Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD (2015)
PLoS Biol 13(6): e1002180.
写真を見た際の（負の）感情評価を「全脳のボクセルを対象にしたfMRI MVPA（実際に効いているのは全ボクセルの1.6%）」を用いて高精度（正解率90%以上）に予測できる。 http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002180
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.