fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits
This work addresses the need for diagnostic tools in clinical settings by using learning patterns from neurofeedback, though it appears incremental as it builds on existing fMRI methods.
The researchers tackled the problem of predicting personal and clinical traits by developing a personal signature based on fMRI neurofeedback learning patterns, specifically predicting Amygdala activity across sessions, and found it stronger than previous approaches for such signatures.
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.