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精神分裂症和健康人群大数据的多模态关联分析研究
罗娜
Subtype博士
Thesis Advisor隋婧
2019-12-03
Degree Grantor中国科学院大学
Place of Conferral中国科学院大学
Degree Discipline模式识别与智能系统
Keyword多模态,关联分析,精神分裂症,健康人群大数据,成人毕生发展
Abstract

多模态磁共振成像技术能够从多个维度对大脑进行全方位地描述,通过融合多个模态的数据信息,能够发现与脑疾病病变相关的多模态影像、基因共变成分,弥补单模态成像技术的局限性。精神分裂症是一种高遗传的重型精神疾病,尽管研究者们已经发现精神分裂症患者存在基因、脑结构、脑功能以及认知的异常,但是对于这四者通过哪些通路而相互关联,是否存在中介调控效应,以及不同模态在不同疾病阶段的变化速率是否存在差异等问题还不够明确。本论文借助多模态脑影像、基因、认知行为等大数据,采用基于数据驱动的多模态数据关联分析方法,从多个视角深入探讨了精神分裂症病情发展与脑区损伤的关系,以及影像-基因-认知能力之间的调控关系。除此之外,通过研究健康人群大数据(n>15000)的脑结构和脑功能的一致性关联及其随年龄的变化关系,本研究从多个视角揭示了人脑发育和老化过程中脑功能、脑结构和遗传变量之间共有的以及各自独特的变化关系。本论文的主要创新性工作包括:

1) 结合了单核苷酸多态性(single nucleotide polymorphism, SNP)、静息态低频振幅比率(fractional amplitude of low-frequency fluctuations, fALFF)和灰质体积(grey matter, GM)三种特征,首次基于905例中国汉族精神分裂症大样本数据开展了多模态脑影像和基因融合分析。研究结果发现精神分裂症患者的GM相对于正常人在丘脑、壳核、双侧颞叶、楔叶处降低,与之共变的fALFF成分在额叶处也相较于正常人降低。同时,该影像成分与一组强调CSMD1, CNTNAP2, DCCGABBR2等精神分裂症易感基因的SNP成分显著相关;上述结果在另一独立验证集上可复现。后续相关分析发现,上述共变的影像遗传成分与描述工作记忆能力的数字广度评分正相关,且进一步地中介分析揭示了,慢性精神分裂症患者灰质体积介导了从SNP对局部脑功能的影响,进而影响了工作记忆表现。

2) 在首发未用药精分患者、慢性精分患者和正常对照三组人群比较了功能连接(functional connectivity, FC)GMSNP三个模态随病程变化速率的差异。结果发现:1)GMFC均随着病程发展表现出逐渐损伤的变化趋势,而SNP位点则不会随着疾病的发展而变化;2)精神分裂症患者的GMFC相对于正常对照在海马,颞叶和小脑处降低,且与强调了GABBR2SATB2CACNA1CPDE4B等易感基因的SNP成分相关;3)FC在正常对照和首发未用药患者之间的组间差异显著高于GM,且与病程表现出更强的负相关关系(p=0.0006)

3) 基于独立成分分析和空间相关分析,首次在包含15000+例被试的大样本上分析了脑结构与功能独立成分的空间一致性,以及上述结构-功能一致性在不同脑区子网络中的差异和随年龄变化的趋势。结果发现: 1)45组结构-功能匹配对在测试集和验证集中均表现出高一致性(皮尔逊相关系数|r|>0.25和互信息MI>0.2),提出了一个结构-功能匹配模板;2)初级皮层(尤其是感知运动网络和视觉网络)的结构-功能一致性最高,而高级联络皮层(尤其是额顶网络)的结构-功能一致性最差, 可能的原因是高级皮层相对于初级皮层发育得晚且个体间差异更大; 3)结构-功能的一致性随年龄增加呈现U型非线性变化趋势,即在大脑发育早期下降,在30岁至50岁之间达到最低点,之后开始振荡式地缓慢增加,这与人脑发育和衰老的变化规律相匹配。

4) 首次基于约6000例被试,以一岁为步长,应用独立成分分析方法和多变量线性回归模型,系统地分析了GM、结构网络相关(structural network correlation, SNC)和功能网络连接(functional network  connectivity, FNC)三种影像学特征从13岁到72岁的发展规律。结果发现:1)GM在大部分脑区随着年龄增加呈线性下降的趋势,旁海马呈倒U型的关系。小脑内的SNC与年龄呈正U型的关系,而在与默认网络和额顶网络相关的SNC主要呈倒U型的关系;2)FNC在网络内的连接与年龄呈线性下降的趋势,尤其是视觉网络和默认网络内。而网络间的连接则在早期与年龄呈线性增加的关系,比如与额顶网络和默认网络相关的连接,在晚年期间开始下降,形成一个倒U型关系。正U型关系则主要出现在腹侧注意网络和皮下核团相关的连接,这些连接在老年阶段为了弥补认知损伤会出现短暂地增加。3)SNCFNC在枕中回-脑导和楔前叶-小脑这两条连接表现出相似的变化趋势。

Other Abstract

With the development of neuroimaging technology, various imaging techniques have been developed to describe the human brain in different views. Investigating association between different modalities could provide complementary information of the brain, which has been considered as an important manner to dig up the impaired brain regions and potential biomarkers. In this dissertation, based on the brain imaging big data, genetic variants and cognitive scores, we applied multiple data fusion methods to explore the covarying and modality-specific multimodal associations in schizophrenia development from a variety of perspectives, as well as the structural-functional correspondence and its variation across lifespan in a very large healthy control cohort(15000+).

First, gray matter volume, fractional amplitude of low-frequency fluctuations (fALFF) of functional magnetic resonance imaging data, and 4522 schizophrenia- susceptible single nucleotide polymorphisms (SNP) from 450 schizophrenia patients and 455 healthy controls were first jointly analyzed to investigate the multimodal association. One significantly linked imaging-genetic pattern was then identified to be group discriminative, and was replicated in an independent Chinese cohort (166 subjects), which was also associated with working memory performance. Particularly, Gray matter reduction in thalamus, putamen and bilateral temporal gyrus in schizophrenia associated with fALFF decrease in medial prefrontal cortex, both associated with genetic factors enriched in neuron development, synapse organization and axon pathways, highlighting genes including CSMD1, CNTNAP2, DCC, GABBR2 etc. Further mediation analyses indicate that gray matter alterations significantly mediated the association from SNP to fALFF, while fALFF mediated the association from SNP and gray matter to working memory performance. Collectively, these findings not only verify the impaired imaging-genetic association in very large Chinese population (905+166 subjects), but also initially reveal a potential genetic-brain-cognition mediation pathway, indicating that polygenic risk factors could exert impact on phenotypic measures from brain structure to function, thus further affect cognition in schizophrenia.

Second, the progress of schizophrenia at various stages is an intriguing question, which has been explored to some degree using single-modality brain imaging data, e.g. grey matter (GM) or functional connectivity (FC). However it remains unclear how those changes from different modalities are correlated with each other and if the sensitivity to duration of illness and disease stages across modalities is different. In this work, we jointly analyzed FC, GM volume and single nucleotide polymorphisms (SNPs) data of 159 individuals including healthy controls (HC), drug-naïve first-episode schizophrenia (FESZ) and chronic schizophrenia patients (CSZ), aiming to evaluate the links among SNP, FC and GM patterns, and their sensitivity to duration of illness and disease stages in schizophrenia. Our results suggested : 1) both GM and FC highlighted impairments in hippocampal, temporal gyrus and cerebellum in schizophrenia, which were significantly correlated with genes like SATB2, GABBR2, PDE4B, CACNA1C etc. 2) GM and FC presented gradually decrease trend (HC>FESZ>CSZ), while SNP indicated a non-gradual variation trend with un-significant group difference observed between FESZ and CSZ; 3) Group difference between HC and FESZ of FC was more remarkable than GM, and FC presented a stronger negative correlation with duration of illness than GM (p=0.0006). Collectively, these results highlight the benefit of leveraging multimodal data and provide additional clues regarding the impact of mental illness at various disease stages. 

Third, brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architecture match the intrinsic functional networks among different anatomical domains, and how this may vary with age across lifespan remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of well-matched structural-functional (S-F) component maps, distributing across 9 anatomical domains, in both a discovery cohort (n=6005) and a replication cohort (UK Biobank, n=9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g. somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Moreover, the age-varying S-F similarity revealed a U-shape ranging from 13 to 75 years, consistent with the trends of human brain development and decline. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of change compared to unimodal cortex, which may lead to higher inter-individual variability and lower S-F coherence. 

Last, though there are multiple studies which investigated the relationship between age and brain imaging data, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC) and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: 1)GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. 2) FNC tended to linearly decrease within resting-state networks (RSNs), especially in visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. 3) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

Pages130
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28378
Collection脑网络组研究中心
Recommended Citation
GB/T 7714
罗娜. 精神分裂症和健康人群大数据的多模态关联分析研究[D]. 中国科学院大学. 中国科学院大学,2019.
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