Because of sophisticated machine-learning algorithms, big data, and increases in computing power, artificial intelligence has markedly advanced. Artificial intelligence techniques have been utilized for decoding in systems neuroscience. Functional connectivity analysis for resting state fMRI data revealed disorder-specific abnormalities of functional connections. Artifical intelligence techniques have enabled the development of disorder-specific classifiers or biomarkers that can be generalized to external validation datasets. By combining such biomarkers and decoding techniques with fMRI real-time neurofeedback methods, sophisticated neurofeedback techniques can be developed as future generation therapies for mental and developmental disorders.
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