Tinnitus and fMRI Neurofeedback

Background

Chronic tinnitus, the perception of a persistent "ringing" or sound, is a debilitating condition. Functional MRI neurofeedback (fMRI-NF) is a novel and promising intervention where patients learn to volitionally control brain activity in targeted regions. This project will analyze a unique and rich fMRI dataset from a recent clinical trial to understand how this intervention changes brain networks. We aim to assess network reconfiguration and identify changes in brain network integration/segregation that result from fMRI neurofeedback.

Project Description

This project involves analyzing a longitudinal fMRI dataset from 21 patients across 15 fMRI sessions. The student will learn to apply advanced "temporal analyses" and connectomics methods to fMRI data. The primary goal is to map how brain networks reconfigure over the course of the intervention and to identify "markers for improved recovery" by correlating network changes with clinical scores (e.g., Tinnitus Handicap Inventory, THI). The project is in collaboration with colleagues at EPFL.

Requirements

• Interest in networks, computational neuroscience, topological data science, and tinnitus.
• Good knowledge of at least one programming language (e.g. Python, Julia, Matlab).
• Experience with fMRI data analysis is desirable but not required.