Perceiving and thinking are essential aspects of human consciousness. Understanding the origins and realization of consciousness, particularly in relation to brain functions (or even the entire body), constitutes fundamental and intriguing inquiries into the human mind. Fortunately, advancing modern neuroimaging techniques such as M/EEG, fMRI, ECoG, and fNIRS enables us to explore and examine human mental activity by observing and analyzing brain activities.

Neuroimaging data is typically rich in information but also inevitably contains noise. Additionally, the presence of individual variations poses challenges in drawing generalizable conclusions. Therefore, well-modeled neuroimaging data often provide more informative and sensitive insights into our dynamic internal processes.

This Methods Collection aims to expand the readers' perspectives by showcasing cutting-edge neuroimaging analytical techniques based on computational modeling. These computational models encompass various approaches, including Bayesian probabilities, time-frequency analysis, machine learning, deep learning, functional connectivity, and neural mass models. Any computational model that advances the decoding of neural or behavioral data aligns with the scope of this collection.