Abstract
With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.
Highlights
Depression is a complex mental illness in which the sufferer will continuously feel depressed, negative, and pessimistic and even have suicidal thoughts. is does great harm to the patient himself and imposes a burden on his family, friends, and the people around him [1].is disease poses great challenges to the accurate diagnosis and effective and timely treatment of the medical community
We compare five other common machine learning models, such as support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a normal deep neural network (DNN) model, and a convolutional neural network (CNN) model represented by GoogleNet Inception-residual networks (ResNet)-v2 with the model proposed in this paper
Our highest classification accuracy of the model proposed in this paper reached 94.68% when listening to all types of music, 93.61% when listening to positive music, and 89.36% when listening to negative music
Summary
Depression is a complex mental illness in which the sufferer will continuously feel depressed, negative, and pessimistic and even have suicidal thoughts. is does great harm to the patient himself and imposes a burden on his family, friends, and the people around him [1].is disease poses great challenges to the accurate diagnosis and effective and timely treatment of the medical community. Rosa et al [29] used SVM to establish the fMRI data discrimination modeling framework based on sparse network and the pattern recognition which was used to distinguish MDD between normal people and patients with deep depression and obtained the accuracy rate of 85%.
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