基于脑皮层指标的遗传度分析与智力预测研究

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	基于脑皮层指标的遗传度分析与智力预测研究
	朱美芳
	2018-05
学位类型	工学硕士
中文摘要	揭示人类智商的本质是近几个世纪以来在认知神经学研究中的热点。近来医学影像成像技术的不断发展和进步，给我们关于大脑神经生物学和智商关系的研究带来了极大的便利。在这其中，流体智力作为与遗传有着密切联系的认知指标被选中为本文的研究对象。与此同时，关于大脑皮层结构的相关研究在随着医疗影像技术的发展，也有了不少新的发现；比如，研究表明大脑的皮层结构指标，如皮层厚度和脑沟深度是与遗传高度相关的；此外，大脑的皮层结构指标也与个体常见的精神疾病和神经系统疾病，如精神分裂症和阿尔兹海默症等存在关联性。因为皮层结构指标和流体智力均于遗传存在相关性，所以本文主要研究的是皮层结构指标与流体智力之间的关系。首先本文利用HCP (Human Connectome Project) 提供的公开数据集S900来进行大脑皮层结构指标的遗传度计算。值得一提的是，已有研究对于常见的大脑皮层结构指标均有了比较翔实的研究结果，本文主要在皮层厚度，脑沟深度和皮层沟回曲率上对计算遗传度的计算模型进行验证，而后对皮层结构指标中还未有遗传度量化分析的皮层髓鞘化进行全脑的遗传模式分析。髓鞘是指包裹在神经元轴突外侧的绝缘鞘，对于大脑的神经系统的正常发挥功能起到了至关重要的作用。髓鞘作为表征大脑神经系统的一个重要结构指标，其与疾病的关联性已有很多相关的研究，但是大脑皮层的髓鞘化在全脑的遗传模式及其与基因之间的表达还未有相关的研究。因此希望通过研究皮层髓鞘化指标的遗传模式和其与基因表达之间的关系，以期不仅能够给临床上与髓鞘相关的疾病的发现和诊断提供帮助，也能为下一步研究不同皮层结构指标与流体智力之间的关系打下基础。 S900数据集包含了897名通过HCP测试的健康被试，利用SOLAR (Sequential Oligogenic Linkage Analysis Routines) 软件计算基于Brainnetome Atlas图谱的左右脑210个脑区的皮层髓鞘化的遗传度，得到全脑的遗传模式；而后基于AIBS (Allen Brain Institute for Brain Science) 机构提供的基因表达数据，根据文献中提及的137个与皮层髓鞘化相关的基因的表达，研究基因表达与大脑皮层髓鞘化之间的关系。在遗传度计算结果的分析中我们发现，大脑皮层髓鞘化的分布与其他大脑结构指标类似，呈现左右脑对称的形式；同时我们发现遗传度的分布呈现从前侧到后侧不断梯度递增的规律，这与文献中关于髓鞘发育早晚和遗传关系的假说一致；而在基因表达的研究中，我们没有发现与髓鞘相关的基因表达和大脑皮层髓鞘化分布之间的关联性。在此基础上，本文还进行了基于大脑皮层结构指标进行对流体智力的预测。我们选取了大脑中常见的皮层结构指标，大脑皮层厚度，脑沟深度，大脑皮层沟回曲率和大脑皮层髓鞘化4个指标；先利用皮尔逊相关在210个脑区中找到与流体智力相关的脑区作为被选取脑区的特征集合，利用被选取的特征集和支持向量回归模型对个体流体智力进行了预测。预测结果表明除了大脑皮层沟回曲率以外，其他3个皮层结构指标均能够对个体的流体智力有预测效果。而后我们计算这三个指标在所有脑区上的遗传度，在被选取脑区和未被选取脑区之间进行了遗传度的T检验。在脑沟深度和皮层髓鞘化指标上发现了遗传度的显著差异性。该研究结果说明了个体的流体智力与大脑皮层结构指标之间存在关联，且脑沟深度指标在脑区遗传度的差异性说明了脑沟深度与遗传之间的高度相关性，这与先前关于沟回与遗传之间的文献的结论一致。
英文摘要	Revealing the essence of human intelligence quotient is a hot topic in the study of cognitive neurology in recent centuries. With the continuous development and advancement of medical image imaging technology, it have greatly facilitated our research on the relationship between brain neurobiology and IQ (intelligence quotient). The fluid intelligence, as an important cognitive ability closely related to genetic, is chosen as the research object of this article. At the same time, related research on the structure of the cerebral cortex has also made new discoveries along with the development of medical imaging technology; for example, studies on the cerebral cortex structures show that the cerebral cortical structure, such as cortical thickness and cortical sulc is highly related to heredity; in addition, cortical structures are also associated with common mental and neurological diseases such as schizophrenia and Alzheimer's disease. Because cortical structures and fluid intelligence are both genetically related, we focuses on the relationship between cortical structures and fluid intelligence in this study. We applied the public data set S900 released by the Human Connectome Project (HCP) to calculate the heritability of the cortical structures. It is worth mentioning that there have been more detailed studies on common cortical structure measures (cortical thickness, cortical sulc and cortical curvature) already. We focused on the heritability distribution model of cortical myelination in this paper as there is no genetic quantification analysis of the cortical myelination for the whole brain genetic model analysis. We validates the computational model by calculating heritability on the cortical thickness and cortical surface area. Myelin refers to the insulating sheath that is wrapped around the axons of neurons and plays a crucial role in the normal function of the brain's nervous system, and there are lot of related research about its correlation with the disease, but the myelination of the cerebral cortex in the whole brain's genetic model and its expression with the gene has not yet investigated. Therefore, it is hoped that by studying the genetic model of the cortical myelination and its relationship with gene expression, in order to not only provide clinical assistance for the discovery and diagnosis of myelin-related diseases, but also to study relationship between cortical structure and fluid intelligence for the next step. The S900 dataset included 897 healthy subjects who passed the HCP test and used SOLAR (Sequential Oligogenic Linkage Analysis Routines) software to calculate the heritability of 210 brain regions in the left and right hemispheres based on the Brainnetome Atlas, then we obtained the heritability distribution of whole brain. The gene expression data provided by the AIBS (Allen Brain Institute for Brain Science) organization, we studied the relationship between gene expression and myelination of the cerebral cortex based on the expression of 137 mylination-related genes mentioned in the literature. In the analysis of heritability estimation results, we found that the heritability distribution of cortical myelination in the cerebral cortex is similar to other cortical structure, showing left and right hemisphere symmetry; at the same time, we found that the distribution of heritability shows a constant gradient decrease from the anterior to the posterior. This is consistent with the hypothesis that the myelin sheath is involved in the early and late development and genetic relationship in the literature; in the study of gene expression, we did not find a correlation between myelin-related genes expression and the cortical myelination. On this basis, this article also carried out the prediction of individual fluid intelligence based on the cortical structure measurements. We applied cortical thickness, cortical sulcus, cortical curvature, and cortical myelination to prediction the fluid intelligence by using the machine learning algorithm SVR. We performed the feature selection by calculating the pearson correlation between each feature and the individual general fluid intelligence in 4 cortical measures. All the feature which the pearson correlation less than 0.05 were selected. The prediction results show that all the cortical structure measurements except the cortical curvature can predict the individual's fluid intelligence. Then we calculated the heritability of cortical structures in all regions, and conducted a T-test between heritability of the selected brain regions and the unselected brain regions. Significant differences in heritability were found in the cortical sulcus and cortical myelination. The results of this study demonstrated a correlation between individual fluid intelligence and cortical structure measurements, and the genetic diversity of the cortical sulcus in the brain region suggests a high correlation between cortical sulcus and genetics. The conclusions are consistent with the result in the literature proposed by Yann Le.
关键词	皮层结构指标大脑皮层髓鞘化遗传度流体智力
文献类型	学位论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20977
专题	毕业生_硕士学位论文
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	朱美芳. 基于脑皮层指标的遗传度分析与智力预测研究[D]. 北京. 中国科学院研究生院,2018.

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