ArticleNeuroImage. Clinical2022
Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset.
Article in NeuroImage. Clinical, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 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
- Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.Patterns (New York, N.Y.) · 2026Article
- 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
- Spatial transcriptomics implicates the thalamus and cortex in autism and schizophrenia.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus.bioRxiv : the preprint server for biology · 2026Article
- Unraveling the Autism Spectrum Heterogeneity: Insights From ABIDE I Database Using Data/Model-Driven Permutation Testing Approaches.Brain and behavior · 2026Article
- Vision transformer and complex network analysis for autism spectrum disorder classification in T1 structural MRI.Japanese journal of radiology · 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
- An evaluation of image-based and statistical techniques for harmonizing brain volume measurements.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.Scientific data · 2024Article
- Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders.Brain informatics · 2024Article
- Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation.Frontiers in neuroinformatics · 2024Article
- Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing.Frontiers in neuroinformatics · 2024Article
- Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations.Journal of ophthalmology · 2024Article
- Effect of data harmonization of multicentric dataset in ASD/TD classification.Brain informatics · 2023Article
- A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework.Scientific reports · 2023Article
- Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.NeuroImage · 2023Review
Corrections and comments
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8 authors.
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Abstract
Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 ± 0.04) to a still low, but reasonably significant AUC = 0.67 ± 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 ± 0.02, AUC = 0.65 ± 0.03 and AUC = 0.69 ± 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups.
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