Computational and dynamic models in neuroimaging
Open accessAbstract
This article reviews the substantial impact computational neuroscience has had on neuroimaging over the past years. It builds on the distinction between models of the brain as a computational machine and computational models of neuronal dynamics per se; i.e., models of brain function and biophysics. Both sorts of model borrow heavily from computational neuroscience, and both have enriched the analysis of neuroimaging data and the type of questions we address. To illustrate the role of functional models in imaging neuroscience, we focus on optimal control and decision (game) theory; the models used here provide a mechanistic account of neuronal computations and the latent (mental) states represent by the brain. In terms of biophysical modelling, we focus on dynamic causal modelling, with a special emphasis on recent advances in neural-mass models for hemodynamic and electrophysiological time series. Each example emphasises the role of generative models, which embed our hypotheses or questions, and the importance of model comparison (i.e., hypothesis testing). We will refer to this theme, when trying to contextualise recent trends in relation to each other.
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MMN is widely considered a robust marker of psychosis, thought to reflect an attempt to minimize prediction error and capture its disruption.
“MMN is widely considered to be a robust marker of psychosis15, thought to reflect an attempt to minimize this prediction error and capture its disruption16–19.”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [16]
The section “Forward and backward connections in the brain” (page unavailable) links repetition suppression that might explain MMN to predictive coding; the Discussion describes suppression of sensory prediction error. These passages support the proposed mechanism, but they do not say that MMN captures disruption of prediction-error minimization. The psychosis-marker statement has a separate citation in the citing sentence and is not attributed to this source. (LLM, conf=0.82)
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concatenation underlies interactions between gamma and beta rhythms in neocortex
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Brain coordination dynamics: True and false faces of phase synchrony and metastability
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Dynamic causal modelling of induced responses
Chen, C. C., Kiebel, S. J., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.026 · Cited by 136 · see citing works
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Mean-field modeling of the basal ganglia-thalamocortical system. II
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Stochastic models of neuronal dynamics
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Functional and Effective Connectivity: A Review
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Collignon, O., Dormal, G., de Heering, A., Lepore, F., Lewis, T. L., & Maurer, D. (2015) · Current Biology · doi:10.1016/j.cub.2015.07.036 · Cited by 94 · see citing works
Vignando, M., Ffytche, D., Mazibuko, N., Palma, G., Bhat, A., Montagnese, M., Dave, S., Tai, Y. F., Batzu, L., Leta, V., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00669-7
“MMN is widely considered to be a robust marker of psychosis 15 , thought to reflect an attempt to minimize this prediction error and capture its disruption 16–19 .”
- Authors
- Friston, K. J., & Dolan, R. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Friston, Karl J.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 52
- Issue
- 3
- Pages
- 752-765
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2910283
- OpenAlex
- W2012993788
- Cited by
- 131
- References
- 156
- Citations by year
- 2026: 4 · 2025: 5 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 5 · 2019: 27 · 2018: 5 · 2017: 3 · 2016: 3 · 2015: 15 · 2014: 14 · 2013: 8 · 2012: 12
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (088130)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- computational neuroscience, neuroimaging, optimal control, decision theory, dynamic causal modeling, neural mass model, generative models, model comparison, hypothesis testing, neural dynamics
- MeSH terms
- Animals, Brain, Brain Mapping, Computer Simulation, Game Theory, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Models, Theoretical
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