fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them . In this paper, we present a mixture of experts model for predicting brain activity patterns . Given a new stimulus, the model predicts the entire brain activation as a weighted linear combination of activation of multiple experts . We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also helps in understanding which of the brain regions get activated together , given a certain kind of stimulus . Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles URLanization of function in the brain, namely that of specialization and integration  ] .
fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. State-of-the-art encoding models use a single global model (linear or non-linear) to predict brain activation given the stimulus. However, the critical assumption in these methods is that a priori different brain regions respond the same way to all the stimuli, that is, there is no modularity or specialization assumed for any region. This goes against the modularity theory, supported by many cognitive neuroscience investigations suggesting that there are functionally specialized regions in the brain . In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model. The key idea here is that each linear expert captures the behaviour of similar brain regions . Given a new stimulus, the utility of the proposed model is twofold (i) predicts the brain activation as a weighted linear combination of the activations of multiple linear experts and (ii) to learn multiple experts corresponding to different brain regions . We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. This study helps in understanding the brain regions that are activated together given different kinds of stimuli . Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles URLanization of function in the brain, namely that of specialization and integration . Experiments on fMRI data from paradigm 1 1]where participants view linguistic stimuli show that the proposed MoRE model has better prediction accuracy compared to that of conventional models .
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Classical
State-of-the-art
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linear multivariate methods
a single global model (linear or non-linear)
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(all the voxels)
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these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them
the critical assumption in these methods is that a priori different brain regions respond the same way to all the stimuli, that is, there is no modularity or specialization assumed for any region. This goes against the modularity theory, supported by many cognitive neuroscience investigations suggesting that there are functionally specialized regions in the brain
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present a mixture of experts model for predicting brain activity patterns
achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model. The key idea here is that each linear expert captures the behaviour of similar brain regions
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model predicts the entire
utility of the proposed model is twofold (i) predicts the
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activation of multiple experts
the activations of multiple linear experts and (ii) to learn multiple experts corresponding to different brain regions
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Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also
This study
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which of
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get activated together , given a certain kind of stimulus
that are activated together given different kinds of stimuli
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. Experiments on fMRI data from paradigm 1
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1
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where participants view linguistic stimuli show that the proposed MoRE model has better prediction accuracy compared to that of conventional models