ArticlePeerJ2020
Facilitating open-science with realistic fMRI simulation: validation and application.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- DeepCor: denoising fMRI data with contrastive autoencoders.Nature methods · 2026Article
- Motion impact score for detecting spurious brain-behavior associations.Nature communications · 2025Article
- Emotions in the Brain Are Dynamic and Contextually Dependent: Using Music to Measure Affective Transitions.eNeuro · 2025Article
- Multi-Metric Approach for the Comparison of Denoising Techniques for Resting-State fMRI.Human brain mapping · 2025Article
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Computational Language Modeling and the Promise of In Silico Experimentation.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.bioRxiv : the preprint server for biology · 2023Article
- Static and dynamic functional connectomes represent largely similar information.bioRxiv : the preprint server for biology · 2023Article
- Optimizing cognitive neuroscience experiments for separating event- related fMRI BOLD responses in non-randomized alternating designs.Frontiers in neuroimaging · 2023Article
- Static and dynamic fMRI-derived functional connectomes represent largely similar information.Network neuroscience (Cambridge, Mass.) · 2023Article
- A Computational Neural Model for Mapping Degenerate Neural Architectures.Neuroinformatics · 2022Article
- Advances in the field of intranasal oxytocin research: lessons learned and future directions for clinical research.Molecular psychiatry · 2021Review
- BrainIAK: The Brain Imaging Analysis Kit.Aperture neuro · 2021Article
- Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain.PLoS computational biology · 2020Article
- Fully synthetic neuroimaging data for replication and exploration.NeuroImage · 2020Article
- Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis.Neuropsychologia · 2020Review
- A validation framework for neuroimaging software: The case of population receptive fields.PLoS computational biology · 2020Article
- Exploring the impact of analysis software on task fMRI results.Human brain mapping · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. We present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. We validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. The fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data.
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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.