Predicting the imagined contents using brain activation

TL;DR

This study demonstrates that machine learning can classify whether individuals are imagining or perceiving visual stimuli based on midbrain activation patterns in fMRI data, revealing neural correlates of mental imagery.

Contribution

It introduces a method to predict imagined contents from brain activation patterns using support vector machines, highlighting neural similarities between perception and imagery.

Findings

Midbrain regions activate during both imagined and perceived rewards.

Support vector machine achieved 75% accuracy in classifying imagery.

Brain imaging can decode cognitive states related to mental imagery.

Abstract

Mental imagery refers to percept-like experiences in the absence of sensory input. Brain imaging studies suggest common, modality-specific, neural correlates imagery and perception. We associated abstract visual stimuli with either visually presented or imagined monetary rewards and scrambled pictures. Brain images for a group of 12 participants were collected using functional magnetic resonance imaging. Statistical analysis showed that human midbrain regions were activated irrespective of the monetary rewards being imagined or visually present. A support vector machine trained on the midbrain activation patterns to the visually presented rewards predicted with 75% accuracy whether the participants imagined the monetary reward or the scrambled picture during imagination trials. Training samples were drawn from visually presented trials and classification accuracy was assessed for imagination trials. These results suggest the use of machine learning technique for classification of underlying cognitive states from brain imaging data.