Diffusion Models for Computational Neuroimaging: A Survey

• DOI: 10.48550/arxiv.2502.06552
• Publication Date: 2025-02-10
• Authors: Haokai Zhao; Haowei Lou; Lina Yao; Wei Peng; Ehsan Adeli; Kilian M. Pohl; Yu Zhang
• Keywords: Computational model

Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition behavior. While diffusion models have shown stability high-quality generation in natural images, there is increasing interest adapting them analyze data various neurological tasks such as enhancement, disease diagnosis decoding. This survey provides an overview of recent efforts integrate into computational neuroimaging. We begin by introducing the common modalities, follow with formulations conditioning mechanisms. Then we discuss how variations denoising starting point, condition input target are developed enhance specific tasks. For a comprehensive ongoing research, publicly available repository at https://github.com/JoeZhao527/dm4neuro.