Abstract：
BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control.
METHODS: The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared.
RESULTS: The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively.
CONCLUSIONS: The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.
METHODS: The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared.
RESULTS: The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively.
CONCLUSIONS: The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.
摘要：
背景：由于战争，创伤后应激障碍（PTSD）的发病率目前正在增加，恐怖主义,和大流行性疾病的情况。因此,PTSD的准确检测对患者的治疗至关重要，为此,本研究旨在对PTSD患者与健康对照者进行分类.
方法：使用19名PTSD和24名健康对照男性受试者的静息状态功能MRI（rs-fMRI）扫描，使用组水平独立成分分析（ICA）和t检验来识别大多数受影响的大脑区域的激活模式。将受创伤后应激障碍影响的受试者与健康对照的六种机器学习技术进行分类，包括随机森林，天真的贝叶斯,支持向量机，决策树,K-最近邻,线性判别分析，和深度学习三维3D-CNN的数据进行了比较。
结果：分析了最常见的11个创伤暴露区域和健康大脑的rs-fMRI扫描，以观察其激活水平。杏仁核和脑岛区域被确定为PTSD受试者大脑中感兴趣区域中最激活的区域。此外,机器学习技术已应用于从ICA提取的组件，但模型提供低分类精度。ICA分量也被馈送到3D-CNN模型中，用5倍交叉验证方法训练。3D-CNN模型表现出很高的准确性，如98.12%，98.25%,和98.00%的平均训练，验证,和测试数据集，分别。
结论：研究结果表明，3D-CNN是一种超越其他六种技术的方法，它有助于准确识别PTSD患者。
方法：使用19名PTSD和24名健康对照男性受试者的静息状态功能MRI（rs-fMRI）扫描，使用组水平独立成分分析（ICA）和t检验来识别大多数受影响的大脑区域的激活模式。将受创伤后应激障碍影响的受试者与健康对照的六种机器学习技术进行分类，包括随机森林，天真的贝叶斯,支持向量机，决策树,K-最近邻,线性判别分析，和深度学习三维3D-CNN的数据进行了比较。
结果：分析了最常见的11个创伤暴露区域和健康大脑的rs-fMRI扫描，以观察其激活水平。杏仁核和脑岛区域被确定为PTSD受试者大脑中感兴趣区域中最激活的区域。此外,机器学习技术已应用于从ICA提取的组件，但模型提供低分类精度。ICA分量也被馈送到3D-CNN模型中，用5倍交叉验证方法训练。3D-CNN模型表现出很高的准确性，如98.12%，98.25%,和98.00%的平均训练，验证,和测试数据集，分别。
结论：研究结果表明，3D-CNN是一种超越其他六种技术的方法，它有助于准确识别PTSD患者。
