Figure 1.
Four hypotheses for emotion processing in the brain have been put forward.
(Figure based on [15]. The right hemisphere hypothesis (a) assumes that emotion is processed predominantly in the right hemisphere. The valence hypothesis (b) suggests the right hemisphere to be dominant in processing negative emotions and the left hemisphere to be dominant in processing positive emotions. The one-network hypothesis (c) posits that all emotions may be processed by a set of brain regions not specific to a respective emotion category, while the localist hypothesis (d) is that processing of different emotions specifically corresponds to activation in distinct brain regions.
Table 1.
Participant details and acquisition parameters.
Figure 2.
The PANAS captures two independent dimensions of Positive Affect (PA) and Negative Affect (NA).
(Figure based on [32]). In this circumplex model of affective space, they fall between the Pleasantness and Arousal spectra, two dimensions which are orthogonal themselves. The PANAS scales emerge after rotation of these two factors. The correlation between the PA and NA scales is low enough to suggest relative independence when taking the measurement error into account.
Table 2.
Positive and Negative Schedule (PANAS) items.
Figure 3.
Following standard pre-processing of the resting state fMRI data, connectivity was calculated between the time courses of each of the 200 functional seeds and all the voxels in the brain. These connectivity scores were correlated with PA and NA scores in separate analyses, which were followed by a group-level multiple regression and two conjunction analyses.
Figure 4.
No correlation between the scales.
A correlation analysis revealed no correlation between individual scores of NA and PA (r2 = 0.025, p>0.4), as expected from their construction as orthogonal scales (see Figure 2).
Table 3.
Details of connections for NA.
Table 4.
Details of connections for PA.
Figure 5.
Networks correlated with Positive Affect (PA) and Negative Affect (NA).
Whole-brain connectivity analysis revealed networks that were correlated with PA or NA. While some of the regions were common to both PA and NA functional connectivity patterns, others were dissociative of the respective affective domain, here depicted in different colors. A trend for overall right-hemispheric dominance was observed.
Figure 6.
Examples of connections correlated with Negative Affect.
NA was reflected in two small networks: within the positively correlated network, greater connectivity was observed with higher NA, whereas within the negatively correlated network greater connectivity was observed with lower NA.
Figure 7.
Examples of connections correlated with Positive Affect.
We detected a large network that was negatively correlated to the PA score; greater connectivity within this network was observed to correlate with lower PA.