ArticleNeuroImage2018
Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- From heterogeneity to translation: data‑driven subtyping of autism in a multilevel framework.Translational psychiatry · 2026Review
- Transdiagnostic behavioral and sociodemographic influences on the cognitive-adaptive functioning gap in neurodivergent children.Scientific reports · 2026Article
- Development of a Metagenomics-Guided Personalized Synbiotic Protocol for Children with Autism Spectrum Disorder: An Exploratory Case Series.Nutrients · 2026Article
- Autism Sensory Profiles Predict Stimulus- Evoked Insula Connectivity.medRxiv : the preprint server for health sciences · 2026Article
- Advantages of combining multiple eye-tracking paradigms for distinguishing young autistic from non-autistic children.Molecular autism · 2026Article
- Article
- Brain functional network connectivity interpolation characterizes the neuropsychiatric continuum and heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Generating Biologically Relevant Subtypes of Autism Spectrum Disorder with differential responses to Acute Oxytocin Administration in a Randomized Trial using Random Forest Models and K-means Clustering.medRxiv : the preprint server for health sciences · 2026Article
- SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers.Frontiers in psychiatry · 2026Article
- Targeted stool metabolomics suggests exploratory catecholamine- and tryptophan-linked metabolic features in autism spectrum disorder.Frontiers in neuroscience · 2026Article
- Perinatal Western-style diet exposure associated with altered sensory functional connectivity in infant Japanese macaques.Physiological reports · 2025Article
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.Journal of imaging informatics in medicine · 2025Article
- Decomposition of phenotypic heterogeneity in autism reveals underlying genetic programs.Nature genetics · 2025Article
- Integrated bioinformatics and machine learning identify S100A9 and VGLL1 as hub genes for schizophrenia.Frontiers in psychiatry · 2025Article
- Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging.Cognitive, affective & behavioral neuroscience · 2024Article
- Using Machine Learning to Study Factors Affecting Discharge Destination in Recovery Units.Cureus · 2024Article
- Participation as a Predictor of Quality of Life among Japanese Children with Neurodevelopmental Disorders Analyzed Using a Machine Learning Algorithm.Children (Basel, Switzerland) · 2024Article
- Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.Journal of neural engineering · 2023Article
- Maternal Perinatal Stress Trajectories and Negative Affect and Amygdala Development in Offspring.The American journal of psychiatry · 2023Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.
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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.