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

On pattern recognition of brain connectivity in resting-state functional MRI

Dissertação de mestrado integrado em Biomedical Engineering (specialization on Medical Informatics)The human urge and pursuit for information have led to the development of increasingly complex technologies, and new means to study and understand the most advanced and intricate biological system: the human brain. Large-scale neuronal communication within the brain, and how it relates to human behaviour can be inferred by delving into the brain network, and searching for patterns in connectivity. Functional connectivity is a steady characteristic of the brain, and it has been proved to be very useful for examining how mental disorders affect connections within the brain. The detection of abnormal behaviour in brain networks is performed by experts, such as physicians, who limit the process with human subjectivity, and unwittingly introduce errors in the interpretation. The continuous search for alternatives to obtain faster and robuster results have put Machine Learning and Deep Learning in the leading position of computer vision, as they enable the extraction of meaningful patterns, some beyond human perception. The aim of this dissertation is to design and develop an experiment setup to analyse functional connectivity at a voxel level, in order to find functional patterns. For the purpose, a pipeline was outlined to include steps from data download to data analysis, resulting in four methods: Data Download, Data Preprocessing, Dimensionality Reduction, and Analysis. The proposed experiment setup was modeled using as materials resting state fMRI data from two sources: Life and Health Sciences Research Institute (Portugal), and Human Connectome Project (USA). To evaluate its performance, a case study was performed using the In-House data for concerning a smaller number of subjects to study. The pipeline was successful at delivering results, although limitations concerning the memory of the machine used restricted some aspects of this experiment setup’s testing. With appropriate resources, this experiment setup may support the process of analysing and extracting patterns from any resting state functional connectivity data, and aid in the detection of mental disorders.O desejo e a busca intensos do ser humano por informação levaram ao desenvolvimento de tecnologias cada vez mais complexas e novos meios para estudar e entender o sistema biológico mais avançado e intrincado: o cérebro humano. A comunicação neuronal em larga escala no cérebro, e como ela se relaciona com o comportamento humano, pode ser inferida investigando a rede neuronal cerebral e procurando por padrões de conectividade. A conectividade funcional é uma característica constante do cérebro e provou ser muito útil para examinar como os distúrbios mentais afetam as conexões cerebrais. A deteção de anormalidades em imagens de ressonância magnética é realizada por especialistas, como médicos, que limitam o processo com a subjetividade humana e, inadvertidamente, introduzem erros na interpretação. A busca contínua de alternativas para obter resultados mais rápidos e robustos colocou as técnicas de machine learning e deep learning na posição de liderança de visão computacional, pois permitem a extração de padrões significativos e alguns deles para além da percepção humana. O objetivo desta dissertação é projetar e desenvolver uma configuração experimental para analisar a conectividade funcional ao nível do voxel, a fim de encontrar padrões funcionais. Nesse sentido, foi delineado um pipeline para incluir etapas a começar no download de dados até à análise desses mesmos dados, resultando assim em quatro métodos: Download de Dados, Pré-processamento de Dados, Redução de Dimensionalidade e Análise. A configuração experimental proposta foi modelada usando dados de ressonância magnética funcional de resting-state de duas fontes: Instituto de Ciências da Vida e Saúde (Portugal) e Human Connectome Project (EUA). Para avaliar o seu desempenho, foi realizado um estudo de caso usando os dados internos por considerar um número menor de participantes a serem estudados. O pipeline foi bem-sucedido em fornecer resultados, embora limitações relacionadas com a memória da máquina usada tenham restringido alguns aspetos do teste desta configuração experimental. Com recursos apropriados, esta configuração experimental poderá servir de suporte para o processo de análise e extração de padrões de qualquer conjunto de dados de conectividade funcional em resting-state e auxiliar na deteção de transtornos mentais

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis