ArticleTranslational psychiatry2016
Brain responses to biological motion predict treatment outcome in young children with autism.
Article in Translational psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- Scoping review of precision child and youth mental health research: dwelling in possibility.Frontiers in psychiatry · 2025Pooled it
- A Pivotal Response Treatment Package for Children With Autism Spectrum Disorder: An RCT.Pediatrics · 2019Trial
- Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning.Medical image analysis · 2026Article
- The Cost of Social Integration: Distinct Neural Inefficiency in Autistic Children Underlies Symptom Severity.Autism research : official journal of the International Society for Autism Research · 2026Article
- STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC.Proceedings of machine learning research · 2026Article
- Structural Brain Imaging Biomarkers of Autism Spectrum Disorder.Advances in neurobiology · 2024Review
- Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation.Machine learning in clinical neuroimaging : 6th international workshop, MLCN 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MLCN (Workshop) (6th : 2023 : Vancouver, B.C.) · 2023Article
- Disentangling disorder-specific variation is key for precision psychiatry in autism.Frontiers in behavioral neuroscience · 2023Article
- Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity.Human brain mapping · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Re-Examining Labels in Neurocognitive Research: Evidence from Bilingualism and Autism as Spectrum-Trait Cases.Brain sciences · 2022Review
- Attentional influences on neural processing of biological motion in typically developing children and those on the autism spectrum.Molecular autism · 2022Article
- Preference for biological motion is reduced in ASD: implications for clinical trials and the search for biomarkers.Molecular autism · 2021Article
- Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Investigating Changes in Reward-Related Neural Correlates After PEERS Intervention in Adolescents With ASD: Preliminary Evidence of a "Precision Medicine" Approach.Frontiers in psychiatry · 2021Article
- Diffusion kurtosis imaging of gray matter in young adults with autism spectrum disorder.Scientific reports · 2020Article
- Examining the value of body gestures in social reward contexts.NeuroImage · 2020Article
- Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
- Increased Neural Reward Responsivity in Adolescents with ASD after Social Skills Intervention.Brain sciences · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Autism spectrum disorders (ASDs) are common yet complex neurodevelopmental disorders, characterized by social, communication and behavioral deficits. Behavioral interventions have shown favorable results-however, the promise of precision medicine in ASD is hampered by a lack of sensitive, objective neurobiological markers (neurobiomarkers) to identify subgroups of young children likely to respond to specific treatments. Such neurobiomarkers are essential because early childhood provides a sensitive window of opportunity for intervention, while unsuccessful intervention is costly to children, families and society. In young children with ASD, we show that functional magnetic resonance imaging-based stratification neurobiomarkers accurately predict responses to an evidence-based behavioral treatment-pivotal response treatment. Neural predictors were identified in the pretreatment levels of activity in response to biological vs scrambled motion in the neural circuits that support social information processing (superior temporal sulcus, fusiform gyrus, amygdala, inferior parietal cortex and superior parietal lobule) and social motivation/reward (orbitofrontal cortex, insula, putamen, pallidum and ventral striatum). The predictive value of our findings for individual children with ASD was supported by a multivariate pattern analysis with cross validation. Predicting who will respond to a particular treatment for ASD, we believe the current findings mark the very first evidence of prediction/stratification biomarkers in young children with ASD. The implications of the findings are far reaching and should greatly accelerate progress toward more precise and effective treatments for core deficits in ASD.
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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.