Generative embedding for model-based classification of fMRI data
Open accessAbstract
Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups.
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Generative embedding classification or clustering results have a mechanistic interpretation because the dimensions of the feature space are given by specific model parameters.
“the classification or clustering results have a mechanistic interpretation since the dimensions of the feature space are given by specific model parameters.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [64]
Generative embedding typically results in substantially higher performance than conventional machine learning when one has a good model, because the model acts as a theory-led feature selection device that retains only dimensions of interest and discards irrelevant data features.
“provided one has a good model, generative embedding typically results in substantially higher performance since the model is used as a theory-led feature selection device, which retains only dimensions of interest and discards irrelevant data features.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [64]
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Flagged first, then by citations
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- Authors
- Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- Wellcome Centre for Human Neuroimaging (GB)
- ETH Zurich (CH)
- Corresponding author
- Brodersen, Kay H.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-06-23
- Volume
- 7
- Issue
- 6
- Pages
- e1002079
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3121683
- OpenAlex
- W1967541074
- Cited by
- 165
- References
- 117
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 4 · 2023: 5 · 2022: 7 · 2021: 13 · 2020: 7 · 2019: 17 · 2018: 15 · 2017: 22 · 2016: 13 · 2015: 12 · 2014: 10 · 2013: 16 · 2012: 10
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (091593, ME033459MES, 056750)
- National Institute for Health and Care Research
- Universität Zürich
- SystemsX.ch
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- fMRI classification, dynamic causal modeling, support vector machine, mechanistic interpretability, speech processing, high-dimensional small-sample data
- MeSH terms
- Adult, Aged, Algorithms, Aphasia, Bayes Theorem, Brain, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Nervous System Diseases, Pattern Recognition, Automated, Speech Perception, Reproducibility of Results, Databases, Factual, Computational Biology, Principal Component Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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