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精神分裂症脑结构异常及模式分类研究
李健
学位类型工程硕士
导师蒋田仔
2016-05-26
学位授予单位中国科学院大学
学位授予地点北京
关键词精神分裂症 磁共振成像 脑结构异常 模式分类
摘要精神分裂症是一种高复发率、高致残率、病因复杂的重性精神类疾病,其神经病理学机制尚未明了。研究者们提出了多种假说试图解释其发病机制,包括神经发育假说和多巴胺假说等,但目前还没有确切定论。在临床上,精神分裂症的诊断主要基于患者日常行为和评分量表,一方面,目前的诊断方式费时费力,另一方面,这种诊断方式过于依赖患者或家属的主观描述以及医生的个人经验。如果能借助影像学数据实现精神分裂症的自动诊断,上述问题都将得到解决,使精神分裂症的诊断更方便和更客观。要实现精神分裂症的高准确自动诊断,寻找精神分裂症的潜在生物学标记是关键。以往大量研究表明精神分裂症会造成患者脑结构异常,主要表现为灰质体积的减小或灰质皮层变薄。然而,由于多种因素的影响,诸如精神分裂症的多相性、实验数据的差异性等,不同的研究关于具体的脑结构异常区域常常得到不一致的结论。为了尽量降低其它因素对研究结果的影响,本文利用了多中心的精神分裂症及健康对照的结构磁共振成像(MRI)数据,在大样本上进行了一系列实验。
首先,我们采用基于体素的形态学分析方法(Voxel-based morphometry,VBM)考察精神分裂症患者和正常人两组被试的灰质体积差异,通过综合多中心的结果来确定相对较稳定的、可重复的灰质体积差异。研究结果表明,与对照组相比,精神分裂症患者组大脑灰质体积有显著减小,在多中心实验重复验证试验表明,一致性差异区域主要位于颞上回和脑岛。
然后,考虑到精神分裂症症状复杂,不同的症状可能对患者脑结构产生不同影响,分析单一症状精神分裂症患者脑结构异常可能会得到更清晰的结果,我们决定针对精神分裂症特定症状人群进行分析,尝试发现特定症状对患者脑结构的影响。言语性幻听(auditory verbal hallucination,AVH)是精神分裂症的常见症状,幻听产生机制尚不明了,研究者们对幻听的产生机制也提出了多种假说,比如内部言语(inner speech)假说。为了找出精神分裂症中幻听患者脑结构上的相对稳定的异常,进一步为相关假说提供依据,本文又考察了精神分裂症患者中有幻听症状和没有幻听症状的两组人群之间的脑结构差异,结果表明,与非幻听组相比,幻听组在左侧颞中回中段有显著的灰质皮层变薄。这一结果一定程度上可以解释“内部言语”假说,大脑对内部言语的错误解读导致幻听的产生,与此同时,造成参与语言与语义记忆的颞中回负担加重,导致灰质可塑性修剪的发生,进而表现为皮层变薄。
最后,本文利用多中心结构磁共振成像数据构建独立的训练集(340个精神分裂症患者,300个正常人)和测试集(322个精神分裂症患者,313个正常人),提取形态结构特征(灰质、白质和脑脊液)训练机器学习模型,尝试模拟真实应用场景实现精神分裂症患者和健康对照的自动分类,最终在独立测试集上达到75.5%的分类准确率,虽然较前人结果有一定提升,但离实用仍有不少差距。
其他摘要

Schizophrenia is one of the most complex psychiatric disorder, although the underlying neuropathology is not clear. Researchers have proposed several hypothesis trying to explain the pathogenesis of schizophrenia, including neural development hypothesis and dopamine hypothesis. The exact mechanisms of schizophrenia still remain unresolved and controversially discussed due to the diversity of schizophrenia symptoms. The clinical diagnosis of schizophrenia mainly base on the behavior of patients and rating scales. On the one hand, this kind of diagnosis is labour-intensive and time-consuming; on the other hand, it’s too dependent on descriptions of patients or their relatives and experience of doctors. These problems could disappear if we can build an automatic diagnosis system based on MRI data, making diagnosis more convenient and objective. Finding the potential biomakers of schizophrenia is the key to achieve high accuracy and automatic diagnosis of schizophrenia. Previous studies have revealed widespread abnormalities associated with schizophrenia, especially the reduction of brain gray matter volume and surface thickness. However, these studies showed some heterogeneity due to various factors such as heterogeneity of schizophrenia. In order to reduce the impact of other factors, we conducted a series of experiments on multi-center data trying to find biomarkers of schizophrenia and build automatic diagnosis system.

First, we conducted Voxel-based morphometry (VBM) analysis on multi-center data, in order to reveal the consistent abnormalities in schizophrenia. The results showed that schizophrenia group has significant gray matter reduction and the reduction in superior temporal lobe (STG) and insular are consistent across multi-center data study.

Furthermore, we examined structural abnormalities between schizophrenia patients with auditory verbal hallucinations (AVH) and schizophrenia patients without AVHs, considering AVHs is one of the most devastating symptoms of schizophrenia. Results showed that schizophrenia patients with AVHs has significant thickness reduction in left middle temporal gyrus compared to patients without AVHs.

At last, we trained a model to classify schizophrenia patient and healthy control using morphological features (GM, WM, CSF) extracted from MRI data. We built the model from 640 subjects scanned with Siemens scanner, and tested the unaltered model on a completely independent sample of 635 subjects scanned with different scanners. The classification rate of the independent sample was 75.5%. Feature importance analysis results showed that features extracted from STG and insula have higher importance scores compare to features extracted from other regions, which was consistent with VBM analysis results. These findings suggests that anatomical deficits in STG and insula lobe may modulate the pathogenesis of schizophrenia.

文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/11607
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
推荐引用方式
GB/T 7714
李健. 精神分裂症脑结构异常及模式分类研究[D]. 北京. 中国科学院大学,2016.
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