 纤维示踪成 像 多模态 |
| 学位名称 | 理学硕士 |
| 学位专业 | 认知神经科学 |
| 其他题名 | The Fusion of Structural and Functional Connectivity and its Application in Autism |
| 中文摘要 | Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with an incidence rate of nearly 1.7%, appearing in early childhood and having a significant impact on affected families. Although symptoms may manifest as early as 1-2 years of age, diagnosis typically occurs at 3-6 years of age and currently relies on subjective clinical judgment rather than objective biomarkers, hindering the diagnosis and treatment of ASD. Magnetic Resonance Imaging (MRI), a non-invasive and cost-effective brain imaging method, has the potential to serve as an objective diagnostic tool for ASD. In recent years, diffusion Magnetic Resonance Imaging (dMRI) and Resting-state functional Magnetic Resonance Imaging (R-fMRI) have been widely used to identify potential biological markers of ASD, but there is no standardized processing pipeline for dMRI analysis tools and existing structural connectivity tools vary widely, impacting research outcomes. This study aims to develop a standardized, integrated, and user-friendly toolbox for fiber tracking and structural connectivity analysis. Additionally, previous unimodal analyses using dMRI or R-fMRI failed to identify consistent biological markers or establish objective diagnostic criteria, highlighting the need for multimodal fusion analysis methods such as Structure-Function Coupling (SFC) and Track-weighted Functional Connectivity (TW-FC) which have the potential to identify robust ASD biomarkers but lack simple and accessible tools. This study consists of three sub-studies. Sub-study 1 is the development of a tool, while sub-studies 2 and 3 involve the use of analytical tools for unimodal and multimodal analysis to identify biomarkers, while verifying the effectiveness of the developed tool. Specifically, sub-study 1 developed a fiber tracking and structural connectivity toolbox, DPABIFiber, which combines commonly used toolboxes and is programmed using MATLAB. It features a user-friendly graphical user interface and allows for fast and easy parameter setting, enabling researchers who are unfamiliar with fiber tracking methodology to quickly and accurately analyze fiber tracking and structural connectivity. Sub-study 2 aims to identify unimodal biomarkers for autism spectrum disorder (ASD) by using DPABIFiber and DPABISurf, a resting-state functional magnetic resonance imaging analysis tool based on the cortex, to analyze diffusion-weighted magnetic resonance imaging (dMRI) and resting-state functional magnetic resonance imaging (R-fMRI) data from both ASD patients and typically developing (TD) individuals. The results were analyzed using tract-based spatial statistics (TBSS), structural connectivity (SC), and functional connectivity (FC) based on fibers, and features were identified that could serve as unimodal biomarkers for ASD. Sub-study 3 explored multimodal biomarkers by calculating the SFC based on the structural and functional connectivity matrices computed in sub-study 2, and using the individual space functional connectivity map and TW-FC computed using whole-brain fibers. SFC and TW-FC reflect the relationship between structure and function, and the integration of structural and functional information, respectively, and their multimodal changes may reflect ASD multimodal biomarkers. Overall, this study developed a fiber tracking and structural connectivity toolbox that provides researchers with a fast and accurate method for fiber tracking and structural connectivity analysis. Based on this toolbox, the study conducted unimodal biomarker analysis for ASD and further conducted multimodal biomarker analysis using advanced methods. At the same time, the effectiveness of the analysis toolbox was also validated. This study is inspirational for the scientific community in terms of multimodal analysis methods and ASD biomarker research. It is hoped that this new open-source toolbox can help more novice and expert users, promote the development of advanced structural analysis methods, and their application in clinical translational research. |
| 英文摘要 | 孤独症谱系障碍(Autism Spectrum Disorder, ASD)作为一种在儿童发育早期出现的神经发育障碍，发病率接近1.7，对患儿家庭影响巨大。但是其诊断年龄常常是在3-6岁，却在1-2岁的时候就会显现出症状。然而目前ASD的临床诊断仍然是基于症状学的主观判断，缺乏客观的生物标志(biomarker，这严重制约了ASD的诊断和治疗效果。如果使用客观的诊断方法，就能对ASD患儿进行更早的筛查，从而让患儿获得更早的干预治疗。磁共振成像(Magnetic Resonance Image, MRI)作为一种非侵入式、成本较为低廉的脑成像方法，有潜力成为ASD的客观诊断方法。近年来弥散磁共振成像(diffusion Magnetic Resonance Imaging, dMRI)和静息态功能磁共振成像(Resting-state functional Magnetic Resonance Imaging, R-fMRI)被广泛应用于寻找ASD可能的生物标志。然而，现有dMRI分析处理工具多种多样，处理流程各异，没有一个统一的标准流程。虽然新工具QSIPrep把常用的dMRI分析工具整合了起来，但是其仍需要进一步完善，方便研究人员使用。同时，由于当前结构连接的工具多种多样且结构连接的分析方法多种多样，会对研究结果产生影响，因此本研究致力于开发一个标准化的、整合的、用户友好的纤维示踪和结构连接分析工具箱。此外，现有的研究多为单独使用dMRI或R-fMRI进行的单模态(unimodal分析，未能发现具有共识的生物标志，客观诊断标准不明，需要进一步明确。近年来新兴的多模态(multimodal的融合分析方法反映了脑区间结构和功能的综合连接，具有能更灵敏地发现ASD生物标志的潜力。目前有两种新颖的多模态MRI分析方法，分别是结构连接祸合(Structure-Function Coupling, SFC)和纤维加权功能连接C Track-weighted Functional Connectivity, TW-FC，这两种方法可能能找到稳健的ASD生物标志，但是这种结构连接和功能连接的融合方法缺乏简单易用的工具。 本研究共有3个子研究，研究一是开发一个工具，研究二三在使用分析工具进行单模态和多模态分析寻找生物标志的同时，验证该工具分析的有效性。具体来说，研究一是纤维示踪和结构连接工具箱开发，该子研究结合了当前常用的工具包，使用MATLAB进行编程开发，开发出了一个纤维示踪和结构连接分析工具一一DPABIFiber。它具有用户友好的图形用户界面、能快速便捷地进行参数设置，使得对计算机不熟悉和不精于纤维示踪方法学的研究人员也能快速上手、快速且准确地进行纤维示踪和结构连接的分析。研究二是寻找ASD结构和功能连接单模态生物标志，该子研究使用研究一中开发的纤维示踪和结构连接分析工具DPABIFiber和基于皮层的静息态功能磁共振分析工具DPABISurf对ASD患者和典型发育(Typical Development, TD)的dMRI和R-fMRI数据进行分析，得出基于纤维的空间统计(Tract-Based Spatial Statistics, TBSS)、结构连接(S tructural Connectivity, SC)和功能连接(Functional Connectivity, FC)的结果，获得了能成为ASD单模态生物标志的特征。研究三探究了ASD结构和功能连接融合的生物标志，该子研究基于研究二中计算出的结构连接和功能连接矩阵计算结构功能祸合(SFC)，并使用个体空间功能连接图和全脑纤维计算的纤维加权功能连接 (TW-FC。这两种方法前者代表着结构和功能的关系，后者代表着综合结构和功能的信息考虑，SFC和TW-FC的多模态变化可能反映了ASD多模态生物标志。 总的来说，本研究开发了一个纤维示踪和结构连接的工具箱，提供给研究人员进行快速准确的纤维示踪和结构连接分析;基于该工具箱，本研究对ASD进行了单模态生物标志分析，并进一步使用先进的方法进行了多模态的生物标志分析;与此同时也对该分析工具进行了有效性验证。本研究对科学界的多模态分析方法和ASD的生物标志研究具有启发性意义，并希望这个新的开源工具箱能够帮助更多的新手和专家用户，促进先进的结构分析方法的发展及其在临床转化研究中的应用。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/46084]  |
| 专题 | 心理研究所_认知与发展心理学研究室 |
| 推荐引用方式 GB/T 7714 | 邓昭宇. 结构连接和功能连接的融合及其在孤独症中的应用[D]. 中国科学院心理研究所. 中国科学院大学. 2023. |
入库方式： OAI收割
来源：心理研究所
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