ArticleCerebral cortex (New York, N.Y. : 1991)2023
A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Connectome-based substrates of emotional N-back performance in the adolescent brain cognitive development study.Developmental cognitive neuroscience · 2026Article
- Article
- Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.Nature. Mental health · 2026Article
- Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer's disease.Brain communications · 2025Article
- Identifying dynamic reproducible brain states using a predictive modelling approach.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.bioRxiv : the preprint server for biology · 2024Article
- Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance.Frontiers in dementia · 2023Article
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Authors and funding
21 authors.
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Abstract
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
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