Dimensional emotions are represented by distinct topographical brain networks

The ability to recognize others’ facial emotions has become increasingly important after the COVID-19 pandemic, which causes stressful situations in emotion regulation. Considering the importance of emotion in maintaining a social life, emotion knowledge to perceive and label emotions of oneself and others requires an understanding of affective dimensions, such as emotional valence and emotional arousal. However, limited information is available about whether the behavioral representation of affective dimensions is similar to their neural representation. To explore the relationship between the brain and behavior in the representational geometries of affective dimensions, we constructed a behavioral paradigm in which emotional faces were categorized into geometric spaces along the valence, arousal, and valence and arousal dimensions. Moreover, we compared such representations to neural representations of the faces acquired by functional magnetic resonance imaging. We found that affective dimensions were similarly represented in the behavior and brain. Specifically, behavioral and neural representations of valence were less similar to those of arousal. We also found that valence was represented in the dorsolateral prefrontal cortex, frontal eye fields, precuneus, and early visual cortex, whereas arousal was represented in the cingulate gyrus, middle frontal gyrus, orbitofrontal cortex, fusiform gyrus, and early visual cortex. In conclusion, the current study suggests that dimensional emotions are similarly represented in the behavior and brain and are presented with differential topographical organizations in the brain. (AU)

Dimensional emotions are represented by distinct topographical brain networks.

The ability to recognize others' facial emotions has become increasingly important after the COVID-19 pandemic, which causes stressful situations in emotion regulation. Considering the importance of emotion in maintaining a social life, emotion knowledge to perceive and label emotions of oneself and others requires an understanding of affective dimensions, such as emotional valence and emotional arousal. However, limited information is available about whether the behavioral representation of affective dimensions is similar to their neural representation. To explore the relationship between the brain and behavior in the representational geometries of affective dimensions, we constructed a behavioral paradigm in which emotional faces were categorized into geometric spaces along the valence, arousal, and valence and arousal dimensions. Moreover, we compared such representations to neural representations of the faces acquired by functional magnetic resonance imaging. We found that affective dimensions were similarly represented in the behavior and brain. Specifically, behavioral and neural representations of valence were less similar to those of arousal. We also found that valence was represented in the dorsolateral prefrontal cortex, frontal eye fields, precuneus, and early visual cortex, whereas arousal was represented in the cingulate gyrus, middle frontal gyrus, orbitofrontal cortex, fusiform gyrus, and early visual cortex. In conclusion, the current study suggests that dimensional emotions are similarly represented in the behavior and brain and are presented with differential topographical organizations in the brain.

Functional Connectivity in the Mouse Brainstem Represents Signs of Recovery from Concussion.

Mild traumatic brain injury (mTBI) is one of the most frequent neurological disorders. Diagnostic criteria for mTBI are based on cognitive or neurological symptoms without fully understanding the neuropathological basis for explaining behaviors. From the neuropathological perspective of mTBI, recent neuroimaging studies have focused on structural or functional differences in motor-related cortical regions but did not compare topological network properties between the post-concussion days in the brainstem. We investigated temporal changes in functional connectivity and evaluated network properties of functional networks in the mouse brainstem. We observed a significantly decreased functional connectivity and global and local network properties on post-concussion day 7, which normalized on post-concussion day 14. Functional connectivity and local network properties on post-concussion day 2 were also significantly decreased compared with those on post-concussion day 14, but there were no significant group differences in global network properties between days 2 and 14. We also observed that the local efficiency and clustering coefficient of the brainstem network were significantly correlated with anxiety-like behaviors on post-concussion days 7 and 14. This study suggests that functional connectivity in the mouse brainstem provides vital recovery signs from concussion through functional reorganization.

Perception of a surface split induced by globally inconsistent kinetic occlusion: Objects with salient parts break apart easily.

Unlike previous studies of part-based shape representations that mostly investigated static images, we tested whether part segmentation can affect the perception of ambiguous dynamic events, involving globally inconsistent kinetic occlusion: An object moved horizontally across another stationary object, with either its top or bottom half occluding and the other half being occluded by the stationary one, which could be perceived as one object being split into halves by the other object. We manipulated an object's part structure by introducing concave or convex cusps along the contour of an object and found that objects with concave cusps were more likely perceived as being split than those with no or convex cusps. This study provides a new insight into a broad framework of spatiotemporal perceptual organization, by demonstrating that salient parts are readily perceived as broken apart in a physical sense, which in turn alters the perception of a motion event and its causal structure. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

State estimation and prediction using clustered particle filters.

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.

Conceptual dynamical models for turbulence.

Understanding the complexity of anisotropic turbulent processes in engineering and environmental fluid flows is a formidable challenge with practical significance because energy often flows intermittently from the smaller scales to impact the largest scales in these flows. Conceptual dynamical models for anisotropic turbulence are introduced and developed here which, despite their simplicity, capture key features of vastly more complicated turbulent systems. These conceptual models involve a large-scale mean flow and turbulent fluctuations on a variety of spatial scales with energy-conserving wave-mean-flow interactions as well as stochastic forcing of the fluctuations. Numerical experiments with a six-dimensional conceptual dynamical model confirm that these models capture key statistical features of vastly more complex anisotropic turbulent systems in a qualitative fashion. These features include chaotic statistical behavior of the mean flow with a sub-Gaussian probability distribution function (pdf) for its fluctuations whereas the turbulent fluctuations have decreasing energy and correlation times at smaller scales, with nearly Gaussian pdfs for the large-scale fluctuations and fat-tailed non-Gaussian pdfs for the smaller-scale fluctuations. This last feature is a manifestation of intermittency of the small-scale fluctuations where turbulent modes with small variance have relatively frequent extreme events which directly impact the mean flow. The dynamical models introduced here potentially provide a useful test bed for algorithms for prediction, uncertainty quantification, and data assimilation for anisotropic turbulent systems.