ReviewTranslational psychiatry2019
Neurodevelopmental heterogeneity and computational approaches for understanding autism.
Review in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review.Translational psychiatry · 2021Pooled it
- Computerized Adaptive Tests for Rapid and Accurate Assessment of Autism.JAMA network open · 2026Article
- Machine learning-based prediction of intellectual disability in children with autism spectrum disorder: using behavioral observation techniques.BMC psychiatry · 2026Article
- E/I imbalance and internal noise cause weak neural representations and face recognition challenges in ASD.Communications biology · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Population-level transitions in observed difficulties through childhood and adolescence.Developmental psychology · 2025Article
- A transdiagnostic study of theory of mind in children and youth with neurodevelopmental conditions.Molecular autism · 2025Article
- Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.Psychiatry and clinical neurosciences · 2025Article
- Computational Neuroscience's Influence on Autism Neuro-Transmission Research: Mapping Serotonin, Dopamine, GABA, and Glutamate.Biomedicines · 2025Review
- High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals.GigaScience · 2025Article
- A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue.Personality neuroscience · 2025Article
- University students' attitudes toward individuals with Autism Spectrum Disorder in Egypt.International journal of developmental disabilities · 2025Article
- Assessing executive functions and metacognition: translational potential of the Metacognitive Wisconsin Card Sorting Test for developmental neuropsychology.Frontiers in behavioral neuroscience · 2025Review
- Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Dysregulated acetylcholine-mediated dopamine neurotransmission in the eIF4E Tg mouse model of autism spectrum disorders.Cell reports · 2024Article
- Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging.Brain sciences · 2024Review
- Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks? Incorporating a new neurodevelopmental spectrum.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024Article
- Individual differences in autism-like traits are associated with reduced goal emulation in a computational model of observational learning.Nature. Mental health · 2024Article
- Single cell spatial biology over developmental time can decipher pediatric brain pathologies.Neurobiology of disease · 2024Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to streamline ASD's diagnostic methods, to discern similarities and differences from comorbid diagnoses, and to follow developmentally variable outcomes. Both supervised machine learning models for classification outcome and unsupervised approaches to identify new dimensions and subgroups are discussed. We provide an illustrative example of how computational analytic methods and a longitudinal design can improve our inferential ability to detect early dysfunctional behaviors that may or may not reach threshold levels for formal diagnoses. Specifically, an unsupervised machine learning approach of anomaly detection is used to illustrate how community samples may be utilized to investigate early autism risk, multidimensional features, and outcome variables. Because ASD symptoms and challenges are not static within individuals across development, computational approaches present a promising method to elucidate subgroups of etiological contributions to phenotype, alternative developmental courses, interactions with biomedical comorbidities, and to predict potential responses to therapeutic interventions.
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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.