Emotionotopy in the human right temporo-parietal cortex

AbstractHumans use emotions to decipher complex cascades of internal events. However, which mechanisms link descriptions of affective states to brain activity is unclear, with evidence supporting either local or distributed processing. A biologically favorable alternative is provided by the notion of gradient, which postulates the isomorphism between functional representations of stimulus features and cortical distance. Here, we use fMRI activity evoked by an emotionally charged movie and continuous ratings of the perceived emotion intensity to reveal the topographic organization of affective states. Results show that three orthogonal and spatially overlapping gradients encode the polarity, complexity and intensity of emotional experiences in right temporo-parietal territories. The spatial arrangement of these gradients allows the brain to map a variety of affective states within a single patch of cortex. As this organization resembles how sensory regions represent psychophysical properties (e.g., retinotopy), we propose emotionotopy as a principle of emotion coding

Local and Widespread Slow Waves in Stable NREM Sleep: Evidence for Distinct Regulation Mechanisms.

Previous work showed that two types of slow waves are temporally dissociated during the transition to sleep: widespread, large and steep slow waves predominate early in the falling asleep period ( <i>type I</i> ), while smaller, more circumscribed slow waves become more prevalent later ( <i>type II</i> ). Here, we studied the possible occurrence of these two types of slow waves in stable non-REM (NREM) sleep and explored potential differences in their regulation. A heuristic approach based on slow wave synchronization efficiency was developed and applied to high-density electroencephalographic (EEG) recordings collected during consolidated NREM sleep to identify the potential <i>type I</i> and <i>type II</i> slow waves. Slow waves with characteristics compatible with those previously described for <i>type I</i> and <i>type II</i> were identified in stable NREM sleep. Importantly, these slow waves underwent opposite changes across the night, with only <i>type II</i> slow waves displaying a clear homeostatic regulation. In addition, we showed that the occurrence of <i>type I</i> slow waves was often followed by larger <i>type II slow waves</i> , whereas the occurrence of <i>type II</i> slow waves was usually followed by smaller <i>type I</i> waves. Finally, <i>type II</i> slow waves were associated with a relative increase in spindle activity, while <i>type I</i> slow waves triggered periods of high-frequency activity. Our results provide evidence for the existence of two distinct slow wave synchronization processes that underlie two different types of slow waves. These slow waves may have different functional roles and mark partially distinct "micro-states" of the sleeping brain

Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

fMRI is used to investigate brain functional connectivity after removing nonneural components by General Linear Model (GLM) approach with a reference ventricle-derived signal as covariate. Ventricle signals are related to low-frequency modulations of cardiac and respiratory rhythms, which are nonstationary activities. Herein, we employed an adaptive filtering approach to improve removing physiological noise from BOLD signals. Comparisons between filtering approaches were performed by evaluating the amount of removed signal variance and the connectivity between homologous contralateral regions of interest (ROIs). The global connectivity between ROIs was estimated with a generalized correlation named RV coefficient. The mean ROI decrease of variance was -52% and -11%, for adaptive filtering and GLM, respectively. Adaptive filtering led to higher connectivity between grey matter ROIs than that obtained with GLM. Thus, adaptive filtering is a feasible method for removing the physiological noise in the low frequency band and to highlight resting state functional networks

Common spatiotemporal processing of visual features shapes object representation

none10Biological vision relies on representations of the physical world at different levels of complexity. Relevant features span from simple low-level properties, as contrast and spatial frequencies, to object-based attributes, as shape and category. However, how these features are integrated into coherent percepts is still debated. Moreover, these dimensions often share common biases: for instance, stimuli from the same category (e.g., tools) may have similar shapes. Here, using magnetoencephalography, we revealed the temporal dynamics of feature processing in human subjects attending to objects from six semantic categories. By employing Relative Weights Analysis, we mitigated collinearity between model-based descriptions of stimuli and showed that low-level properties (contrast and spatial frequencies), shape (medial-axis) and category are represented within the same spatial locations early in time: 100-150 ms after stimulus onset. This fast and overlapping processing may result from independent parallel computations, with categorical representation emerging later than the onset of low-level feature processing, yet before shape coding. Categorical information is represented both before and after shape, suggesting a role for this feature in the refinement of categorical matching.nonePapale, Paolo; Betta, Monica; Handjaras, Giacomo; Malfatti, Giulia; Cecchetti, Luca; Rampinini, Alessandra; Pietrini, Pietro; Ricciardi, Emiliano; Turella, Luca; Leo, AndreaPapale, Paolo; Betta, Monica; Handjaras, Giacomo; Malfatti, Giulia; Cecchetti, Luca; Rampinini, Alessandra; Pietrini, Pietro; Ricciardi, Emiliano; Turella, Luca; Leo, Andre

Eight weddings and six funerals: An fMRI study on autobiographical memories

\u201cAutobiographical memory\u201d (AM) refers to remote memories from one's own life. Previous neuroimaging studies have highlighted that voluntary retrieval processes from AM involve different forms of memory and cognitive functions. Thus, a complex and widespread brain functional network has been found to support AM. The present functional magnetic resonance imaging (fMRI) study used a multivariate approach to determine whether neural activity within the AM circuit would recognize memories of real autobiographical events, and to evaluate individual differences in the recruitment of this network. Fourteen right-handed females took part in the study. During scanning, subjects were presented with sentences representing a detail of a highly emotional real event (positive or negative) and were asked to indicate whether the sentence described something that had or had not really happened to them. Group analysis showed a set of cortical areas able to discriminate the truthfulness of the recalled events: medial prefrontal cortex, posterior cingulate/retrosplenial cortex, precuneus, bilateral angular, superior frontal gyri, and early visual cortical areas. Single-subject results showed that the decoding occurred at different time points. No differences were found between recalling a positive or a negative event. Our results show that the entire AM network is engaged in monitoring the veracity of AMs. This process is not affected by the emotional valence of the experience but rather by individual differences in cognitive strategies used to retrieve AMs

The effects of visual control and distance in modulating peripersonal spatial representation

In the presence of vision, finalized motor acts can trigger spatial remapping, i.e., reference frames transformations to allow for a better interaction with targets. However, it is yet unclear how the peripersonal space is encoded and remapped depending on the availability of visual feedback and on the target position within the individual’s reachable space, and which cerebral areas subserve such processes. Here, functional magnetic resonance imaging (fMRI) was used to examine neural activity while healthy young participants performed reach-to-grasp movements with and without visual feedback and at different distances of the target from the effector (near to the hand–about 15 cm from the starting position–vs. far from the hand–about 30 cm from the starting position). Brain response in the superior parietal lobule bilaterally, in the right dorsal premotor cortex, and in the anterior part of the right inferior parietal lobule was significantly greater during visually-guided grasping of targets located at the far distance compared to grasping of targets located near to the hand. In the absence of visual feedback, the inferior parietal lobule exhibited a greater activity during grasping of targets at the near compared to the far distance. Results suggest that in the presence of visual feedback, a visuo-motor circuit integrates visuo-motor information when targets are located farther away. Conversely in the absence of visual feedback, encoding of space may demand multisensory remapping processes, even in the case of more proximal targets