Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition.

Prefrontal cortex (PFC) is postulated to exert "top-down control" on information processing throughout the brain to promote specific behaviors. However, pathways mediating top-down control remain poorly understood. In particular, knowledge about direct prefrontal connections that might facilitate top-down control of hippocampal information processing remains sparse. Here we describe monosynaptic long-range GABAergic projections from PFC to hippocampus. These preferentially inhibit vasoactive intestinal polypeptide-expressing interneurons, which are known to disinhibit hippocampal microcircuits. Indeed, stimulating prefrontal-hippocampal GABAergic projections increases hippocampal feedforward inhibition and reduces hippocampal activity in vivo. The net effect of these actions is to specifically enhance the signal-to-noise ratio for hippocampal encoding of object locations and augment object-induced increases in spatial information. Correspondingly, activating or inhibiting these projections promotes or suppresses object exploration, respectively. Together, these results elucidate a top-down prefrontal pathway in which long-range GABAergic projections target disinhibitory microcircuits, thereby enhancing signals and network dynamics underlying exploratory behavior.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit.

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration.

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning.

Are Internal Fragments Observable in Electron Based Top-Down Mass Spectrometry?

Protein tandem mass spectrometry (MS/MS) often generates sequence-informative fragments from backbone bond cleavages near the termini. This lack of fragmentation in the protein interior is particularly apparent in native top-down mass spectrometry (MS). Improved sequence coverage, critical for reliable annotation of posttranslational modifications and sequence variants, may be obtained from internal fragments generated by multiple backbone cleavage events. However, internal fragment assignments can be error prone due to isomeric/isobaric fragments from different parts of a protein sequence. Also, internal fragment generation propensity depends on the chosen MS/MS activation strategy. Here, we examine internal fragment formation in electron capture dissociation (ECD) and electron transfer dissociation (ETD) following native and denaturing MS, as well as LC/MS of several proteins. Experiments were undertaken on multiple instruments, including quadrupole time-of-flight, Orbitrap, and high-field Fourier-transform ion cyclotron resonance (FT-ICR) across four laboratories. ECD was performed at both ultrahigh vacuum and at similar pressure to ETD conditions. Two complementary software packages were used for data analysis. When feasible, ETD-higher energy collision dissociation MS3 was performed to validate/refute potential internal fragment assignments, including differentiating MS3 fragmentation behavior of radical versus even-electron primary fragments. We show that, under typical operating conditions, internal fragments cannot be confidently assigned in ECD or ETD. On the other hand, such fragments, along with some b-type terminal fragments (not typically observed in ECD/ETD spectra) appear at atypical ECD operating conditions, suggesting they originate from a separate ion-electron activation process. Furthermore, atypical fragment ion types, e.g., x ions, are observed at such conditions as well as upon EThcD, presumably due to vibrational activation of radical z-type ions.

Human-like scene interpretation by a guided counterstream processing.

In modeling vision, there has been a remarkable progress in recognizing a range of scene components, but the problem of analyzing full scenes, an ultimate goal of visual perception, is still largely open. To deal with complete scenes, recent work focused on the training of models for extracting the full graph-like structure of a scene. In contrast with scene graphs, humans' scene perception focuses on selected structures in the scene, starting with a limited interpretation and evolving sequentially in a goal-directed manner [G. L. Malcolm, I. I. A. Groen, C. I. Baker, Trends. Cogn. Sci. 20, 843-856 (2016)]. Guidance is crucial throughout scene interpretation since the extraction of full scene representation is often infeasible. Here, we present a model that performs human-like guided scene interpretation, using an iterative bottom-up, top-down processing, in a "counterstream" structure motivated by cortical circuitry. The process proceeds by the sequential application of top-down instructions that guide the interpretation process. The results show how scene structures of interest to the viewer are extracted by an automatically selected sequence of top-down instructions. The model shows two further benefits. One is an inherent capability to deal well with the problem of combinatorial generalization-generalizing broadly to unseen scene configurations, which is limited in current network models [B. Lake, M. Baroni, 35th International Conference on Machine Learning, ICML 2018 (2018)]. The second is the ability to combine visual with nonvisual information at each cycle of the interpretation process, which is a key aspect for modeling human perception as well as advancing AI vision systems.

Hierarchical Gradients of Encoded Spatial and Sensory Information in the Neocortex Are Attenuated by Dorsal Hippocampal Lesions.

During navigation, the neocortex actively integrates learned spatial context with current sensory experience to guide behaviors. However, the relative encoding of spatial and sensorimotor information among cortical cells, and whether hippocampal feedback continues to modify these properties after learning, remains poorly understood. Thus, two-photon microscopy of male and female Thy1-GCaMP6s mice was used to longitudinally image neurons spanning superficial retrosplenial cortex and layers II-Va of primary and secondary motor cortices before and after bilateral dorsal hippocampal lesions. During behavior on a familiar cued treadmill, the locations of two obstacles were interchanged to decouple place-tuning from cue-tuning among position-correlated cells with fields at those locations. Subpopulations of place and cue cells each formed interareal gradients such that higher-level cortical regions exhibited higher fractions of place cells, whereas lower-level regions exhibited higher fractions of cue cells. Position-correlated cells in the motor cortex also formed translaminar gradients; more superficial cells were more likely to exhibit fields and were more sparsely and precisely tuned than deeper cells. After dorsal hippocampal lesions, a neural representation of the learned environment persisted, but retrosplenial cortex exhibited significantly increased cue-tuning, and, in motor cortices, both position-correlated cell recruitment and population activity at the unstable obstacle locations became more homogeneously elevated across laminae. Altogether, these results support that the hippocampus continues to modulate cortical responses in familiar environments, and the relative impact of descending feedback obeys hierarchical interareal and interlaminar gradients opposite to the flow of ascending sensory inputs.

Group membership modulates empathic neural responses to pain in deaf individuals.

Empathy deficiencies are prevalent among deaf individuals. It has yet to be determined whether they exhibit an ingroup bias in empathic responses. This study employed explicit and implicit empathy tasks (i.e. attention-to-pain-cue [A-P] task and attention-to-nonpain-cue [A-N] task) to explore the temporal dynamics of neural activities when deaf individuals were processing painful/nonpainful stimuli from both ingroup models (deaf people) and outgroup models (hearing people), which aims to not only assist deaf individuals in gaining a deeper understanding of their intergroup empathy traits but also to aid in the advancement of inclusive education. In the A-P task, we found that (i) ingroup priming accelerated the response speed to painful/nonpainful pictures; (ii) the N2 amplitude of painful pictures was significantly more negative than that of nonpainful pictures in outgroup priming trials, whereas the N2 amplitude difference between painful and nonpainful pictures was not significant in ingroup priming trials. For N1 amplitude of the A-N task, we have similar findings. However, this pattern was reversed for P3/late positive component amplitude of the A-P task. These results suggest that the deaf individuals had difficulty in judging whether hearing individuals were in pain. However, their group identification and affective responses could shape the relatively early stage of pain empathy.

Serial Dependence in Perception.

Much evidence has shown that perception is biased towards previously presented similar stimuli, an effect recently termed serial dependence. Serial dependence affects nearly every aspect of perception, often causing gross perceptual distortions, especially for weak and ambiguous stimuli. Despite unwanted side-effects, empirical evidence and Bayesian modeling show that serial dependence acts to improve efficiency and is generally beneficial to the system. Consistent with models of predictive coding, the Bayesian priors of serial dependence are generated at high levels of cortical analysis, incorporating much perceptual experience, but feed back to lower sensory areas. These feedback loops may drive oscillations in the alpha range, linked strongly with serial dependence. The discovery of top-down predictive perceptual processes is not new, but the new, more quantitative approach characterizing serial dependence promises to lead to a deeper understanding of predictive perceptual processes and their underlying neural mechanisms.

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Reintroducing bison results in long-running and resilient increases in grassland diversity.

The widespread extirpation of megafauna may have destabilized ecosystems and altered biodiversity globally. Most megafauna extinctions occurred before the modern record, leaving it unclear how their loss impacts current biodiversity. We report the long-term effects of reintroducing plains bison (Bison bison) in a tallgrass prairie versus two land uses that commonly occur in many North American grasslands: 1) no grazing and 2) intensive growing-season grazing by domesticated cattle (Bos taurus). Compared to ungrazed areas, reintroducing bison increased native plant species richness by 103% at local scales (10 m2) and 86% at the catchment scale. Gains in richness continued for 29 y and were resilient to the most extreme drought in four decades. These gains are now among the largest recorded increases in species richness due to grazing in grasslands globally. Grazing by domestic cattle also increased native plant species richness, but by less than half as much as bison. This study indicates that some ecosystems maintain a latent potential for increased native plant species richness following the reintroduction of native herbivores, which was unmatched by domesticated grazers. Native-grazer gains in richness were resilient to an extreme drought, a pressure likely to become more common under future global environmental change.