ArticleIEEE transactions on bio-medical engineering2021
Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Construction of a small-sample brain imaging data augmentation and explainable diagnostic model for autism based on generative adversarial networks.BMC medical imaging · 2026Article
- Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors.Autism research : official journal of the International Society for Autism Research · 2026Article
- Learning brain dynamics across distinct scaling regimes reveals psychiatric signatures.Communications biology · 2026Article
- Bridging Brain Science and Technology: How AI Is Shaping the Future of Neuroimaging in Autism.Diagnostics (Basel, Switzerland) · 2026Article
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Structural brain alterations associated with brain age may link to social dysfunction in male adults with autism spectrum disorder.Frontiers in neuroscience · 2026Article
- Functional network organization is locally atypical in children, adolescents, and young adults with congenital heart disease.NeuroImage. Clinical · 2026Article
- Four-Source Voxel-Level 3D-CNN With Hierarchical Feature Fusion of rs-fMRI and sMRI Derivatives for ADHD Classification.IEEE open journal of engineering in medicine and biology · 2026Article
- Vision transformer and complex network analysis for autism spectrum disorder classification in T1 structural MRI.Japanese journal of radiology · 2025Article
- MBrain sciences · 2025Article
- Hidden Rhythms of a Developing Brain: Multimetric rs-fMRI Insights Into Typical Youth Maturation.Human brain mapping · 2025Article
- Temporal and spatial variability of large-scale dynamic brain networks in ASD.European child & adolescent psychiatry · 2025Article
- Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.Medical image analysis · 2025Article
- Language networks in Autism Spectrum Disorder: A systematic review of connectivity-based fMRI studies.Review journal of autism and developmental disorders · 2025Article
- A lightweight, end-to-end explainable, and generalized attention-based graph neural network model trained on high-order spatiotemporal organization of dynamic functional connectivity to classify autistics from typically developing.Network neuroscience (Cambridge, Mass.) · 2025Article
- Multi-view united transformer block of graph attention network based autism spectrum disorder recognition.Frontiers in psychiatry · 2025Article
- Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study.Journal of child psychology and psychiatry, and allied disciplines · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized.
methodsWe address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data.
resultsOur results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism.
conclusionMulti-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE: ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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