# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
### Autism Detection Using Resting-State Functional MRI Data  
[PERSON] et al. (2020) used whole-brain point-to-point functional connectivity, including resting-state functional magnetic resonance imaging (fMRI) data comprising 964 subjects collected from 16 different international sites. Raw image data were preprocessed in MATLAB (Mathworks, Natick, MA, USA) ([PERSON] et al., 2020) using SPM8 (Wellcome Trust, London, UK) distributor software ([PERSON] et al., 2020). After preprocessing, mean BOLD signals for each subject were extracted from 7266 grey matter ROIs. A 7266 \(\times\) 7266 association matrix representing functional connectivity between every ROI pair was computed from Pearson correlation coefficients for every subject. Each ROI pair was defined as a connection. Connections were categorized into several bins, and a general linear model classifier was fitted on bins containing connections. A very low accuracy of merely 60% was achieved for whole-brain classification.  
[PERSON] et al. (2020) applied a deep learning algorithm combining a multilayer perceptron (MLP) along with autoencoders and obtained 70% mean classification accuracy using connectivity features derived from the Craddock 200 (CC200) brain atlas. However, it took a huge training time (over 32 h). [PERSON] et al. (2020) proposed ASD-Diagnet, a framework for detecting ASD using only resting-state fMRI data. A combined learning procedure using an autoencoder and a single-layered perceptron (SLP) was used. Furthermore, to increase the number of training subjects, a data augmentation strategy based on linear interpolation on available feature vectors was implemented. However, this strategy could only improve accuracy by 1% and presented a mean classification accuracy of 70.1%. [PERSON] et al. [39] performed ASD and control classification utilizing two types of fMRI imaging modalities. A 3D Resnet and MLP classifier were used in the classification process. This approach obtained 74% classification accuracy.  
To compete with the advancement of technologies in the neuroimaging field, there exists a scope for improving the accuracy of existing methods related to diagnosing autism spectrum disorder utilizing functional MRI. The primary goal of this study is to achieve a superior accuracy to the existing methods that have utilized mean time series signal based ASD detection and also to reduce the training time. In this regard, we have utilized the resting-state fMRI data of 866 subjects comprising 402 ASD and 464 control subjects from the Autism Brain Imaging Data Exchange (ABIDE) dataset. A detailed description of this dataset has been represented in Section 3.1, Table 1.  
The principal contributions of this work are listed below:  
1. Achieved maximum accuracy of 88% using only resting-state functional MRI (rsfMRI) data.
2. Reduced training time to less than 10 min, whereas other studies required more than 32 h in [37] and 41 min in [38].
3. Reached the conclusion that the Bootstrap Analysis of Stable Clusters (BASC) atlas using 122 ROIs yield a higher predictive power than other predefined atlases from comparative analysis.  
The subsequent contents of this study are organized as mentioned. Section 2 and its subsections describe our proposed model to detect ASD in detail. Section 3 describes the experimental results, discussions, and overall performance analysis for our proposed model. Section 4 contains a brief conclusion regarding our research findings and implications.