Predicting Personality Traits From Cognitive Task Performance Through Task-fMRI Analysis

*This project was conducted by a project group in Neuromatch Academy Computational Neuroscience.

In this mini-project that was conducted as part of the Neuromatch Academy: Computational Neuroscience 2023, we were interested in...

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Project Description

Personality plays a significant role in human performance and decision-making processes. Neuroimaging techniques are employed to investigate brain activations and understand how they relate to personality traits. Using fMRI, functional correlates of the Big Five personality traits were found in the brain. The existing literature has found that behavioral task performance differs across various personality traits. However, there is limited evidence on how brain activation moderates this relationship between task performance and personality. Here, we investigate how individual differences in task performance scores can serve as a predictive measure for personality trait scores. To this end, we aim to decode neural activity using a classifier to find correlations between brain network activations and personality traits across various tasks within the fMRI task battery to accurately predict task performance. Using task-fMRI data from Human Connectome Project (HCP) and the personality trait measures from NEO Five-Factor Inventory (NEO-FFI), we present a decoding model featuring these metrics. Unlike traditional brain-wide association studies that rely on resting-state fMRI data, here we investigate individual differences in task performance scores. Our findings underscore the lack of a "one-size-fits-all" approach linking human traits to brain activation patterns. This study sheds light on the importance of considering task-related brain activity for a more differentiated understanding of the interplay between neural processes and human traits.

Code Organization

• Here, you should detail how the code and filesystem are structured within this repo... The code was written in Python using Google Colaboratory.