ArticleNeuroImage2021
Mapping individual differences across brain network structure to function and behavior with connectome embedding.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
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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
22 citing papers in PubMed, 38 citations in OpenAlex.
- Trial
- Article
- Connectome-based predictive modeling of concurrent and longitudinal substance use vulnerability in adolescence.Developmental cognitive neuroscience · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Glymphatic System Dysfunction in Central Nervous System Diseases.CNS neuroscience & therapeutics · 2026Review
- The network architecture of general intelligence in the human connectome.Nature communications · 2026Article
- Increased functional integration of emotional control network across the adult lifespan.Aging brain · 2026Article
- Connectome-Based Predictive Modeling of Concurrent and Prospective Substance Use in Adolescence.bioRxiv : the preprint server for biology · 2025Article
- Krakencoder: a unified brain connectome translation and fusion tool.Nature methods · 2025Article
- Multi-voxel pattern analysis for developmental cognitive neuroscientists.Developmental cognitive neuroscience · 2025Review
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- The Brain Connectome for Clinical Neuroscience.Advances in experimental medicine and biology · 2024Review
- Weighting the structural connectome: Exploring its impact on network properties and predicting cognitive performance in the human brain.Network neuroscience (Cambridge, Mass.) · 2024Article
- Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability.Human brain mapping · 2023Article
- Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning.Human brain mapping · 2023Article
- A Goldilocks theory of cognitive control: Balancing precision and efficiency with low-dimensional control states.Current opinion in neurobiology · 2022Review
- Modular community structure of the face network supports face recognition.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Local structure-function relationships in human brain networks across the lifespan.Nature communications · 2022Article
- Edges in brain networks: Contributions to models of structure and function.Network neuroscience (Cambridge, Mass.) · 2022Review
- Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays.Frontiers in neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The connectome, a comprehensive map of the brain's anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes "context" is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
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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.