ArticleFrontiers in psychiatry2019
Evaluation of Altered Functional Connections in Male Children With Autism Spectrum Disorders on Multiple-Site Data Optimized With Machine Learning.
Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.Neuroradiology · 2021Pooled it
- Patterns of Behavioral and Emotional Problems in Young Children With Developmental Disabilities: Cluster Analysis and Longitudinal Follow-Up.Journal of autism and developmental disorders · 2026Article
- Temporal and spatial variability of large-scale dynamic brain networks in ASD.European child & adolescent psychiatry · 2025Article
- Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.Molecular psychiatry · 2025Article
- Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.Human brain mapping · 2024Article
- The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.Interdisciplinary sciences, computational life sciences · 2024Article
- Effect of data harmonization of multicentric dataset in ASD/TD classification.Brain informatics · 2023Article
- Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey.Biomedicines · 2023Article
- Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study.Bioengineering (Basel, Switzerland) · 2023Article
- Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder.Frontiers in neuroinformatics · 2023Article
- Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images.Autism research and treatment · 2023Article
- Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.PLoS biology · 2022Article
- Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.Scientific reports · 2022Article
- mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity.Nature communications · 2021Article
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.Brain and behavior · 2021Article
- Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation.Frontiers in neuroscience · 2021Article
- Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning.Frontiers in human neuroscience · 2021Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
No univocal and reliable brain-based biomarkers have been detected to date in Autism Spectrum Disorders (ASD). Neuroimaging studies have consistently revealed alterations in brain structure and function of individuals with ASD; however, it remains difficult to ascertain the extent and localization of affected brain networks. In this context, the application of Machine Learning (ML) classification methods to neuroimaging data has the potential to contribute to a better distinction between subjects with ASD and typical development controls (TD). This study is focused on the analysis of resting-state fMRI data of individuals with ASD and matched TD, available within the ABIDE collection. To reduce the multiple sources of heterogeneity that impact on understanding the neural underpinnings of autistic condition, we selected a subgroup of 190 subjects (102 with ASD and 88 TD) according to the following criteria: male children (age range: 6.5-13 years); rs-fMRI data acquired with open eyes; data from the University sites that provided the largest number of scans (KKI, NYU, UCLA, UM). Connectivity values were evaluated as the linear correlation between pairs of time series of brain areas; then, a Linear kernel Support Vector Machine (L-SVM) classification, with an inter-site cross-validation scheme, was carried out. A permutation test was conducted to identify over-connectivity and under-connectivity alterations in the ASD group. The mean L-SVM classification performance, in terms of the area under the ROC curve (AUC), was 0.75 ± 0.05. The highest performance was obtained using data from KKI, NYU and UCLA sites in training and data from UM as testing set (AUC = 0.83). Specifically, stronger functional connectivity (FC) in ASD with respect to TD involve (p < 0.001) the angular gyrus with the precuneus in the right (R) hemisphere, and the R frontal operculum cortex with the pars opercularis of the left (L) inferior frontal gyrus. Weaker connections in ASD group with respect to TD are the intra-hemispheric R temporal fusiform cortex with the R hippocampus, and the L supramarginal gyrus with L planum polare. The results indicate that both under- and over-FC occurred in a selected cohort of ASD children relative to TD controls, and that these functional alterations are spread in different brain networks.
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