ArticleFrontiers in neuroscience2020
Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.Human brain mapping · 2023Pooled it
- Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.Brain sciences · 2026Article
- Prob-sparse self-attention extraction of time-aligned dynamic functional connectivity for ASD diagnosis.Heliyon · 2025Article
- High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.GigaScience · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- Altered functional network topology and connectivity in female nurses with shift work sleep disorder.Frontiers in systems neuroscience · 2025Article
- Abnormal intrinsic functional hubs and connectivity in nurses with occupational burnout: a resting-state fMRI study.Frontiers in public health · 2025Article
- Diagnosis of Autism Spectrum Disorder (ASD) by Dynamic Functional Connectivity Using GNN-LSTM.Sensors (Basel, Switzerland) · 2024Article
- Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.Scientific reports · 2024Article
- Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm.Neuroinformatics · 2024Article
- Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.Brain sciences · 2024Article
- The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.Interdisciplinary sciences, computational life sciences · 2024Article
- Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder.Frontiers in human neuroscience · 2023Article
- Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images.Autism research and treatment · 2023Article
- Functional Connectome-Based Predictive Modeling in Autism.Biological psychiatry · 2022Review
- Aberrant dynamic functional connectivity features within default mode network in patients with autism spectrum disorder: evidence from dynamical conditional correlation.Cognitive neurodynamics · 2022Article
- Brain Network Alterations in Rectal Cancer Survivors With Depression Tendency: Evaluation With Multimodal Magnetic Resonance Imaging.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of "correlation's correlation" to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.