# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 2 Related Work  
In the last 10 years, rs-fMRI techniques have been used in the study of brain activity for diagnosis of ASD using machine learning and deep learning algorithms [16; 17; 18]. Table A1 in the Appendix A presents the latest AI technologies in ASD classification using rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE). As shown in Table A1, support vectors machines (SVMs) are a traditional machine learning classifier that has been widely used in previous studies. [PERSON] et al. [19] proposed a discriminative model using an SVM to classify the selected FC features based on the F-score method. With 240 ASD and 128 NC subjects, the data were collected from six different sites of ABIDE, producing 79.17% classification accuracy based on 10-fold cross validation (CV). [PERSON] et al. [20] also used SVM for classification of ASD, taking in-phase synchronization features of the FC network extracted from 30 subjects from one of the ABIDE sites: the LEUVEN dataset. Their classification technique achieved an average accuracy of 86.7% using 30-fold CV. Recently, [PERSON] et al. [21] extracted the phase synchrony of the FC network and used principal component analysis (PCA) to reduce the dimensionality of the FC network by selecting the best FC features as the feature vector for the SVM classifier. Using a dataset of 90 subjects, the classifier achieved 78.9% accuracy in ASD classification from NC run on a 10-fold CV framework. As shown above, the selection of discriminative FC features in fMRI data is crucial for good performance of discriminative models for ASD detection. On the other hand, deep learning (DL) algorithms based on FC networks of rs-fMRI data have been used to classify ASD. [PERSON] et al. [16] concluded that DL algorithms should use unsupervised methods to extract relevant features while minimizing human intervention. They transferred 19,900 features from the FC network to a deep neural network (DNN) using two stacked denoising auto-encoders and achieved an average accuracy of 70%. [PERSON] et al. [22] achieved 93.2% accuracy by using SVM with recursive feature elimination (RFE) to select the top-ranked FC features from an FC network; their results were based on 10-fold CV among the full ABIDE dataset. [PERSON] et al. [17] attempted to improve the automated ASD classifier by converting the vector of FC features to two-dimensional (2D) matrices and using the images as input to a convolutional neural network (CNN). Their proposed model achieved an average accuracy of 70.2% based on 10-fold CV. In the same context, [PERSON] et al. [18] achieved 76.4% average classification accuracy using 2D images of selected FC features as input to a deep belief network (DBN). [PERSON] et al. [18] first filtered the FC network using a heuristic graph-based feature selection method that considered both external and internal FC network measurements. [PERSON] et al. [23] achieved 87.9% ASD classification accuracy by training a DNN using the FC features of rs-fMRI data from ABIDE. Based on the quality assessment of the fMRI data, the authors selected 866 patients from a total number of 1035, including 402 ASD and 464 NC subjects. It is worth mentioning that the brain network pattern FC plays a key role in the performance of ASD classification models. As can be seen from Table 1, there are two main patterns of FC: static and dynamic. To determine the static FC network, the Pearson correlation between the BOLD signals of the brain nodes is used. Most studies that have used rs-fMRI data to classify ASD utilized static FC [16; 17; 18]. On the other hand, dynamic connections can be obtained by representing the FC in the time-frequency domain, resulting in more informative connectivity features. For example, [PERSON] et al. [19] examined the resting-state FC in ASD over two frequency bands: the slow-4 (0.01-0.027 Hz) and slow-5 (0.027-0.073 Hz). [PERSON] et al. [20] attempted to study FC networks based on in-phase synchronization of coherence between signals, and [PERSON] et al.