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基于独立成分子空间支持向量机的精神疾病磁共振影像学分类研究
高爽
Subtype博士
Thesis Advisor隋婧
2019-12-03
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword独立成分分析 子空间相似度 支持向量机 磁共振影像 精神疾病分类
Abstract

       近年来,基于神经影像学的精神疾病个体水平的分类预测越来越受到重视。利用多种神经影像成像技术,如功能磁共振(fMRI)、结构磁共振(sMRI)和扩散磁共振成像(dMRI),结合机器学习方法,对脑疾病患者进行精确分类的研究有着巨大的潜力,但是仍然存在着一定的挑战性。大多数研究仍存在特征选择有偏、过拟合、样本量较小、或泛化性一般的问题。因此,基于对之前工作的总结,本文改进了相关精神疾病分类研究中应用最为广泛的支持向量机(SVM)分类算法,并结合独立成分分析(ICA)、子空间相似性度量、特征选择、多模态融合及多核分类等机器学习技术,在不同的精神疾病上开展了分类研究,并对于分类结果和算法选出的判别性脑区进行了深入分析。本文的主要创新性工作包括:
       (1)提出了一种基于独立成分子空间支持向量机的分类算法,首先提取出每个被试个体水平的空间独立成分(IC),然后利用子空间距离构建不同被试ICs张成的子空间之间的相似度矩阵,并进一步在核空间对相似度矩阵进行划分,最后以此作为核函数嵌入SVM分类器进行分类,同时完成对判别性独立成分组合的选择。本方法用于双相情感障碍(BD)和抑郁症(MDD)患者的早期区别诊断,分类精度高达92.4%。选择得到的5个对分类贡献最大的固有连接网络(ICN)包括默认网络、凸显网络、背侧注意网络、额顶中央执行网络以及皮下脑区包括尾状核、丘脑和海马旁回。基于上述网络判别12例临床难以确诊的情绪障碍患者的组别(BD/MDD),并根据其药物治疗后对应的正确组别做对比,盲测精度高达91.7%,展示了本方法具有广大的临床应用前景。
       (2)提出了一种采用统一组水平参考信号的多核SVM分类算法,在前述独立成分子空间分类算法的基础上,采用大样本正常被试计算统一的组水平参考信号模板,之后提取个体水平的IC,对于每个脑网络对应的独立成分利用子空间相似度构建该脑网络上的核函数,然后使用多核SVM将各个脑网络结合起来进行分类。在多个中心的双相情感障碍(BD)及精神分裂症(SZ)患者的分类中验证了多核分类的优势。研究发现BD与SZ患者差异性脑区位于认知控制网络、皮下核团、视觉网络及小脑。该方法为不同中心之间独立成分的比较提供了统一的模板,对于多核/子空间、全域/功能域、成分个数、线性/非线性的对比进行了全面的实验验证,最优BD/SZ的分类精度达到80.2%,并找出了不同中心数据重叠的判别性成分。
       (3)提出了一种针对多模态MRI特征的多核SVM分类算法,首先采用多模态融合方法(mCCA+jICA)提取不同模态间对应的组水平独立成分用于制作不同模态特征的掩模,之后结合嵌套十倍交叉验证划分样本并进行特征及成分的选择,对于每个模态使用选择得到的特征成分构建不同被试之间的子空间相似度作为该模态的核,最后使用多核SVM将各个模态结合起来进行分类。该算法在两个独立的多模态数据集上进行了SZ患者与正常被试(HC)的分类,实验比单模态分类精度提升6-30%;此外,在两个数据集上选择出的判别性独立成分脑网络也有较高的可重复性,验证了算法的准确性和可靠性。
       (4)将基于独立成分子空间的SVM算法应用于多中心分类和泛化性能测试。首先在每个中心使用本文方法提取组水平IC,构建分类模型并选择判别性成分,然后在其他中心以这些组水平IC为参考信号提取个体水平IC并使用已构建好的模型进行分类测试。将本方法应用于两个中心的注意缺陷多动障碍(ADHD)患者与健康对照的分类研究,ADHD是较难预测分类的精神疾病,已有分类研究精度多在60-70%的水平,本方法展示了良好的跨中心预测性能(单中心分类精度80%以上,跨中心预测精度70%以上),发现了ADHD患者在注意网络、默认网络、控制网络、尾状核及丘脑的异常改变,说明本论文提出的方法具有更优的泛化性能。
       综上所述,本文提出了一系列针对磁共振影像,尤其是fMRI,且基于独立成分子空间支持向量机的分类算法,并结合不同的机器学习技术如组信息独立成分分析、多核技术、多模态融合等应用于不同的精神疾病分类问题中,验证了本文提出的方法在独立盲测、多模态、多中心分类上的有效性,有望为发掘多种精神疾病潜在的异常影像学标记提供方法学支撑。

Other Abstract

    In recent years, more and more attention has been paid to the individual level classification and prediction of psychiatric disorders based on neuroimaging. Using a variety of neuroimaging modalities such as functional magnetic resonance (fMRI), structural magnetic resonance (sMRI) and diffusion magnetic resonance imaging (dMRI), along with machine learning techniques, more than hundreds of studies have been carried out on the accurate classification of brain disorders. However these classification studies still have some disadvantages, such as biased feature selection, over fitting, small sample size, and poor generalization. Therefore, based on the summary of previous studies, this paper improved the support vector machine (SVM) classification algorithm, which was the most widely used method in psychiatric disorder classification. Combined with independent component analysis (ICA), subspace similarity, feature selection, multimodal fusion, multi-kernel classification and other machine learning technologies, the proposed methods were performed on the classification of different psychiatric disorders, and the classification results and the selected discriminative brain regions were further analyzed. The main achievements of our study are as follows:
    (1) A classification algorithm of support vector machine based on subspace of independent components was proposed. Firstly, the individual level spatial independent component (IC) of each subject was extracted, and then the similarity matrix between the subspaces spanned by different subjects’ ICs was constructed by using the specific subspace distance. The similarity matrix was further partitioned in the kernel space. Finally, the similarity matrix was embedded into SVM classifier as kernel function to classify subjects and simultaneously to select the corresponding discriminative independent component combination. This method was applied to the early differential diagnosis of patients with bipolar disorder (BD) and major depression disorder (MDD). The accuracy of classification was as high as 92.4%, and five maximally contributory IC brain networks were identified. These intrinsic connection networks (ICNs) included default mode network, salience network, dorsal attention network, frontoparietal central executive network, subcortical regions including caudate body, thalamus and parahippocampal gyrus. Based on networks mentioned above, 12 complicated patients with unclear mood disorders were classified as possible groups (BD/MDD) and compared with the corresponding medication-class of response, and the blind test accuracy was 91.7%, demonstrating this method had broad clinical application prospects.
    (2) A multi-kernel SVM classification algorithm using uniform group level reference signal was proposed. Based on the algorithm proposed above, the uniform group level reference signal template calculated by large samples of healthy controls was adopted to extract individual level ICs. For each independent component of specific brain network, subspace similarity method was adopted to construct the kernel of the brain network, and then multi-kernel SVM was adopted to combine different brain networks for classification. In the classification of bipolar disorder (BD) and schizophrenic (SZ) patients from multiple sites, the superiority of multi-kernel classification was verified, and the discriminative brain regions between BD and SZ patients were found located in cognitive control network, subcortical regions, visual network and cerebellum. This method provided a unified template for the comparison of independent components among different sites. Comprehensive experiments of multi-kernel / subspace, all / functional domains, number of components and linear / nonlinear comparison were carried out. Under the optimal experimental condition, the classification accuracy was 80.2%, and the overlapping discriminative components among different sites were found.
    (3) A multi-kernel SVM classification algorithm for multi-modal features was proposed. Firstly, the group level corresponding independent components of different modalities were extracted by multi-modal fusion method (mCCA + jICA) to make masks for different modal features. Then, the subjects were divided by nested 10 fold cross validation to select features and components. The subspace similarity between different subjects based on selected feature components was used as the kernel of each modality. Finally, multi-kernel SVM was used to combine each modality for classification. Two independent multi-modal datasets were used to classify the SZ patients and healthy controls (HC). Experiments demonstrated that the accuracy of multi-modal methods were 6-30% higher than that of single-modal method. The discriminative independent component brain networks selected from the two datasets were also correspondent to a certain extent.
    (4) The performance of multi-site classification of SVM based on subspace of ICs was verified. Firstly, in each site, the method proposed in this paper was used to extract group level ICs, select discriminative components and build classification models. Then in other sites, the group level ICs were used as reference signals to extract individual level ICs and the models built before were adopted for classification test. This method was applied to the multi-site classification of ADHD patients and HCs from two sites. ADHD is a kind of psychiatric disorders which is difficult to predict and classify, and the classification accuracies are mostly 60-70% in existing studies. The cross-site classification of the proposed method was verified. (The accuracies of single site classification were both more than 80%, and those of multiple sites prediction were both more than 70%.) The abnormal changes of functional brain networks in ADHD patients including attention network, default mode network, control network, caudate and thalamus were found, indicating the promising generalization.
    In this paper, a series of classification algorithms based on MRI, especially fMRI, of SVM based on subspace of ICs were proposed, and combined with different machine learning technologies including group information guided independent component analysis, multi-kernel technique, multi-modal fusion etc. These methods were applied to different psychiatric disorder classification, to verify the effectiveness of the proposed methods in independent blind testing, multi-modal and multi-site classification and to provide the basis of exploring the potential abnormal biomarkers of various psychiatric disorders. 

Pages118
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28368
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
高爽. 基于独立成分子空间支持向量机的精神疾病磁共振影像学分类研究[D]. 中国科学院自动化研究所. 中国科学院大学,2019.
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