Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder.

Mol Autism

Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering, Aalto University, Rakentajanaukio 2 C, FI-02150 Espoo, Finland ; Advanced Magnetic Imaging Centre, Aalto University, Otakaari 5, FI-02150 Espoo, Finland.

Published: December 2015

Background: Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain's wiring diagram, i.e., the connectome.

Methods: We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60-90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.

Results: In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.

Conclusions: Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681075PMC
http://dx.doi.org/10.1186/s13229-015-0058-4DOI Listing

Publication Analysis

Top Keywords

weighted networks
12
autism spectrum
8
spectrum disorder
8
graph theoretical
8
unweighted weighted
8
structural brain
8
brain networks
8
subjects asd
8
decreased unweighted
8
unweighted networks
8

Similar Publications

Background: Ankylosing spondylitis (AS) is a chronic autoimmune disease characterized by inflammation of the sacroiliac joints and spine. Cuproptosis is a newly recognized copper-induced cell death mechanism. Our study explored the novel role of cuproptosis-related genes (CRGs) in AS, focusing on immune cell infiltration and molecular clustering.

View Article and Find Full Text PDF

Background: Chronic kidney disease (CKD) is a progressive condition that arises from diverse etiological factors, resulting in structural alterations and functional impairment of the kidneys. We aimed to establish the Anoikis-related gene signature in CKD by bioinformatics analysis.

Methods: We retrieved 3 datasets from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) of them, which were intersected with Anoikis-related genes (ARGs) to derive Anoikis-related differentially expressed genes (ARDEGs).

View Article and Find Full Text PDF

Lightweight Retinal Layer Segmentation With Global Reasoning.

IEEE Trans Instrum Meas

May 2024

School of Mechanical Engineering, Shandong University, Jinan 250061, Shandong, China.

Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be light-weight to be deployed for practical clinical applications.

View Article and Find Full Text PDF

In a Canadian cohort with HIV, 61% gained weight, 26% lost weight, and 12% remained stable in the first year of antiretroviral therapy. Weight gain was not associated with regimen type and slowed in years 2 to 3, with 44%, 34%, and 23% experiencing increasing, decreasing, and stable trajectories. Although 23% had significant weight gain year 1, many subsequently lost weight despite continuing antiretroviral therapy.

View Article and Find Full Text PDF

Introduction: Artificial intelligence and neuroimaging enable accurate dementia prediction, but 'black box' models can be difficult to trust. Explainable artificial intelligence (XAI) describes techniques to understand model behaviour and the influence of features, however deciding which method is most appropriate is non-trivial. Vision transformers (ViT) have also gained popularity, providing a self-explainable, alternative to traditional convolutional neural networks (CNN).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!