基于静息态功能磁共振成像的孤独症分类算法研究
文献类型：学位论文
| 作者 | 梁玲燕 |
| 答辩日期 | 2022-12 |
| 文献子类 | 硕士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 李晶 |
| 关键词 | 孤独症 深度学习 静息态功能磁共振成像 局部一致性 脑图谱 |
| 学位名称 | 理学硕士 |
| 学位专业 | 发展与教育心理学 |
| 其他题名 | Research on Autism Sbectrum Disorder Classification Algorithms based on Resting State Functional MRI Imaging |
| 中文摘要 | In recent years, the incidence of Autism Spectrum Disorder (ASD) has increased significantly worldwide, but diagnosing ASD is still difficult, and the analysis of ASD's pathological causes always is a big problem in this filed. Therefore, it is very meaningful to research autism classification method based on machine learning algorithms, which will provide more objective indicators to assist doctors in diagnosis and to improve the accuracy and efficiency of autism diagnosis. Based on the open dataset ABIDE-I, this thesis researches the autism classification algorithm by using temporal information and spatial information of resting state functional magnetic resonance imaging data (rs-fMRI) by means of the depth learning theory. In addition, this thesis also analyses the possible pathological causes of ASD by using functional connectivity's information between possible brain regions. In time domain, the autism classification model built with the long short term memory (LSTM) structure is trained by the time sequence information of Anatomical Automatic Labeling atlas (AAL) and Craddock 200 atlas (CC200) respectively. In this process, in order to reduce the impact of the noise from different acquisition devices, this study performs data cleaning and data enhancement on ABIDE-I data. Finally, the classification methods have got the accuracy of 59.23% (AAL), and 68.65% (CC200) for classifying ASD and typically developing (TD). In spatial domain, the autism classification model built with multi-layer perceptron (MLP) is trained by CC200's functional connectivity information, and the autism classification model built with three-dimension convolutional neural network is trained by Regional Homogeneity (ReHo) information. On ABIDE-I database, the classifier trained by CC200 information has achieved 70.3% classification accuracy, and the classifier trained by ReHo has achieved 69.3% classification accuracy. In addition, it further studies the fusion strategy of features which are from CC200 and ReHo, and finally has obtained 79.2% classification accuracy on ABIDE-I database. Through the analysis method of weight parameters in deep learning, from the perspective of brain functional regions, this thesis researches ASD's possible abnormal brain functional regions based on the pre-trained LSTM network's model parameters in time domain, and has found that the main abnormal brain functional regions of ASD include Frontal, Cuneus, Cingulum, Angular, Calcarine, ParaHippocampal, Amygdala, etc. From the perspective of brain functional connectivity, this thesis researches possible abnormal brain functional connectivity regions based on the pre-trained MLP network's model parameters of CC200's connectivity matrix in space domain, and has found that some brain functional connectivity of ASD between left and right brain regions are weaker than that of typically developing (TD). Furthermore, based on the triple network structure, this thesis also analyses the functional connectivity of "brain network" and has found that the functional connectivity of ASD between anterior cingulate cortex (ACC) and prefrontal cortex (PFC) is weaker than that of TD. The experimental result proves that ASD classifiers learnt by deep learning technology based on resting state functional magnetic imaging, can diagnose ASD patients effectively. In addition, the experiment result also proves that it is useful and feasible to perform ASD's pathological exploration in brain functional regions and brain functional connectivity based on ASD classifiers' weight parameters, which provides a new research idea for ASD's pathological cause exploration. |
| 英文摘要 | 近年来，在世界范围内，孤独症谱系障碍(Autism Spectrum Disorder, ASD的发病率显著上升，但孤独症的病理原因分析和诊断一直是该领域的大难题，因此研究基于机器学习的孤独症诊断方法来辅助孤独症的临床诊断将是一项非常有意义的工作，这将提供更为客观的指标辅助医生进行诊断，提高诊断的准确性和效率。本研究以国际公开数据库ABIDE-I为研究对象，基于静息态功能磁共振成像数据，分别从时域信息和空域信息两个角度，以提高ASD分类算法的分类准确率为目标，采用深度学习理论进行ASD分类算法的研究，并从脑功能区和脑功能连接角度同步探讨ASD可能的病理原因。 在时域中，为了减少不同采集设备时间不同步的噪声影响，本研究对ABIDE-I数据进行了数据清洗和增强工作，然后分别基于自动解剖标记图谱(Anatomical Automatic Labeling, AAL)时序信息和Craddock 200 ( CC200)图谱时序信息，采用长短期记忆模型训练ASD分类器，最终分别得到了59.23 % C AAL)和68.65070C CC200)的分类准确率。 在空域中，根据CC200连接矩阵信息和局部一致性(Regional Homogeneity ReHo)信息的不同特点，分别设计了不同结构的多层感知机模型和三维卷积神经网络模型进行ASD分类器的训练学习。在ABIDE-I数据库上，基于CC200连接矩阵训练得到的分类器取得了70.3%的分类准确率，基于ReHo训练得到的分类器取得了69.3%的分类准确率。另外本研究基于CC200连接矩阵特征和ReHo特征做了进一步的特征融合实验，最终在ABIDE-I数据库上得到79.2%的分类准确率。 另外借鉴于深度学习网络中的权重参数分析法，从脑功能区角度，基于时域信息训练得到的网络模型参数，对ASD患者可能的异常脑功能区进行分析发现，ASD患者主要存在异常的脑功能区有额叶、楔叶、扣带回、角回、距状裂周围皮层、海马旁回、杏仁核等区域;从脑功能连接角度，通过基于空域CC200连接矩阵训练得到的多层感知模型参数分析发现，ASD患者在部分左右脑区间的功能连接弱于典型发育人群。此外，本研究进一步基于三重网络结构，从“脑网络”功能连接角度进行分析发现，ASD患者在前扣带回皮层和前额叶皮层之前的脑功能连接强度弱于典型发育人群。 通过实验研究证明，基于静息态功能磁共振成像数据，采用深度神经网络技术学习得到的ASD分类器，能有效对ASD患者和典型发育人群进行分类。同时通过实验发现基于网络模型权重参数分析法进行ASD患者在脑功能区和脑功能连接上的病理探究是可行的，该方法为ASD的病理原因探究提供了新的研究思路。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/45069]  |
| 专题 | 心理研究所_认知与发展心理学研究室 |
| 推荐引用方式 GB/T 7714 | 梁玲燕. 基于静息态功能磁共振成像的孤独症分类算法研究[D]. 中国科学院心理研究所. 中国科学院大学. 2022. |
入库方式： OAI收割
来源：心理研究所
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