Interrogating autism from a multidimensional perspective: an integrative framework.

Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully elucidated by scientists. At present, the diagnosis of ASD typically involves a set of gold-standard diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS), the Autism Diagnostic Interview-Revised (ADI-R), and the more cost-effective Social Responsive Scale (SRS). Administering these diagnostic tests, which involve assessing communication and behavioral patterns, along with obtaining a clinical history, requires the expertise of a team of qualified clinicians. This process is time-consuming, effortful, and involves a degree of subjectivity due to the reliance on clinical judgment. Aside from conventional observational assessments, recent developments in neuroimaging and machine learning offer a fast and objective alternative for diagnosing ASD using brain imaging. This comprehensive work explores the use of different imaging modalvities, namely structural MRI (sMRI) and resting-state functional MRI (rs-fMRI), to investigate their potential for autism diagnosis. The proposed study aims to offer a new approach and perspective in comprehending ASD as a multidimensional problem, within a behavioral space that is defined by one of the available ASD diagnostic tools. This dissertation introduces a thorough investigation of the utilization of feature engineering tools to extract distinctive insights from various brain imaging modalities, including the application of novel feature representations. Additionally, the use of a machine learning framework to aid in the precise classification of individuals with autism is also explored in detail. This extensive research, which draws upon large publicly available datasets, sheds light on the influence of various decisions made throughout the pipeline on diagnostic accuracy. Furthermore, it identifies brain regions that may be impacted and contribute to an autism diagnosis. The attainment of high global state-of-the-art cross-validated, and hold-out set accuracy validates the advantages of feature representation and engineering in extracting valuable information, as well as the potential benefits of employing neuroimaging for autism diagnosis. Furthermore, a suggested diagnostic report has been put forth to assist physicians in mapping diagnoses to underlying neuroimaging markers. This approach could enable an earlier, automated, and more objective personalized diagnosis

The role of MRI in diagnosing autism: a machine learning perspective.

There is approximately 1 in every 44 children in the United States suffers from autism spectrum disorder (ASD), a disorder characterized by social and behavioral impairments. Communication difficulties, interpersonal difficulties, and behavioral difficulties are the top common symptoms. Even though symptoms can begin as early as infancy, it may take multiple visits to a pediatric specialist before an accurate diagnosis can be made. In addition, the diagnosis can be subjective, and different specialists may give different scores. There is a growing body of research suggesting differences in brain development and/or environmental and/or genetic factors contribute to autism development, but scientists have yet to identify exactly the pathology of this disorder. ASD can currently be diagnosed by a set of diagnostic evaluations, regarded as the gold standard, such as the Autism Diagnostic Observation Schedule (ADOS) or the Autism Diagnostic Interview-Revised (ADI-R). A team of qualified clinicians is needed for performing the behavioral and communication tests as well as clinical history information, hence a considerable amount of time, effort, and subjective judgment is involved in using these gold-standard diagnostic instruments. In addition to standard observational assessment, recent advancements in neuroimaging and machine learning suggest a rapid and objective alternative, using brain imaging. An investigation of the employment of different imaging modalities, namely Diffusion Tensor Imaging (DTI), and resting state functional MRI (rs-fMRI) for autism diagnosis is presented in this comprehensive work. A detailed study of the implementation of feature engineering tools to find discriminant insights from different brain imaging modalities, including the use of novel feature representations, and the use of a machine learning framework to assist in the accurate classification of autistic individuals is introduced in this dissertation. Based on three large publicly available datasets, this extensive research highlights different decisions along the pipeline and their impact on diagnostic accuracy. It also identifies potentially impacted brain regions that contribute to an autism diagnosis. Achieving high global state-of-the-art cross-validated accuracy confirms the benefits of feature representation and feature engineering in extracting useful information, as well as the potential benefits of utilizing neuroimaging in the diagnosis of autism. This should enable an early, automated, and more objective personalized diagnosis

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

Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study

[EN] Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.This research was partially funded by the Ministerio de Economia y Competitividad (MINECO), through the project BFU2015-64380-C2-2-R. U.P.-R. is funded by the Spanish Ministerio de Educacion, Cultura y Deporte (MECD) under grant FPU13/03537. We are thankful to the initiative ABIDE to provide the huge public release and open sharing autism database what has made possible to carry out this study in better conditions and improve the results obtained significantly.Borràs-Ferrís, L.; Pérez-Ramírez, MÚ.; Moratal, D. (2019). Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study. Diagnostics. 9(1):1-10. https://doi.org/10.3390/diagnostics9010032S11091Hull, J. V., Dokovna, L. B., Jacokes, Z. J., Torgerson, C. M., Irimia, A., & Van Horn, J. D. (2017). Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Frontiers in Psychiatry, 7. doi:10.3389/fpsyt.2016.00205Mertz, L. (2017). Sharing Data to Solve the Autism Riddle: An Interview with Adriana Di Martino and Michael Milham of ABIDE. IEEE Pulse, 8(6), 6-9. doi:10.1109/mpul.2017.2750819Cociu, B. A., Das, S., Billeci, L., Jamal, W., Maharatna, K., Calderoni, S., … Muratori, F. (2018). 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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

ARIANNA: A research environment for neuroimaging studies in autism spectrum disorders

The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation