Altered Functional Connectivity in Children With Low-Function Autism Spectrum Disorders

Xu, Shou jun; Li, Meng; Yang, Chun; Fang, Xiang ling; Ye, Miao ting; Wei, Lei; Liu, Jian; Li, Bao juan; Gan, Yun gen; Yang, Binrang; Wen, Huang; Meng, Xian lei; Wu, Yi‐Long; Jiang, Gui hua

doi:10.3389/fnins.2019.00806

Cited by 41 publications

(27 citation statements)

References 46 publications

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“…Recent studies of structural and functional brain networks in patients with ASD suggest that cognitive deficits may be attributed to abnormal connections between different brain regions (such as the superior occipital gyrus) [45][46][47][48]. Consistent with the results of our study, the superior occipital gyrus of participants with autism showed significant abnormalities as the task complexity increased when participating in the test task compared with the normal control group [45].…”

Section: Superior Occipital Gyrussupporting

confidence: 90%

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Shi

Zhang

2020

Symmetry

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder originating in infancy and childhood that may cause language barriers and social difficulties. However, in the diagnosis of ASD, the current machine learning methods still face many challenges in determining the location of biomarkers. Here, we proposed a novel feature selection method based on the minimum spanning tree (MST) to seek neuromarkers for ASD. First, we constructed an undirected graph with nodes of candidate features. At the same time, a weight calculation method considering both feature redundancy and discriminant ability was introduced. Second, we utilized the Prim algorithm to construct the MST from the initial graph structure. Third, the sum of the edge weights of all connected nodes was sorted for each node in the MST. Then, N features corresponding to the nodes with the first N smallest sum were selected as classification features. Finally, the support vector machine (SVM) algorithm was used to evaluate the discriminant performance of the aforementioned feature selection method. Comparative experiments results show that our proposed method has improved the ASD classification performance, i.e., the accuracy, sensitivity, and specificity were 86.7%, 87.5%, and 85.7%, respectively.

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Section: Superior Occipital Gyrussupporting

confidence: 90%

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Shi

Zhang

2020

Symmetry

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“…In depressed patients, reduced activation in the right middle frontal gyrus and increased activation in the amygdala and hippocampus were associated with memory suppression involving negatively valenced stimuli 41 . Reduced activation in the inferior frontal gyrus and supramarginal gyri were related to though suppression 42 .…”

Section: Neuroimaging Findings On Impulsivity and Thought Suppressionmentioning

confidence: 94%

Impulsivity and Thought Suppression in Behavioral Addiction: Associated Neural Connectivity and Neural Networks

Wan¹,

Zha²,

Ren³

et al. 2020

Preprint

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Impulsivity and thought suppression are two psychological traits that have great variation in healthy population. In extreme cases, both are closely related to mental illness and play an important role in behavioral addiction. We have known the role of the top-down mechanism in impulsivity and thought suppression, but we do not know how the related neural nuclei are functionally connected and interact with each other. In the study, we selected excessive internet users (EIU) as our target population and investigated the relationship between thought suppression and impulsivity in the following aspects: their correlations to psychological symptoms; the associated neural networks; and the associated brain morphometric changes. We acquired data from 131 excessive internet users, with their psychological, resting-state fMRI and T1-MRI data collected. With the whole brain analysis, graph theory analysis, replication with additional brain atlas, replication with additional MRI data, and analysis of brain structure, we found that: (i) implusivity and thought suppression shared common neural connections in the top-down mechanism; (ii) thought suppression was associated with the neural network that connected to the occipital lobe in the resting-state brain but not the morphometric change of the occipital lobe. The study confirmed the overlap between impulsivity and thought suppression in terms of neural connectivity and suggested the role of thought suppression and the occipital network in behavioral addiction. Studying thought suppression provided a new insight into behavioral addiction research. The neural network study helped further understanding of behavioral addiction in terms of information interaction in the brain.

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“…To exemplify this idea, we can consider research in neurodevelopmental disorders, which has often focused on characterizing trait-dependent sources of variance. Research on autism spectrum disorder (ASD), which is characterized by impairments in social communication and restrictive, repetitive behaviors, points to distinct FC patterns that distinguish ASD from typically developing controls across rest and task ( Easson et al, 2019 ; Hull et al, 2017 ; Long et al, 2016 ; Xu et al, 2019 ). Less frequent, however, are studies that characterize both task-specific (state) and disorder-specific (trait) effects within the same sample.…”

Section: Concurrent Changes Of State and Trait Neurodevelopmental Varmentioning

confidence: 99%

Predictive modeling of neurobehavioral state and trait variation across development

Sanchez-Alonso

Aslin

2020

Developmental Cognitive Neuroscience

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A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

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Altered Functional Connectivity in Children With Low-Function Autism Spectrum Disorders

Cited by 41 publications

References 46 publications

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

Impulsivity and Thought Suppression in Behavioral Addiction: Associated Neural Connectivity and Neural Networks

Predictive modeling of neurobehavioral state and trait variation across development

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