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Pattern-Information Analysis: Columnar Sensitivity, Stimulus Decoding, and Computational-Model Testing

Pattern-Information Analysis: Columnar Sensitivity, Stimulus Decoding, and Computational-Model Testing

This video was recorded at BBCI Workshop: Advances in Neurotechnologies, Berlin 2012. Pattern-information fMRI has become a popular method in neuroscience. The technique is motivated by the idea that spatial patterns of fMRI activity reflect the neuronal population codes of perception, cognition, and action. I will address the question to what extent we can expect to investigate columnar-scale neuronal pattern information with fMRI at 3T using conventional resolutions of 2-3 mm voxel width. I will then compare existing approaches with a focus on the question of what we can learn from them in terms of brain theory. The most popular and widespread method is stimulus decoding by response-pattern classification. This approach addresses the question whether activity patterns in a given region carry information about the stimulus category. Pattern classification uses generic models of the stimulus-response relationship that do not mimic brain information processing and treats the stimulus space as categorical – a simplification that is often helpful, but also limiting in terms of the questions that can be addressed. Beyond pattern classification, a major new direction is the integration of computational models of brain information processing into pattern-information analysis. This approach enables us to address the question to what extent competing computational models are consistent with the stimulus representations in a brain region. Two methods that test computational models are voxel receptive-field modeling and representational similarity analysis. These methods sample the stimulus (or mental-state) space more richly, estimate a separate response pattern for each stimulus, and can generalize from the stimulus sample to a stimulus population.


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