Journal
HUMAN BRAIN MAPPING
Volume 43, Issue 10, Pages 3062-3085Publisher
WILEY
DOI: 10.1002/hbm.25835
Keywords
decoding; EEG; encoding; MEG; single trial task dynamics
Funding
- Biotechnology and Biological Sciences Research Council [BB/R010803/1]
- European Research Council [ERC-StG2019-850404]
- James S. McDonnell Foundation [JSMF220020372]
- Medical Research Council [RG89702, RG94383]
- NIHR Oxford Health Biomedical Research Centre
- Novo Nordisk [NNF19OC-0054895]
- Wellcome Trust [104765/Z/14/Z, 106183/Z/14/Z, 203139/Z/16/Z, 214314/Z/18/Z, 215573/Z/19/Z, 219525/Z/19/Z]
- EU-Project euSNN [MSCA-ITN H2020-860563]
- Wellcome Trust [214314/Z/18/Z, 219525/Z/19/Z] Funding Source: Wellcome Trust
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The use of generative encoding model framework allows for the simultaneous inference of spatial patterns of brain activity and variable timing of these patterns in individual trials, providing insights into the dynamic characteristics of brain activity.
An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion maps from these parameters to the equivalent decoding model, allowing predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in the future.
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