Suprateek Kundu, Bani K. Mallick, Veera Baladandayuthapani.

Source: Bayesian Analysis, Volume 14, Number 2, 449--476.

Abstract:
There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for large dimensional settings where the dimension increases with the sample size, by decoupling model fitting and covariance selection. First, a full model based on a complete graph is fit under a novel class of mixtures of inverse–Wishart priors, which induce shrinkage on the precision matrix under an equivalence with Cholesky-based regularization, while enabling conjugate updates. Subsequently, a post-fitting model selection step uses penalized joint credible regions to perform model selection. This allows our methods to be computationally feasible for large dimensional settings using a combination of straightforward Gibbs samplers and efficient post-fitting inferences. Theoretical guarantees in terms of selection consistency are also established. Simulations show that the proposed approach compares favorably with competing methods, both in terms of accuracy metrics and computation times. We apply this approach to a cancer genomics data example.

M. W. McLean, M. P. Wand.

Source: Bayesian Analysis, Volume 14, Number 2, 371--398.

Abstract:
We build on recent work concerning message passing approaches to approximate fitting and inference for arbitrarily large regression models. The focus is on regression models where the response variable is modeled to have an elaborate distribution, which is loosely defined to mean a distribution that is more complicated than common distributions such as those in the Bernoulli, Poisson and Normal families. Examples of elaborate response families considered here are the Negative Binomial and $t$ families. Variational message passing is more challenging due to some of the conjugate exponential families being non-standard and numerical integration being needed. Nevertheless, a factor graph fragment approach means the requisite calculations only need to be done once for a particular elaborate response distribution family. Computer code can be compartmentalized, including that involving numerical integration. A major finding of this work is that the modularity of variational message passing extends to elaborate response regression models.

Lloyd T. Elliott, Maria De Iorio, Stefano Favaro, Kaustubh Adhikari, Yee Whye Teh.

Source: Bayesian Analysis, Volume 14, Number 2, 313--339.

Abstract:
We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.

Peter J. Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsame Ella, Kisito Ogoussan, Katherine Gass.

Source: Statistical Science, Volume 35, Number 1, 42--50.

Abstract:
Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper, we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one is to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; the other is to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection among infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decision-making in two ways. One is to ensure that, in a resource-limited setting, prevalence surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainty attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met.

Adam R. Brentnall, Jack Cuzick.

Source: Statistical Science, Volume 35, Number 1, 14--30.

Abstract:
Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer–Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.

Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong.

Source: Statistical Science, Volume 35, Number 1, 2--13.

Abstract:
Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are under-explored in the area of single-cell genomics, and have the advantage of quantifying the uncertainty of the clustering result. Here we develop a model-based approach for the integrative analysis of single-cell chromatin accessibility and gene expression data. We show that combining these two types of data, we can achieve a better separation of the underlying cell types. An efficient Markov chain Monte Carlo algorithm is also developed.

T. Tony Cai.

Source: Statistical Science, Volume 34, Number 4, 635--656.

Abstract:
A wide range of nonparametric function estimation models have been studied individually in the literature. Among them the homoscedastic nonparametric Gaussian regression is arguably the best known and understood. Inspired by the asymptotic equivalence theory, Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) developed a unified approach to turn a collection of non-Gaussian function estimation models into a standard Gaussian regression and any good Gaussian nonparametric regression method can then be used. These Gaussianization Machines have two key components, binning and transformation. When combined with BlockJS, a wavelet thresholding procedure for Gaussian regression, the procedures are computationally efficient with strong theoretical guarantees. Technical analysis given in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) shows that the estimators attain the optimal rate of convergence adaptively over a large set of Besov spaces and across a collection of non-Gaussian function estimation models, including robust nonparametric regression, density estimation, and nonparametric regression in exponential families. The estimators are also spatially adaptive. The Gaussianization Machines significantly extend the flexibility and scope of the theories and methodologies originally developed for the conventional nonparametric Gaussian regression. This article aims to provide a concise account of the Gaussianization Machines developed in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046), Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433).

Andreas Buja, Arun Kumar Kuchibhotla, Richard Berk, Edward George, Eric Tchetgen Tchetgen, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 606--620.

Abstract:
We respond to the discussants of our articles emphasizing the importance of inference under misspecification in the context of the reproducibility/replicability crisis. Along the way, we discuss the roles of diagnostics and model building in regression as well as connections between our well-specification framework and semiparametric theory.

Dalia Ghanem, Todd A. Kuffner.

Source: Statistical Science, Volume 34, Number 4, 604--605.

David Whitney, Ali Shojaie, Marco Carone.

Source: Statistical Science, Volume 34, Number 4, 591--598.

Anthony C. Davison, Erwan Koch, Jonathan Koh.

Source: Statistical Science, Volume 34, Number 4, 584--590.

Abstract:
This discussion focuses on areas of disagreement with the papers, particularly the target of inference and the case for using the robust ‘sandwich’ variance estimator in the presence of moderate mis-specification. We also suggest that existing procedures may be appreciably more powerful for detecting mis-specification than the authors’ RAV statistic, and comment on the use of the pairs bootstrap in balanced situations.

Roderick J. Little.

Source: Statistical Science, Volume 34, Number 4, 580--583.

Dag Tjøstheim.

Source: Statistical Science, Volume 34, Number 4, 575--579.

Nikki L. B. Freeman, Xiaotong Jiang, Owen E. Leete, Daniel J. Luckett, Teeranan Pokaprakarn, Michael R. Kosorok.

Source: Statistical Science, Volume 34, Number 4, 572--574.

Jerald F. Lawless.

Source: Statistical Science, Volume 34, Number 4, 569--571.

Abstract:
I comment on the papers Models as Approximations I and II, by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, M. Traskin, L. Zhao and K. Zhang.

Sara van de Geer.

Source: Statistical Science, Volume 34, Number 4, 566--568.

Abstract:
We discuss the papers “Models as Approximations” I & II, by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, M. Traskin, L. Zao and K. Zhang (Part I) and A. Buja, L. Brown, A. K. Kuchibhota, R. Berk, E. George and L. Zhao (Part II). We present a summary with some details for the generalized linear model.

Andreas Buja, Lawrence Brown, Arun Kumar Kuchibhotla, Richard Berk, Edward George, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 545--565.

Abstract:
We develop a model-free theory of general types of parametric regression for i.i.d. observations. The theory replaces the parameters of parametric models with statistical functionals, to be called “regression functionals,” defined on large nonparametric classes of joint ${x extrm{-}y}$ distributions, without assuming a correct model. Parametric models are reduced to heuristics to suggest plausible objective functions. An example of a regression functional is the vector of slopes of linear equations fitted by OLS to largely arbitrary ${x extrm{-}y}$ distributions, without assuming a linear model (see Part I). More generally, regression functionals can be defined by minimizing objective functions, solving estimating equations, or with ad hoc constructions. In this framework, it is possible to achieve the following: (1) define a notion of “well-specification” for regression functionals that replaces the notion of correct specification of models, (2) propose a well-specification diagnostic for regression functionals based on reweighting distributions and data, (3) decompose sampling variability of regression functionals into two sources, one due to the conditional response distribution and another due to the regressor distribution interacting with misspecification, both of order $N^{-1/2}$, (4) exhibit plug-in/sandwich estimators of standard error as limit cases of ${x extrm{-}y}$ bootstrap estimators, and (5) provide theoretical heuristics to indicate that ${x extrm{-}y}$ bootstrap standard errors may generally be preferred over sandwich estimators.

Andreas Buja, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 523--544.

Abstract:
In the early 1980s, Halbert White inaugurated a “model-robust” form of statistical inference based on the “sandwich estimator” of standard error. This estimator is known to be “heteroskedasticity-consistent,” but it is less well known to be “nonlinearity-consistent” as well. Nonlinearity, however, raises fundamental issues because in its presence regressors are not ancillary, hence cannot be treated as fixed. The consequences are deep: (1) population slopes need to be reinterpreted as statistical functionals obtained from OLS fits to largely arbitrary joint ${x extrm{-}y}$ distributions; (2) the meaning of slope parameters needs to be rethought; (3) the regressor distribution affects the slope parameters; (4) randomness of the regressors becomes a source of sampling variability in slope estimates of order $1/sqrt{N}$; (5) inference needs to be based on model-robust standard errors, including sandwich estimators or the ${x extrm{-}y}$ bootstrap. In theory, model-robust and model-trusting standard errors can deviate by arbitrary magnitudes either way. In practice, significant deviations between them can be detected with a diagnostic test.

Daniele Durante, Tommaso Rigon.

Source: Statistical Science, Volume 34, Number 3, 472--485.

Abstract:
Variational Bayes (VB) is a common strategy for approximate Bayesian inference, but simple methods are only available for specific classes of models including, in particular, representations having conditionally conjugate constructions within an exponential family. Models with logit components are an apparently notable exception to this class, due to the absence of conjugacy among the logistic likelihood and the Gaussian priors for the coefficients in the linear predictor. To facilitate approximate inference within this widely used class of models, Jaakkola and Jordan ( Stat. Comput. 10 (2000) 25–37) proposed a simple variational approach which relies on a family of tangent quadratic lower bounds of the logistic log-likelihood, thus restoring conjugacy between these approximate bounds and the Gaussian priors. This strategy is still implemented successfully, but few attempts have been made to formally understand the reasons underlying its excellent performance. Following a review on VB for logistic models, we cover this gap by providing a formal connection between the above bound and a recent Pólya-gamma data augmentation for logistic regression. Such a result places the computational methods associated with the aforementioned bounds within the framework of variational inference for conditionally conjugate exponential family models, thereby allowing recent advances for this class to be inherited also by the methods relying on Jaakkola and Jordan ( Stat. Comput. 10 (2000) 25–37).

Anna L. Smith, Dena M. Asta, Catherine A. Calder.

Source: Statistical Science, Volume 34, Number 3, 428--453.

Abstract:
We review the class of continuous latent space (statistical) models for network data, paying particular attention to the role of the geometry of the latent space. In these models, the presence/absence of network dyadic ties are assumed to be conditionally independent given the dyads’ unobserved positions in a latent space. In this way, these models provide a probabilistic framework for embedding network nodes in a continuous space equipped with a geometry that facilitates the description of dependence between random dyadic ties. Specifically, these models naturally capture homophilous tendencies and triadic clustering, among other common properties of observed networks. In addition to reviewing the literature on continuous latent space models from a geometric perspective, we highlight the important role the geometry of the latent space plays on properties of networks arising from these models via intuition and simulation. Finally, we discuss results from spectral graph theory that allow us to explore the role of the geometry of the latent space, independent of network size. We conclude with conjectures about how these results might be used to infer the appropriate latent space geometry from observed networks.

Sijia Xiang, Weixin Yao, Guangren Yang.

Source: Statistical Science, Volume 34, Number 3, 391--404.

Abstract:
Finite mixture models have offered a very important tool for exploring complex data structures in many scientific areas, such as economics, epidemiology and finance. Semiparametric mixture models, which were introduced into traditional finite mixture models in the past decade, have brought forth exciting developments in their methodologies, theories, and applications. In this article, we not only provide a selective overview of the newly-developed semiparametric mixture models, but also discuss their estimation methodologies, theoretical properties if applicable, and some open questions. Recent developments are also discussed.

Roger Koenker, Jiaying Gu.

Source: Statistical Science, Volume 34, Number 2, 209--213.

Abstract:
Efron’s elegant approach to $g$-modeling for empirical Bayes problems is contrasted with an implementation of the Kiefer–Wolfowitz nonparametric maximum likelihood estimator for mixture models for several examples. The latter approach has the advantage that it is free of tuning parameters and consequently provides a relatively simple complementary method.

Nicole Bohme Carnegie.

Source: Statistical Science, Volume 34, Number 1, 90--93.

Abstract:
With a thorough exposition of the methods and results of the 2016 Atlantic Causal Inference Competition, Dorie et al. have set a new standard for reproducibility and comparability of evaluations of causal inference methods. In particular, the open-source R package aciccomp2016, which permits reproduction of all datasets used in the competition, will be an invaluable resource for evaluation of future methodological developments. Building upon results from Dorie et al., we examine whether a set of potential modifications to Bayesian Additive Regression Trees (BART)—multiple chains in model fitting, using the propensity score as a covariate, targeted maximum likelihood estimation (TMLE), and computing symmetric confidence intervals—have a stronger impact on bias, RMSE, and confidence interval coverage in combination than they do alone. We find that bias in the estimate of SATT is minimal, regardless of the BART formulation. For purposes of CI coverage, however, all proposed modifications are beneficial—alone and in combination—but use of TMLE is least beneficial for coverage and results in considerably wider confidence intervals.

Irune Diaz-Aparicio
Feb 12, 2020; 40:1453-1482
Development Plasticity Repair

Sibylle Béchet
Apr 8, 2020; 40:3104-3118
Neurobiology of Disease

Amanda Avona
May 29, 2019; 39:4323-4331
Systems/Circuits

Xiao-Jing Wang
Oct 15, 1996; 16:6402-6413
Articles

In what could be a trial run for more of the same, Red Hat last week held a first-ever virtual technical summit to spread the word about its latest cloud tech offerings. CEO Paul Cormier welcomed online viewers to the conference, which attracted more than 80,000 virtual attendees. The company made several key announcements during the online gathering and highlighted customer innovations.

If you are looking for a well-designed Linux distro that is far from mainstream, loaded with performance features not found elsewhere, check out the 2020 upgrade of the MakuluLinux Core distro. It could change your perspective on what a daily computing driver should offer. Developer Jacque Montague Raymer recently released the 2020 edition of MakuluLinux Core OS.

Krabbe's disease is an infantile neurodegenerative disease, which is affected by mutations in the lysosomal enzyme galactocerebrosidase, leading to the accumulation of its metabolite psychosine. We have shown previously that the S1P receptor agonist fingolimod (FTY720) attenuates psychosine-induced glial cell death and demyelination both in vitro and ex vivo models. These data, together with a lack of therapies for Krabbe's disease, prompted the current preclinical study examining the effects of fingolimod in twitcher mice, a murine model of Krabbe's disease. Twitcher mice, both male and female, carrying a natural mutation in the galc gene were given fingolimod via drinking water (1 mg/kg/d). The direct impact of fingolimod administration was assessed via histochemical and biochemical analysis using markers of myelin, astrocytes, microglia, neurons, globoid cells, and immune cells. The effects of fingolimod on twitching behavior and life span were also demonstrated. Our results show that treatment of twitcher mice with fingolimod significantly rescued myelin levels compared with vehicle-treated animals and also regulated astrocyte and microglial reactivity. Furthermore, nonphosphorylated neurofilament levels were decreased, indicating neuroprotective and neurorestorative processes. These protective effects of fingolimod on twitcher mice brain pathology was reflected by an increased life span of fingolimod-treated twitcher mice. These in vivo findings corroborate initial in vitro studies and highlight the potential use of S1P receptors as drug targets for treatment of Krabbe's disease.

SIGNIFICANCE STATEMENT This study demonstrates that the administration of the therapy known as fingolimod in a mouse model of Krabbe's disease (namely, the twitcher mouse model) significantly rescues myelin levels. Further, the drug fingolimod also regulates the reactivity of glial cells, astrocytes and microglia, in this mouse model. These protective effects of fingolimod result in an increased life span of twitcher mice.

Although the etiology of schizophrenia is still unknown, it is accepted to be a neurodevelopmental disorder that results from the interaction of genetic vulnerabilities and environmental insults. Although schizophrenia's pathophysiology is still unclear, postmortem studies point toward a dysfunction of cortical interneurons as a central element. It has been suggested that alterations in parvalbumin-positive interneurons in schizophrenia are the consequence of a deficient signaling through NMDARs. Animal studies demonstrated that early postnatal ablation of the NMDAR in corticolimbic interneurons induces neurobiochemical, physiological, behavioral, and epidemiological phenotypes related to schizophrenia. Notably, the behavioral abnormalities emerge only after animals complete their maturation during adolescence and are absent if the NMDAR is deleted during adulthood. This suggests that interneuron dysfunction must interact with development to impact on behavior. Here, we assess in vivo how an early NMDAR ablation in corticolimbic interneurons impacts on mPFC and ventral hippocampus functional connectivity before and after adolescence. In juvenile male mice, NMDAR ablation results in several pathophysiological traits, including increased cortical activity and decreased entrainment to local gamma and distal hippocampal theta rhythms. In addition, adult male KO mice showed reduced ventral hippocampus-mPFC-evoked potentials and an augmented low-frequency stimulation LTD of the pathway, suggesting that there is a functional disconnection between both structures in adult KO mice. Our results demonstrate that early genetic abnormalities in interneurons can interact with postnatal development during adolescence, triggering pathophysiological mechanisms related to schizophrenia that exceed those caused by NMDAR interneuron hypofunction alone.

SIGNIFICANCE STATEMENT NMDAR hypofunction in cortical interneurons has been linked to schizophrenia pathophysiology. How a dysfunction of GABAergic cortical interneurons interacts with maturation during adolescence has not been clarified yet. Here, we demonstrate in vivo that early postnatal ablation of the NMDAR in corticolimbic interneurons results in an overactive but desynchronized PFC before adolescence. Final postnatal maturation during this stage outspreads the impact of the genetic manipulation toward a functional disconnection of the ventral hippocampal-prefrontal pathway, probably as a consequence of an exacerbated propensity toward hippocampal-evoked depotentiation plasticity. Our results demonstrate a complex interaction between genetic and developmental factors affecting cortical interneurons and PFC function.

The detection and segmentation of meaningful figures from their background is one of the primary functions of vision. While work in nonhuman primates has implicated early visual mechanisms in this figure–ground modulation, neuroimaging in humans has instead largely ascribed the processing of figures and objects to higher stages of the visual hierarchy. Here, we used high-field fMRI at 7 Tesla to measure BOLD responses to task-irrelevant orientation-defined figures in human early visual cortex (N = 6, four females). We used a novel population receptive field mapping-based approach to resolve the spatial profiles of two constituent mechanisms of figure–ground modulation: a local boundary response, and a further enhancement spanning the full extent of the figure region that is driven by global differences in features. Reconstructing the distinct spatial profiles of these effects reveals that figure enhancement modulates responses in human early visual cortex in a manner consistent with a mechanism of automatic, contextually driven feedback from higher visual areas.

SIGNIFICANCE STATEMENT A core function of the visual system is to parse complex 2D input into meaningful figures. We do so constantly and seamlessly, both by processing information about visible edges and by analyzing large-scale differences between figure and background. While influential neurophysiology work has characterized an intriguing mechanism that enhances V1 responses to perceptual figures, we have a poor understanding of how the early visual system contributes to figure–ground processing in humans. Here, we use advanced computational analysis methods and high-field human fMRI data to resolve the distinct spatial profiles of local edge and global figure enhancement in the early visual system (V1 and LGN); the latter is distinct and consistent with a mechanism of automatic, stimulus-driven feedback from higher-level visual areas.

Endogenous neuropeptide Y (NPY) and corticotrophin-releasing factor (CRF) modulate the responses of the basolateral amygdala (BLA) to stress and are associated with the development of stress resilience and vulnerability, respectively. We characterized persistent effects of repeated NPY and CRF treatment on the structure and function of BLA principal neurons in a novel organotypic slice culture (OTC) model of male rat BLA, and examined the contributions of specific NPY receptor subtypes to these neural and behavioral effects. In BLA principal neurons within the OTCs, repeated NPY treatment caused persistent attenuation of excitatory input and induced dendritic hypotrophy via Y₅ receptor activation; conversely, CRF increased excitatory input and induced hypertrophy of BLA principal neurons. Repeated treatment of OTCs with NPY followed by an identical treatment with CRF, or vice versa, inhibited or reversed all structural changes in OTCs. These structural responses to NPY or CRF required calcineurin or CaMKII, respectively. Finally, repeated intra-BLA injections of NPY or a Y₅ receptor agonist increased social interaction, a validated behavior for anxiety, and recapitulated structural changes in BLA neurons seen in OTCs, while a Y₅ receptor antagonist prevented NPY's effects both on behavior and on structure. These results implicate the Y₅ receptor in the long-term, anxiolytic-like effects of NPY in the BLA, consistent with an intrinsic role in stress buffering, and highlight a remarkable mechanism by which BLA neurons may adapt to different levels of stress. Moreover, BLA OTCs offer a robust model to study mechanisms associated with resilience and vulnerability to stress in BLA.

SIGNIFICANCE STATEMENT Within the basolateral amygdala (BLA), neuropeptide Y (NPY) is associated with buffering the neural stress response induced by corticotropin releasing factor, and promoting stress resilience. We used a novel organotypic slice culture model of BLA, complemented with in vivo studies, to examine the cellular mechanisms associated with the actions of NPY. In organotypic slice cultures, repeated NPY treatment reduces the complexity of the dendritic extent of anxiogenic BLA principal neurons, making them less excitable. NPY, via activation of Y5 receptors, additionally inhibits and reverses the increases in dendritic extent and excitability induced by the stress hormone, corticotropin releasing factor. This NPY-mediated neuroplasticity indicates that resilience or vulnerability to stress may thus involve neuropeptide-mediated dendritic remodeling in BLA principal neurons.

Synaptic dysfunction provoking dysregulated cortical neural circuits is currently hypothesized as a key pathophysiological process underlying clinical manifestations in Alzheimer's disease and related neurodegenerative tauopathies. Here, we conducted PET along with postmortem assays to investigate time course changes of excitatory and inhibitory synaptic constituents in an rTg4510 mouse model of tauopathy, which develops tau pathologies leading to noticeable brain atrophy at 5-6 months of age. Both male and female mice were analyzed in this study. We observed that radiosignals derived from [¹¹C]flumazenil, a tracer for benzodiazepine receptor, in rTg4510 mice were significantly lower than the levels in nontransgenic littermates at 2-3 months of age. In contrast, retentions of (E)-[¹¹C]ABP688, a tracer for mGluR5, were unaltered relative to controls at 2 months of age but then gradually declined with aging in parallel with progressive brain atrophy. Biochemical and immunohistochemical assessment of postmortem brain tissues demonstrated that inhibitory, but not excitatory, synaptic constituents selectively diminished without overt loss of somas of GABAergic interneurons in the neocortex and hippocampus of rTg4510 mice at 2 months of age, which was concurrent with enhanced immunoreactivity of cFos, a well-characterized immediate early gene, suggesting that impaired inhibitory neurotransmission may cause hyperexcitability of cortical circuits. Our findings indicate that tau-induced disruption of the inhibitory synapse may be a critical trigger of progressive neurodegeneration, resulting in massive neuronal loss, and PET assessments of inhibitory versus excitatory synapses potentially offer in vivo indices for hyperexcitability and excitotoxicity early in the etiologic pathway of neurodegenerative tauopathies.

SIGNIFICANCE STATEMENT In this study, we examined the in vivo status of excitatory and inhibitory synapses in the brain of the rTg4510 tauopathy mouse model by PET imaging with (E)-[¹¹C]ABP688 and [¹¹C]flumazenil, respectively. We identified inhibitory synapse as being significantly dysregulated before brain atrophy at 2 months of age, while excitatory synapse stayed relatively intact at this stage. In line with this observation, postmortem assessment of brain tissues demonstrated selective attenuation of inhibitory synaptic constituents accompanied by the upregulation of cFos before the formation of tau pathology in the forebrain at young ages. Our findings indicate that selective degeneration of inhibitory synapse with hyperexcitability in the cortical circuit constitutes the critical early pathophysiology of tauopathy.

The transcription factor Nurr1 has been identified to be ectopically induced in the striatum of rodents expressing l-DOPA-induced dyskinesia (LID). In the present study, we sought to characterize Nurr1 as a causative factor in LID expression. We used rAAV2/5 to overexpress Nurr1 or GFP in the parkinsonian striatum of LID-resistant Lewis or LID-prone Fischer-344 (F344) male rats. In a second cohort, rats received the Nurr1 agonist amodiaquine (AQ) together with l-DOPA or ropinirole. All rats received a chronic DA agonist and were evaluated for LID severity. Finally, we performed single-unit recordings and dendritic spine analyses on striatal medium spiny neurons (MSNs) in drug-naïve rAAV-injected male parkinsonian rats. rAAV-GFP injected LID-resistant hemi-parkinsonian Lewis rats displayed mild LID and no induction of striatal Nurr1 despite receiving a high dose of l-DOPA. However, Lewis rats overexpressing Nurr1 developed severe LID. Nurr1 agonism with AQ exacerbated LID in F344 rats. We additionally determined that in l-DOPA-naïve rats striatal rAAV-Nurr1 overexpression (1) increased cortically-evoked firing in a subpopulation of identified striatonigral MSNs, and (2) altered spine density and thin-spine morphology on striatal MSNs; both phenomena mimicking changes seen in dyskinetic rats. Finally, we provide postmortem evidence of Nurr1 expression in striatal neurons of l-DOPA-treated PD patients. Our data demonstrate that ectopic induction of striatal Nurr1 is capable of inducing LID behavior and associated neuropathology, even in resistant subjects. These data support a direct role of Nurr1 in aberrant neuronal plasticity and LID induction, providing a potential novel target for therapeutic development.

SIGNIFICANCE STATEMENT The transcription factor Nurr1 is ectopically induced in striatal neurons of rats exhibiting levodopa-induced dyskinesia [LID; a side-effect to dopamine replacement strategies in Parkinson's disease (PD)]. Here we asked whether Nurr1 is causing LID. Indeed, rAAV-mediated expression of Nurr1 in striatal neurons was sufficient to overcome LID-resistance, and Nurr1 agonism exacerbated LID severity in dyskinetic rats. Moreover, we found that expression of Nurr1 in l-DOPA naïve hemi-parkinsonian rats resulted in the formation of morphologic and electrophysiological signatures of maladaptive neuronal plasticity; a phenomenon associated with LID. Finally, we determined that ectopic Nurr1 expression can be found in the putamen of l-DOPA-treated PD patients. These data suggest that striatal Nurr1 is an important mediator of the formation of LID.

N-methyl-D-aspartate receptors (NMDARs) are glutamate-gated ion channels that play critical roles in neuronal development and nervous system function. Here, we developed a model to study NMDARs in early development in zebrafish, by generating CRISPR-mediated lesions in the NMDAR genes, grin1a and grin1b, which encode the obligatory GluN1 subunits. While receptors containing grin1a or grin1b show high Ca²⁺ permeability, like their mammalian counterpart, grin1a is expressed earlier and more broadly in development than grin1b. Both grin1a^–/– and grin1b^–/– zebrafish are viable. Unlike in rodents, where the grin1 knockout is embryonic lethal, grin1 double-mutant fish (grin1a^–/–; grin1b^–/–), which lack all NMDAR-mediated synaptic transmission, survive until ~10 d dpf (days post fertilization), providing a unique opportunity to explore NMDAR function during development and in generating behaviors. Many behavioral defects in the grin1 double-mutant larvae, including abnormal evoked responses to light and acoustic stimuli, prey-capture deficits, and a failure to habituate to acoustic stimuli, are replicated by short-term treatment with the NMDAR antagonist MK-801, suggesting that they arise from acute effects of compromised NMDAR-mediated transmission. Other defects, however, such as periods of hyperactivity and alterations in place preference, are not phenocopied by MK-801, suggesting a developmental origin. Together, we have developed a unique model to study NMDARs in the developing vertebrate nervous system.

SIGNIFICANCE STATEMENT Rapid communication between cells in the nervous system depends on ion channels that are directly activated by chemical neurotransmitters. One such ligand-gated ion channel, the NMDAR, impacts nearly all forms of nervous system function. It has been challenging, however, to study the prolonged absence of NMDARs in vertebrates, and hence their role in nervous system development, due to experimental limitations. Here, we demonstrate that zebrafish lacking all NMDAR transmission are viable through early development and are capable of a wide range of stereotypic behaviors. As such, this zebrafish model provides a unique opportunity to study the role of NMDAR in the development of the early vertebrate nervous system.

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The show ran for just five days before the London museum closed due to COVID-19

The nine classes span contemporary art, fashion and photography

Remote Isle au Haut is integrating time-tested technology with emerging innovations to create its own microgrid

<span class="topsy_trackback_comment"><span class="topsy_twitter_username"><span class="topsy_trackback_content">Squeekville, model train amusement park, on display at Children's ...: Squeekville, model train amusement park, ... http://bit.ly/9x4oFS</span></span>

<span class="topsy_trackback_comment"><span class="topsy_twitter_username"><span class="topsy_trackback_content">RT @imconair Song Contest: Raab und Engelke sollen Eurovision-Finale moderieren http://bit.ly/fsr6gD #IMCRadio.net: Song Contest: Raa...</span></span>

Matteo Tranchellini and Moreno Monti created a coffee table book called CHICken to showcase the natural beauty of the ubiquitous birds.

The post 6th Graders Discover 3D Modeling with SOLIDWORKS Apps for Kids Classroom appeared first on SOLIDWORKS Education Blog.

Bank of England Working Papers by Aydan Dogan and Ida Hjortsoe

European Systemic Risk Board Occasional Papers by Greg Ros

AppleInsider has rounded up the best 16-inch MacBook Pro deals going on right now, with coupon savings knocking up to $450 off every single model. Whether you're in the market for a standard config or looking for a loaded Core i9 model, it pays to check out the cash discounts.

eDrawings Pro 2020 now supports choosing your own 360˚ images as your custom environment in VR! This blog post will help walk you through the process of creating a 360˚ equirectangular image in SOLIDWORKS Visualize and adding it to your Virtual Reality scene in eDrawings Pro 2020.

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The post Creating Your Own Sky for VR Mode in eDrawings Professional 2020 using SOLIDWORKS Visualize appeared first on SOLIDWORKS Tech Blog.

Every year SOLIDWORKS hosts one of the largest engineering conferences in the world. Since SOLIDWORKS World 2000, Model Mania® has been an attraction for many engineers wanting to show off their SOLIDWORKS skills. Model Mania, for those not familiar, is a

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The post 21 Years of Model Mania® appeared first on SOLIDWORKS Tech Blog.