scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2020 ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L. Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

AbstractEarly identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved outcomes. Use of electroencephalography (EEG) in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment in ASD, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly within the first postnatal year, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared predictive accuracy at 6-months (during phoneme learning) versus 12-months (after phoneme learning). Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Using Pearson correlation feature selection and support vector machine with radial basis function classifier, 100% predictive diagnostic accuracy was observed at both ages. However, predictive features selected at the two ages differed and came from different scalp locations. We also report that performance across multiple machine learning algorithms was highly variable and declined when the 12-month sample size and behavioral heterogeneity was increased. These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes in order to develop clinically relevant classification algorithms.

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L. Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2021 ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

Abstract Background: Early identification of autism spectrum disorder (ASD) provides opportunity for early intervention and improved outcomes. Electroencephalography (EEG) use in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Methods: Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during phoneme learning) versus 12 months (after phoneme learning), and identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Features included a combination of power and nonlinear measures across 10-20 electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Results: Using a combination Pearson correlation feature selection and support vector machine classifier 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12- month data. At 6-months, predictive features were biased to measures from central electrodes, power measures, and measures in the alpha range. At 12-months, predictive features were more distributed between power and nonlinear measures, and biased toward measures in the beta range. Conclusions: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Fostering Emotion Recognition in Children with Autism Spectrum Disorder

2021 ◽  
Vol 5 (10) ◽  
pp. 57
Author(s):  
Vinícius Silva ◽  
Filomena Soares ◽  
João Sena Esteves ◽  
Cristina P. Santos ◽  
Ana Paula Pereira
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Social Communication ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Robotic Platform ◽  
Children With Asd

Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

scholarly journals English As Secondary Language Learning and Autism Spectrum Disorder: The Obstacles in Teaching and Learning the Language

2021 ◽  
Vol 12 (2) ◽  
pp. 22-30
Author(s):  
Haida Umiera Hashim ◽  
Melor Md Yunus ◽  
Helmi Norman
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Learning ◽  
Teaching And Learning ◽  
English Language ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
English Language Learning ◽  
Number Of Children ◽  
Learning Journey ◽  
The World

To this date, there has been an increasing number of children across the globe diagnosed with an autism spectrum disorder. There has been much literature that discussed the issues and obstacles common learners face in their English language learning journey. Yet, not much spotlight and acknowledgment were given to the learners with Autism in their voyage of English language learning. In conjunction to that, this paper intends to investigate the obstacles that the learners with Autism face in their English as secondary language learning. In addition to that, teachers are not to be forgotten as teachers are also believed to be playing a role in learners with Autism’s English as a second language(ESL) learning process which is why this research also intends to investigate the challenges faced by the teachers who are in charge of teaching learners with Autism. A qualitative research with the observation and interview as the instruments represents this research. This research involved two Autism Centres located in an urban area in Malaysia. The observation and interviews were conducted with forty-five learners with Autism and fourteen teachers. The findings have gathered that the world of Autism Spectrum Disorder is indeed full of obstacles and challenges but none that could not be overcome. It is believed that this research will provide a better insight of the real world of ESL teaching and learning among learners with Autism. Discussions and recommendations are further explained in this research.

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