Knowledge-Driven Contrast Gain Control is Characterized by Two Distinct Electrocortical Markers

Sensitivity to variations in luminance (contrast) is fundamental to perception because contrasts define the edges and textures of visual objects. Recent research has shown that contrast sensitivity, in addition to being controlled by purely stimulus-driven mechanisms, is also affected by expectations and prior knowledge about the contrast of upcoming stimuli. The ability to adjust contrast sensitivity based on expectations and prior knowledge could help to maximize the information extracted when scanning familiar visual scenes. In the present study we used the event-related potentials (ERP) technique to resolve the stages that mediate knowledge-driven aspects of contrast gain control. Using groupwise independent components analysis and multivariate partial least squares, we isolated two robust spatiotemporal patterns of electrical brain activity associated with preparation for upcoming targets whose contrast was predicted by a cue. The patterns were sensitive to the informative value of the cue. When the cues were informative, these patterns were also able to differentiate among cues that predicted low-contrast targets and cues that predicted high-contrast targets. Both patterns were localized to parts of occipitotemporal cortex, and their morphology, latency, and topography resembled P2/N2 and P3 potentials. These two patterns provide electrophysiological markers of knowledge-driven preparation for impending changes in contrast and shed new light on the manner in which top-down factors modulate sensory processing

Prefrontal Compensatory Engagement in TBI is due to Altered Functional Engagement Of Existing Networks and not Functional Reorganization

Functional neuroimaging studies of traumatic brain injury (TBI) have demonstrated altered neural recruitment, specifically within prefrontal cortex (PFC). This is manifest typically as increased recruitment of homologous regions of PFC (e.g., right ventrolateral PFC during performance of a verbal working memory task, possibly in response to damage involving the left PFC). The behavioral correlates of these functional changes are poorly understood. We used fMRI and multivariate analytic methods to investigate changes in spatially distributed activity patterns and their behavioral correlates in a sample of TBI patients with diffuse axonal injury (DAI, but without focal injury) and matched healthy controls. Participants performed working memory tasks with varying memory load and executive demand. We identified networks within left and right PFC that uniquely and positively correlated with performance in our control and TBI samples respectively, providing evidence of compensatory functional recruitment. Next we combined brain–behavior and functional connectivity analyses to investigate whether compensatory brain changes were facilitated by functional reorganization (i.e., recruitment of brain regions not engaged by our control sample) or altered functional engagement (i.e., differential recruitment of similar brain regions between the two groups based on task demands). In other words, does altered recruitment represent the instantiation of novel neural networks to support working memory performance after injury or the unmasking of extant, but behaviorally latent, functional connectivity? Our results support an altered functional engagement hypothesis. Areas within PFC that are normally coactivated during working memory are behaviorally relevant at an earlier stage of difficulty for TBI patients as compared to controls. This altered functional engagement, also evident in the aging literature, is attributable to distributed changes owing to significant DAI

Functional Embedding Predicts the Variability of Neural Activity

Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region’s activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding

Editorial: State-dependent brain computation

International audienceThe brain is a self-organizing system, which has evolved such that neuronal responses and related behavior are continuously adapted with respect to the external and internal context. This powerful capability is achieved through the modulation of neuronal interactions depending on the history of previously processed information. In particular, the brain updates its connections as it learns successful versus unsuccessful strategies. The resulting connectivity changes, together with stochastic processes (i.e., noise) influence ongoing neuronal dynamics. The role of such state-dependent fluctuations may be one of the fundamental computational properties of the brain, being pervasively present in human behavior and leaving a distinctive fingerprint in neuroscience data. This development is captured by the present Frontiers Research Topic, " State-Dependent Brain Computation

Use of \u3cem\u3eTris\u3c/em\u3e-Quaternary Ammonium Salts as Pain Modulating Agents

Provided are tris-quatemary ammonium compounds which are modulators of nociception and pain

A macaque connectome for large-scale network simulations in TheVirtualBrain

© 2019, The Author(s). Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data

Brain Activity Patterns Uniquely Supporting Visual Feature Integration after Traumatic Brain Injury

Traumatic brain injury (TBI) patients typically respond more slowly and with more variability than controls during tasks of attention requiring speeded reaction time. These behavioral changes are attributable, at least in part, to diffuse axonal injury (DAI), which affects integrated processing in distributed systems. Here we use a multivariate method sensitive to distributed neural activity to compare brain activity patterns of patients with chronic phase moderate to-severe TBI to those of controls during performance on a visual feature integration task assessing complex attentional processes that has previously shown sensitivity to TBI. The TBI patients were carefully screened to be free of large focal lesions that can affect performance and brain activation independently of DAI. The task required subjects to hold either one or three features of a Target in mind while suppressing responses to distracting information. In controls, the multi-feature condition activated a distributed network including limbic, prefrontal, and medial temporal structures. TBI patients engaged this same network in the single-feature and baseline conditions. In multi-feature presentations, TBI patients alone activated additional frontal, parietal, and occipital regions. These results are consistent with neuroimaging studies using tasks assessing different cognitive domains, where increased spread of brain activity changes was associated with TBI. Our results also extend previous findings that brain activity for relatively moderate task demands in TBI patients is similar to that associated with of high task demands in controls

Selective activation of resting state networks following focal stimulation in a connectome- based network model of the human brain

Imaging studies suggest that the functional connectivity patterns of resting state networks (RS-networks) reflect underlying structural connectivity (SC). If the connectome constrains how brain areas are functionally connected, the stimulation of specific brain areas should produce a characteristic wave of activity ultimately resolving into RS-networks. To systematically test this hypothesis, we use a connectome-based network model of the human brain with detailed realistic SC. We systematically activate all possible thalamic and cortical areas with focal stimulation patterns and confirm that the stimulation of specific areas evokes network patterns that closely resemble RS-networks. For some sites, one or no RS-network is engaged, whereas for other sites more than one RS-network may evolve. Our results confirm that the brain is operating at the edge of criticality, wherein stimulation produces a cascade of functional network recruitments, collapsing onto a smaller subspace that is constrained in part by the anatomical local and long-range SCs. We suggest that information flow, and subsequent cognitive processing, follows specific routes imposed by connectome features, and that these routes explain the emergence of RS-networks. Since brain stimulation can be used to diagnose/treat neurological disorders, we provide a look-up table showing which areas need to be stimulated to activate specific RS-networks.Comment: 25 pages (in total), 7 figures, 2 table

Functional connectivity-based subtypes of individuals with and without autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder, characterized by impairments in social communication and restricted, repetitive behaviors. Neuroimaging studies have shown complex patterns and functional connectivity (FC) in ASD, with no clear consensus on brain-behavior relationships or shared patterns of FC with typically developing controls. Here, we used a dimensional approach to characterize two distinct clusters of FC patterns across both ASD participants and controls using k-means clustering. Using multivariate statistical analyses, a categorical approach was taken to characterize differences in FC between subtypes and between diagnostic groups. One subtype was defined by increased FC within resting-state networks and decreased FC across networks compared with the other subtype. A separate FC pattern distinguished ASD from controls, particularly within default mode, cingulo-opercular, sensorimotor, and occipital networks. There was no significant interaction between subtypes and diagnostic groups. Finally, a dimensional analysis of FC patterns with behavioral measures of IQ, social responsiveness, and ASD severity showed unique brain-behavior relations in each subtype and a continuum of brain-behavior relations from ASD to controls within one subtype. These results demonstrate that distinct clusters of FC patterns exist across ASD and controls, and that FC subtypes can reveal unique information about brain-behavior relationships. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, with high variation in the types of severity of impairments in social communication and restricted, repetitive behaviors. Neuroimaging studies have shown complex patterns of communication between brain regions, or functional connectivity (FC), in ASD. Here, we defined two distinct FC patterns and relationships between FC and behavior in a group of participants consisting of individuals with and without ASD. One subtype was defined by increased FC within distinct networks of brain regions and decreased FC between networks compared with the other subtype. A separate FC pattern distinguished ASD from controls. The interaction between subtypes and diagnostic groups was not significant. Dimensional analyses of FC patterns with behavioral measures revealed unique information about brain-behavior relations in each subtype