Introduction | Other models, such as the Gaussian Nai've Bayes approach [14], rely on differnces in activity patterns without capturing any of the interactions among brain regions that are likely critical for differentiating affective states [20,22,23]. |
Network Co-activation Differences among Emotion Categories | 3 shows that each emotion category was associated with a qualitatively different configuration of co-activation between cortical networks and subcortical brain regions . |
New Implications for Emotion Theories | Many theories also assume that the signature for an emotion type should correspond to activation in a specific brain region or anatomically modular circuit (e.g., [50]), usually Within sub-cortical tissue (e.g., [51] ). |
New Implications for Emotion Theories | Valence may be an aspect of emotional responses that is particularly important subjectively, but is not the principal determinant of which brain regions are engaged during emotional experience at an architectural level. |
New Implications for Emotion Theories | Our findings place an important constraint on emotion theories that identify emotions with discrete brain regions or circuits. |
Abstract | Moreover, these biases impede the comparison of oscillations across brain regions , neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. |
Author Summary | These, previously neglected, biases hinder the comparison of oscillations across brain regions , neuronal types and behavioral states, leading inevitably to severe misinterpretation of experimental results. |
Discussion | In the mammalian brain, the range of firing rates between brain areas and neuronal types is considerable, and even within a specific brain region and a specific neuronal type, the heterogeneity of firing rates within the population is large. |
Discussion | In conclusion, the modulation indeX can provide an objective quantification of spike train oscillations, and thus an unbiased comparison across brain regions , behavioral states and separate recordings with different recording lengths. |
Discussion | simulated how network structure affects the phase lead/lag relationship between brain regions in a realistic brain network model [19]. |
Introduction | For example, a study analyzing the electroencephalogram (EEG) recorded from human volunteers demonstrated that if a brain region is topologically more accessible to other brain regions , then it has a larger variability in its local activity [16]. |
Introduction | As another example, a magnetoencephalogram (MEG) study showed that variability in the MEG sources determines the direction of information flow between local brain regions [17, 18]. |