fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

TL;DR

This study develops a deep learning-based method to predict individual learning progress in fMRI neurofeedback tasks, linking brain activity patterns to personal and clinical traits, and demonstrating its potential as a diagnostic tool.

Contribution

The paper introduces a novel predictive signature derived from fMRI neurofeedback learning patterns using deep neural networks, outperforming previous methods in personal trait prediction.

Findings

Predictive power exceeds previous approaches.

Signature correlates with personal and clinical traits.

Method demonstrates potential as a diagnostic tool.

Abstract

We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person's progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person's learning pattern may be used as a diagnostic tool. Our code has been made available, and data would be shared, subject to ethical approvals.