Article Figures & Data
Figures
- Fig. 1.
Design of the fMRI experiment. A 2 × 2 factorial design was used, with factors of preference (juice or water) and predictability (predictable or unpredictable). Subjects received 0.8 ml boluses of juice and water in either a predictable or unpredictable sequence. Using event-related fMRI, brain activation was analyzed in terms of preference and predictability, as well as the interaction between them.
- Fig. 2.
Neural network model of the experiment and the brain regions associated with information processing. A, Diagram indicates our hypothesis for how the sequence of stimuli could influence dopaminergic output. In this hypothesis, we have indicated that changes in dopaminergic output could influence target neural structures in a manner detectable in a fMRI BOLD measurement. The juice and water are shown to have both sensory (projection from finite timewindow box) and reward (the r pathways) representations in their influence on dopaminergic activity. To generate an expected hemodynamic response from this hypothesis, we made a finite time window (small boxes for juice and water), which determined the value of the immediate rewardr(t) (1 if juice occurred, 0.5 if water occurred, and 0 if no stimulus occurred). This maneuver arbitrarily set juice to twice the value of water. This is not important for the main expectation generated by the model.B, Predicted dopamine effect for predictable and unpredictable sequences of juice and water delivery. Horizontal axis is scan number. Vertical axis is the expected hemodynamic response predicted by a temporal difference model. The scale on the vertical axis is arbitrary. The important point to note is that the predictable run progresses to 0, whereas the unpredictable run remains high-amplitude throughout. The traces were generated by convolving a hemodynamic response kernel with the output of a temporal difference model. This suggested that the average BOLD response would be greater when the stimuli were unpredictable.
- Fig. 3.
The main effect of predictability showed that reward-related regions had a greater BOLD response to the unpredictable stimuli. A, Planes centered at (0, 4, −4) show that bilateral nucleus accumbens/ventral striatum (NAC) and bilateral superior parietal cortex were more active in the predictable condition. B, A small region in the right superior temporal gyrus was relatively more activated by the predictable stimuli. Significance was thresholded atp < 0.001 and an extent >10 contiguous voxels.
Tables
- Table 1.
Brain regions displaying significant changes in measured activity (p < 0.001 uncorrected; cluster size >10 voxels, except where noted)
Brain region MNI coordinates (x, y, z) Peak t value Cluster size Preferred > nonpreferred L somatosensory cortex −60, −12, 16 4.19 17 (p < 0.01) Nonpreferred > preferred None Unpredictable > predictable R medial orbitofrontal cortex including 20, 36, −12 7.31 77 Bilateral nucleus accumbens 0, 16, −8 7.17 Bilateral paracentral lobule 12, −20, 64 6.35 239 L mediodorsal nucleus of thalamus −4, −20, 4 5.86 12 R cerebellum 44, −68, −24 5.85 12 16, −76, −28 4.86 26 Predictable > unpredictable R superior temporal gyrus 56, −40, 16 5.16 98 L precentral gyrus −48, 4, 36 4.28 14 R lateral orbitofrontal gyrus 32, 16, −16 4.22 13 (Preferred–nonpreferred) × (unpredictable–predictable) L superior temporal gyrus −48, −4, −16 3.15 5 (p < 0.01) (Preferred–nonpreferred) × (predictable–unpredictable) R insula 44, 20, 12 3.61 17 (p < 0.01) L posterior cingulate gyrus −12, −40, 8 3.59 46 (p < 0.01) R cerebellum 28, −52, −32 3.56 13 (p < 0.01) L, Left; R, right.
In this issue
