A Novel Automated Approach for Deep Learning on Stereotypical Autistic Motor Movements

2021 ◽  
pp. 54-68
Author(s):  
Mohammad Shabaz ◽  
Parveen Singla ◽  
Malik Mustafa Mohammad Jawarneh ◽  
Himayun Mukhtar Qureshi
Keyword(s):  
Neural Networks ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
State Of The Art ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Motor Disorders ◽  
Convolution Neural Networks ◽  
Motor Movements

Autism spectrum disorder (ASD) is an ongoing neurodevelopmental disorder, with repeated behavior called stereotypical movement autism (SMM). Some recent experiments with accelerometer features as feedback to computer classifiers demonstrate positive findings in persons with autistic motor disorders for the automobile detection of stereotypical motor motions (SMM). To date, several methods for detecting and recognizing SMMs have been introduced. In this context, the authors suggest an approach of deep learning for recognition of SMM, namely deep convolution neural networks (DCNN). They also implemented a robust DCNN model for the identification of SMM in order to solve stereotypical motor movements (SMM), which thus outperform state-of-the-art SMM classification work.

scholarly journals A Novel Deep Learning Approach for Recognizing Stereotypical Motor Movements within and across Subjects on the Autism Spectrum Disorder

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Lamyaa Sadouk ◽  
Taoufiq Gadi ◽  
El Hassan Essoufi
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Transfer Learning ◽  
Neurodevelopmental Disorder ◽  
Real Life ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Svm Classifier ◽  
Learning Approach ◽  
Motor Movements

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties including repetitive patterns of behavior known as stereotypical motor movements (SMM). So far, several techniques have been implemented to track and identify SMMs. In this context, we propose a deep learning approach for SMM recognition, namely, convolutional neural networks (CNN) in time and frequency-domains. To solve the intrasubject SMM variability, we propose a robust CNN model for SMM detection within subjects, whose parameters are set according to a proper analysis of SMM signals, thereby outperforming state-of-the-art SMM classification works. And, to solve the intersubject variability, we propose a global, fast, and light-weight framework for SMM detection across subjects which combines a knowledge transfer technique with an SVM classifier, therefore resolving the “real-life” medical issue associated with the lack of supervised SMMs per testing subject in particular. We further show that applying transfer learning across domains instead of transfer learning within the same domain also generalizes to the SMM target domain, thus alleviating the problem of the lack of supervised SMMs in general.

scholarly journals Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ke Niu ◽  
Jiayang Guo ◽  
Yijie Pan ◽  
Xin Gao ◽  
Xueping Peng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Clinical Diagnosis ◽  
Cross Validation ◽  
Personal Characteristic ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Learning Models ◽  
Automated Diagnosis

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

scholarly journals Reproducible Neuroimaging Features for Diagnosis of Autism Spectrum Disorder with Machine Learning

2021 ◽  
Author(s):  
Cooper J. Mellema ◽  
Kevin P. Nguyen ◽  
Alex Treacher ◽  
Albert Montillo
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
State Of The Art ◽  
Neurodevelopmental Disorder ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Accurate Diagnosis ◽  
Spectrum Disorder ◽  
Learning Models ◽  
Model Search

Abstract Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to measure alterations manifest in ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets. The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.

scholarly journals Reproducible neuroimaging features in Autism spectrum disorder that enable accurate diagnosis with machine learning models

2021 ◽  
Author(s):  
Cooper J Mellema ◽  
Kevin P Nguyen ◽  
Alex Treacher ◽  
Albert Montillo
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
State Of The Art ◽  
Neurodevelopmental Disorder ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Accurate Diagnosis ◽  
Spectrum Disorder ◽  
Learning Models ◽  
Model Search

Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to measure alterations manifest in ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets. The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellum biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.

Specialized Diet Therapies: Exploration for Improving Behavior in Autism Spectrum Disorder (ASD)

2020 ◽  
Vol 27 (40) ◽  
pp. 6771-6786
Author(s):  
Geir Bjørklund ◽  
Nagwa Abdel Meguid ◽  
Maryam Dadar ◽  
Lyudmila Pivina ◽  
Joanna Kałużna-Czaplińska ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Neurodevelopmental Disorder ◽  
Caloric Intake ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nutritional Deficiencies ◽  
Childhood And Adolescence ◽  
Restricted Interests ◽  
Balanced Diet ◽  
The Individual

As a major neurodevelopmental disorder, Autism Spectrum Disorder (ASD) encompasses deficits in communication and repetitive and restricted interests or behaviors in childhood and adolescence. Its etiology may come from either a genetic, epigenetic, neurological, hormonal, or an environmental cause, generating pathways that often altogether play a synergistic role in the development of ASD pathogenesis. Furthermore, the metabolic origin of ASD should be important as well. A balanced diet consisting of the essential and special nutrients, alongside the recommended caloric intake, is highly recommended to promote growth and development that withstand the physiologic and behavioral challenges experienced by ASD children. In this review paper, we evaluated many studies that show a relationship between ASD and diet to develop a better understanding of the specific effects of the overall diet and the individual nutrients required for this population. This review will add a comprehensive update of knowledge in the field and shed light on the possible nutritional deficiencies, metabolic impairments (particularly in the gut microbiome), and malnutrition in individuals with ASD, which should be recognized in order to maintain the improved socio-behavioral habit and physical health.

scholarly journals Intranasal oxytocin administration ameliorates social behavioral deficits in a POGZWT/Q1038R mouse model of autism spectrum disorder

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kohei Kitagawa ◽  
Kensuke Matsumura ◽  
Masayuki Baba ◽  
Momoka Kondo ◽  
Tomoya Takemoto ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Mouse Model ◽  
Mouse Models ◽  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
De Novo Mutation ◽  
Behavioral Deficits

AbstractAutism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by core symptoms of impaired social behavior and communication. Recent studies have suggested that the oxytocin system, which regulates social behavior in mammals, is potentially involved in ASD. Mouse models of ASD provide a useful system for understanding the associations between an impaired oxytocin system and social behavior deficits. However, limited studies have shown the involvement of the oxytocin system in the behavioral phenotypes in mouse models of ASD. We have previously demonstrated that a mouse model that carries the ASD patient-derived de novo mutation in the pogo transposable element derived with zinc finger domain (POGZWT/Q1038R mice), showed ASD-like social behavioral deficits. Here, we have explored whether oxytocin (OXT) administration improves impaired social behavior in POGZWT/Q1038R mice and found that intranasal oxytocin administration effectively restored the impaired social behavior in POGZWT/Q1038R mice. We also found that the expression level of the oxytocin receptor gene (OXTR) was low in POGZWT/Q1038R mice. However, we did not detect significant changes in the number of OXT-expressing neurons between the paraventricular nucleus of POGZWT/Q1038R mice and that of WT mice. A chromatin immunoprecipitation assay revealed that POGZ binds to the promoter region of OXTR and is involved in the transcriptional regulation of OXTR. In summary, our study demonstrate that the pathogenic mutation in the POGZ, a high-confidence ASD gene, impairs the oxytocin system and social behavior in mice, providing insights into the development of oxytocin-based therapeutics for ASD.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

scholarly journals In Silico Exploration of the Potential Role of Acetaminophen and Pesticides in the Etiology of Autism Spectrum Disorder

Toxics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 97
Author(s):  
Tristan Furnary ◽  
Rolando Garcia-Milian ◽  
Zeyan Liew ◽  
Shannon Whirledge ◽  
Vasilis Vasiliou
Keyword(s):  
Autism Spectrum Disorder ◽  
Prenatal Exposure ◽  
Pesticide Exposure ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Biomedical Literature ◽  
Spectrum Disorder ◽  
Biological Processes ◽  
Genetic Associations ◽  
Increased Risk

Recent epidemiological studies suggest that prenatal exposure to acetaminophen (APAP) is associated with increased risk of Autism Spectrum Disorder (ASD), a neurodevelopmental disorder affecting 1 in 59 children in the US. Maternal and prenatal exposure to pesticides from food and environmental sources have also been implicated to affect fetal neurodevelopment. However, the underlying mechanisms for ASD are so far unknown, likely with complex and multifactorial etiology. The aim of this study was to explore the potential effects of APAP and pesticide exposure on development with regards to the etiology of ASD by highlighting common genes and biological pathways. Genes associated with APAP, pesticides, and ASD through human research were retrieved from molecular and biomedical literature databases. The interaction network of overlapping genetic associations was subjected to network topology analysis and functional annotation of the resulting clusters. These genes were over-represented in pathways and biological processes (FDR p < 0.05) related to apoptosis, metabolism of reactive oxygen species (ROS), and carbohydrate metabolism. Since these three biological processes are frequently implicated in ASD, our findings support the hypothesis that cell death processes and specific metabolic pathways, both of which appear to be targeted by APAP and pesticide exposure, may be involved in the etiology of ASD. This novel exposures-gene-disease database mining might inspire future work on understanding the biological underpinnings of various ASD risk factors.

scholarly journals Mealtime Behaviors and Food Preferences of Students with Autism Spectrum Disorder

Foods ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 49
Author(s):  
Hae Jin Park ◽  
Su Jin Choi ◽  
Yuri Kim ◽  
Mi Sook Cho ◽  
Yu-Ri Kim ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Nutrition Education ◽  
Neurodevelopmental Disorder ◽  
Food Preferences ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Online Questionnaire ◽  
Mealtime Behavior ◽  
High Level

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a lack of social communication and restrictive, repetitive behaviors or interests. This study aimed to examine the mealtime behaviors and food preferences of students with ASD. An online questionnaire on mealtime behavior and food preferences of ASD students was conducted by caregivers including parents, and the average age of ASD students was 14.1 ± 6.1. The analysis of mealtime behavior resulted in classification into three clusters: cluster 1, the “low-level problematic mealtime behavior group”; cluster 2, the “mid-level problematic mealtime behavior group”; and cluster 3, the “high-level problematic mealtime behavior group”. Cluster 1 included older students than other clusters and their own specific dietary rituals. Meanwhile, cluster 3 included younger students than other clusters, high-level problematic mealtime behavior, and a low preference for food. In particular, there were significant differences in age and food preference for each subdivided ASD group according to their eating behaviors. Therefore, the content and method of nutrition education for ASD students’ needs a detailed approach according to the characteristics of each group.

Sign in / Sign up

Export Citation Format

Share Document