scholarly journals Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 51 ◽  
Author(s):  
Aitana Pascual-Belda ◽  
Antonio Díaz-Parra ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Management Functions ◽  
The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

Classification of Autism Spectrum Disorder from EEG-based Functional Brain Connectivity Analysis

2021 ◽  
pp. 1-27
Author(s):  
Noura Alotaibi ◽  
Koushik Maharatna
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Phase Locking ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
Graph Theoretic ◽  
Phase Locking Value

Abstract Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.

scholarly journals Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

2019 ◽  
Author(s):  
Anish K. Simhal ◽  
Kimberly L.H. Carpenter ◽  
Saad Nadeem ◽  
Joanne Kurtzberg ◽  
Allen Song ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Ricci Curvature ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Global Changes ◽  
Before And After ◽  
The Brain

ABSTRACTRicci curvature is a method for measuring the robustness of networks. In this work, we use Ricci curvature to measure robustness of brain networks affected by autism spectrum disorder (ASD). Subjects with ASD are given a stem cell infusion and are imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantify both local and global changes in the brain networks correlated with the infusion. Our results find changes in regions associated with ASD that were not detected via traditional brain network analysis.

scholarly journals Music therapy as treatment of Autism Spectrum Disorder: Systematic Review

2021 ◽  
Author(s):  
Paloma Abrantes de Oliveira ◽  
Maria Eduarda Duarte de Oliveira ◽  
Petrina Rezende de Souza ◽  
Rafaela Duarte Silva ◽  
Ronald Godinho de Oliveira Silva ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Music Therapy ◽  
Randomized Clinical Trials ◽  
Brain Connectivity ◽  
Neurodevelopmental Disorder ◽  
Well Being ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Socially Responsive ◽  
The Brain

Introduction: Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication1 .Music is great for individuals with ASD, because it interacts with the brain, contributing with cognitive and psychosocial benefits2,3. Furthermore, music is emotional and promotes movement synchronization, being a non-pharmacological option for the treatment of ASD3 . Objective: To investigate the positive impacts of using music therapy for cognition in patients with ASD. Methodology: Controlled and randomized clinical trials, in English, performed on humans, in the last 5 years, indexed on PubMed, were selected from the descriptors “autism spectrum disorder” and “music therapy”. This review was registered on PROSPERO by protocol 254495 and the PRISMA recommendation was used to improve its organization. Results: Music therapy was efficient in improving the symptoms of children with ASD, because of the better sensitivity of individuals to music than words4 . In addition, music is able to restore brain connectivity, which is altered in TEA5 . Musical and emotional attunement allows synchronization, integrating the senses and affective regulation, improving self-awareness6. During therapy, there was a reduction in the diagnostic scale of ASD, indicating an improvement in communicative and social skills. Furthermore, it was noted that children became more socially responsive, but there were differences, suggesting the need to balance individual and methodological treatment7 . Finally, music therapy contributes to children’s well-being and health8,9. Conclusion: Evidence suggests that patients with ASD can benefit from music therapy, as it explores and expands the physical and mental limits of the autistic person, stimulating agility, communication and motor control.

scholarly journals The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

2019 ◽  
Author(s):  
Amirali Kazeminejad ◽  
Roberto C Sotero
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Networks ◽  
Model Performance ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Network Graph ◽  
Functional Connections ◽  
Functional Brain Networks

AbstractIn recent years, there has been a significant growth in the number of applications of machine learning (ML) techniques to the study and identification of neurological disorders. These methods rely heavily on what features are made available to the ML algorithm. Features such as graph theoretical metrics of resting-state fMRI-based brain networks have proven useful. However, the computation of functional brain networks relies on making an arbitrary choice about whether the obtained anti-correlations, representing the strengths of functional connections in the brain, should be discarded or not. In this study, we examine how this choice affects the performance of a support vector machine (SVM) model for classifying autism spectrum disorder. We extracted graph theoretical features using three different pipelines for constructing the functional network graph. These pipelines primarily used positive weights, negative weights (anti-correlations) and only the absolute value of weights of the correlation matrix derived from fMRI time-series. Our results suggest that in the presence of Global Signal Regression (GSR) the features extracted from anti-correlations play a major role in improving model performance. However, this does not undermine the importance of features from other pipelines.

Possibility that the Onset of Autism Spectrum Disorder is Induced by Failure of the Glutamine-Glutamate Cycle

2020 ◽  
Vol 14 (2) ◽  
pp. 170-174
Author(s):  
Koichi Kawada ◽  
Nobuyuki Kuramoto ◽  
Seisuke Mimori
Keyword(s):  
Autism Spectrum Disorder ◽  
Endoplasmic Reticulum ◽  
Environmental Factors ◽  
Endoplasmic Reticulum Stress ◽  
Synapse Formation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neurodevelopmental Disease ◽  
Number Of Patients ◽  
The Brain

: Autism spectrum disorder (ASD) is a neurodevelopmental disease, and the number of patients has increased rapidly in recent years. The causes of ASD involve both genetic and environmental factors, but the details of causation have not yet been fully elucidated. Many reports have investigated genetic factors related to synapse formation, and alcohol and tobacco have been reported as environmental factors. This review focuses on endoplasmic reticulum stress and amino acid cycle abnormalities (particularly glutamine and glutamate) induced by many environmental factors. In the ASD model, since endoplasmic reticulum stress is high in the brain from before birth, it is clear that endoplasmic reticulum stress is involved in the development of ASD. On the other hand, one report states that excessive excitation of neurons is caused by the onset of ASD. The glutamine-glutamate cycle is performed between neurons and glial cells and controls the concentration of glutamate and GABA in the brain. These neurotransmitters are also known to control synapse formation and are important in constructing neural circuits. Theanine is a derivative of glutamine and a natural component of green tea. Theanine inhibits glutamine uptake in the glutamine-glutamate cycle via slc38a1 without affecting glutamate; therefore, we believe that theanine may prevent the onset of ASD by changing the balance of glutamine and glutamate in the brain.

Are there anthropometric and body composition differences between children with autism spectrum disorder and children with typical development? Analysis by age and spectrum severity in a school population

Autism ◽  
2021 ◽  
pp. 136236132098772
Author(s):  
Patricia Esteban-Figuerola ◽  
Paula Morales-Hidalgo ◽  
Victoria Arija-Val ◽  
Josefa Canals-Sans
Keyword(s):  
Autism Spectrum Disorder ◽  
Cardiovascular Risk ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
School Age Children ◽  
School Age ◽  
Typical Development ◽  
Height Ratio ◽  
Waist Height Ratio

Overweight and obesity have been reported to be more prevalent in populations with autism spectrum disorder than in children with typical development. The aim of this study was to compare the anthropometric status of children with autism spectrum disorder (diagnosed and subclinical) and children with typical development and analyse which variables can affect the anthropometric and health status of children with autism spectrum disorder. We present a two-phase epidemiological study in a school population of two age groups which assesses autism spectrum disorder diagnosis, anthropometric data and bioelectrical impedance analysis. From an initial sample of 3,713 children, 79 with autism spectrum disorder, 42 with subclinical autism spectrum disorder and 350 with typical development participated in the study. Pre-schoolers with autism spectrum disorder were taller than pre-schoolers with typical development. School-age children with autism spectrum disorder showed a significantly higher body mass index and rate of overweight/obesity than children with typical development (63.4% vs 46.3%). No significant differences were found for bioelectrical impedance analysis, but school-age children with autism spectrum disorder showed a significantly higher waist circumference, waist/height ratio and cardiovascular risk than children with typical development. The quality of the diet was lower in children with autism spectrum disorder than in children with typical development. Multiple regression analyses showed that having autism spectrum disorder and internalizing psychological problems were associated with waist/height ratio and high cardiovascular risk in school-age children. Lay abstract This study makes a comparison between the growth status of pre-school and school-age children with autism spectrum disorder and typical development children. Pre-schoolers with autism spectrum disorder were taller than children with typical development. School-age children with autism spectrum disorder were more overweight/obese, had more body fat and a greater waist circumference and waist/height ratio than children with typical development. The presence of autism spectrum disorder and internalizing problems was associated with cardiovascular risk in school-age children.

scholarly journals Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Vânia Tavares ◽  
Luís Afonso Fernandes ◽  
Marília Antunes ◽  
Hugo Ferreira ◽  
Diana Prata
Keyword(s):  
Autism Spectrum Disorder ◽  
Sex Differences ◽  
Resting State ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Control Networks ◽  
Highly Sensitive ◽  
Female Specific ◽  
Main Effects

AbstractFunctional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.

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