Rotem Leibovitz -fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Electrical Engineering Systems Seminar

 Speaker: Rotem Leibovitz

M.Sc. student under the supervision of Prof. Lior Wolf

Wednesday, 3^rd April 2024, at 15:00

Room 011, Kitot Building, Faculty of Engineering

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

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.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר