Philippe Gagnon, Alain Desgagné, Mylène Bédard.

Source: Bayesian Analysis, Volume 15, Number 2, 389--414.

Abstract:
Linear regression is ubiquitous in statistical analysis. It is well understood that conflicting sources of information may contaminate the inference when the classical normality of errors is assumed. The contamination caused by the light normal tails follows from an undesirable effect: the posterior concentrates in an area in between the different sources with a large enough scaling to incorporate them all. The theory of conflict resolution in Bayesian statistics (O’Hagan and Pericchi (2012)) recommends to address this problem by limiting the impact of outliers to obtain conclusions consistent with the bulk of the data. In this paper, we propose a model with super heavy-tailed errors to achieve this. We prove that it is wholly robust, meaning that the impact of outliers gradually vanishes as they move further and further away from the general trend. The super heavy-tailed density is similar to the normal outside of the tails, which gives rise to an efficient estimation procedure. In addition, estimates are easily computed. This is highlighted via a detailed user guide, where all steps are explained through a simulated case study. The performance is shown using simulation. All required code is given.

Abhishek Bishoyi, Xiaojing Wang, Dipak K. Dey.

Source: Bayesian Analysis, Volume 15, Number 1, 215--239.

Abstract:
Missing data often appear as a practical problem while applying classical models in the statistical analysis. In this paper, we consider a semiparametric regression model in the presence of missing covariates for nonparametric components under a Bayesian framework. As it is known that Gaussian processes are a popular tool in nonparametric regression because of their flexibility and the fact that much of the ensuing computation is parametric Gaussian computation. However, in the absence of covariates, the most frequently used covariance functions of a Gaussian process will not be well defined. We propose an imputation method to solve this issue and perform our analysis using Bayesian inference, where we specify the objective priors on the parameters of Gaussian process models. Several simulations are conducted to illustrate effectiveness of our proposed method and further, our method is exemplified via two real datasets, one through Langmuir equation, commonly used in pharmacokinetic models, and another through Auto-mpg data taken from the StatLib library.

Qingpo Cai, Jian Kang, Tianwei Yu.

Source: Bayesian Analysis, Volume 15, Number 1, 79--102.

Abstract:
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Jon Cockayne, Chris J. Oates, Ilse C.F. Ipsen, Mark Girolami.

Source: Bayesian Analysis, Volume 14, Number 3, 937--1012.

Abstract:
A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is employed. However, for more challenging systems a substantial error can be present even after many iterations have been performed. The estimates obtained in this case are of little value unless further information can be provided about, for example, the magnitude of the error. In this paper we propose a novel statistical model for this error, set in a Bayesian framework. Our approach is a strict generalisation of the conjugate gradient method, which is recovered as the posterior mean for a particular choice of prior. The estimates obtained are analysed with Krylov subspace methods and a contraction result for the posterior is presented. The method is then analysed in a simulation study as well as being applied to a challenging problem in medical imaging.

Lizhen Lin, Niu Mu, Pokman Cheung, David Dunson.

Source: Bayesian Analysis, Volume 14, Number 3, 907--926.

Abstract:
Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although there is an increasingly vast literature on applications, methods, theory and algorithms related to GPs, the overwhelming majority of this literature focuses on the case in which the input domain corresponds to a Euclidean space. However, particularly in recent years with the increasing collection of complex data, it is commonly the case that the input domain does not have such a simple form. For example, it is common for the inputs to be restricted to a non-Euclidean manifold, a case which forms the motivation for this article. In particular, we propose a general extrinsic framework for GP modeling on manifolds, which relies on embedding of the manifold into a Euclidean space and then constructing extrinsic kernels for GPs on their images. These extrinsic Gaussian processes (eGPs) are used as prior distributions for unknown functions in Bayesian inferences. Our approach is simple and general, and we show that the eGPs inherit fine theoretical properties from GP models in Euclidean spaces. We consider applications of our models to regression and classification problems with predictors lying in a large class of manifolds, including spheres, planar shape spaces, a space of positive definite matrices, and Grassmannians. Our models can be readily used by practitioners in biological sciences for various regression and classification problems, such as disease diagnosis or detection. Our work is also likely to have impact in spatial statistics when spatial locations are on the sphere or other geometric spaces.

Mengyang Gu.

Source: Bayesian Analysis, Volume 14, Number 3, 877--905.

Abstract:
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some objective priors, such as the reference prior, are studied in the context of emulating (approximating) computationally expensive mathematical models. In this work, we introduce a new class of priors, called the jointly robust prior, for both the emulation and calibration. This prior is designed to maintain various advantages from the reference prior. In emulation, the jointly robust prior has an appropriate tail decay rate as the reference prior, and is computationally simpler than the reference prior in parameter estimation. Moreover, the marginal posterior mode estimation with the jointly robust prior can separate the influential and inert inputs in mathematical models, while the reference prior does not have this property. We establish the posterior propriety for a large class of priors in calibration, including the reference prior and jointly robust prior in general scenarios, but the jointly robust prior is preferred because the calibrated mathematical model typically predicts the reality well. The jointly robust prior is used as the default prior in two new R packages, called “RobustGaSP” and “RobustCalibration”, available on CRAN for emulation and calibration, respectively.

Brian Neelon.

Source: Bayesian Analysis, Volume 14, Number 3, 849--875.

Abstract:
Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of latent variables that are represented as scale mixtures of normals, where the precision terms follow independent Pólya-Gamma distributions. Conditional on the latent variables, inference proceeds via straightforward Gibbs sampling. For fixed-effects models, our approach is comparable to existing methods. However, our model can accommodate more complex data structures, including multivariate and spatiotemporal data, settings in which current approaches often fail due to computational challenges. Using simulation studies, we highlight key features of the method and compare its performance to other estimation procedures. We apply the approach to a spatiotemporal analysis examining the number of annual inpatient admissions among United States veterans with type 2 diabetes.

Luis Gutiérrez, Andrés F. Barrientos, Jorge González, Daniel Taylor-Rodríguez.

Source: Bayesian Analysis, Volume 14, Number 2, 649--675.

Abstract:
We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.

Łukasz Rajkowski.

Source: Bayesian Analysis, Volume 14, Number 2, 477--494.

Abstract:
Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components. To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs). It is therefore important to develop an understanding of the strengths and weaknesses of this approach. This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution). Some desirable properties of the MAP partition are proved: ‘almost disjointness’ of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity). Consequently, the number of such clusters remains bounded. Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter. As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.

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).

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).

Lei Liu, Ya-Chen Tina Shih, Robert L. Strawderman, Daowen Zhang, Bankole A. Johnson, Haitao Chai.

Source: Statistical Science, Volume 34, Number 2, 253--279.

Abstract:
Zero-inflated nonnegative continuous (or semicontinuous) data arise frequently in biomedical, economical, and ecological studies. Examples include substance abuse, medical costs, medical care utilization, biomarkers (e.g., CD4 cell counts, coronary artery calcium scores), single cell gene expression rates, and (relative) abundance of microbiome. Such data are often characterized by the presence of a large portion of zero values and positive continuous values that are skewed to the right and heteroscedastic. Both of these features suggest that no simple parametric distribution may be suitable for modeling such type of outcomes. In this paper, we review statistical methods for analyzing zero-inflated nonnegative outcome data. We will start with the cross-sectional setting, discussing ways to separate zero and positive values and introducing flexible models to characterize right skewness and heteroscedasticity in the positive values. We will then present models of correlated zero-inflated nonnegative continuous data, using random effects to tackle the correlation on repeated measures from the same subject and that across different parts of the model. We will also discuss expansion to related topics, for example, zero-inflated count and survival data, nonlinear covariate effects, and joint models of longitudinal zero-inflated nonnegative continuous data and survival. Finally, we will present applications to three real datasets (i.e., microbiome, medical costs, and alcohol drinking) to illustrate these methods. Example code will be provided to facilitate applications of these methods.

Georg Lindgren.

Source: Statistical Science, Volume 34, Number 1, 100--128.

Abstract:
We describe and compare how methods based on the classical Rice’s formula for the expected number, and higher moments, of level crossings by a Gaussian process stand up to contemporary numerical methods to accurately deal with crossing related characteristics of the sample paths. We illustrate the relative merits in accuracy and computing time of the Rice moment methods and the exact numerical method, developed since the late 1990s, on three groups of distribution problems, the maximum over a finite interval and the waiting time to first crossing, the length of excursions over a level, and the joint period/amplitude of oscillations. We also treat the notoriously difficult problem of dependence between successive zero crossing distances. The exact solution has been known since at least 2000, but it has remained largely unnoticed outside the ocean science community. Extensive simulation studies illustrate the accuracy of the numerical methods. As a historical introduction an attempt is made to illustrate the relation between Rice’s original formulation and arguments and the exact numerical methods.

London : Cleanair (33 Stillness Rd, London, SE23 1NG), [198-?]

London, [29 January 1994]

[London], 2019.

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Italian translation of the BIS press release about the BIS Quarterly Review, December 2017

Italian translation of the BIS press release on the presentation of the Annual Economic Report 2018, 24 June 2018. Le autorità possono fare in modo che l'attuale ripresa economica si mantenga oltre il breve termine avviando riforme strutturali, ridando margine di manovra alle politiche monetarie e di bilancio per affrontare eventuali future minacce, e incoraggiando la pronta attuazione delle riforme regolamentari, scrive la Banca dei Regolamenti Internazionali (BRI) nella sua Relazione economica annuale. ...

Italian version of BIS Press Release - BIS Quarterly Review, 1 March 2020 - Definire il futuro dei pagamenti: Rassegna trimestrale BRI

French translation of "Frequently asked questions on market risk capital requirements" by the Basel Committee, March 2018.

Spanish translation of "Pillar 3 disclosure requirements - updated framework", December 2018

Spanish translation of the speech by Mr Agustín Carstens, General Manager of the BIS, on the occasion of the Bank's Annual General Meeting, Basel, 30 June 2019.

Spanish translation of the BIS press release on the presentation of the Annual Economic Report 2019, 30 June 2019.

These days are either the most trying time for encouraging employee engagement or the best we could expect. With so many people working remotely, many businesses need extra ways to communicate with the rank and file, and this might present a prime opportunity to try new things. We make a big deal of engaging the customer, and in most CRM circles engagement outranks simple customer experience.

Mitochondrial fission catalyzed by dynamin-related protein 1 (Drp1) is necessary for mitochondrial biogenesis and maintenance of healthy mitochondria. However, excessive fission has been associated with multiple neurodegenerative disorders, and we recently reported that mice with smaller mitochondria are sensitized to ischemic stroke injury. Although pharmacological Drp1 inhibition has been put forward as neuroprotective, the specificity and mechanism of the inhibitor used is controversial. Here, we provide genetic evidence that Drp1 inhibition is neuroprotective. Drp1 is activated by dephosphorylation of an inhibitory phosphorylation site, Ser637. We identify Bβ2, a mitochondria-localized protein phosphatase 2A (PP2A) regulatory subunit, as a neuron-specific Drp1 activator in vivo. Bβ2 KO mice of both sexes display elongated mitochondria in neurons and are protected from cerebral ischemic injury. Functionally, deletion of Bβ2 and maintained Drp1 Ser637 phosphorylation improved mitochondrial respiratory capacity, Ca²⁺ homeostasis, and attenuated superoxide production in response to ischemia and excitotoxicity in vitro and ex vivo. Last, deletion of Bβ2 rescued excessive stroke damage associated with dephosphorylation of Drp1 S637 and mitochondrial fission. These results indicate that the state of mitochondrial connectivity and PP2A/Bβ2-mediated dephosphorylation of Drp1 play a critical role in determining the severity of cerebral ischemic injury. Therefore, Bβ2 may represent a target for prophylactic neuroprotective therapy in populations at high risk of stroke.

SIGNIFICANCE STATEMENT With recent advances in clinical practice including mechanical thrombectomy up to 24 h after the ischemic event, there is resurgent interest in neuroprotective stroke therapies. In this study, we demonstrate reduced stroke damage in the brain of mice lacking the Bβ2 regulatory subunit of protein phosphatase 2A, which we have shown previously acts as a positive regulator of the mitochondrial fission enzyme dynamin-related protein 1 (Drp1). Importantly, we provide evidence that deletion of Bβ2 can rescue excessive ischemic damage in mice lacking the mitochondrial PKA scaffold AKAP1, apparently via opposing effects on Drp1 S637 phosphorylation. These results highlight reversible phosphorylation in bidirectional regulation of Drp1 activity and identify Bβ2 as a potential pharmacological target to protect the brain from stroke injury.

Gamma-band oscillations (GBOs) elicited by transient nociceptive stimuli are one of the most promising biomarkers of pain across species. Still, whether these GBOs reflect stimulus encoding in the primary somatosensory cortex (S1) or nocifensive behavior in the primary motor cortex (M1) is debated. Here we recorded neural activity simultaneously from the brain surface as well as at different depths of the bilateral S1/M1 in freely-moving male rats receiving nociceptive stimulation. GBOs measured from superficial layers of S1 contralateral to the stimulated paw not only had the largest magnitude, but also showed the strongest temporal and phase coupling with epidural GBOs. Also, spiking of superficial S1 interneurons had the strongest phase coherence with epidural GBOs. These results provide the first direct demonstration that scalp GBOs, one of the most promising pain biomarkers, reflect neural activity strongly coupled with the fast spiking of interneurons in the superficial layers of the S1 contralateral to the stimulated side.

SIGNIFICANCE STATEMENT Nociceptive-induced gamma-band oscillations (GBOs) measured at population level are one of the most promising biomarkers of pain perception. Our results provide the direct demonstration that these GBOs reflect neural activity coupled with the spike firing of interneurons in the superficial layers of the primary somatosensory cortex (S1) contralateral to the side of nociceptive stimulation. These results address the ongoing debate about whether nociceptive-induced GBOs recorded with scalp EEG or epidurally reflect stimulus encoding in the S1 or nocifensive behavior in the primary motor cortex (M1), and will therefore influence how experiments in pain neuroscience will be designed and interpreted.

Nitric oxide (NO) is an important signaling molecule that fulfills diverse functional roles as a neurotransmitter or diffusible second messenger in the developing and adult CNS. Although the impact of NO on different behaviors such as movement, sleep, learning, and memory has been well documented, the identity of its molecular and cellular targets is still an area of ongoing investigation. Here, we identify a novel role for NO in strengthening inhibitory GABA_A receptor-mediated transmission in molecular layer interneurons of the mouse cerebellum. NO levels are elevated by the activity of neuronal NO synthase (nNOS) following Ca²⁺ entry through extrasynaptic NMDA-type ionotropic glutamate receptors (NMDARs). NO activates protein kinase G with the subsequent production of cGMP, which prompts the stimulation of NADPH oxidase and protein kinase C (PKC). The activation of PKC promotes the selective strengthening of α3-containing GABA_ARs synapses through a GABA receptor-associated protein-dependent mechanism. Given the widespread but cell type-specific expression of the NMDAR/nNOS complex in the mammalian brain, our data suggest that NMDARs may uniquely strengthen inhibitory GABAergic transmission in these cells through a novel NO-mediated pathway.

SIGNIFICANCE STATEMENT Long-term changes in the efficacy of GABAergic transmission is mediated by multiple presynaptic and postsynaptic mechanisms. A prominent pathway involves crosstalk between excitatory and inhibitory synapses whereby Ca²⁺-entering through postsynaptic NMDARs promotes the recruitment and strengthening of GABA_A receptor synapses via Ca²⁺/calmodulin-dependent protein kinase II. Although Ca²⁺ transport by NMDARs is also tightly coupled to nNOS activity and NO production, it has yet to be determined whether this pathway affects inhibitory synapses. Here, we show that activation of NMDARs trigger a NO-dependent pathway that strengthens inhibitory GABAergic synapses of cerebellar molecular layer interneurons. Given the widespread expression of NMDARs and nNOS in the mammalian brain, we speculate that NO control of GABAergic synapse efficacy may be more widespread than has been appreciated.

To determine whether Cav1.2 voltage-gated Ca²⁺ channels contribute to astrocyte activation, we generated an inducible conditional knock-out mouse in which the Cav1.2 α subunit was deleted in GFAP-positive astrocytes. This astrocytic Cav1.2 knock-out mouse was tested in the cuprizone model of myelin injury and repair which causes astrocyte and microglia activation in the absence of a lymphocytic response. Deletion of Cav1.2 channels in GFAP-positive astrocytes during cuprizone-induced demyelination leads to a significant reduction in the degree of astrocyte and microglia activation and proliferation in mice of either sex. Concomitantly, the production of proinflammatory factors such as TNFα, IL1β and TGFβ1 was significantly decreased in the corpus callosum and cortex of Cav1.2 knock-out mice through demyelination. Furthermore, this mild inflammatory environment promotes oligodendrocyte progenitor cells maturation and myelin regeneration across the remyelination phase of the cuprizone model. Similar results were found in animals treated with nimodipine, a Cav1.2 Ca²⁺ channel inhibitor with high affinity to the CNS. Mice of either sex injected with nimodipine during the demyelination stage of the cuprizone treatment displayed a reduced number of reactive astrocytes and showed a faster and more efficient brain remyelination. Together, these results indicate that Cav1.2 Ca²⁺ channels play a crucial role in the induction and proliferation of reactive astrocytes during demyelination; and that attenuation of astrocytic voltage-gated Ca²⁺ influx may be an effective therapy to reduce brain inflammation and promote myelin recovery in demyelinating diseases.

SIGNIFICANCE STATEMENT Reducing voltage-gated Ca²⁺ influx in astrocytes during brain demyelination significantly attenuates brain inflammation and astrocyte reactivity. Furthermore, these changes promote myelin restoration and oligodendrocyte maturation throughout remyelination.

For visually guided navigation, the use of environmental cues is essential. Particularly, detecting local boundaries that impose limits to locomotion and estimating their location is crucial. In a series of three fMRI experiments, we investigated whether there is a neural coding of navigational distance in the human visual cortex (both female and male). We used virtual reality software to systematically manipulate the distance from a viewer perspective to different types of a boundary. Using a multivoxel pattern classification employing a linear support vector machine, we found that the occipital place area (OPA) is sensitive to the navigational distance restricted by the transparent glass wall. Further, the OPA was sensitive to a non-crossable boundary only, suggesting an importance of the functional constraint of a boundary. Together, we propose the OPA as a perceptual source of external environmental features relevant for navigation.

SIGNIFICANCE STATEMENT One of major goals in cognitive neuroscience has been to understand the nature of visual scene representation in human ventral visual cortex. An aspect of scene perception that has been overlooked despite its ecological importance is the analysis of space for navigation. One of critical computation necessary for navigation is coding of distance to environmental boundaries that impose limit on navigator's movements. This paper reports the first empirical evidence for coding of navigational distance in the human visual cortex and its striking sensitivity to functional constraint of environmental boundaries. Such finding links the paper to previous neurological and behavioral works that emphasized the distance to boundaries as a crucial geometric property for reorientation behavior of children and other animal species.

Neonatal stroke is as frequent as stroke in the elderly, but many pathophysiological injury aspects are distinct in neonates, including immune signaling. While myeloid cells can traffic into the brain via multiple routes, the choroid plexus (CP) has been identified as a uniquely educated gate for immune cell traffic during health and disease. To understand the mechanisms of myeloid cell trafficking via the CP and their influence on neonatal stroke, we characterized the phenotypes of CP-infiltrating myeloid cells after transient middle cerebral artery occlusion (tMCAO) in neonatal mice of both sexes in relation to blood-brain barrier permeability, injury, microglial activation, and CX3CR1-CCR2 signaling, focusing on the dynamics early after reperfusion. We demonstrate rapid recruitment of multiple myeloid phenotypes in the CP ipsilateral to the injury, including inflammatory CD45⁺CD11b⁺Ly6c^highCD86⁺, beneficial CD45⁺CD11b⁺Ly6c^lowCD206⁺, and CD45⁺CD11b⁺Ly6c^lowLy6g^high cells, but only minor leukocyte infiltration into acutely ischemic-reperfused cortex and negligible vascular albumin leakage. We report that CX3CR1-CCR2-mediated myeloid cell recruitment contributes to stroke injury. Considering the complexity of inflammatory cascades triggered by stroke and a role for TLR2 in injury, we also used direct TLR2 stimulation as an independent injury model. TLR2 agonist rapidly recruited myeloid cells to the CP, increased leukocytosis in the CSF and blood, but infiltration into the cortex remained low over time. While the magnitude and the phenotypes of myeloid cells diverged between tMCAO and TLR2 stimulation, in both models, disruption of CX3CR1-CCR2 signaling attenuated both monocyte and neutrophil trafficking to the CP and cortex.

SIGNIFICANCE STATEMENT Stroke during the neonatal period leads to long-term disabilities. The mechanisms of ischemic injury and inflammatory response differ greatly between the immature and adult brain. We examined leukocyte trafficking via the choroid plexus (CP) following neonatal stroke in relation to blood-brain barrier integrity, injury, microglial activation, and signaling via CX3CR1 and CCR2 receptors, or following direct TLR2 stimulation. Ischemia-reperfusion triggered marked unilateral CX3CR1-CCR2 dependent accumulation of diverse leukocyte subpopulations in the CP without inducing extravascular albumin leakage or major leukocyte infiltration into the brain. Disrupted CX3CR1-CCR2 signaling was neuroprotective in part by attenuating monocyte and neutrophil trafficking. Understanding the migratory patterns of CP-infiltrating myeloid cells with intact and disrupted CX3CR1-CCR2 signaling could identify novel therapeutic targets to protect the neonatal brain.

MECP2 gain-of-function and loss-of-function in genetically engineered monkeys recapitulates typical phenotypes in patients with autism, yet where MECP2 mutation affects the monkey brain and whether/how it relates to autism pathology remain unknown. Here we report a combination of gene–circuit–behavior analyses including MECP2 coexpression network, locomotive and cognitive behaviors, and EEG and fMRI findings in 5 MECP2 overexpressed monkeys (Macaca fascicularis; 3 females) and 20 wild-type monkeys (Macaca fascicularis; 11 females). Whole-genome expression analysis revealed MECP2 coexpressed genes significantly enriched in GABA-related signaling pathways, whereby reduced β-synchronization within fronto-parieto-occipital networks was associated with abnormal locomotive behaviors. Meanwhile, MECP2-induced hyperconnectivity in prefrontal and cingulate networks accounted for regressive deficits in reversal learning tasks. Furthermore, we stratified a cohort of 49 patients with autism and 72 healthy controls of 1112 subjects using functional connectivity patterns, and identified dysconnectivity profiles similar to those in monkeys. By establishing a circuit-based construct link between genetically defined models and stratified patients, these results pave new avenues to deconstruct clinical heterogeneity and advance accurate diagnosis in psychiatric disorders.

SIGNIFICANCE STATEMENT Autism spectrum disorder (ASD) is a complex disorder with co-occurring symptoms caused by multiple genetic variations and brain circuit abnormalities. To dissect the gene–circuit–behavior causal chain underlying ASD, animal models are established by manipulating causative genes such as MECP2. However, it is unknown whether such models have captured any circuit-level pathology in ASD patients, as demonstrated by human brain imaging studies. Here, we use transgenic macaques to examine the causal effect of MECP2 overexpression on gene coexpression, brain circuits, and behaviors. For the first time, we demonstrate that the circuit abnormalities linked to MECP2 and autism-like traits in the monkeys can be mapped to a homogeneous ASD subgroup, thereby offering a new strategy to deconstruct clinical heterogeneity in ASD.