ArticleNeuroImage2022
Enhancing task fMRI preprocessing via individualized model-based filtering of intrinsic activity dynamics.
Article in NeuroImage, 2022. 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.
- Task-induced topological and geometrical changes in whole-brain dynamics predict cognitive individual differences.bioRxiv : the preprint server for biology · 2026Article
- On the control of recurrent neural networks using constant inputs.IEEE transactions on automatic control · 2026Article
- Distinguishing task-evoked dynamic brain networks from intrinsic activity with tensor component analysis.Brain imaging and behavior · 2026Article
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- Article
- Neural Mechanisms Supporting the Relationship between Working Memory Capacity and Proactive Control.bioRxiv : the preprint server for biology · 2025Article
- Neural Patterns of Social Pain in the Brain-Wide Representations Across Social Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Precision data-driven modeling of cortical dynamics reveals idiosyncratic mechanisms underlying canonical oscillations.bioRxiv : the preprint server for biology · 2023Article
- Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement.Annual reviews in control · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first "filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.
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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.