  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.
