Brain-computer interfaces based on fMRI enable real-time feedback of circumscribed brain regions to learn volitional regulation of those regions. This is an emerging field of intense research, with potential for multiple applications in neuroscientific research in brain plasticity and reorganization, movement restoration due to stroke, clinical rehabilitation of emotional disorders, quality assurance of fMRI experiments, and teaching functional imaging. This article presents a general architecture of an fMRI-BCI, with descriptions of each of its subsystems, and factors influencing their performance. The study has attempted to describe and compare a variety of approaches toward signal acquisition, preprocessing, analysis, and feedback. Technological advancement in higher-field MRI scanners, data acquisition sequences and image reconstruction techniques, preprocessing algorithms to correct for artifacts, more intelligent and robust analysis and interpretation methods, and faster feedback and visualization technology are anticipated to make fMRI-BCI widely applicable. FMRI-BCI could potentially be used for training patients to learn self-regulation of specific brain areas for transferring them later on to a more portable EEG-BCI system. FMRI-BCI has the potential of establishing itself as a tool for neuroscientific research and experimentation and also as an aid for psychophysiological treatment.