# fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

arXiv: https://arxiv.org/abs/2112.11014 · HTML: https://mlsanity.com/paper/2112.11014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [A critical analysis of self-supervision, or what we can learn from a single image](https://mlsanity.com/paper/1904.13132/) (2019)
- [Magnetic Resonance Fingerprinting using Recurrent Neural Networks](https://mlsanity.com/paper/1812.08155/) (2018)
- [Learning Personal Representations from fMRIby Predicting Neurofeedback Performance](https://mlsanity.com/paper/2112.04902/) (2021)

## Abstract (arXiv)

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.