CASIA OpenIR  > 脑网络组研究
基于多中心功能磁共振影像的精神分裂症脑连接研究
徐凯彬
Subtype硕士
Thesis Advisor蒋田仔
2020-07-29
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Name工学硕士
Degree Discipline计算机应用技术
Keyword精神分裂症 功能磁共振 功能连接 分类 批处理
Abstract

精神分裂症(Schizophrenia)是一种是以精神活动异常为主要特征的重症精神疾病,伴有行为、思维、情感等多方面障碍。该病病因复杂且尚未明确,遗传、环境因素可能共同作用,发病年龄多始于青少年时期,且难以治愈,给社会经济和治安带来沉重负担。阐明精神分裂症的发病机制,找寻预测指标,指导诊断及治疗,是目前精神分裂症研究中亟待解决的全球性问题。

本文主要利用多中心精神分裂症的功能磁共振影像,通过组间比较探寻客观可测的脑影像生物学标记,为临床诊断及早期预测该病提供影像学支持。同时,评估了现有常用精神分裂症分类模型的分类性能,分析被错误分类的精神分裂症患者间的共同属性。在此过程中,开发并完善基于MATLAB的一站式功能磁共振影像处理软件包BRANT。

本文研究内容主要包括以下几个部分:

  1. 基于多中心精神分裂症功能磁共振影像的功能连接研究。在本研究中,使用了来自7个数据集的大样本功能磁共振影像,来研究精神分裂症患者和正常对照组的组间功能连接差异。结果发现额颞叶、丘脑和默认网络皮质功能连接一致性异常,并且在精神分裂症患者中降低的左侧颞上回和右侧枕下侧回之间的功能连接与PANSS阴性症状总分呈显著负相关,与既往研究提示的精神分裂症多感觉整合功能受损有关。
  2. 基于多中心精神分裂症功能磁共振影像的分类研究。在对精神分裂症患者和正常对照组的模式分类研究中,约有不少于10%的精神分裂症患者一直被错误分类,但很少被讨论。因此,寻找被错误分类的的精神分裂症患者的共同特征有一定的临床意义。本研究首先对一个包含1082例被试的大数据集,使用四个常用的分类模型进行了二分类。在分类结果中,四个不同的分类器的分类准确率达到81.96% ~ 83.54%。分类后,收集了所有分类器分类错误或正确的精神分裂症患者,并在两组之间进行比较。两组中显著的功能连接主要分布在额下回、眶额皮层、岛叶、岛盖区域、内侧前额叶以及内侧额叶的子区之间。在错误分类的精神分裂症别试中,降低的PANSS阳性症状总分,和升高的左侧额下回子区A45r和左侧岛叶子区vId/vIg之间连接存在显著的负相关(R=-0.36,P=0.006),而在正确分类的精神分裂症被试中,该连接和PANSS评分之间不存在相关关系(R=0.05,P=0.26)。结果表明,在错误分类组中,存在的大量较正确分类组升高的功能连接,同时,额下回-岛叶子区间功能连接与精神分裂症阳性症状之间的负相关关系,可能是该组被试更容易被错误分类成正常对照的原因。
  3. 静息态功能磁共振影像一站式批处理软件。静息状态功能磁共振的数据处理工具箱为我们提供了强大的工具和友好的图形用户交互界面。但是,许多工具箱只包括了特定类别的功能,并且使用专门设计的图形用户交互界面。为了方便数据处理和减轻手工绘制图形用户交互界面的负担,本文开发了一个通用、可扩展、基于MATLAB的静息态功能磁共振影像处理软件BRANT(BRAinNetome fMRI Toolkit)。该软件具有优化的计算、高效的文件处理方法和代码生成的图形用户交互界面。BRANT的功能涵盖了常用的静息状态功能磁共振影像处理方法,包括批处理流程、脑自发活动分析、功能连接分析、复杂网络分析、统计分析和结果可视化。使用BRANT时,用户既可以利用高效的批量处理功能分析数据,也可以使用代码生成的图形用户交互界面以便于快速实现新的算法。
Other Abstract

Schizophrenia is a complex mental disorder characterized by abnormalities of behavior, thinking, emotion, etc. The cause of the disorder is unclear, and both genetic and environmental factors were reported to work together. The onset of the disorder is mostly found at teenagerhood, and thereafter patients are hard to be cured, leading to heavy economic burden to family and security burden to society. Therefore, to elucidate the pathogenesis and to find biological markers for diagnosis and treatment, become urgent global-wide tasks for researchers of the disorder.

In the current paper, we use resting-state functional magnetic resonance imaging (rs-fMRI) to search for objective biological markers of the disorder with sample-wise comparisons. Later, we tested classifiers with rs-fMRI measurements as feature to perform automatic classification. The classification performance of the existing schizophrenia classification models was evaluated and the common attributes among the misclassified schizophrenia patients were analyzed. During the period processing rs-fMRI data, we developed a one-stop fMRI data processing software package based on MATLAB, called BRANT, with functions covering almost all processing steps.

The main contributions are summarized as follows:

  1. Study of abnormal functional connectivity in schizophrenia using a multi-center schizophrenia dataset. In the current study, we used a large sample of rs-fMRI data from seven datasets to investigate group-wise difference of functional connectivity between schizophrenia patients and normal controls. We have found abnormal functional connectivity in among subareas of frontal-parietal lobe, frontotemporal lobe, thalamus, and default mode network. Besides, we found the reduced functional connectivity of left superior temporal gyrus (A22c) and right inferior occipital gyrus (iOccG) in schizophrenia was negatively correlated with the negative PANSS score, which was associated with the impaired multisensory integration function in schizophrenia suggested by previous studies.
  2. Classification of schizophrenia patients and normal controls. In previous classification studies of schizophrenia, a proportion of schizophrenia patients ranging from 10% to 30% is consistently misclassified but rarely discussed. Therefore, it is interesting for us to find out common characteristics shared by misclassified schizophrenia patients. In the current study, we performed binary classification on a large dataset of 1082 subjects using four popular models. The overall classification accuracy reached a range between 81.96% and 83.54%. Then, we collected schizophrenia patients that are either misclassified or correctly classified across all classifiers and conducted sample-wise comparisons between the two groups. In the results 148 significantly increased functional connectivity in the misclassed group were found among subareas of inferior frontal gyrus, orbital frontal gyrus, insula, insular-opercular, medial prefrontal cortex and medial frontal gyrus. Among the increased functional connectivities, the left inferior frontal gyrus (A45r) – left insula (vId/vIg) connectivity is found having significant negative correlation with positive PANSS score (R=-0.36, P=0.006) in the misclassified group, but not in the correctly classified group (R=0.05, P=0.26). Our results suggest the relationship between inferior frontal gyrus-insular functional connectivity and patients’ positive symptoms is different in the misclassified group, leading to failure of functional connectivity-based classification.
  3. One-stop fMRI batch processing software. Existing data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us powerful tools and friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs for new functions, we have developed a versatile and extendable MATLAB-based software, BRANT (BRAinNetome fMRI Toolkit), with optimized calculations, efficient file handling method, and code-generated GUIs. Functions of BRANT cover a wide range of fMRI data processing, including batch preprocessing pipeline, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization. With BRANT, users can find efficient functions for batch processing, while developers can quickly publish scripts with code-generated GUIs.
Pages73
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40387
Collection脑网络组研究
Corresponding Author徐凯彬
Recommended Citation
GB/T 7714
徐凯彬. 基于多中心功能磁共振影像的精神分裂症脑连接研究[D]. 北京. 中国科学院大学,2020.
Files in This Item:
File Name/Size DocType Version Access License
徐凯彬-硕士论文-v3-20200906(3274KB)学位论文 开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[徐凯彬]'s Articles
Baidu academic
Similar articles in Baidu academic
[徐凯彬]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[徐凯彬]'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.