fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

• URL: http://arxiv.org/abs/2112.11014v1
• Date: Tue, 21 Dec 2021 06:52:48 GMT
• Title: fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits
• Authors: Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Guy Gurevitch and Talma Hendler
• Abstract summary: 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.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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