ArticleNeuroImage2021
Resample aggregating improves the generalizability of connectome predictive modeling.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.BMC medical informatics and decision making · 2026Pooled it
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Representing Brain-Behavior Associations by Retaining High-Motion Minoritized Youth.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- The network architecture of general intelligence in the human connectome.Nature communications · 2026Article
- Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Multimodal neuroimaging data boosts the prediction of multifaceted cognition.Research square · 2025Article
- Machine learning for identifying caregiving adversities associated with greatest risk for mental health problems in children.Nature. Mental health · 2025Article
- Identifying dynamic reproducible brain states using a predictive modelling approach.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations.bioRxiv : the preprint server for biology · 2024Article
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach.Human brain mapping · 2023Article
- Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns.NeuroImage · 2022Article
- Brain connectivity at rest predicts individual differences in normative activity during movie watching.NeuroImage · 2022Article
- Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.Science advances · 2022Article
- Article
- Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.
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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.