scholarly journals Adults with autism spectrum disorder show atypical patterns of thoughts and feelings during rest

Autism ◽  
2021 ◽  
pp. 136236132199092
Author(s):  
Sonja Simpraga ◽  
Ricarda F Weiland ◽  
Huibert D Mansvelder ◽  
Tinca JC Polderman ◽  
Sander Begeer ◽  
...  

Mind wandering constitutes a major part of everyday experience and is inherently related to how we feel and identify ourselves. Thus, probing the character and content of thoughts and feelings experienced during mind-wandering episodes could lead to a better understanding of the human mind in health and disease. How mind wandering and spontaneous thought processes are affected in disorders such as autism is poorly understood. Here, we used the eyes-closed rest condition to stimulate mind wandering and quantified the subjective experiences using the Amsterdam Resting-State Questionnaire—which quantifies subjective psychological states of resting-state cognition across 10 domains—in 88 adults with autism spectrum disorder and 90 controls. We observed an atypical pattern of both thoughts and feelings in the autism spectrum disorder cohort, specifically in the domains of Theory of Mind, Comfort, and Discontinuity of Mind. We propose that the use of the Amsterdam Resting-State Questionnaire as a standardized cognitive instrument could advance our understanding of thoughts and feelings in autism spectrum disorder as well as in a wide variety of other brain disorders and how these may change due to therapeutic interventions. Lay abstract Everyone knows the feeling of letting one’s mind wander freely in a quiet moment. The thoughts and feelings experienced in those moments have been shown to influence our well-being—and vice versa. In this study, we looked at which thoughts and feelings are being experienced by adults with autism spectrum disorder and compared them to adults without autism spectrum disorder. In total, 88 adults with autism spectrum disorder and 90 adults without autism spectrum disorder were asked to rest for 5 min with their eyes closed and let their mind wander. Directly after, they filled in the Amsterdam Resting-State Questionnaire, which probes what participants were feeling and thinking during the period of rest. We found that adults with autism spectrum disorder tend to think less about others, felt less comfortable, and had more disrupted thoughts during the rest compared to adults without autism spectrum disorder. Interestingly, autism spectrum disorder participants reporting lower levels of comfort during the rest also reported more autism spectrum disorder symptoms, specifically in social behaviors and skills, attention switching, and imagination. We propose to use the eyes-closed rest condition in combination with the Amsterdam Resting-State Questionnaire more widely to shed light on aberrant thoughts and feelings in brain disorders and to study the effect of therapeutic interventions.

2017 ◽  
Vol 47 (6) ◽  
pp. 729-735 ◽  
Author(s):  
Giusy Olivito ◽  
Michela Lupo ◽  
Fiorenzo Laghi ◽  
Silvia Clausi ◽  
Roberto Baiocco ◽  
...  

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba

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.


Sign in / Sign up

Export Citation Format

Share Document