Decoding Spatial Representations from Functional Magnetic Resonance Imaging Data
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2016-08-25
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en
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Abstract
The hippocampus is essential to our ability to navigate our environment. Rodent research
on the underlying hippocampal code has revealed specialized place, grid, and head direction
cells. Recent studies showed that humans employ a similar memory network to encode
spatial representations in hippocampal regions. In this study we used recurrent neural
networks to decode the trajectories of participants navigating a virtual environment from
functional magnetic resonance imaging (fMRI) data. Encoding models were implemented
trying to encode evoked blood-oxygenation-level-dependent (BOLD) responses from location
feature sequences. Subsequently, decoding models were trained to predict the location
sequence from BOLD responses. Although the models fell short in achieving sufficient
performance, the study provides useful insights to improve future approaches to decode
trajectories from fMRI data.
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Faculteit der Sociale Wetenschappen