CASIA OpenIR  > 毕业生  > 博士学位论文
基于神经影像的脑发育模式及其个体化评估研究
赵雨馨
2023-11-28
Pages132
Subtype博士
Abstract

脑发育是心智成长的生物基础。人脑更长的发育期带来了更强的可塑性,但也导致了更高的疾病易感性。发育期是各项认知能力涌现和高速发展的黄金时期,也是多种精神疾病高发的时期。因此,理解脑发育规律不仅有助于全面开发个体的智能潜能,而且对神经发育型疾病的早期防治具有深远意义。近年来,磁共振等非侵入式成像技术的快速发展、基因组学、转录组学等多组学数据的涌现以及机器学习、统计分析等前沿数据分析方法的成熟,极大地推进了脑发育规律及其遗传机制的研究。

脑发育包括脑功能发育与脑结构发育。已有的脑功能发育研究多是针对单一脑网络,但人脑功能的实现依赖于大脑的层级组织,而大脑层级的发育规律及其分子机制仍有待研究。对于脑结构发育,得益于脑结构指标较高的重测信度,群组水平的脑结构发育研究已较为成熟,而个体化脑发育的研究仍屈指可数。此外,自闭症谱系障碍(autism spectrum disorder,ASD)、注意力缺陷多动症(attention deficit hyperactivity disorder,ADHD)等神经发育型疾病多与脑的异常发育有关,因此迫切需要对脑异常发育个体进行早期预警。针对以上问题,本文基于大样本神经影像数据,并融合基因组学、转录组学以及行为量表等多层次信息,采用机器学习、统计分析等前沿数据分析方法,系统地研究了脑发育规律及其行为特征和遗传基础,并进一步在一般人群中进行了脑异常发育个体的早期预警。主要工作与创新性贡献归纳如下:

1. 脑功能层级发育模式及其分子机制研究

从感知到认知依赖人脑的信息处理层级,目前该层级的发育规律及其分子机制尚不清楚。基于功能磁共振影像、转录组学等多层次数据,采用非线性降维、聚类分析以及统计分析等方法,本文揭示了广泛发育期内脑功能层级的发育模式及其潜在分子机制。基于三个独立的发育期功能神经影像数据集(2–21 岁,总人数1500+),本文采用非线性降维方法计算了个体的脑功能层级,并借助一般线性模型分析了脑功能层级的发育模式,三个数据集上的研究结果均表明初级网络(如视觉网络)与高级联合网络(如默认网络)逐渐分离,提示了广泛发育期内较为一致的脑功能层级发育规律。进一步采用影像转录组关联分析、基因表达轨迹聚类分析以及富集分析等方法发现该脑功能层级的发育与两簇基因相关(产前/产后基因簇),且分别与不同疾病风险、细胞类型以及生物学过程有关。具体地,产前基因簇与ASD、ADHD 等神经发育型疾病、抑制性神经元以及发育进程相关的生物学过程有关,而产后基因簇则与可卡因依赖等成瘾型疾病、兴奋性神经元以及细胞信号相关的生物学过程有关。本部分研究从宏观脑影像和微观分子机制两个层面为理解脑功能发育提供了新的见解,也为ASD 等神经发育型疾病的病理机制提供了新的线索。

2. 个体化脑发育定量评估及行为特征研究

脑发育的个体化评估是脑异常发育个体早期预警的关键,目前尚缺乏个体化定量评估方法。基于结构神经影像、基因组学以及行为量表等数据,结合高斯混合模型及统计分析方法,本文创新性地揭示了一种个体化脑异常发育状态及其遗传基础。借助脑生长发育图表,本文在一般人群中将个体的全脑皮层厚度量化为跨年龄跨性别可比的个体化脑结构发育状态,并采用高斯混合模型识别出一种稳定鲁棒的脑异常发育状态。随后针对认知行为的统计分析结果表明该脑异常发育状态对应的外化问题更严重且认知能力偏差。进一步结合多基因风险分数的分析发现,这种脑异常发育状态对应的IQ 多基因分数显著偏低,而ADHD 多基因风险分数显著偏高,验证了上述脑-行为关联的同时,也为该脑异常发育状态的遗传基础提供了重要的证据支持。综上,本部分研究为个体化脑发育评估提供了新的研究思路,也为ADHD 等神经发育型疾病的早期预警奠定了基础。

3. 脑异常发育个体的早期预警及进展分析

脑异常发育是ADHD 等神经发育型疾病的典型表现,目前尚缺少脑异常发育个体的早期预警研究。本文创新性地从发育角度出发,构建了上述脑异常发育状态的早期预警模型并在一般人群中进行了验证。本文首先在大样本数据集中构建了脑异常发育状态的预警模型,并在另一个独立的大样本数据集中进行了脑异常发育个体的预测,针对高风险个体的行为分析结果验证了该预警模型的有效性。此外,本文还分析了该脑异常发育状态个体的脑功能网络特点,结果表明脑异常发育状态对应的脑功能网络模式(初级网络与高级网络间变得更加整合)与本文前期工作发现的典型发育趋势(初级网络与高级网络间逐渐分离)相反,提示了脑结构异常发育个体的脑功能也发生了异常改变,为脑功能-结构耦合提供了新的证据支持。针对不同组别个体的认知行为进展分析结果表明,从脑异常发育转为脑典型发育状态的个体的发育趋势会逐渐逼近健康个体的发育趋势,提示了脑异常发育个体早期预警的必要性。本部分研究为脑异常发育个体的早期预警提供了新的研究思路与见解。

Other Abstract

Brain development is the biological basis of mental growth. The longer development period of the human brain brings greater plasticity, but also leads to greater susceptibility to neuropsychiatric disorders. The developmental period is a golden period for the emergence and rapid development of various cognitive abilities as well as a period of high incidence of various mental diseases. Therefore, understanding the principles of brain development not only helps to fully develop an individual's intellectual potential, but also has far-reaching significance for the early prevention and treatment of neurodevelopmental disorders. In recent years, the rapid development of non-invasive imaging technologies such as magnetic resonance imaging, the emergence of multi-omics data such as genomics and transcriptomics, and the maturity of cutting-edge data analysis methods such as machine learning and statistical analysis have greatly advanced studies on brain development and its genetic mechanisms.

Brain development includes brain functional development and brain structural development. Most of the existing research on brain functional development focuses on a single brain network, but the realization of human brain functions depends on the hierarchical organization of the brain. However, the developmental principles and molecular mechanisms of the functional hierarchies remain to be studied. For brain structural development, thanks to the high test-retest reliability of brain structural indicators, research on brain structural development at the group level has become much more mature, but there are still only a handful of studies on individualized brain development. In addition, neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are broadly believed to be associated with abnormal brain development. Therefore, there is an urgent need for early warning/diagnosis of individuals with abnormal brain development. In response to the above problems, combining a large sample of neuroimaging data, integrating multi-level information such as genomics, transcriptomics, and behavioral scales, and using cutting-edge data analysis methods such as machine learning and statistical analysis, we systematically investigated the principles of brain development, its behavioral characteristics as well as the genetic basis, and further detected the high-risk individuals with abnormal brain development in the general population. The main work and innovative contributions are summarized as follows:

1. Research on the development of brain functional hierarchy and their molecular mechanisms.

From perception to cognition, we rely on the information-processing hierarchy of the human brain. The developmental principles and molecular mechanisms of the hierarchy are currently unclear. Combining multi-level data such as functional magnetic resonance imaging and transcriptomics and methods such as nonlinear dimensionality reduction, cluster analysis, and statistical analysis, we revealed the developmental patterns and underlying molecular mechanisms of brain functional hierarchy over a wide range of developmental periods. Based on three independent developmental functional neuroimaging datasets (2-21 years old, total number of people: 1500+), we used a nonlinear dimensionality reduction method to calculate the individual brain functional hierarchy and utilized a general linear model to analyze the brain functional hierarchy development, the research results on the three datasets all showed that the primary network (such as the visual network) and the high-level association network (such as the default mode network) gradually separate, suggesting a relatively consistent development pattern of the brain functional hierarchy across a wide range of developmental periods. Furthermore, imaging-transcriptomics analysis, gene expression trajectory cluster analysis, and enrichment analysis suggested that the development of the brain functional hierarchy is associated with two clusters of genes (prenatal/postnatal gene clusters), and are respectively involved in different disease risks, cell types and biological processes. Specifically, the prenatal gene cluster is associated with neurodevelopmental disorders such as ASD and ADHD, inhibitory neurons, and developmental processes-related biological processes, while the postnatal gene cluster is associated with addictive diseases such as cocaine dependence, excitatory neurons, and cell signaling-related biological processes. This part of the research provides new insights into understanding brain functional development from both macroscopic neuroimaging and microscopic molecular mechanisms, and sheds light on the pathological mechanisms of neurodevelopmental disorders such as ASD.

2. Quantitative assessment and behavioral characteristics analysis of individualized brain development.

Individualized assessment of brain development is the key to early detection of individuals with abnormal brain development. Currently, there is a lack of individualized quantitative assessment methods. Utilizing structural neuroimaging, genomics, and behavioral data, combined with Gaussian mixture models and statistical analysis methods, we innovatively revealed an individualized abnormal brain development status and its genetic basis. Leveraging brain charts, we quantified individualized whole-brain cortical thickness in the general population into individualized brain developmental statuses that are comparable across different ages and genders. The Gaussian mixture model was used to identify a stable and robust abnormal brain developmental status. Subsequent statistical analysis of behavior characteristics showed that the abnormal brain developmental status corresponded to more severe externalizing problems and cognitive ability deviations. Following polygenic risk score analysis indicated that the IQ polygenic score corresponding to the abnormal brain developmental status was significantly lower, while the ADHD polygenic risk score was significantly higher, strongly supporting the above-mentioned brain-behavior association as well as the genetic basis of the abnormal developmental status. In summary, this part of the research provides new insights for assessment of individualized brain development and lays the foundation for early warning of neurodevelopmental disorders such as ADHD.

3. Early warning and progression analysis of individuals with abnormal brain development.

Abnormal brain development is a typical manifestation of neurodevelopmental disorders such as ADHD. There is currently a lack of early warning research on individuals with abnormal brain development. We innovatively constructed an early warning model for the above-mentioned abnormal brain developmental status from a developmental perspective and verified it in the general population. We first constructed an early warning model for abnormal brain development in a large sample dataset and predicted individuals with abnormal brain development in another independent large sample data set. The behavioral analysis results of high-risk individuals verified the effectiveness of our early warning model. In addition, we analyzed the functional network characteristics of individuals with abnormal brain structural development and found that the brain functional network patterns (primary networks and high-level networks become more integrated) of the abnormal brain developmental status are opposite to the typical developmental trend (primary networks and high-level networks are gradually segregated) discovered in the previous work of this thesis, suggesting that the brain function of individuals with abnormal brain structure development also undergoes abnormal changes, further providing new evidence for brain function-structure coupling. Progression analysis of the neurocognition and behavior of individuals in different groups showed that the development trend of individuals who transition from abnormal brain developmental status into typical developmental status would gradually approach the developmental trend of healthy individuals, suggesting the necessity of early warning for individuals with abnormal brain development. This part of the research provides a new perspective for early warning of individuals with abnormal brain development.

Keyword脑功能层级 脑结构 个体化脑发育 异常发育 早期预警
Indexed By其他
Language中文
Sub direction classification脑网络分析
planning direction of the national heavy laboratory认知机理与类脑学习
Paper associated data
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/54525
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
赵雨馨. 基于神经影像的脑发育模式及其个体化评估研究[D],2023.
Files in This Item:
File Name/Size DocType Version Access License
博士学位论文-赵雨馨.pdf(28444KB)学位论文 限制开放CC BY-NC-SA
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[赵雨馨]'s Articles
Baidu academic
Similar articles in Baidu academic
[赵雨馨]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[赵雨馨]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.