Anonymizationof fMRI Images with Generative Models

Functional Magnetic Resonance Imaging (fMRI) augmentation has emerged as a promising avenue in neuroimaging research, offering enhanced insights into brain function and connectivity. In our work we expect to introduce a novel approach to this field by incorporating a transformer model, a powerful tool in natural language processing. This model will be available as open source Furthermore, this paper presents a comprehensive review of existing studies in the domain, focusing on the quality and anonymity of augmented data. By synthesizing findings from a range of research papers, this study aims to elucidate the strengths and limitations of various augmentation techniques employed in fMRI data analysis. Through this investigation, our work seeks to provide valuable insights into the evolving landscape of fMRI anonymization, offering guidance for researchers and practitioners in selecting appropriate methods for enhancing the quality and privacy of neuroimaging data.