Enhancing Friedreich Ataxia Research: Data Augmentation and Multimodal Fusion Focusing on the Spine-Brain Axis

One of the biggest challenges in FA research is the lack of reliable methods to track disease progression and treatment effects, largely due to the slow nature of the disease. Dr Dadsena aims to address this challenge by applying advanced artificial intelligence (AI) techniques to analyze brain and spinal cord images, along with clinical data from FA patients. To overcome the limitation of small datasets, “data augmentation” will be used to generate synthetic (computer-created) images, increasing the amount of data available for analysis. This enhanced larger dataset will help improve the performance of AI models by reducing overfitting and enhancing their ability to generalize across different sub-populations within the FA patient group. Additionally, by combining different types of medical imaging—such as brain structure, brain connectivity, and metabolism scans—with clinical data, Dr Dadsena aims to get a more complete picture of how FA affects the body. The goal is to develop accurate markers that can be used in clinical trials to detect changes in the brain and spinal cord, which will allow researchers to better track disease progression and evaluate the effectiveness of treatments. Furthermore, the project will enable the creation of AI models that predict how FA progresses over time, leading to more personalized treatment approaches. By working with data from multiple research groups, the findings from this study will be applicable to a wide range of FA patients, advancing the development of effective therapies.