ArticleNeuroImage2022
A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS.Scientific reports · 2022Trial
- Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.Neuroinformatics · 2026Article
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- Connectome-Based Predictive Models of General and Specific Executive Functions.Human brain mapping · 2025Article
- Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Functional connectome stability and optimality are markers of cognitive performance.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.Frontiers in neuroscience · 2023Article
- Network controllability mediates the relationship between rigid structure and flexible dynamics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory.Journal of cognitive neuroscience · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model context-dependent changes. Here, we present a novel connectome-to-connectome (C2C) transformation framework that enables us to model the brain's functional reorganization from one connectome state to another in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-related connectomes from their task-free (resting-state) connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions with increased power, achieving similar performance with just a third of the subjects needed when relying on resting-state data alone. Finally, the C2C model reveals how the brain reorganizes between cognitive states. Our observations support the existence of reliable state-specific subsystems in the brain and demonstrate that we can quantitatively model how the connectome reconfigures to different cognitive states, enabling more accurate predictions of behavior with fewer subjects.
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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.