Temporal Graph Representation Learning for Autism spectrum disorder Brain Networks

2021 ◽  
Author(s):  
Peng Cao ◽  
Guangqi Wen ◽  
Lanting Li ◽  
Xiaoli Liu ◽  
Jinzhu Yang ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Networks ◽  
Representation Learning ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Graph Representation ◽  
Temporal Graph

scholarly journals Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder

2020 ◽  
Author(s):  
Shuxia Yao ◽  
Menghan Zhou ◽  
Yuan Zhang ◽  
Feng Zhou ◽  
Qianqian Zhang ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Visual Processing ◽  
Brain Networks ◽  
Cingulate Cortex ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Volume Changes ◽  
Processing Network

AbstractWhile a number of functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent, reflecting dysregulated developmental trajectories, although no studies have investigated whether homotopic interhemispheric rsFC alterations occur in ASD children. The present study conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 SD and 175 typically developing children under age 10 and examined associations with symptom severity in the Autism Brain Imaging Data Exchange datasets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes in ASD children. Results demonstrated decreased homotopic rsFC in ASD children in the medial prefrontal cortex, precuneus and posterior cingulate cortex of the default mode network (DMN), the dorsal anterior cingulate cortex of the salience network, the precentral gyrus and inferior parietal lobule of the mirror neuron system, the lingual, fusiform and inferior occipital gyri of the visual processing network and thalamus. Symptom severity was associated with homotopic rsFC in regions in the DMN and visual processing network. There were no significant CC volume changes in ASD children. The present study shows that reduced homotopic interhemispheric rsFC in brain networks in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker.

Lateralization of Brain Networks and Clinical Severity in Toddlers with Autism Spectrum Disorder: A HARDI Diffusion MRI Study

2015 ◽  
Vol 9 (3) ◽  
pp. 382-392 ◽  
Author(s):  
Eugenia Conti ◽  
Sara Calderoni ◽  
Anna Gaglianese ◽  
Kerstin Pannek ◽  
Sara Mazzotti ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Severity ◽  
Mri Study

scholarly journals Measuring Robustness of Brain Networks in Autism Spectrum Disorder with Ricci Curvature

2019 ◽  
Author(s):  
Anish K. Simhal ◽  
Kimberly L.H. Carpenter ◽  
Saad Nadeem ◽  
Joanne Kurtzberg ◽  
Allen Song ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Ricci Curvature ◽  
Diffusion Mri ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Global Changes ◽  
Before And After ◽  
The Brain

ABSTRACTRicci curvature is a method for measuring the robustness of networks. In this work, we use Ricci curvature to measure robustness of brain networks affected by autism spectrum disorder (ASD). Subjects with ASD are given a stem cell infusion and are imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantify both local and global changes in the brain networks correlated with the infusion. Our results find changes in regions associated with ASD that were not detected via traditional brain network analysis.

scholarly journals Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 51 ◽  
Author(s):  
Aitana Pascual-Belda ◽  
Antonio Díaz-Parra ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Management Functions ◽  
The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

scholarly journals Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128474-128486 ◽  
Author(s):  
Sakib Mostafa ◽  
Lingkai Tang ◽  
Fang-Xiang Wu
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Identification of Autism Spectrum Disorder With Functional Graph Discriminative Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Jingcong Li ◽  
Fei Wang ◽  
Jiahui Pan ◽  
Zhenfu Wen
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Graph Representation ◽  
Restricted Interests ◽  
Neuroscience Research ◽  
Functional Graph ◽  
Communication Impairments

Autism spectrum disorder (ASD) is a specific brain disease that causes communication impairments and restricted interests. Functional connectivity analysis methodology is widely used in neuroscience research and shows much potential in discriminating ASD patients from healthy controls. However, due to heterogeneity of ASD patients, the performance of conventional functional connectivity classification methods is relatively poor. Graph neural network is an effective graph representation method to model structured data like functional connectivity. In this paper, we proposed a functional graph discriminative network (FGDN) for ASD classification. On the basis of pre-built graph templates, the proposed FGDN is able to effectively distinguish ASD patient from health controls. Moreover, we studied the size of training set for effective training, inter-site predictions, and discriminative brain regions. Discriminative brain regions were determined by the proposed model to investigate its applicability and biomarkers for ASD identification. For functional connectivity classification and analysis, FGDN is not only an effective tool for ASD identification but also a potential technique in neuroscience research.

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