ReviewMolecular psychiatry2019
Big data approaches to decomposing heterogeneity across the autism spectrum.
Review in Molecular psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 249 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
249 citing papers in PubMed, 8 syntheses or guidelines pooled it, 531 citations in OpenAlex.
- Predicting early intervention outcomes in autism via individual participant data mega-analysis.Molecular autism · 2026Pooled it
- Associations Between Communication Skills and Social Anxiety in Children and Adolescents With Autism Spectrum Disorder: A Systematic Review.Journal of speech, language, and hearing research : JSLHR · 2026Pooled it
- Applicability and Psychometric Properties of General Mental Health Assessment Tools in Autistic People: A Systematic Review.Journal of autism and developmental disorders · 2025Pooled it
- Experiences of females on the autism spectrum through the perspective of minority stress theory: a review.Frontiers in psychiatry · 2025Pooled it
- Gray matter covariations in autism: out-of-sample replication using the ENIGMA autism cohort.Molecular autism · 2024Pooled it
- A systematic review and meta-analysis of suicidality in autistic and possibly autistic people without co-occurring intellectual disability.Molecular autism · 2023Pooled it
- A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders.PloS one · 2023Pooled it
- Autism and Down syndrome: early identification and diagnosis.Arquivos de neuro-psiquiatria · 2022Pooled it
- Trial
- A Longitudinal RCT of P-ESDM With and Without Parental Mindfulness Based Stress Reduction: Impact on Child Outcomes.Journal of autism and developmental disorders · 2022Trial
- Gray matter subtyping with SuStaIn reveals neuroanatomical patterns in autism spectrum disorder.Brain imaging and behavior · 2026Article
- Effects of High-Phenolic Extra Virgin Olive Oil on Sensory Processing in Children With Autism Spectrum Disorder: A Pilot Study.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Short-term outcomes and six-month follow-up of BEAR: a blended, pre-emptive intervention for infants and toddlers at elevated likelihood for autism.European child & adolescent psychiatry · 2026Article
- Heterogeneous functional state dynamics and its structural substrates in male individuals with autism spectrum disorder.Molecular psychiatry · 2026Article
- Distinct Temporal Stages of Infant Brain Processing Associate With Early Versus Later Autism Diagnosis.Developmental science · 2026Article
- Dynamic Functional Synchronization Profiles in Autism Differ by Spatial Scale and Along Hierarchical Cortical Gradients.Biological psychiatry global open science · 2026Article
- Heterogeneity of Response to Early Start Denver Model: Identifying Developmental Trajectories and Predictors of Cognitive Outcomes.Autism research : official journal of the International Society for Autism Research · 2026Article
- Navigating Mixed-Neurotype Dialogue: Environmental and Affective Influences on Autistic Speakers.Autism : the international journal of research and practice · 2026Article
- Visual Responsivity in Autism: Measuring Visual Responses in Autistic and Non-Autistic Adults Using Psychophysics, fMRI, and EEG.Autism research : official journal of the International Society for Autism Research · 2026Article
- Myelin, white matter, and social deficits in autism spectrum disorder.Neural regeneration research · 2026Article
189 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 5 countries.
Funding
Abstract
Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.
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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.