Arf4 Determines Dentate Gyrus-Mediated Pattern Separation by Regulating Dendritic Spine Development

The ability to distinguish between similar experiences is a critical feature of episodic memory and is primarily regulated by the dentate gyrus (DG) region of the hippocampus. However, the molecular mechanisms underlying such pattern separation tasks are poorly understood. We report a novel role for the small GTPase ADP ribosylation factor 4 (Arf4) in controlling pattern separation by regulating dendritic spine development. Arf4^+/− mice at 4–5 months of age display severe impairments in a pattern separation task, as well as significant dendritic spine loss and smaller miniature excitatory post-synaptic currents (mEPSCs) in granule cells of the DG. Arf4 knockdown also decreases spine density in primary neurons, whereas Arf4 overexpression promotes spine development. A constitutively active form of Arf4, Arf4-Q71L, promotes spine density to an even greater extent than wildtype Arf4, whereas the inactive Arf4-T31N mutant does not increase spine density relative to controls. Arf4′s effects on spine development are regulated by ASAP1, a GTPase-activating protein that modulates Arf4 GTPase activity. ASAP1 overexpression decreases spine density, and this effect is partially rescued by concomitant overexpression of wildtype Arf4 or Arf4-Q71L. In addition, Arf4 overexpression rescues spine loss in primary neurons from an Alzheimer''s disease-related apolipoprotein (apo) E4 mouse model. Our findings suggest that Arf4 is a critical modulator of DG-mediated pattern separation by regulating dendritic spine development.     

Prefrontal cortical activity predicts the occurrence of nonlocal hippocampal representations during spatial navigation

The receptive field of a neuron describes the regions of a stimulus space where the neuron is consistently active. Sparse spiking outside of the receptive field is often considered to be noise, rather than a reflection of information processing. Whether this characterization is accurate remains unclear. We therefore contrasted the sparse, temporally isolated spiking of hippocampal CA1 place cells to the consistent, temporally adjacent spiking seen within their spatial receptive fields (“place fields”). We found that isolated spikes, which occur during locomotion, are strongly phase coupled to hippocampal theta oscillations and transiently express coherent nonlocal spatial representations. Further, prefrontal cortical activity is coordinated with and can predict the occurrence of future isolated spiking events. Rather than local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical–hippocampal process consistent with the generation of nonlocal scenario representations during active navigation.

This study of active navigation shows that, rather than being local noise within the hippocampus, sparse, isolated place cell spiking reflects a coordinated cortical-hippocampal process consistent with the generation of non-local scenario representations.     

Amplification of Asynchronous Inhibition-Mediated Synchronization by Feedback in Recurrent Networks

Synchronization of 30–80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells) is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called “stochastic synchronization”, wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model.     

A statistical model for describing and simulating microbial community profiles

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA’s model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. “taxa”) or between features and “phenotypes” to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA’s performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA’s utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.     

Pattern separation: a common function for new neurons in hippocampus and olfactory bulb

While adult-born neurons in the olfactory bulb (OB) and the dentate gyrus (DG) subregion of the hippocampus have fundamentally different properties, they may have more in common than meets the eye. Here, we propose that new granule cells in the OB and DG may function as modulators of principal neurons to influence pattern separation and that adult neurogenesis constitutes an adaptive mechanism to optimally encode contextual or olfactory information. See the related Perspective from Aimone, Deng, and Gage, "Resolving New Memories: A Critical Look at the Dentate Gyrus, Adult Neurogenesis, and Pattern Separation," in this issue of Neuron.     

Rapid compensatory changes in the expression of EAAT-3 and GAT-1 transporters during seizures in cells of the CA1 and dentate gyrus

Background

Epilepsy is a neurological disorder produced by an imbalance between excitatory and inhibitory neurotransmission, in which transporters of both glutamate and GABA have been implicated. Hence, at different times after local administration of the convulsive drug 4-aminopyridine (4-AP) we analyzed the expression of EAAT-3 and GAT-1 transporter proteins in cells of the CA1 and dentate gyrus.

Methods

Dual immunofluorescence was used to detect the co-localization of transporters and a neuronal marker. In parallel, EEG recordings were performed and convulsive behavior was rated using a modified Racine Scale.

Results

By 60 min after 4-AP injection, EAAT-3/NeuN co-labelling had increased in dentate granule cells and decreased in CA1 pyramidal cells. In the latter, this decrease persisted for up to 180 min after 4-AP administration. In both the DG and CA1, the number of GAT-1 labeled cells increased 60 min after 4-AP administration, although by 180 min GAT-1 labeled cells decreased in the DG alone. The increase in EAAT-3/NeuN colabelling in DG was correlated with maximum epileptiform activity and convulsive behavior.

Conclusions

These findings suggest that a compensatory mechanism exists to protect against acute seizures induced by 4-AP, whereby EAAT-3/NeuN cells is rapidly up regulated in order to enhance the removal of glutamate from the extrasynaptic space, and attenuating seizure activity.     

Hippocampal Synaptic Expansion Induced by Spatial Experience in Rats Correlates with Improved Information Processing in the Hippocampus

Spatial water maze (WM) overtraining induces hippocampal mossy fiber (MF) expansion, and it has been suggested that spatial pattern separation depends on the MF pathway. We hypothesized that WM experience inducing MF expansion in rats would improve spatial pattern separation in the hippocampal network. We first tested this by using the the delayed non-matching to place task (DNMP), in animals that had been previously trained on the water maze (WM) and found that these animals, as well as animals treated as swim controls (SC), performed better than home cage control animals the DNMP task. The “catFISH” imaging method provided neurophysiological evidence that hippocampal pattern separation improved in animals treated as SC, and this improvement was even clearer in animals that experienced the WM training. Moreover, these behavioral treatments also enhance network reliability and improve partial pattern separation in CA1 and pattern completion in CA3. By measuring the area occupied by synaptophysin staining in both the stratum oriens and the stratun lucidum of the distal CA3, we found evidence of structural synaptic plasticity that likely includes MF expansion. Finally, the measures of hippocampal network coding obtained with catFISH correlate significantly with the increased density of synaptophysin staining, strongly suggesting that structural synaptic plasticity in the hippocampus induced by the WM and SC experience is related to the improvement of spatial information processing in the hippocampus.     

Evaluating the effectiveness of ensemble voting in improving the accuracy of consensus signals produced by various DTWA algorithms from step-current signals generated during nanopore sequencing

Nanopore sequencing device analysis systems simultaneously generate multiple picoamperage current signals representing the passage of DNA or RNA nucleotides ratcheted through a biomolecule nanopore array by motor proteins. Squiggles are a noisy and time-distorted representation of an underlying nucleotide sequence, “gold standard model”, due to experimental and algorithmic artefacts. Other research fields use dynamic time warped-space averaging (DTWA) algorithms to produce a consensus signal from multiple time-warped sources while preserving key features distorted by standard, linear-averaging approaches. We compared the ability of DTW Barycentre averaging (DBA), minimize mean (MM) and stochastic sub-gradient descent (SSG) DTWA algorithms to generate a consensus signal from squiggle-space ensembles of RNA molecules Enolase, Sequin R1-71-1 and Sequin R2-55-3 without knowledge of their associated gold standard model. We propose techniques to identify the leader and distorted squiggle features prior to DTWA consensus generation. New visualization and warping-path metrics are introduced to compare consensus signals and the best estimate of the “true” consensus, the study’s gold standard model. The DBA consensus was the best match to the gold standard for both Sequin studies but was outperformed in the Enolase study. Given an underlying common characteristic across a squiggle ensemble, we objectively evaluate a novel “voting scheme” that improves the local similarity between the consensus signal and a given fraction of the squiggle ensemble. While the gold standard is not used during voting, the increase in the match of the final voted-on consensus to the underlying Enolase and Sequin gold standard sequences provides an indirect success measure for the proposed voting procedure in two ways: First is the decreased least squares warped distance between the final consensus and the gold model, and second, the voting generates a final consensus length closer to the known underlying RNA biomolecule length. The results suggest considerable potential in marrying squiggle analysis and voted-on DTWA consensus signals to provide low-noise, low-distortion signals. This will lead to improved accuracy in detecting nucleotides and their deviation model due to chemical modifications (a.k.a. epigenetic information). The proposed combination of ensemble voting and DTWA has application in other research fields involving time-distorted, high entropy signals.     

Visual Receptive Field Properties of Neurons in the Mouse Lateral Geniculate Nucleus

The lateral geniculate nucleus (LGN) is increasingly regarded as a “smart-gating” operator for processing visual information. Therefore, characterizing the response properties of LGN neurons will enable us to better understand how neurons encode and transfer visual signals. Efforts have been devoted to study its anatomical and functional features, and recent advances have highlighted the existence in rodents of complex features such as direction/orientation selectivity. However, unlike well-researched higher-order mammals such as primates, the full array of response characteristics vis-à-vis its morphological features have remained relatively unexplored in the mouse LGN. To address the issue, we recorded from mouse LGN neurons using multisite-electrode-arrays (MEAs) and analysed their discharge patterns in relation to their location under a series of visual stimulation paradigms. Several response properties paralleled results from earlier studies in the field and these include centre-surround organization, size of receptive field, spontaneous firing rate and linearity of spatial summation. However, our results also revealed “high-pass” and “low-pass” features in the temporal frequency tuning of some cells, and greater average contrast gain than reported by earlier studies. In addition, a small proportion of cells had direction/orientation selectivity. Both “high-pass” and “low-pass” cells, as well as direction and orientation selective cells, were found only in small numbers, supporting the notion that these properties emerge in the cortex. ON- and OFF-cells showed distinct contrast sensitivity and temporal frequency tuning properties, suggesting parallel projections from the retina. Incorporating a novel histological technique, we created a 3-D LGN volume model explicitly capturing the morphological features of mouse LGN and localising individual cells into anterior/middle/posterior LGN. Based on this categorization, we show that the ON/OFF, DS/OS and linear response properties are not regionally restricted. Our study confirms earlier findings of spatial pattern selectivity in the LGN, and builds on it to demonstrate that relatively elaborate features are computed early in the visual pathway.     

Simulation Studies as Designed Experiments: The Comparison of Penalized Regression Models in the “Large p,Small n” Setting

New algorithms are continuously proposed in computational biology. Performance evaluation of novel methods is important in practice. Nonetheless, the field experiences a lack of rigorous methodology aimed to systematically and objectively evaluate competing approaches. Simulation studies are frequently used to show that a particular method outperforms another. Often times, however, simulation studies are not well designed, and it is hard to characterize the particular conditions under which different methods perform better. In this paper we propose the adoption of well established techniques in the design of computer and physical experiments for developing effective simulation studies. By following best practices in planning of experiments we are better able to understand the strengths and weaknesses of competing algorithms leading to more informed decisions about which method to use for a particular task. We illustrate the application of our proposed simulation framework with a detailed comparison of the ridge-regression, lasso and elastic-net algorithms in a large scale study investigating the effects on predictive performance of sample size, number of features, true model sparsity, signal-to-noise ratio, and feature correlation, in situations where the number of covariates is usually much larger than sample size. Analysis of data sets containing tens of thousands of features but only a few hundred samples is nowadays routine in computational biology, where “omics” features such as gene expression, copy number variation and sequence data are frequently used in the predictive modeling of complex phenotypes such as anticancer drug response. The penalized regression approaches investigated in this study are popular choices in this setting and our simulations corroborate well established results concerning the conditions under which each one of these methods is expected to perform best while providing several novel insights.     

High-fidelity reconstitution of stress granules and nucleoli in mammalian cellular lysate

Liquid–liquid phase separation (LLPS) is a mechanism of intracellular organization that underlies the assembly of a variety of RNP granules. Fundamental biophysical principles governing LLPS during granule assembly have been revealed by simple in vitro systems, but these systems have limitations when studying the biology of complex, multicomponent RNP granules. Visualization of RNP granules in cells has validated key principles revealed by simple in vitro systems, but this approach presents difficulties for interrogating biophysical features of RNP granules and provides limited ability to manipulate protein, nucleic acid, or small molecule concentrations. Here, we introduce a system that builds upon recent insights into the mechanisms underlying RNP granule assembly and permits high-fidelity reconstitution of stress granules and the granular component of nucleoli in mammalian cellular lysate. This system fills the gap between simple in vitro systems and live cells and allows for a variety of studies of membraneless organelles, including the development of therapeutics that modify properties of specific condensates.     

Pharmacotherapy with Fluoxetine Restores Functional Connectivity from the Dentate Gyrus to Field CA3 in the Ts65Dn Mouse Model of Down Syndrome

Down syndrome (DS) is a high-incidence genetic pathology characterized by severe impairment of cognitive functions, including declarative memory. Impairment of hippocampus-dependent long-term memory in DS appears to be related to anatomo-functional alterations of the hippocampal trisynaptic circuit formed by the dentate gyrus (DG) granule cells - CA3 pyramidal neurons - CA1 pyramidal neurons. No therapies exist to improve cognitive disability in individuals with DS. In previous studies we demonstrated that pharmacotherapy with fluoxetine restores neurogenesis, granule cell number and dendritic morphology in the DG of the Ts65Dn mouse model of DS. The goal of the current study was to establish whether treatment rescues the impairment of synaptic connectivity between the DG and CA3 that characterizes the trisomic condition. Euploid and Ts65Dn mice were treated with fluoxetine during the first two postnatal weeks and examined 45–60 days after treatment cessation. Untreated Ts65Dn mice had a hypotrophyc mossy fiber bundle, fewer synaptic contacts, fewer glutamatergic contacts, and fewer dendritic spines in the stratum lucidum of CA3, the terminal field of the granule cell projections. Electrophysiological recordings from CA3 pyramidal neurons showed that in Ts65Dn mice the frequency of both mEPSCs and mIPSCs was reduced, indicating an overall impairment of excitatory and inhibitory inputs to CA3 pyramidal neurons. In treated Ts65Dn mice all these aberrant features were fully normalized, indicating that fluoxetine can rescue functional connectivity between the DG and CA3. The positive effects of fluoxetine on the DG-CA3 system suggest that early treatment with this drug could be a suitable therapy, possibly usable in humans, to restore the physiology of the hippocampal networks and, hence, memory functions.     

How Informative Are Spatial CA3 Representations Established by the Dentate Gyrus?

In the mammalian hippocampus, the dentate gyrus (DG) is characterized by sparse and powerful unidirectional projections to CA3 pyramidal cells, the so-called mossy fibers. Mossy fiber synapses appear to duplicate, in terms of the information they convey, what CA3 cells already receive from entorhinal cortex layer II cells, which project both to the dentate gyrus and to CA3. Computational models of episodic memory have hypothesized that the function of the mossy fibers is to enforce a new, well separated pattern of activity onto CA3 cells, to represent a new memory, prevailing over the interference produced by the traces of older memories already stored on CA3 recurrent collateral connections. Can this hypothesis apply also to spatial representations, as described by recent neurophysiological recordings in rats? To address this issue quantitatively, we estimate the amount of information DG can impart on a new CA3 pattern of spatial activity, using both mathematical analysis and computer simulations of a simplified model. We confirm that, also in the spatial case, the observed sparse connectivity and level of activity are most appropriate for driving memory storage – and not to initiate retrieval. Surprisingly, the model also indicates that even when DG codes just for space, much of the information it passes on to CA3 acquires a non-spatial and episodic character, akin to that of a random number generator. It is suggested that further hippocampal processing is required to make full spatial use of DG inputs.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Adult neurogenesis and pattern separation in rodents: A critical evaluation of data,tasks and interpretation

The ability to discriminate and store similar inputs as distinct representations in memory is thought to rely on a process called pattern separation in the dentate gyrus of the hippocampus. Recent computational and empirical findings support a role for adult-born granule neurons in spatial pattern separation. We reviewed rodent studies that have manipulated both hippocampal adult neurogenesis and assessed pattern separation. The majority of studies report a supporting role of adult born neurons in pattern separation as measured at the behavioral level. However, closer evaluation of the published findings reveals variation in both pattern separation tasks and in the interpretation of behavioral performance that, taken together, suggests that the role of hippocampal adult neurogenesis in pattern separation may be less established than is currently assumed. Assessment of pattern separation at the network level through the use of immediate early gene expression, optogenetic, pharmacogenetic and/or in vivo electrophysiology studies could be instrumental in further confirming a role of adult born neurons in pattern separation further. Finally, hippocampal adult neurogenesis and pattern separation are not an exclusive pair, as evidence for hippocampal adult neurogenesis contributing to the temporal separation of events in memory, forgetting and cognitive flexibility has also been found. We conclude that whereas current empirical evidence for the involvement of hippocampal adult neurogenesis in pattern separation seems supportive, there is a need for careful interpretation of behavioral findings and an integration of the various proposed functions of adult born neurons.     

Visual perception from the perspective of a representational,non-reductionistic,level-dependent account of perception and conscious awareness

This article proposes a new model to interpret seemingly conflicting evidence concerning the correlation of consciousness and neural processes. Based on an analysis of research of blindsight and subliminal perception, the reorganization of elementary functions and consciousness framework suggests that mental representations consist of functions at several different levels of analysis, including truly localized perceptual elementary functions and perceptual algorithmic modules, which are interconnections of the elementary functions. We suggest that conscious content relates to the ‘top level’ of analysis in a ‘situational algorithmic strategy’ that reflects the general state of an individual. We argue that conscious experience is intrinsically related to representations that are available to guide behaviour. From this perspective, we find that blindsight and subliminal perception can be explained partly by too coarse-grained methodology, and partly by top-down enhancing of representations that normally would not be relevant to action.     

Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms

Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vegetation. A wide range of automatic thresholding or binarization algorithms exists to classify the photographs. The variety in methodology hampers ability to compare results across studies. To identify an optimal threshold selection method, this study assessed the accuracy of seven binarization methods implemented in software currently available for the processing of hemispherical photographs. Therefore, binarizations obtained by the algorithms were compared to reference data generated through a manual binarization of a stratified random selection of pixels. This approach was adopted from the accuracy assessment of map classifications known from remote sensing studies. Percentage correct () and kappa-statistics () were calculated. The accuracy of the algorithms was assessed for photographs taken with automatic exposure settings (auto-exposure) and photographs taken with settings which avoid overexposure (histogram-exposure). In addition, gap fraction values derived from hemispherical photographs were compared with estimates derived from the manually classified reference pixels. All tested algorithms were shown to be sensitive to overexposure. Three of the algorithms showed an accuracy which was high enough to be recommended for the processing of histogram-exposed hemispherical photographs: “Minimum” ( 98.8%; 0.952), “Edge Detection” ( 98.1%; 0.950), and “Minimum Histogram” ( 98.1%; 0.947). The Minimum algorithm overestimated gap fraction least of all (11%). The overestimation by the algorithms Edge Detection (63%) and Minimum Histogram (67%) were considerably larger. For the remaining four evaluated algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an incompatibility with photographs containing overexposed pixels was detected. When applied to histogram-exposed photographs, these algorithms overestimated the gap fraction by at least 180%.     

Reduction of Pavlovian Bias in Schizophrenia: Enhanced Effects in Clozapine-Administered Patients

The negative symptoms of schizophrenia (SZ) are associated with a pattern of reinforcement learning (RL) deficits likely related to degraded representations of reward values. However, the RL tasks used to date have required active responses to both reward and punishing stimuli. Pavlovian biases have been shown to affect performance on these tasks through invigoration of action to reward and inhibition of action to punishment, and may be partially responsible for the effects found in patients. Forty-five patients with schizophrenia and 30 demographically-matched controls completed a four-stimulus reinforcement learning task that crossed action (“Go” or “NoGo”) and the valence of the optimal outcome (reward or punishment-avoidance), such that all combinations of action and outcome valence were tested. Behaviour was modelled using a six-parameter RL model and EEG was simultaneously recorded. Patients demonstrated a reduction in Pavlovian performance bias that was evident in a reduced Go bias across the full group. In a subset of patients administered clozapine, the reduction in Pavlovian bias was enhanced. The reduction in Pavlovian bias in SZ patients was accompanied by feedback processing differences at the time of the P3a component. The reduced Pavlovian bias in patients is suggested to be due to reduced fidelity in the communication between striatal regions and frontal cortex. It may also partially account for previous findings of poorer “Go-learning” in schizophrenia where “Go” responses or Pavlovian consistent responses are required for optimal performance. An attenuated P3a component dynamic in patients is consistent with a view that deficits in operant learning are due to impairments in adaptively using feedback to update representations of stimulus value.