Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study

Qin, Bin; Wang, Longlun; Zhang, Yun; Cai, Jinhua; Chen, Jie; Li, Tingyu

doi:10.3389/fpsyt.2018.00278

Cited by 20 publications

(19 citation statements)

References 47 publications

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“…Using six graph theoretical measures (i.e., CC, cPL, normalized CC, normalized cPL, SW, and modularity), the authors reported that the ASD group showed significantly lower modularity, but no significant differences were found in the other five measures. In a study conducted later by Qin et al ( 21 ), children with ASD (2.89 ± 0.97 years old) and TD children (3.15 ± 1.12 years old) were recruited. Among seven measures (i.e., CC, cPL, normalized CC, normalized cPL, SW, global and local efficiency), their DTI-derived structural network showed significantly lower cPL and higher global/local efficiency in children with ASD.…”

Section: Introductionmentioning

confidence: 99%

Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach

et al. 2021

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Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60–89 months old) and for 25 typically developing (TD) control children (10 girls, 60–91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan–Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD.

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Section: Introductionmentioning

confidence: 99%

Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach

et al. 2021

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“…Brain tractography has been widely applied in developmental age, both in neurodevelopmental disorders, as ADHD [33] and autism spectrum disorders [34][35][36], and in neurological disorders, as leukodystrophies, cerebral palsy or cerebellar diseases [37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Neural substrates of neuropsychological profiles in dystrophynopathies: A pilot study of diffusion tractography imaging

et al. 2021

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Introduction Cognitive difficulties and neuropsychological alterations in Duchenne and Becker muscular dystrophy (DMD, BMD) boys are not yet sufficiently explored, although this topic could have a relevant impact, finding novel biomarkers of disease both at genetics and neuroimaging point of view. The current study aims to: 1) analyze the neuropsychological profile of a group of DMD and BMD boys without cognitive impairment with an assessment of their executive functions; 2) explore the structural connectivity in DMD, BMD, and age-matched controls focusing on cortico-subcortical tracts that connect frontal cortex, basal ganglia, and cerebellum via the thalamus; 3) explore possible correlations between altered structural connectivity and clinical neuropsychological measures. Materials and methods This pilot study included 15 boys (5 DMD subjects, 5 BMD subjects, and 5 age-matched typically developing, TD). They were assessed using a neuropsychological assessment protocol including cognitive and executive functioning assessment and performed a 1.5T MRI brain exam including advance Diffusion Weighted Imaging (DWI) method for tractography. Structural connectivity measurements were extracted along three specific tracts: Cortico-Ponto-Cerebellar Tract (CPCT), Cerebellar-Thalamic Tract (CTT), and Superior Longitudinal Fasciculus (SLF). Cortical-Spinal Tract (CST) was selected for reference, as control tract. Results Regarding intellectual functioning, a major impairment in executive functions compared to the general intellectual functioning was observed both for DMD (mean score = 86.20; SD = 11.54) and for BMD children (mean score = 88; SD = 3.67). Mean FA resulted tendentially always lower in DMD compared to both BMD and TD groups for all the examined tracts. The differences in FA were statistically significant for the right CTT (DMD vs BMD, p = 0.002, and DMD vs TD, p = 0.0015) and the right CPCT (DMD vs TD, p = 0.008). Concerning DMD, significant correlations emerged between FA-R-CTT and intellectual quotients (FIQ, p = 0.044; ρs = 0.821), and executive functions (Denomination Total, p = 0.044, ρs = 0.821; Inhibition Total, p = 0.019, ρs = 0.900). BMD showed a significant correlation between FA-R-CPCT and working memory index (p = 0.007; ρs = 0.949). Discussion and conclusion In this pilot study, despite the limitation of sample size, the findings support the hypothesis of the involvement of a cerebellar-thalamo-cortical loop for the neuropsychological profile of DMD, as the CTT and the CPCT are involved in the network and the related brain structures are known to be implied in executive functions. Our results suggest that altered WM connectivity and reduced fibre organization in cerebellar tracts, probably due to the lack of dystrophin in the brain, may render less efficient some neuropsychological functions in children affected by dystrophinopathies. The wider multicentric study could help to better establish the role of cerebellar connectivity in neuropsychological profile for dystrophinopathies, identifying possible novel diagnostic and prognostic biomarkers.

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“…Graph theory can be used to measure the brain network segregation (clustering coefficient and transitivity), integration (characteristic path length and efficiency), and centrality (betweenness centrality, eigenvector centrality, participation coefficient and within module z-score). Recent brain imaging studies have found topological differences between ASD and normal brains which can be quantified using graph theory, such as global alterations of characteristic path length and efficiency in ASD (Rudie et al, 2013; Itahashi et al, 2014; Zeng et al, 2017; Qin et al, 2018) as well as alterations to segregation measures (Barttfeld et al, 2011; Rudie et al, 2013; Leung et al, 2014; Keown et al, 2017; Zeng et al, 2017) and centrality measures(Di Martino et al, 2013; Leung et al, 2014; Balardin et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification

Kazeminejad

Sotero

2019

Front. Neurosci.

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Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) has challenged researchers for many years. With enough data and computational power, machine learning (ML) algorithms can be used to interpret the data and extract the best biomarkers from thousands of candidates. In this study, we used the fMRI data of 816 individuals enrolled in the Autism Brain Imaging Data Exchange (ABIDE) to introduce a new biomarker extraction pipeline for ASD that relies on the use of graph theoretical metrics of fMRI-based functional connectivity to inform a support vector machine (SVM). Furthermore, we split the dataset into 5 age groups to account for the effect of aging on functional connectivity. Our methodology achieved better results than most state-of-the-art investigations on this dataset with the best model for the >30 years age group achieving an accuracy, sensitivity, and specificity of 95, 97, and 95%, respectively. Our results suggest that measures of centrality provide the highest contribution to the classification power of the models.

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Enhanced Topological Network Efficiency in Preschool Autism Spectrum Disorder: A Diffusion Tensor Imaging Study

Cited by 20 publications

References 47 publications

Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach

Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach

Neural substrates of neuropsychological profiles in dystrophynopathies: A pilot study of diffusion tractography imaging

Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification

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