fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Talma Hendler, Guy Gurevitch

Keywords: fMRI, Amygdala-neurofeedback, imaging-based diagnosis, psychiatry, InverseReinforcement Learning

TL;DR: A new framework that uses raw fMRI images to predict clinical and demographics characteristics, by creating a personal signature

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.

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Paper Type: methodological development

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Integration of Imaging and Clinical Data

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/fmri-neurofeedback-learning-patterns-are/code)