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ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

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ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

Taban Eslami et al. Front Neuroinform.

Abstract

Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

Keywords: ABIDE; ASD; SLP; autoencoder; classification; data augmentation; fMRI.

Figures

Figure 1
Figure 1
Structure of an autoencoder consisting of an encoder that receives the input data and encodes it into a lower dimensional representation at the bottleneck layer, and a decoder that reconstructs the original input from the bottleneck layer.
Figure 2
Figure 2
Generating new artificial data: Step (1) Selecting a sample (p). Step (2) Find k-nearest neighbors of p from the same class, and pick one random neighbor (qr). Step (3) Generate new sample p′ using p and qr by linear interpolation.
Figure 3
Figure 3
Workflow of ASD-DiagNet: (A) Pairwise Pearson's correlations for each subject in the training set is computed. The average of all correlation arrays is computed and the position of 1/4 largest and 1/4 smallest values in the average array is considered as a mask. Masked correlation array of each sample is considered as its feature vectors. (B) A set of artificial samples is generated using the feature vectors of training samples. (C) Autoencoder and SLP are jointly trained by adding up their training loss in each iteration. (D) For a test subject, the features are extracted using the mask generated in part A, followed by passing the features through the encoder part of the autoencoder, and finally predicting its label using the trained SLP.
Figure 4
Figure 4
ROC curves of different methods for classification of whole dataset using CC-200 parcellation.
Figure 5
Figure 5
ROC curves of different methods for classification of whole dataset using AAL parcellation.
Figure 6
Figure 6
ROC curves of different methods for classification of whole dataset using TT parcellation.
Figure 7
Figure 7
ROC curves of different methods for classification of subjects below the age of 15 using CC-200 parcellation.

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