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Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional magnetic resonance imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we use 10-minute movie clips as ecologically valid emotion-evoking procedures in n=52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (400 atlas based parcels). Employing machine-learning based decoding and cross validation procedures allowed to develop predictive FC profiles that can accurately distinguish sustained happiness and sadness and that generalize across movies and subjects. Both functional brain network-based and subnetwork-based emotion prediction results suggest that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results show that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibit a strong contribution to emotion prediction. To further estimate the cumulative effect of naturalistic long-term movie-based video-evoking emotions, we divide the 10-min episode into three stages: early stimulation (1 ~ 200 s), middle stimulation (201 ~ 400 s), and late stimulation (401 ~ 600 s) and examine the emotion prediction performance at different stimulation stages. We found that the late stimulation has a stronger predictive ability (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrates that sustained sadness and happiness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidate the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

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