Abstract
Analyzing functional magnetic resonance imaging (fMRI) data from the encoding perspective provides a powerful tool to explore human vision. Using voxel-wise encoding models, previous studies predicted the brain activity evoked by external stimuli successfully. However, these models constructed a regularized regression model for each single voxel separately, which overlooked the intrinsic spatial property of fMRI data. In this work, we proposed a multi-target regression model that predicts the activities of adjacent voxels simultaneously. Different from the previous models, the spatial constraint is considered in our model. The effectiveness of the proposed model is demonstrated by comparing it with two state-of-the-art voxel-wise models on a publicly available dataset. Results indicate that the proposed method can predict voxel responses more accurately than the competing methods.
Highlights
One important goal of neuroscience is to understand the relationship between external visual stimulus and human brain activity
In functional brain mapping, combining local brain activity often results in more consistent patterns across subjects (Kriegeskorte et al 2006). All these results suggest that spatial structure of functional magnetic resonance imaging (fMRI) data should be considered in encoding models
We compare our proposed multi-target model with two state-of-the-art voxel-wise models (Ridge and Lasso); these two models were widely employed in fMRI encoding models (Agrawal et al 2014; Kay et al 2008; Schoenmakers et al 2013; Güçlü and van Gerven 2014)
Summary
One important goal of neuroscience is to understand the relationship between external visual stimulus and human brain activity. We can gain the understanding by analyzing fMRI data from the mirror perspectives of neural decoding and neural encoding (Naselaris et al 2011). In the view of neural decoding, we often attempt to predict information of stimuli from measured brain activity. In the view of neural encoding, we try to model how brain activity varies corresponding to external stimulus and attempt to predict brain activity from stimuli features. Previous studies have indicated that encoding models are more efficient in describing the function of brain areas than decoding models (Naselaris et al 2011), suggesting the advantages of analyzing fMRI in the encoding view
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