Neural Correlates of Audiovisual Temporal Binding Window in Individuals With Schizotypal and Autistic Traits: Evidence From Resting‐State Functional Connectivity

2020 ◽  
Author(s):  
Han‐yu Zhou ◽  
Yong‐ming Wang ◽  
Rui‐ting Zhang ◽  
Eric F. C. Cheung ◽  
Christos Pantelis ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Autistic Traits ◽  
Neural Correlates ◽  
Resting State Functional Connectivity ◽  
Temporal Binding ◽  
Temporal Binding Window

scholarly journals Belief in Communism and Theory of Mind

2021 ◽  
Vol 12 ◽  
Author(s):  
Outong Chen ◽  
Fang Guan ◽  
Yu Du ◽  
Yijun Su ◽  
Hui Yang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Theory Of Mind ◽  
Resting State ◽  
Brain Activity ◽  
Neural Correlates ◽  
Resting State Functional Connectivity ◽  
Neural Basis ◽  
Spontaneous Brain Activity ◽  
Multiple Regions ◽  
Left Thalamus

A belief in communism refers to the unquestionable trust and belief in the justness of communism. Although former studies have discussed the political aim and social value of communism, the cognitive neural basis of a belief in communism remains largely unknown. In this study, we determined the behavioral and neural correlates between a belief in communism and a theory of mind (ToM). For study 1, questionnaire scores were measured and for study 2, regional homogeneity (ReHo) and resting-state functional connectivity (rsFC) were used as an index for resting-state functional MRI (rs-fMRI), as measured by the Belief in Communism Scale (BCS). The results showed that a belief in communism is associated with higher ReHo in the left thalamus and lower ReHo in the left medial frontal gyrus (MFG). Furthermore, the results of the rsFC analysis revealed that strength of functional connectivity between the left thalamus and the bilateral precuneus is negatively associated with a belief in communism. Hence, this study provides evidence that spontaneous brain activity in multiple regions, which is associated with ToM capacity, contributes to a belief in communism.

Neural correlates of future-oriented coping: Preliminary evidence from a resting-state functional connectivity study

PsyCh Journal ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 239-247 ◽  
Author(s):  
Rui-ting Zhang ◽  
Tian-xiao Yang ◽  
Yi Wang ◽  
Yu-xiu Sui ◽  
Jing-jing Yao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Neural Correlates ◽  
Preliminary Evidence ◽  
Resting State Functional Connectivity

scholarly journals Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism

2015 ◽  
Vol 112 (48) ◽  
pp. E6699-E6706 ◽  
Author(s):  
Mark Plitt ◽  
Kelly Anne Barnes ◽  
Gregory L. Wallace ◽  
Lauren Kenworthy ◽  
Alex Martin
Keyword(s):  
Functional Connectivity ◽  
Adaptive Behavior ◽  
Resting State ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Anterior Cingulate ◽  
Longitudinal Change ◽  
Adaptive Behaviors ◽  
Resting State Functional Connectivity

Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome—adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement.

scholarly journals More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

2020 ◽  
Author(s):  
Joe Bathelt ◽  
Hilde M. Geurts ◽  
Denny Borsboom
Keyword(s):  
Resting State ◽  
Data Exchange ◽  
Autistic Traits ◽  
Neural Correlates ◽  
Brain Regions ◽  
Diagnostic Interview ◽  
Resting State Functional Connectivity ◽  
Psychological Phenomena ◽  
Brain Level ◽  
Network Approaches

ABSTRACTNetwork approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

Relating Disinhibited Eating Behaviors to Resting State Functional Connectivity in Young Children

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1889-P
Author(s):  
ALLISON L.B. SHAPIRO ◽  
SUSAN L. JOHNSON ◽  
BRIANNE MOHL ◽  
GRETA WILKENING ◽  
KRISTINA T. LEGGET ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Young Children ◽  
Resting State ◽  
Eating Behaviors ◽  
Resting State Functional Connectivity ◽  
Disinhibited Eating
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