Application of Deep Learning in fMRI-Based Human Brain Parcellation: A Review

Abstract Functional magnetic resonance imaging (fMRI)-based human brain parcellation reveals brain fundamental organizational principles noninvasively, providing prior guidance for functional analysis and physiological measurement of the brain. Recently, the profound success of deep learning in natural and medical images has attracted an increasing number of deep learning-based investigations for brain parcellation which have accomplished encouraging achievements. This review aims to provide researchers with a comprehensive overview of deep learning-based fMRI brain parcellation and promote the development of this emerging frontier. To begin, we present the history of brain parcellation, emphasizing the current advantages of deep learning in this field. Following a summary of the input modalities, traditional methods, and evaluation criteria for fMRI-based brain parcellation, we comprehensively review the current deep-learning approaches and categorize them based on network architectures. The datasets, models, performance, advantages and limitations are described in detail. Finally, we discuss the current challenges and future directions in methodology, neuroscience, and application, to inspire future investigation in deep learning-based brain parcellation.