scholarly journals A-24 Metacognition and behavioral regulation associated with distinct connectivity patterns in autism spectrum disorder

2019 ◽  
Vol 34 (6) ◽  
pp. 883-883
Author(s):  
H Bednarz ◽  
R Kana
Keyword(s):  
Autism Spectrum Disorder ◽  
Executive Function ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Parent Ratings ◽  
Behavioral Regulation ◽  
Imaging Data ◽  
Frontal Pole

Abstract Objective Executive function (EF) deficits are well documented in children with autism spectrum disorder (ASD)1-2 and are commonly measured clinically using parent ratings3. No studies have investigated whether parent ratings of EF predict brain connectivity in ASD. Aim Examine whether the Behavior Rating Inventory of Executive Function (BRIEF) predicts functional brain connectivity in ASD. Method Resting-state fMRI and behavioral data were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database6 (n = 106 ASD, ages 5-13). ROI-to-ROI (Region of Interest) connectivity was computed for 132 ROIs spanning the whole brain, defined using Conn Toolbox. Multiple regression analyses examined the effect of BRIEF metacognition on connectivity while controlling for BRIEF behavioral regulation, and vice versa. Age, sex, and full-scale IQ were included as covariates. FDR correction was used (p < 0.05). Results More severe deficits in metacognition were associated with stronger connectivity between the left hippocampus and several ROIs, including the cerebellum and planum temporale. More severe deficits in metacognition were associated with weaker right hippocampus – right frontal pole connectivity. More severe deficits in behavioral regulation were associated with stronger connectivity between subcortical regions (i.e., thalamus, putamen, and caudate) and regions involved in motor (superior frontal gyrus) and limbic systems (cingulate gyrus). More severe behavioral regulation deficits were associated with weaker cerebellar-cerebellar connectivity. Conclusions Findings suggest that parent-ratings of metacognitive abilities in children with ASD are associated with hippocampal connectivity, while behavioral regulation abilities are associated with thalamic and striatum connections. These results build upon previous studies of metacognition and behavioral regulation 5,6.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals He said, she said: Autism spectrum diagnosis and gender differentially affect relationships between executive functions and social communication

Autism ◽  
2019 ◽  
Vol 23 (7) ◽  
pp. 1793-1804 ◽  
Author(s):  
Brea Chouinard ◽  
Louise Gallagher ◽  
Clare Kelly
Keyword(s):  
Autism Spectrum Disorder ◽  
Working Memory ◽  
Executive Function ◽  
Executive Functions ◽  
Social Communication ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Male Youth

Autism spectrum disorder is characterized by difficulties with social communication, with a preponderance in males. Evidence supports a relationship between metacognitive executive functions (e.g. planning, working memory) and social communication in autism spectrum disorder, yet relationships with specific metacognitive executive functions and how gender alters the expression of these relationships require further study. We used multiple regression to examine relationships between informant-based measures of metacognitive executive function and social communication in intellectually able (IQ ⩾ 85) female ( n = 111; mean age = 10.2 ± 2.8; 31 autism spectrum disorder) and male youth ( n = 310; mean age = 10.5 ± 1.9; 146 autism spectrum disorder) with and without autism spectrum disorder from the Autism Brain Imaging Data Exchange-II database. Executive function–social communication relationships were different in females and males with autism spectrum disorder. Relationships between the entire metacognitive index and social communication were stronger in males with autism spectrum disorder than without; this pattern was also observed for metacognitive sub-indices ‘monitor’ and ‘working memory’. These patterns were not observed in females. Relationships between executive function and social communication appear different for female and male youth with an autism spectrum disorder diagnosis. To better understand the nature of metacognitive contributions to social communication in autism spectrum disorder, future work should investigate the co-development of monitoring, working memory and social communication, while taking gender into account.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

2018 ◽  
Author(s):  
Evelyn MR Lake ◽  
Emily S Finn ◽  
Stephanie M Noble ◽  
Tamara Vanderwal ◽  
Xilin Shen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Organization ◽  
Functional Brain ◽  
Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

scholarly journals Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6001
Author(s):  
Zarina Rakhimberdina ◽  
Xin Liu ◽  
Tsuyoshi Murata
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Brain Imaging ◽  
Network Architecture ◽  
Data Exchange ◽  
Imaging Techniques ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Model Ensemble

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

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.

scholarly journals Lagging skills contribute to challenging behaviors in children with autism spectrum disorder without intellectual disability

Autism ◽  
2017 ◽  
Vol 22 (8) ◽  
pp. 898-906 ◽  
Author(s):  
Brenna B Maddox ◽  
Patrick Cleary ◽  
Emily S Kuschner ◽  
Judith S Miller ◽  
Anna Chelsea Armour ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Emotion Regulation ◽  
Executive Function ◽  
Intellectual Disability ◽  
Social Skills ◽  
Challenging Behaviors ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Environmental Demands

Many children with autism spectrum disorder display challenging behaviors. These behaviors are not limited to those with cognitive and/or language impairments. The Collaborative and Proactive Solutions framework proposes that challenging behaviors result from an incompatibility between environmental demands and a child’s “lagging skills.” The primary Collaborative and Proactive Solutions lagging skills—executive function, emotion regulation, language, and social skills—are often areas of weakness for individuals with autism spectrum disorder. The purpose of this study was to evaluate whether these lagging skills are associated with challenging behaviors in youth with autism spectrum disorder without intellectual disability. Parents of 182 youth with autism spectrum disorder (6–15 years) completed measures of their children’s challenging behaviors, executive function, language, emotion regulation, and social skills. We tested whether the Collaborative and Proactive Solutions lagging skills predicted challenging behaviors using multiple linear regression. The Collaborative and Proactive Solutions lagging skills explained significant variance in participants’ challenging behaviors. The Depression (emotion regulation), Inhibit (executive function), and Sameness (executive function) scales emerged as significant predictors. Impairments in emotion regulation and executive function may contribute substantially to aggressive and oppositional behaviors in school-age youth with autism spectrum disorder without intellectual disability. Treatment for challenging behaviors in this group may consider targeting the incompatibility between environmental demands and a child’s lagging skills.

scholarly journals Infant quantitative risk for autism spectrum disorder predicts executive function in early childhood

2018 ◽  
Vol 11 (11) ◽  
pp. 1532-1541 ◽  
Author(s):  
Rebecca L. Stephens ◽  
Linda R. Watson ◽  
Elizabeth R. Crais ◽  
J. Steven Reznick
Keyword(s):  
Autism Spectrum Disorder ◽  
Early Childhood ◽  
Executive Function ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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