An encoding framework with brain inner state for natural image identification

TL;DR

This paper introduces a novel brain encoding framework that integrates internal brain states with external stimuli to improve natural image identification accuracy from fMRI data.

Contribution

It proposes a flexible, combined encoding model incorporating inner brain states, enhancing prediction performance over traditional models.

Findings

Significantly improved image identification accuracy.

Robust performance across different dataset sizes.

Effective integration of inner states improves decoding results.

Abstract

Neural encoding and decoding, which aim to characterize the relationship between stimuli and brain activities, have emerged as an important area in cognitive neuroscience. Traditional encoding models, which focus on feature extraction and mapping, consider the brain as an input-output mapper without inner states. In this work, inspired by the fact that human brain acts like a state machine, we proposed a novel encoding framework that combines information from both the external world and the inner state to predict brain activity. The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain. The forward model can be any traditional encoding model, making the framework flexible. The inner state model is a linear model to utilize information in the prediction residuals of the forward model. The proposed encoding framework can achieve much better performance on natural image identification from fMRI response than forwardonly models. The identification accuracy will decrease slightly with the dataset size increasing, but remain relatively stable with different identification methods. The results confirm that the new encoding framework is effective and robust when used for brain decoding.