Cognitive domains affected post-COVID-19; a systematic review and meta-analysis.

BACKGROUND AND PURPOSE: This review aims to characterize the pattern of post-COVID-19 cognitive impairment, allowing better prediction of impact on daily function to inform clinical management and rehabilitation. METHODS: A systematic review and meta-analysis of neurocognitive sequelae following COVID-19 was conducted, following PRISMA-S guidelines. Studies were included if they reported domain-specific cognitive assessment in patients with COVID-19 at >4 weeks post-infection. Studies were deemed high-quality if they had >40 participants, utilized healthy controls, had low attrition rates and mitigated for confounders. RESULTS: Five of the seven primary Diagnostic and Statistical Manual of Mental Disorders (DSM-5) cognitive domains were assessed by enough high-quality studies to facilitate meta-analysis. Medium effect sizes indicating impairment in patients post-COVID-19 versus controls were seen across executive function (standardised mean difference (SMD) -0.45), learning and memory (SMD -0.55), complex attention (SMD -0.54) and language (SMD -0.54), with perceptual motor function appearing to be impacted to a greater degree (SMD -0.70). A narrative synthesis of the 56 low-quality studies also suggested no obvious pattern of impairment. CONCLUSIONS: This review found moderate impairments across multiple domains of cognition in patients post-COVID-19, with no specific pattern. The reported literature was significantly heterogeneous, with a wide variety of cognitive tasks, small sample sizes and disparate initial disease severities limiting interpretability. The finding of consistent impairment across a range of cognitive tasks suggests broad, as opposed to domain-specific, brain dysfunction. Future studies should utilize a harmonized test battery to facilitate inter-study comparisons, whilst also accounting for the interactions between COVID-19, neurological sequelae and mental health, the interplay between which might explain cognitive impairment.

Neuropsychiatric disorders following SARS-CoV-2 infection.

Several large-scale electronic health records studies have reported increased diagnostic rates for neuropsychiatric disorders following Coronavirus disease 2019 [COVID-19 or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 infection)], but many questions remain. To highlight the issues, we selectively review this literature, focusing on mood disorder, anxiety disorder, psychotic disorder, and cognitive impairment ('brain fog'). Eight key questions are addressed, comprising: (i) the nature and magnitude of the risks; (ii) their association with severity of infection; (iii) their duration; (iv) whether the risks differ between adults and children, or between men and women; (v) whether prior vaccination protects against them; (vi) the risk profile associated with different SARS-CoV-2 strains; (vii) what the underlying mechanisms might be; and (viii) whether the sequelae can be predicted. We consider the major unknowns, the limitations of electronic health records for research in this area, and the use of additional approaches to help characterize and understand the neuropsychiatric burden of COVID-19.

Risks of neurological and psychiatric sequelae 2 years after hospitalisation or intensive care admission with COVID-19 compared to admissions for other causes.

The association between COVID-19 and subsequent neurological and psychiatric disorders is well established. However, two important questions remain unanswered. First, what are the risks in those admitted to intensive care unit (ICU) with COVID-19? Admission to ICU is itself associated with neurological and psychiatric sequelae and it is not clear whether COVID-19 further increases those risks or changes their profile. Second, what are the trajectories of neurological and psychiatric risks in patients admitted to hospital or ICU with COVID-19, and when do the risks subside? We sought to answer these two questions using a retrospective cohort study based on electronic health records (EHR) data from the TriNetX Analytics Network (covering 89 million patients, mostly in the USA). Cohorts of patients admitted to hospital or ICU with COVID-19 were propensity score-matched (for 82 covariates capturing risk factors for COVID-19 and more severe COVID-19 illness) to patients admitted to hospital or ICU (respectively) for any other reason. Matched cohorts were followed for up to two years and the risk of 14 neurological and psychiatric outcomes were compared. A total of 280,173 patients admitted to hospital and 46,573 patients admitted to ICU with COVID-19 were successfully matched to an equal number of patients admitted to hospital or ICU for any other reason. Those hospitalised with COVID-19 were found to be at a greater risk of a range of neurological and psychiatric outcomes including seizure/epilepsy, encephalitis, myoneural junction/muscle disease, Guillain-Barré syndrome (GBS), dementia, cognitive deficits, psychotic disorder, mood and anxiety disorders, but not ischaemic stroke or intracranial haemorrhage. When risks were elevated after COVID-19, most remained so for the whole two years of follow-up (except for mood and anxiety disorders). Risk profiles and trajectories were substantially different among those admitted to ICU: compared to those admitted for any other reasons, those admitted with COVID-19 were at a greater risk of myoneural junction/muscle disease, GBS, cognitive deficits and anxiety disorder, but at a significantly lower risk of ischaemic stroke, intracranial haemorrhage, encephalitis, and mood disorder. When elevated, the risks in those admitted to ICU with COVID-19 were mostly short-lived. In summary, risks of neurological and psychiatric sequelae in patients hospitalised with COVID-19 are wide ranging and long standing whereas those in patients admitted to ICU with COVID-19 are similar to, or lower than, the risks observed post-ICU admission for any other cause. These contrasting risk trajectories are relevant for researchers, clinicians, patients, and policymakers.

Long COVID: mechanisms, risk factors and recovery.

NEW FINDINGS: What is the topic of this review? The emerging condition of long COVID, its epidemiology, pathophysiological impacts on patients of different backgrounds, physiological mechanisms emerging as explanations of the condition, and treatment strategies being trialled. The review leads from a Physiological Society online conference on this topic. What advances does it highlight? Progress in understanding the pathophysiology and cellular mechanisms underlying Long COVID and potential therapeutic and management strategies. ABSTRACT: Long COVID, the prolonged illness and fatigue suffered by a small proportion of those infected with SARS-CoV-2, is placing an increasing burden on individuals and society. A Physiological Society virtual meeting in February 2022 brought clinicians and researchers together to discuss the current understanding of long COVID mechanisms, risk factors and recovery. This review highlights the themes arising from that meeting. It considers the nature of long COVID, exploring its links with other post-viral illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome, and highlights how long COVID research can help us better support those suffering from all post-viral syndromes. Long COVID research started particularly swiftly in populations routinely monitoring their physical performance - namely the military and elite athletes. The review highlights how the high degree of diagnosis, intervention and monitoring of success in these active populations can suggest management strategies for the wider population. We then consider how a key component of performance monitoring in active populations, cardiopulmonary exercise training, has revealed long COVID-related changes in physiology - including alterations in peripheral muscle function, ventilatory inefficiency and autonomic dysfunction. The nature and impact of dysautonomia are further discussed in relation to postural orthostatic tachycardia syndrome, fatigue and treatment strategies that aim to combat sympathetic overactivation by stimulating the vagus nerve. We then interrogate the mechanisms that underlie long COVID symptoms, with a focus on impaired oxygen delivery due to micro-clotting and disruption of cellular energy metabolism, before considering treatment strategies that indirectly or directly tackle these mechanisms. These include remote inspiratory muscle training and integrated care pathways that combine rehabilitation and drug interventions with research into long COVID healthcare access across different populations. Overall, this review showcases how physiological research reveals the changes that occur in long COVID and how different therapeutic strategies are being developed and tested to combat this condition.

Sex and age effects on risk of non-traumatic subarachnoid hemorrhage: Retrospective cohort study of 124,234 cases using electronic health records.

OBJECTIVES: The epidemiology of non-traumatic subarachnoid hemorrhage (SAH) is unclear. This study describes the antecedent characteristics of SAH patients, compares the risk of SAH between women and men, and explores if this changes with age. MATERIALS AND METHODS: Retrospective cohort study using an electronic health records network based in the USA (TriNetX). All patients aged 18-90y with at least one healthcare visit were included. Antecedent characteristics of SAH patients (ICD-10 code I60) were measured. The incidence proportion and the relative risk between women and men, were estimated overall, in the 55-90y age group, and in five-year age categories. RESULTS: Of 58.9 million eligible patients, with 190.8 million person-years of observations, 124,234 (0.21%; 63,467 female, 60,671 male) had a first SAH, with a mean age of 56.8 (S.D. 16.8) y (women: 58.2 [16.2] y, men 55.3 [17.2] y). 9,758 SAH cases (7.8%) occurred in people aged 18-30y. Prior to the SAH, an intracranial aneurysm had been diagnosed in 4.1% (women: 5.8% men: 2.5%), hypertension in 25.1% and nicotine dependence in 9.1%. Overall, women had a lower risk of SAH compared to men (RR 0.83, 95% CI 0.83-0.84), with a progressive increase in risk ratio across age groups: from RR 0.36 (0.35-0.37) in people aged 18-24y, to RR 1.07 (1.01-1.13) aged 85-90y. CONCLUSIONS: Men are at greater risk of SAH than women overall, driven by younger adult age groups. Women are at greater risk than men only in the over 75-year age groups. The excess of SAH in young men merits investigation.

Measuring affect dynamics: An empirical framework.

A fast-growing body of evidence from experience sampling studies suggests that affect dynamics are associated with well-being and health. But heterogeneity in experience sampling approaches impedes reproducibility and scientific progress. Leveraging a large dataset of 7016 individuals, each providing over 50 affect reports, we introduce an empirically derived framework to help researchers design well-powered and efficient experience sampling studies. Our research reveals three general principles. First, a sample of 200 participants and 20 observations per person yields sufficient power to detect medium-sized associations for most affect dynamic measures. Second, for trait- and time-independent variability measures of affect (e.g., SD), distant sampling study designs (i.e., a few daily measurements spread out over several weeks) lead to more accurate estimates than close sampling study designs (i.e., many daily measurements concentrated over a few days), although differences in accuracy across sampling methods were inconsistent and of little practical significance for temporally dependent affect dynamic measures (i.e., RMSSD, autocorrelation coefficient, TKEO, and PAC). Third, across all affect dynamics measures, sampling exclusively on specific days or time windows leads to little to no improvement over sampling at random times. Because the ideal sampling approach varies for each affect dynamics measure, we provide a companion R package, an online calculator ( https://sergiopirla.shinyapps.io/powerADapp ), and a series of benchmark effect sizes to help researchers address three fundamental hows of experience sampling: How many participants to recruit? How often to solicit them? And for how long?

Association between serum lithium level and incidence of COVID-19 infection.

An antiviral effect of lithium has been proposed, but never investigated for coronavirus disease 2019 (COVID-19). Using electronic health records of 26 554 patients with documented serum lithium levels during the pandemic, we show that the 6-month COVID-19 infection incidence was lower among matched patients with 'therapeutic' (0.50-1.00) versus 'subtherapeutic' (0.05-0.50) lithium levels (hazard ratio (HR) = 0.82, 95% CI 0.69-0.97, P = 0.017) and among patients with 'therapeutic' lithium levels versus matched patients using valproate (HR = 0.79, 95% CI 0.67-0.92, P = 0.0023). Lower rates of infection were observed for both new COVID-19 diagnoses and positive polymerase chain reaction tests, regardless of underlying psychiatric diagnosis and vaccination status.

Six-month sequelae of post-vaccination SARS-CoV-2 infection: A retrospective cohort study of 10,024 breakthrough infections.

Vaccination has proven effective against infection with SARS-CoV-2, as well as death and hospitalisation following COVID-19 illness. However, little is known about the effect of vaccination on other acute and post-acute outcomes of COVID-19. Data were obtained from the TriNetX electronic health records network (over 81 million patients mostly in the USA). Using a retrospective cohort study and time-to-event analysis, we compared the incidences of COVID-19 outcomes between individuals who received a COVID-19 vaccine (approved for use in the USA) at least 2 weeks before SARS-CoV-2 infection and propensity score-matched individuals unvaccinated for COVID-19 but who had received an influenza vaccine. Outcomes were ICD-10 codes representing documented COVID-19 sequelae in the 6 months after a confirmed SARS-CoV-2 infection (recorded between January 1 and August 31, 2021, i.e. before the emergence of the Omicron variant). Associations with the number of vaccine doses (1 vs. 2) and age (<60 vs. ≥ 60 years-old) were assessed. Among 10,024 vaccinated individuals with SARS-CoV-2 infection, 9479 were matched to unvaccinated controls. Receiving at least one COVID-19 vaccine dose was associated with a significantly lower risk of respiratory failure, ICU admission, intubation/ventilation, hypoxaemia, oxygen requirement, hypercoagulopathy/venous thromboembolism, seizures, psychotic disorder, and hair loss (each as composite endpoints with death to account for competing risks; HR 0.70-0.83, Bonferroni-corrected p < 0.05), but not other outcomes, including long-COVID features, renal disease, mood, anxiety, and sleep disorders. Receiving 2 vaccine doses was associated with lower risks for most outcomes. Associations between prior vaccination and outcomes of SARS-CoV-2 infection were marked in those <60 years-old, whereas no robust associations were observed in those ≥60 years-old. In summary, COVID-19 vaccination is associated with lower risk of several, but not all, COVID-19 sequelae in those with breakthrough SARS-CoV-2 infection. The findings may inform service planning, contribute to forecasting public health impacts of vaccination programmes, and highlight the need to identify additional interventions for COVID-19 sequelae.

A structural brain network of genetic vulnerability to psychiatric illness.

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.

Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19.

BACKGROUND: Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues. METHODS AND FINDINGS: We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score-matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan-Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control. Among COVID-19 survivors (mean [SD] age: 46.3 [19.8], 55.6% female), 57.00% had one or more long-COVID feature recorded during the whole 6-month period (i.e., including the acute phase), and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing (18.71% in the 1- to 180-day period; 7.94% in the 90- to180-day period), fatigue/malaise (12.82%; 5.87%), chest/throat pain (12.60%; 5.71%), headache (8.67%; 4.63%), other pain (11.60%; 7.19%), abdominal symptoms (15.58%; 8.29%), myalgia (3.24%; 1.54%), cognitive symptoms (7.88%; 3.95%), and anxiety/depression (22.82%; 15.49%). All 9 features were more frequently reported after COVID-19 than after influenza (with an overall excess incidence of 16.60% and hazard ratios between 1.44 and 2.04, all p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms. CONCLUSIONS: Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity.

Incidence and outcomes of eating disorders during the COVID-19 pandemic.

There are concerns that eating disorders have become commoner during the coronavirus disease 2019 (COVID-19) pandemic. Using the electronic health records of 5.2 million people aged under 30, mostly in the USA, we show that the diagnostic incidence was 15.3% higher in 2020 overall compared with previous years (relative risk 1.15, 95% CI 1.12-1.19). The relative risk increased steadily from March 2020 onwards, exceeding 1.5 by the end of the year. The increase occurred solely in females, and primarily related to teenagers and anorexia nervosa. A higher proportion of patients with eating disorders in 2020 had suicidal ideation (hazard ratio HR = 1.30, 1.16-1.47) or attempted suicide (HR = 1.69, 1.21-2.35).

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex.

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

Happiness and Social Behavior.

It is often assumed that there is a robust positive symmetrical relationship between happiness and social behavior: Social relationships are viewed as essential to happiness, and happiness is thought to foster social relationships. However, empirical support for this widely held view is surprisingly mixed, and this view does little to clarify which social partner a person will be motivated to interact with when happy. To address these issues, we monitored the happiness and social interactions of more than 30,000 people for a month. We found that patterns of social interaction followed the hedonic-flexibility principle, whereby people tend to engage in happiness-enhancing social relationships when they feel bad and sustain happiness-decreasing periods of solitude and less pleasant types of social relationships that might promise long-term payoff when they feel good. These findings demonstrate that links between happiness and social behavior are more complex than often assumed in the positive-emotion literature.

Hedonism and the choice of everyday activities.

Most theories of motivation have highlighted that human behavior is guided by the hedonic principle, according to which our choices of daily activities aim to minimize negative affect and maximize positive affect. However, it is not clear how to reconcile this idea with the fact that people routinely engage in unpleasant yet necessary activities. To address this issue, we monitored in real time the activities and moods of over 28,000 people across an average of 27 d using a multiplatform smartphone application. We found that people's choices of activities followed a hedonic flexibility principle. Specifically, people were more likely to engage in mood-increasing activities (e.g., play sports) when they felt bad, and to engage in useful but mood-decreasing activities (e.g., housework) when they felt good. These findings clarify how hedonic considerations shape human behavior. They may explain how humans overcome the allure of short-term gains in happiness to maximize long-term welfare.

Assessing the validity of the approximation of diffusion-weighted-MRI signals from crossing fascicles by sums of signals from single fascicles.

PURPOSE: To assess the validity of the superposition approximation for crossing fascicles, i.e., the assumption that the total diffusion-weighted MRI signal is the sum of the signals arising from each fascicle independently, even when the fascicles intermingle in a voxel. METHODS: Monte Carlo simulations were used to study the impact of the approximation on the diffusion-weighted MRI signal and to assess whether this approximate model allows microstructural features of interwoven fascicles to be accurately estimated, despite signal differences. RESULTS: Small normalized signal differences were observed, typically 10-3-10-2. The use of the approximation had little impact on the estimation of the crossing angle, the axonal density index, and the radius index in clinically realistic scenarios wherein the acquisition noise was the predominant source of errors. In the absence of noise, large systematic errors due to the superposition approximation only persisted for the radius index, mainly driven by a low sensitivity of diffusion-weighted MRI signals to small radii in general. CONCLUSION: The use of the superposition approximation rather than a model of interwoven fascicles does not adversely impact the estimation of microstructural features of interwoven fascicles in most current clinical settings. Magn Reson Med 79:2332-2345, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND).

PURPOSE: To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel. THEORY AND METHODS: In prior work, a statistical model of the apparent diffusion coefficient was shown to well-characterize the diffusivity and heterogeneity of the mono-directional diffusion MRI signal. However, this model was unable to characterize the three-dimensional anisotropic diffusion observed in the brain. We introduce a new model that extends the statistical distribution representation to be fully tridimensional, in which apparent diffusion coefficients are extended to be diffusion tensors. The set of compartments present at a voxel is modeled by a finite sum of unimodal continuous distributions of diffusion tensors. Each distribution provides measures of each compartment microstructural diffusivity and heterogeneity. RESULTS: The ability to estimate the tridimensional diffusivity and heterogeneity of multiple fascicles and of free diffusion is demonstrated. CONCLUSION: Our novel tissue model allows for the characterization of the intra-voxel orientational heterogeneity, a prerequisite for accurate tractography while also characterizing the overall tridimensional diffusivity and heterogeneity of each tissue compartment. The model parameters can be estimated from short duration acquisitions. The diffusivity and heterogeneity microstructural parameters may provide novel indicator of the presence of disease or injury. Magn Reson Med 76:963-977, 2016. © 2015 Wiley Periodicals, Inc.