Behavior measures are predicted by how information is encoded in an individual's brain

TL;DR

This paper presents a framework using encoding-models to predict individual behavior based on how their brain encodes information, emphasizing task-specific differences in neural encoding.

Contribution

It introduces a novel encoding-model framework to identify individual brain encoding differences and predict behavior, highlighting the importance of task optimization.

Findings

Encoding-model differences predict behavior effectively.

Task-specific encoding differences are crucial for behavior prediction.

Optimizing task and model improves prediction accuracy.

Abstract

Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region encodes information across individuals may explain their behavior. However, when studying how the brain encodes information, researchers choose different neuroimaging tasks (e.g., language or motor tasks), which can rely on processing different types of information and can modulate different brain regions. We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures. We propose a framework using encoding-models to identify individual differences in brain encoding and test if these differences can predict behavior. We evaluate our framework using task functional magnetic resonance imaging data. Our results indicate that individual differences revealed by encoding-models are a powerful tool for predicting behavior, and that researchers should optimize their choice of task and encoding-model for their behavior of interest.