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

A meta-analysis of machine learning classification tools using rs-fmri data for autism spectrum disorder diagnosis

The Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental condition characterized by cognitive, behavioral, and social dysfunction. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis - currently based on behavioral criteria through a lengthy and timeconsuming process. In particular, the use of Machine Learning (ML) classifiers based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy. Therefore, we conducted a systematic review and meta-analysis to summarize and aggregate the available evidence in the literature so far. The systematic search resulted in the selection of 93 articles, whose data were extracted and analyzed through the systematic review. A bivariate randomeffects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies (132 independent samples) that offered sufficient information for a quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% (95% CI: 71.8-75.8%) and 74.8% (95% CI: 72.3-77.1%), respectively, and Support Vector Machine (SVM) stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for Artificial Neural Network (ANN) classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seem to be promising, achieving specially higher sensitivities (p = 0.002) when compared to rs-fMRI data alone (84.7% - 95% CI: 78.5-89.4% - versus 72.8% - 95% CI: 70.6-74.8%). Lower values of sensitivity/specificity were found when the number of Regions of Interest (ROIs) increased. We also highlight the performance of the approaches using the Automated Anatomical Labelling atlas with 116 ROIs (AAL116). Regarding the features used to train the classifiers, we found better results using the Pearson Correlation (PC) Fishertransformed or other features in comparison to the use of the PC without modifications. Finally, our analysis showed AUC values between acceptable and excellent, but given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.Agência 1O Transtorno do Espectro Autista (TEA) é uma condição complexa e heterogênea que afeta o desenvolvimento cerebral e é caracterizada por disfunções cognitivas, comportamentais e sociais. Muito esforço vem sendo feito para identificar biomarcadores baseados em imagens cerebrais e desenvolver ferramentas que poderiam facilitar o diagnóstico do TEA - atualmente baseado em critérios comportamentais, através de um processo longo e demorado. Em particular, o uso de algoritmos de Aprendizado de Máquina para classificação de dados de Imagens de Ressonância Magnética funcional em estado de repouso (rs-fMRI) é promissor, mas há uma necessidade contínua de pesquisas adicionais a respeito da precisão desses classificadores. Assim, este trabalho realiza uma revisão sistemática e meta-análise de modo a resumir e agregar as evidências disponíveis na literatura da área até o momento. A busca sistemática por artigos resultou na seleção de 93 deles, que tiveram seus dados extraídos e analisados através da revisão sistemática. Um modelo meta-analítico bivariado de efeitos aleatórios foi implementado para investigar a sensibilidade e especificidade dos 55 estudos (132 amostras independentes) que ofereceram informação suficiente para serem utilizados na análise quantitativa. Os resultados obtidos indicaram estimativas gerais de sensibilidade e especificidade de 73.8% (95% IC: 71.8- 75.8%) e 74.8% (95% IC: 72.3-77.1%), respectivamente, e os classificadores baseados em SVM (do inglês, Support Vector Machine) se destacaram como os mais utilizados, apresentando estimativas acima de 76%. Estudos que utilizaram amostras maiores tenderam a obter piores resultados de precisão, com exceção do subgrupo composto por classificadores baseados em Redes Neurais Artificiais. O uso de outros tipos de imagens cerebrais ou dados fenotípicos para complementar as informações obtidas através da rs-fMRI se mostrou promissor, alcançando especialmente sensibilidades mais altas ( = 0.002) em relação aos estudos que utilizaram apenas dados de rs-fMRI (84.7% - 95% IC: 78.5-89.4% - versus 72.8% - 95% IC: 70.6-74.8%). Valores menores de sensibilidade/especificidade foram encontrados quando o número de Regiões de Interesse (ROIs, do inglês Regions of Interest) aumentou. Vale destacar também o desempenho das abordagens utilizando o atlas AAL (do inglês, Automated Anatomical Labelling) com 116 ROIs. Em relação às features usadas para treinar os classificadores, foram encontrados melhores resultados nos estudos que utilizaram a correlação de Pearson em conjunto com a transformação Z de Fisher ou outras features em comparação ao uso da correlação de Pearson sem modifica- ções. Finalmente, a análise revelou valores da área sob a curva ROC (do inglês, Receiver Operating Characteristic) entre aceitável e excelente. Entretanto, considerando as várias limitações que são indicadas no estudo, mais estudos bem desenhados são necessários para estender o uso potencial desses algoritmos de classificação a ambientes clínicos

Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics

Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject\u27s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC-behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain

Leveraging a machine learning based predictive framework to study brain-phenotype relationships

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Phenotypic Characterization with Software Development for Analysis of the Visual System in Animal Models of Neurodevelopmental Diseases

A neurofibromatose tipo 1 (NF1) é uma perturbação do desenvolvimento neurológico com implicações cognitivas adultas. Provoca anomalias do sistema nervoso central e afeta 1 em 3000 indivíduos em todo o mundo. Contudo, pouco se sabe sobre os efeitos no sistema visual e como estes podem estar associados a défices cognitivos e preveem a sua progressão. Neste trabalho, avalia-se as potenciais alterações na fisiologia da retina num modelo genético de murgalho de NF1, utilizando uma técnica neurofisiológica não invasiva, o eletroretinograma (ERG), para determinar o seu potencial diagnóstico. Como um indicador fiável da função da retina em resposta à luz, o ERG tem a capacidade de ajudar a nossa interpretação da fisiopatologia das perturbações do neurodesenvolvimento e neurodegenerativas. Os principais objetivos desta tese são a caracterização fenotípica do sistema visual num modelo animal de NF1 e o desenvolvimento de ferramentas informáticas (MATLAB e Phyton) para processamento de sinais, análise de forma de onda, extração de características, e classificação. Verificou-se que os parâmetros ERG relacionados principalmente com a atividade oscilatória inibitória revelam alterações subtis dependentes do sexo. Para vários potenciais oscilatórios, machos e fêmeas exibem alterações opostas associadas ao genótipo mutante. Além disso, as características do ERG foram utilizadas para formar um classificador de aprendizagem de máquina baseado nos aglomerados significativos encontrados para algumas interações entre indivíduos, um classificador que se destina a ser capaz de receber um sinal e devolver o provável diagnóstico.Neurofibromatosis type 1 (NF1) is a neurodevelopmental disorder with adult cognitive implications. It causes central nervous system anomalies and affects 1 in 3000 individuals worldwide. However, little is known about the effects on the visual system circuitry and how these may be associated with cognitive deficits and predicts its progression. In this work, it was evaluated the potential alterations in retinal physiology in a genetic mouse model of NF1, using a non-invasive neurophysiological technique, the electroretinogram (ERG), to ascertain its diagnostic potential. As a reliable indicator of retinal function in response to light, the ERG has the ability to aid our interpretation of the pathophysiology of neurodevelopmental and neurodegenerative disorders. The main objectives of this thesis are the phenotypic characterization of the visual system in an animal model of NF1 and the development of computer tools (MATLAB and Phyton) for signal processing, waveform analysis, feature extraction, and classification. This work found that ERG parameters mainly related to inhibitory oscillatory activity reveal subtle sex-dependent alterations. For various oscillatory potentials males and females exhibit opposite changes associated with the transgenic background. Furthermore, the ERG features were used to form a machine learning classifier based on the significant clusters found for some interactions between individuals, a classifier that is meant to be able to receive a signal and return the likely diagnosis

Novel Biomarker Identification Approaches for Schizophrenia using fMRI and Retinal Electrophysiology

University of Minnesota Ph.D. dissertation. November 2017. Major: Biomedical Engineering. Advisors: Kelvin Lim, Theoden Netoff. 1 computer file (PDF); vi, 109 pages.Schizophrenia is a chronic mental illness. The exact cause if schizophrenia is not yet known. Extensive research has been done to identify robust biomarkers for the disease using non-invasive brain imaging techniques. A robust biomarker can be informative about pathophysiology of the disease and can guide clinicians into developing more effective interventions. The aim of this dissertation is two folds. First, we seek to identify robust biomarkers using resting state fMRI activity from a cohort of schizophrenic and healthy subjects in a purely data driven approach. We will calculate multivariate network measures and use them as features for classification of the subjects into healthy and diseased. The network measures will be calculated using nodes defined by the AAL anatomical atlas as well as a functional atlas constructed from the fMRI activity. Network measures with high classification rate may be used as potential biomarkers. We will employ double cross-validation to estimate generalizability of our results to a new population of subjects that were not used in biomarker identification. Second, we seek to identify biomarkers using electroretinogram (ERG). We will use a data driven approach to classify individuals based on the pattern of retinal activity they exhibit in response to visual stimulation. Characteristics of the ERG result in high classification rate are presented as potential biomarkers of schizophrenia

Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings

This doctoral thesis outlines advances in multi-way analysis for characterizing electroencephalogram (EEG) recordings from a paediatric population, with the aim to describe new links between EEG data and changes in the brain. This entails establishing the validity of multi-way analysis as a framework for identifying developmental information at the individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear algebraic architecture to identify latent structural relationships in naturally occurring higher order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a multi-way model to efficiently express the complex structures present in paediatric EEG recordings as unique combinations of low-rank matrices, offering new insights into child development. This multi-way CPD framework is explored for both typically developing (TD) children and children with potential developmental delays (DD), e.g. children who suffer from epilepsy or paediatric stroke. Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric EEG via multi-way analysis. Here, the CPD model probes the underlying relationships between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the CPD can reveal distinct population-level features in rEEG that reflect unique developmental traits in varying child populations. These development-affiliated profiles are evaluated with respect to capturing structures well-established in childhood EEG. The identified features are also interrogated for their predictive abilities in anticipating new subjects’ ages. Assessing simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis framework as well suited for identifying developmental profiles from paediatric rEEG. We extend the multi-way analysis scheme to more complex EEG scenarios common in EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of multi-way modelling for interventions where developmental changes often pose as barriers. The multi-way CPD model is expanded to include four modes- task, spatial, spectral and subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual attention task that elucidates a steady-state visual evoked potential and present the advantages gained from the extended CPD model. Through direct multi-linear projection, we demonstrate that linear profiles of the CPD can be capitalized upon for rapid task classification sans individual subject classifier calibration. Incorporating concepts from the multi-way analysis scheme with child development measured by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for inferring development from paediatric EEG. We utilize a common EEG task (button-press) to establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model and cognitive scores from psychometric evaluations then permits joint decomposition of the two datasets to identify common features associated with each representation of development. Use of grid search optimization and a fully cross-validated design supports the JEDI model as another technique for rapidly discerning the developmental status of a child via EEG. We then briefly turn our attention to associating child development as measured by psychometric tests to markers in the EEG using graph network properties. Using graph networks, we show how the functional connectivity can inform on potential developmental delays in very young epileptic children using routine, clinical rEEG measures. This establishes a potential tool complementary to the JEDI model for identifying and inferring links between the established psychometric evaluation of developing children and functional analysis of the EEG. Multi-way analysis of paediatric EEG data offers a new approach for handling the developmental status and profiles of children. The CPD model offers flexibility in terms of identifying development-related features, and can be integrated into EEG tasks common in rehabilitation paradigms. We aim for the multi-way framework and associated techniques pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for characterizing paediatric development

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov