When a visual scene allows multiple interpretations, the percepts may spontaneously alternate despite the stable retinal image and the invariant sensory input transmitted to the brain. To study the brain basis of such multi-stable percepts, we superimposed rapidly changing dynamic noise as regional tags to the Rubin vase-face figure and followed the corresponding tag-related cortical signals with magnetoencephalography. The activity already in the earliest visual cortical areas, the primary visual cortex included, varied with the perceptual states reported by the observers. These percept-related modulations most likely reflect top-down influences that accentuate the neural representation of the perceived object in the early visual cortex and maintain the segregation of objects from the background.
Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even “at rest,” the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple “temporal functional modes,” including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.
Relief fits the definition of a reward. Unlike other reward types the pleasantness of relief depends on the violation of a negative expectation, yet this has not been investigated using neuroimaging approaches. We hypothesized that the degree of negative expectation depends on state (dread) and trait (pessimism) sensitivity. Of the brain regions that are involved in mediating pleasure, the nucleus accumbens also signals unexpected reward and positive prediction error. We hypothesized that accumbens activity reflects the level of negative expectation and subsequent pleasant relief. Using fMRI and two purpose-made tasks, we compared hedonic and BOLD responses to relief with responses during an appetitive reward task in 18 healthy volunteers. We expected some similarities in task responses, reflecting common neural substrates implicated across reward types. However, we also hypothesized that relief responses would differ from appetitive rewards in the nucleus accumbens, since only relief pleasantness depends on negative expectations. The results confirmed these hypotheses. Relief and appetitive reward task activity converged in the ventromedial prefrontal cortex, which also correlated with appetitive reward pleasantness ratings. In contrast, dread and pessimism scores correlated with relief but not with appetitive reward hedonics. Moreover, only relief pleasantness covaried with accumbens activation. Importantly, the accumbens signal appeared to specifically reflect individual differences in anticipation of the adverse event (dread, pessimism) but was uncorrelated to appetitive reward hedonics. In conclusion, relief differs from appetitive rewards due to its reliance on negative expectations, the violation of which is reflected in relief-related accumbens activation.
Variability in opioid analgesia has been attributed to many factors. For example, genetic variability of the μ-opioid receptor (MOR)-encoding gene introduces variability in MOR function and endogenous opioid neurotransmission. Emerging evidence suggests that personality trait related to the experience of reward is linked to endogenous opioid neurotransmission. We hypothesized that opioid-induced behavioral analgesia would be predicted by the trait reward responsiveness (RWR) and the response of the brain reward circuitry to noxious stimuli at baseline before opioid administration. In healthy volunteers using functional magnetic resonance imaging and the μ-opioid agonist remifentanil, we found that the magnitude of behavioral opioid analgesia is positively correlated with the trait RWR and predicted by the neuronal response to painful noxious stimuli before infusion in key structures of the reward circuitry, such as the orbitofrontal cortex, nucleus accumbens, and the ventral tegmental area. These findings highlight the role of the brain reward circuitry in the expression of behavioral opioid analgesia. We also show a positive correlation between behavioral opioid analgesia and opioid-induced suppression of neuronal responses to noxious stimuli in key structures of the descending pain modulatory system (amygdala, periaqueductal gray, and rostral–ventromedial medulla), as well as the hippocampus. Further, these activity changes were predicted by the preinfusion period neuronal response to noxious stimuli within the ventral tegmentum. These results support the notion of future imaging-based subject-stratification paradigms that can guide therapeutic decisions.
Working memory capacity is pivotal for a broad specter of cognitive tasks and develops throughout childhood. This must in part rely on development of neural connections and white matter microstructure maturation, but there is scarce knowledge of specific relations between this and different aspects of working memory. Diffusion tensor imaging (DTI) enables us to study development of brain white matter microstructure. In a longitudinal DTI study of 148 healthy children between 4 and 11 years scanned twice with an on average 1.6 years interval, we characterized change in fractional anisotropy (FA), mean (MD), radial (RD) and axial diffusivity (AD) in 10 major white matter tracts hypothesized to be of importance for working memory. The results showed relationships between change in several tracts and change in visuospatial working memory. Specifically, improvement in visuospatial working memory capacity was significantly associated with decreased MD, RD and AD in inferior longitudinal fasciculus (ILF), inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus (UF) in the right hemisphere, as well as forceps major (FMaj). No significant relationships were found between change in DTI metrics and change in verbal working memory capacity. These findings yield new knowledge about brain development and corresponding working memory improvements in childhood.
Significance The human brain displays an enormous amount of intrinsic activity in the absence of any task or external stimulation. Here we demonstrate that the human spinal cord, the brain’s principal interface with the body, also shows such resting-state activity. We observed biologically plausible and spatially distinct networks that reflect the functional organisation of the spinal cord: networks in the anterior part likely relating to motor function and distinct networks in the posterior part likely reflecting sensory function. These networks were grouped along the spinal cord, consistent with motor output to, and sensory input from, the body. Together with previous brain imaging studies, our data suggest that resting-state activity constitutes a major functional signature of the entire central nervous system.
Product design tend to keep surface modelling as a major means for creating geometry, where excessive time is spent and specialized professions are needed sometimes. Would a change to voxel modelling increase efficiency without reducing surface quality? A comparison between software for surface modeling and voxel modeling was made. Two objects were modeled side-by-side in the two software, one motorcycle tank demanding high surface quality and one automotive seat having detailed form. Surface quality was evaluated using curvature visualization and lead time from sketch to finished CAD object was measured. The tests showed voxel modeling to be much faster than surface modeling for both objects significant difference. However, there was a difference in surface smoothness in favor of surface modeling, although not visible in real-time rendered reflections. Despite the quality difference, voxel modeling might be accurate enough even for products with glossy surfaces, such as automotive exterior panels.
Reproduction and survival in most primate species reflects management of both competitive and cooperative relationships. Here, we investigated the links between neuroanatomy and sociality in free-ranging rhesus macaques. In adults, the number of social partners predicted the volume of the mid–superior temporal sulcus and ventral-dysgranular insula, implicated in social decision-making and empathy, respectively. We found no link between brain structure and other key social variables such as social status or indirect connectedness in adults, nor between maternal social networks or status and dependent infant brain structure. Our findings demonstrate that the size of specific brain structures varies with the number of direct affiliative social connections and suggest that this relationship may arise during development. These results reinforce proposed links between social network size, biological success, and the expansion of specific brain circuits.
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