Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. 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 ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, 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.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.

Genetic factors have been pointed out as the primary root associated with the risk of autism. Recent works indicate that approximately 80% of autistic people have inherited the condition from their parents. However, there are no estimates that indicate the likelihood of an autistic parent having an autistic child. Using Hidden Markov Models, together with the data of autism heritability, we developed a model to investigate the likelihood of autistic parents generating autistic children. Hidden Markov Models are a double-layered stochastic process, and it consists of a nonvisible stochastic process (not observable) that can be predicted through a visible one. Our model was built and validated using statistical data from the association of gender with recurrence of autism among siblings, as well as statistical data from the association of genetic factors with autism. Our results suggest that autistic parents may generate autistic children with probabilities of ≈33% for female children and ≈80% for male children. Such estimates could assist parents in some decision making processes according to the estimated risk of autism in their descendants.

Este trabalho apresenta os resultados de uma Revisão Sistemática nos Anais do Congresso Brasileiro de Informática na Educação sobre o uso de Tecnologias Assistivas no apoio ao desenvolvimento de habilidades em pessoas com autismo. Foram analisados estudos primários das últimas 12 edições do evento, identificando as tecnologias empregadas, as abordagens terapêuticas associadas, as habilidades trabalhadas e as limitações comuns. Um total de 60 estudos foram indexados, dos quais 18 foram analisados para responder às questões de pesquisa. Aplicativos e jogos foram as tecnologias mais empregadas; as abordagens comportamentais foram as mais aplicadas, apesar de quase metade dos trabalhos não se fundamentarem em nenhuma abordagem terapêutica; as habilidades mais trabalhadas foram interação social, comunicação e alfabetização; apesar dos avanços, os resultados evidenciaram algumas lacunas, como, por exemplo, a falta de uma melhor caracterização das pessoas envolvidas (dados demográficos, intensidade do diagnóstico etc), não fundamentar a ferramenta em alguma abordagem terapêutica ou estratégia de ensino, e a ausência de profissionais de saúde no processo de desenvolvimento.

Background: The last decade was marked by increased neuroscience research involving machine Learning (ML) and medical images such as functional magnetic resonance and electroencephalogram (EEG). There are many challenges in this research field, including the need for more data and a standard for presenting the results. Since ML models tend to be sensitive to the input data, different strategies for data acquisition, preprocessing, feature selection, and validation significantly impact the results achieved. Therefore, a significant variation while presenting the results makes it challenging to compare the results. Results: This work aims to tackle the lack of a standard model by presenting a guideline, conform Quadas-2, that covers the most critical data for studies to demonstrate when using EEG and ML for addressing neurological disorders. Conclusions: This guideline allows a structural presentation of the primary data of studies using ML applied to EEG, improving comparison between studies while also allowing fair comparisons.