Dataset Information

Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

ABSTRACT: Introduction:Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population. The aim of this study is to test whether high-functioning adolescents with ASD (HFA) have an abnormal resting-state functional connectivity. Materials and Methods:We performed spatial and temporal analyses on resting-state networks (RSNs) in 13 HFA adolescents and 13 IQ- and age-matched controls. For the spatial analysis, we used probabilistic independent component analysis (ICA) and a permutation statistical method to reveal the RSN differences between the groups. For the temporal analysis, we applied Granger causality to find differences in temporal neurodynamics. Results:Controls and HFA display very similar patterns and strengths of resting-state connectivity. We do not find any significant differences between HFA adolescents and controls in the spatial resting-state connectivity. However, in the temporal dynamics of this connectivity, we did find differences in the causal effect properties of RSNs originating in temporal and prefrontal cortices. Conclusion:The results show a difference between HFA and controls in the temporal neurodynamics from the ventral attention network to the salience-executive network: a pathway involving cognitive, executive, and emotion-related cortices. We hypothesized that this weaker dynamic pathway is due to a subtle trigger challenging the cognitive state prior to the resting state.

SUBMITTER: Bernas A

PROVIDER: S-EPMC5822569 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

Bernas Antoine A Barendse Evelien M EM Aldenkamp Albert P AP Backes Walter H WH Hofman Paul A M PAM Hendriks Marc P H MPH Kessels Roy P C RPC Willems Frans M J FMJ de With Peter H N PHN Zinger Svitlana S Jansen Jacobus F A JFA

Brain and behavior 20180117 2

<h4>Introduction</h4>Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global defa ...[more]

PMID: 29484255

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Project description:IMPORTANCE:Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. OBJECTIVES:To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. DESIGN, SETTING, AND PARTICIPANTS:We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. MAIN OUTCOMES AND MEASURES:We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. RESULTS:Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t???3.13, P?<?.001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r???0.21, P?<?.0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r???-0.09, P?<?.012 corrected), although this association was significant only in typically developing participants (all r???-0.13, P?<?.009 corrected). CONCLUSIONS AND RELEVANCE:Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Project description:Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.

Dataset Information

Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

Publications

Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

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