Sahar Mohamadjani - Medical Imaging Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Morteza Hashemizadeh - Department of Medical Physics, School of Medicine, Ahvaz Jondishapour University of Medical Sciences, Ahvaz, Iran
Fatemeh Tarahomi - Medical Imaging Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

Abstract: Currently, deep learning-aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. Machine learning and its subset, deep learning, are branches of AI, and have shown promising findings in the medical field, especially when applied to imaging data, which have been used in radiological diagnosis, bioinformatics, genome sequencing, drug development, and histopathological image analysis. Deep learning has shown tremendous potential in the field of neuroimaging and brain regulation. Neuroimaging techniques such as MRI, CT, PET/CT, EEG/MEG, optical imaging, and other imaging modalities generate large amounts of comprehensive and complex data, which can be challenging to analyse and interpret. Deep learning techniques such as CNNs, RNNs, and GANs have been proven to be effective in extracting meaning full information from these data and transforming the neuroimaging from qualitative to quantitative imaging modality.for this study, we conducted a comprehensive review of research articles using databases like PubMed, IEEE Xplore, Science Direct, and Google Scholar. We included studies published between ۲۰۱۵ and ۲۰۲۳, focusing on the intersection of deep learning, neuroimaging, and brain regulation. Relevant articles covering advancements in deep learning techniques for neuroimaging analysis and their applications in understanding brain regulation were selected and critically evaluated.Deep learning has shown great promise in the field of neuroimaging and brain regulation, with the potential to improve the accuracy and speed of diagnosis and the treatment of neurological disorders as well as enable new forms of brain–computer interfaces. However, the challenges associated with deep learning must be addressed to ensure that these techniques can be used safely and effectively in clinical settings. Overall, this article reviewed the recent progress of how deep learning is being applied in the medical field of neuroimaging and brain regulation.

Artificial intelligence, Deep learning, Brain regulation, Neuroimaging