ArticleHuman brain mapping2021
Predicting individual variability in task-evoked brain activity in schizophrenia.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Experience-dependent changes in functional connectome fingerprinting.Nature communications · 2026Article
- Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.Human brain mapping · 2026Article
- Continuous Diffusion-Detected Neuroplasticity during Motor Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry.Brain sciences · 2024Article
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- Activity flow mapping over probabilistic functional connectivity.Human brain mapping · 2023Article
- Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features.Psychoradiology · 2022Review
- Predicting individual variability in task-evoked brain activity in schizophrenia.Human brain mapping · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting-state functional connectivity and brain activity during the well-validated N-back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine-learning approach we were able to use resting-state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task-evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.
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Registered trials
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