Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism
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Significance: Few individuals diagnosed with autism spectrum disorder (ASD) go on to achieve high levels of independence or what are considered “very good” outcomes. As such, there is a need to identify predictors of outcomes to improve treatment and services for these individuals. Although behavioral and cognitive variables can predict substantial variance in outcomes, the majority of the variance remains unexplained. In this study, we investigated whether a measure of intrinsic functional brain connectivity [resting-state functional connectivity MRI (rs-fcMRI)] could add meaningful predictive power. Indeed, we found that connectivity involving several brain networks previously implicated in ASD could predict improvements in adaptive behaviors several years after the scan with a high degree of sensitivity.
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Differences in results between resting state connectivity studies could be due to the age of the participants, sex differences, heterogeneity, methodological concerns, or that both connectivity states exist in ASD.
“Differences in results between studies could be due to the age of the participants 82 , sex differences, heterogeneity, methodological concerns 83 or that both connectivity states exist in ASD.”
From Autism spectrum disorder (2020) · cites this paper as [82]
The Connectivity Measures and Feature Matrices section discusses avoiding bias in network selection, and the limitations passage notes a male-only sample and variable follow-up intervals. Neither passage says that methodological concerns—or the other listed factors—explain differences between resting-state connectivity studies, and no passage addresses whether both connectivity states exist in ASD. (LLM, conf=0.72)
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- Authors
- Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L., & Martin, A.
- Affiliations
- National Institutes of Health (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-11-16
- Volume
- 112
- Issue
- 48
- Pages
- E6699-706
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4672806
- OpenAlex
- W2180390996
- Cited by
- 114
- References
- 68
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 7 · 2023: 8 · 2022: 19 · 2021: 13 · 2020: 16 · 2019: 6 · 2018: 13 · 2017: 15 · 2016: 5
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- Attias Family Foundation
- National Institutes of Health
- U.S. Public Health Service (ZIAMH002920)
- National Institute of Mental Health (ZIAMH002920)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, resting-state functional connectivity, autistic traits, adaptive behavior, salience network, default mode network, anterior insula, outcome prediction, longitudinal change
- MeSH terms
- Autism Spectrum Disorder, Machine Learning, Adolescent, Adult, Behavior, Brain, Brain Mapping, Cerebral Cortex, Child, Child, Preschool, Cognition, Female, Follow-Up Studies, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Regression Analysis, Time Factors, Reproducibility of Results, Young Adult
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