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首页> 外文期刊>Journal of Intelligent Information Systems >Depression detection from sMRI and rs-fMRI images using machine learning
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Depression detection from sMRI and rs-fMRI images using machine learning

机译：使用机器学习的SMRI和RS-FMRI图像的抑郁检测

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Major Depression Disorder (MDD) is a common mental disorder that negatively affects many people's lives worldwide. Developing an automated method to find useful diagnostic biomarkers from brain imaging data would help clinicians to detect MDD in its early stages. Depression is known to be a brain connectivity disorder problem. In this paper, we present a brain connectivity-based machine learning (ML) workflow that utilizes similarity/dissimilarity of spatial cubes in brain MRI images as features for depression detection. The proposed workflow provides a unified framework applicable to both structural MRI images and resting-state functional MRI images. Several cube similarity measures have been explored, including Pearson or Spearman correlations, Minimum Distance Covariance, or inverse of Minimum Distance Covariance. Discriminative features from the cube similarity matrix are chosen with the Wilcoxon rank-sum test. The extracted features are fed into machine learning classifiers to train MDD prediction models. To address the challenge of data imbalance in MDD detection, oversampling is performed to balance the training data. The proposed workflow is evaluated through experiments on three independent public datasets, all imbalanced, of structural MRI and resting-state fMRI images with depression labels. Experimental results show good performance on all three datasets in terms of prediction accuracy, specificity, sensitivity, and area under the Receiver Operating Characteristic (ROC) curve. The use of features from both structured MRI and resting state functional MRI is also investigated.
机译：主要抑郁症（MDD）是一种常见的精神障碍，对全世界许多人的生活产生负面影响。开发一种自动化方法，以寻找来自脑成像数据的有用诊断生物标志物，可以帮助临床医生在其早期阶段检测MDD。已知抑郁症是脑连接障碍问题。在本文中，我们提出了一种基于大脑连接的机器学习（ML）工作流程，其利用脑MRI图像中的空间立方体的相似性/异化性作为抑郁检测的特征。所提出的工作流程提供了适用于结构MRI图像和休息状态的功能MRI图像的统一框架。已经探索了几个立方体相似度措施，包括皮尔逊或斯卡曼的相关性，最小距离协方差或最小距离协方差的反向。使用Wilcoxon Rank-Sum测试选择来自立方体相似性矩阵的歧视特征。提取的特征被馈送到机器学习分类器中以训练MDD预测模型。为了解决MDD检测中数据不平衡的挑战，执行过采样以平衡培训数据。通过关于三个独立公共数据集的实验，全部不平衡，结构MRI和休息状态FMRI图像的实验评估所提出的工作流程。实验结果在接收器操作特性（ROC）曲线下的预测精度，特异性，灵敏度和面积方面，所有三个数据集在所有三个数据集上表现出良好的性能。还研究了来自结构化MRI和休息状态功能MRI的特征。

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