Aberrant Intrinsic Brain Activity in Patients with Autism Spectrum Disorder: Insights from EEG Microstates

2018 ◽  
Vol 32 (2) ◽  
pp. 295-303 ◽  
Author(s):  
Huibin Jia ◽  
Dongchuan Yu
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.


Autism ◽  
2020 ◽  
Vol 24 (4) ◽  
pp. 941-953 ◽  
Author(s):  
Carla A Mazefsky ◽  
Amanda Collier ◽  
Josh Golt ◽  
Greg J Siegle

Emotion dysregulation is common in autism spectrum disorder; a better understanding of the underlying neural mechanisms could inform treatment development. The tendency toward repetitive cognition in autism spectrum disorder may also increase susceptibility to perseverate on distressing stimuli, which may then increase emotion dysregulation. Therefore, this study investigated the mechanisms of sustained processing of negative information in brain activity using functional magnetic resonance imaging. We used an event-related task that alternated between emotional processing of personally relevant negative words, neutral words, and a non-emotional task. A priori criteria were developed to define heightened and sustained emotional processing, and feature conjunction analysis was conducted to identify all regions satisfying these criteria. Participants included 25 adolescents with autism spectrum disorder and 23 IQ-, age-, and gender-matched typically developing controls. Regions satisfying all a priori criteria included areas in the salience network and the prefrontal dorsolateral cortex, which are areas implicated in emotion regulation outside of autism spectrum disorder. Collectively, activity in the identified regions accounted for a significant amount of variance in emotion dysregulation in the autism spectrum disorder group. Overall, these results may provide a potential neural mechanism to explain emotion dysregulation in autism spectrum disorder, which is a significant risk factor for poor mental health. Lay abstract Many individuals with autism spectrum disorder struggle with emotions that are intense and interfering, which is referred to as emotion dysregulation. Prior research has established that individuals with autism may be more likely than individuals who are not autistic to have repetitive thoughts. It is possible that persistent thoughts about negative or distressing stimuli may contribute to emotion dysregulation in autism spectrum disorder. This study aimed to identify areas of the brain with evidence of persistent processing of negative information via functional magnetic resonance neuroimaging. We used a task that alternated between emotional processing of personally relevant negative words, neutral words, and a non-emotional task. Criteria were developed to define heightened and persistent emotional processing, and analyses were conducted to identify all brain regions satisfying these criteria. Participants included 25 adolescents with autism spectrum disorder and 23 typically developing adolescents who were similar to the autism spectrum disorder group in IQ, age, and gender ratios. Brain regions identified as having greater and continued processing following negative stimuli in the autism spectrum disorder group as compared with the typically developing group included the salience network and the prefrontal dorsolateral cortex. These areas have been previously implicated in emotion dysregulation outside of autism spectrum disorder. Collectively, brain activity in the identified regions was associated with parent-reported emotion dysregulation in the autism spectrum disorder group. These results help to identify a potential process in the brain associated with emotion dysregulation in autism spectrum disorder. This information may be useful for the development of treatments to decrease emotion dysregulation in autism spectrum disorder.


2018 ◽  
Author(s):  
Štefan Holiga ◽  
Joerg F. Hipp ◽  
Christopher H. Chatham ◽  
Pilar Garces ◽  
Will Spooren ◽  
...  

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.


2020 ◽  
Vol 9 (3) ◽  
pp. 175-185
Author(s):  
Elisabeth V. C. Friedrich

Abstract. The goal of this article was to highlight important issues that have to be considered when designing an electroencephalography (EEG)-based neurofeedback training for children with Autism Spectrum Disorder and to provide practical advice for a successful implementation. Autism is a heterogeneous and complex disorder, which leads to a broad and varied profile of symptoms as well as to huge individual differences between the affected children. This is why the neurofeedback training protocol has to be individually designed based on the specific symptoms as well as in consideration of the existing theories about aberrant brain activity, and why it then needs to be evaluated empirically. Furthermore, neurofeedback training has to be optimized individually regarding the specific control signal, feedback and practical implementations in order to lead to the desired improvements.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiaonan Guo ◽  
Heng Chen ◽  
Zhiliang Long ◽  
Xujun Duan ◽  
Youxue Zhang ◽  
...  

2017 ◽  
Vol 11 (2) ◽  
pp. 342-354 ◽  
Author(s):  
Tamara E. Rosen ◽  
Matthew D. Lerner

2015 ◽  
Vol 56 (3) ◽  
pp. 705 ◽  
Author(s):  
Uk-Su Choi ◽  
Sun-Young Kim ◽  
Hyeon Jeong Sim ◽  
Seo-Young Lee ◽  
Sung-Yeon Park ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document