Model-based feature construction for multivariate decoding
Open accessAbstract
Conventional decoding methods in neuroscience aim to predict discrete brain states from multivariate correlates of neural activity. This approach faces two important challenges. First, a small number of examples are typically represented by a much larger number of features, making it hard to select the few informative features that allow for accurate predictions. Second, accuracy estimates and information maps often remain descriptive and can be hard to interpret. In this paper, we propose a model-based decoding approach that addresses both challenges from a new angle. Our method involves (i) inverting a dynamic causal model of neurophysiological data in a trial-by-trial fashion; (ii) training and testing a discriminative classifier on a strongly reduced feature space derived from trial-wise estimates of the model parameters; and (iii) reconstructing the separating hyperplane. Since the approach is model-based, it provides a principled dimensionality reduction of the feature space; in addition, if the model is neurobiologically plausible, decoding results may offer a mechanistically meaningful interpretation. The proposed method can be used in conjunction with a variety of modelling approaches and brain data, and supports decoding of either trial or subject labels. Moreover, it can supplement evidence-based approaches for model-based decoding and enable structural model selection in cases where Bayesian model selection cannot be applied. Here, we illustrate its application using dynamic causal modelling (DCM) of electrophysiological recordings in rodents. We demonstrate that the approach achieves significant above-chance performance and, at the same time, allows for a neurobiological interpretation of the results.
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The poor signal-to-noise ratio of single-trial recordings poses a serious challenge for modeling.
“The poor signal-to-noise ratio of single-trial recordings poses a serious challenge for modeling (Brodersen et al., 2011)”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Brodersen et al., 2011
The Methods section (‘Trial-by-trial dynamic causal modelling’; page unavailable) describes fitting a model to individual trials, but does not say that poor signal-to-noise ratio makes this difficult. Searches for ‘signal-to-noise,’ ‘noise,’ and challenges in single-trial model estimation found no passage addressing that claim. (LLM, conf=0.70)
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- Brodersen KH Ong CS Stephan KE Buhmann JM Posterior distributions of classification accuracy n.d. under review
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“The poor signal-to-noise ratio of single-trial recordings poses a serious challenge for modeling (Brodersen et al., 2011) and may require adapting hierarchical (empirical Bayesian) estimation schemes (Friston et al., 2016; Raman et al., 2016) to single-trial scenaria as well as sampling schemes for…”
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- Authors
- Brodersen, K. H., Haiss, F., Ong, C. S., Jung, F., Tittgemeyer, M., Buhmann, J. M., Weber, B., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- ETH Zurich (CH)
- Max Planck Institute for Metabolism Research (DE)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Brodersen, Kay H.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 56
- Issue
- 2
- Pages
- 601-615
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3112410
- OpenAlex
- W2142535091
- Cited by
- 36
- References
- 78
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2017: 7 · 2016: 2 · 2015: 1 · 2014: 2 · 2013: 7 · 2012: 3
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Max-Planck-Gesellschaft
- Universität Zürich
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- dynamic causal modeling, multivariate decoding, feature construction, dimensionality reduction, separating hyperplane, electrophysiology, information maps
- MeSH terms
- Animals, Brain, Computer Simulation, Models, Neurological, Rats
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