2015
NeurIPS
NeurIPS 2015
A Reduced-Dimension fMRI Shared Response Model
Abstract
Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.
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Interdisciplinary Bridge
- Artificial Intelligence and Healthcare & Medicine and Interdisciplinary
Authors
Topics
Machine Learning > Core Methods > Clustering
Healthcare & Medicine > Research > Biosignal Processing
Interdisciplinary > Cognitive Science > Cognitive Modeling
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Dimensionality Reduction
Interdisciplinary > Science > Neuroscience