Machine Learning for the Diagnosis of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurological disorder. It refers to a wide range of behavioral and social abnormality and causes problems with social skills, repetitive behaviors, speech, and nonverbal communication. Even though there is no exact cure to ASD, an early diagnosis can help the patient take precautionary steps. Diagnosis of ASD has been of great interest recently, as researchers are yet to find a specific biomarker to detect the disease successfully. For the diagnosis of ASD, subjects need to go through behavioral observation and interview, which are not accurate sometimes. Also, there is a lack of dissimilarity between neuroimages of ASD subjects and healthy control (HC) subjects which make the use of neuroimages difficult for the diagnosis. So, machine learning-based approaches to diagnose ASD are becoming popular day by day. In the machine learning-based approach, features are extracted either from the functional MRI images or the structural MRI images to build the models. In this study at first, I created brain networks from the resting-state functional MRI (rs-fMRI) images, by using the 264 regions based parcellation scheme. These 264 regions capture the functional activity of the brain more accurately compared to regions defined in other parcellation schemes. Next, I extracted spectrum as a raw feature and combined it with other network based topological centralities: assortativity, clustering coefficient, the average degree. By applying a feature selection algorithm on the extracted features, I reduced the dimension of the features to cope up with overfitting. Then I used the selected features in support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR) for the diagnosis of ASD. Using the proposed method on Autism Brain Imaging Data Exchange (ABIDE) I achieved the classification accuracy of 78.4% for LDA, 77.0% for LR, 73.5% for SVM, and 73.8% for KNN. Next, I built a deep neural network model for the classification and feature selection using the autoencoder. In this approach, I used the previously defined features to build the DNN classifier. The DNN classifier is pre-trained using the autoencoder. Due to the pre-training, there has been a significant increase in the performance of the DNN classifier. I also proposed an autoencoder based feature selector. The latent space representation of the autoencoder is used to create a discriminate and compressed representation of the features. To make a more discriminate representation, the autoencoder is pre-trained with the DNN classifier. The classification accuracy of the DNN classifier and the autoencoder based feature selector is 79.2% and 74.6%, respectively. Finally, I studied the structural MRI images and proposed a convolutional autoencoder (CAE) based classification model. The T1-weighted MRI images without any pre-processing are used in this study. As the effect of age is very important when studying the structural images for the diagnosis of ASD, I used the ABIDE 1 dataset, which covers subjects with a wide range of ages. Using the proposed CAE based diagnosis method, I achieved a classification accuracy of 96.6%, which is better than any other study for the diagnosis of ASD using the ABIDE 1 dataset. The results of this thesis demonstrate that the spectrum of the brain networks is an essential feature for the diagnosis of ASD and rather than extracting features from the structural MRI image a more efficient way is to use the images directly into deep learning models. The proposed studies in this thesis can help to build an early diagnosis model for ASD

New Research in Children with Neurodevelopmental Disorders

This book collects recent research in the field of care for neurodevelopmental disorders, emphasizing transdisciplinary work in clinical, educational and family contexts. It presents an opportunity to learn about the impact of participation on children and adolescents with neurodevelopmental disorders. Mainly, new therapeutic approaches are presented in children and adolescents with autism spectrum disorder, attention-deficit/hyperactivity disorder, or motor coordination disorders

Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor

Autism Spectrum Disorder (ASD) has been emerging as a growing public health threat. Early diagnosis of ASD is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in ASD infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and we used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

In recent years, advanced magnetic resonance imaging (MRI) methods including functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) have indicated an increase in the prevalence of neuropsychiatric disorders such as autism spectrum disorder (ASD), effects one out of six children worldwide. Data driven techniques along with medical image analysis techniques, such as computer-assisted diagnosis (CAD), benefiting from deep learning. With the use of artificial intelligence (AI) and IoT-based intelligent approaches, it would be convenient to support autistic children to adopt the new atmospheres. In this paper, we classify and represent learning tasks of the most powerful deep learning network such as convolution neural network (CNN) and transfer learning algorithm on a combination of data from autism brain imaging data exchange (ABIDE I and ABIDE II) datasets. Due to their four-dimensional nature (three spatial dimensions and one temporal dimension), the resting state-fMRI (rs-fMRI) data can be used to develop diagnostic biomarkers for brain dysfunction. ABIDE is a collaboration of global scientists, where ABIDE-I and ABIDE-II consists of 1112 rs-fMRI datasets from 573 typical control (TC) and 539 autism individuals, and 1114 rs-fMRI from 521 autism and 593 typical control individuals respectively, which were collected from 17 different sites. Our proposed optimized version of CNN achieved 81.56% accuracy. This outperforms prior conventional approaches presented only on the ABIDE I datasets

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.Qatar National Librar