Unifying network model links recency and central tendency biases in working memory

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Impact of COVID-19 vaccination on symptoms and immune phenotypes in vaccine-naïve individuals with Long COVID

Background

Long COVID contributes to the global burden of disease. Proposed root cause hypotheses include the persistence of SARS-CoV-2 viral reservoir, autoimmunity, and reactivation of latent herpesviruses. Patients have reported various changes in Long COVID symptoms after COVID-19 vaccinations, leaving uncertainty about whether vaccine-induced immune responses may alleviate or worsen disease pathology.

Methods

In this prospective study, we evaluated changes in symptoms and immune responses after COVID-19 vaccination in 16 vaccine-naïve individuals with Long COVID. Surveys were administered before vaccination and then at 2, 6, and 12 weeks after receiving the first vaccine dose of the primary series. Simultaneously, SARS-CoV-2-reactive TCR enrichment, SARS-CoV-2-specific antibody responses, antibody responses to other viral and self-antigens, and circulating cytokines were quantified before vaccination and at 6 and 12 weeks after vaccination.

Results

Self-report at 12 weeks post-vaccination indicated 10 out of 16 participants had improved health, 3 had no change, 1 had worse health, and 2 reported marginal changes. Significant elevation in SARS-CoV-2-specific TCRs and Spike protein-specific IgG were observed 6 and 12 weeks after vaccination. No changes in reactivities were observed against herpes viruses and self-antigens. Within this dataset, higher baseline sIL-6R was associated with symptom improvement, and the two top features associated with non-improvement were high IFN-β and CNTF, among soluble analytes.

Conclusions

Our study showed that in this small sample, vaccination improved the health or resulted in no change to the health of most participants, though few experienced worsening. Vaccination was associated with increased SARS-CoV-2 Spike protein-specific IgG and T cell expansion in most individuals with Long COVID. Symptom improvement was observed in those with baseline elevated sIL-6R, while elevated interferon and neuropeptide levels were associated with a lack of improvement.

Systematic Review of the Prevalence of Long COVID

Background

Long COVID occurs in those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) whose symptoms persist or develop beyond the acute phase. We conducted a systematic review to determine the prevalence of persistent symptoms, functional disability, or pathological changes in adults or children at least 12 weeks postinfection.

Methods

We searched key registers and databases from January 1, 2020 to November 2, 2021, limited to publications in English and studies with at least 100 participants. Studies in which all participants were critically ill were excluded. Long COVID was extracted as prevalence of at least 1 symptom or pathology, or prevalence of the most common symptom or pathology, at 12 weeks or later. Heterogeneity was quantified in absolute terms and as a proportion of total variation and explored across predefined subgroups (PROSPERO ID CRD42020218351).

Results

One hundred twenty studies in 130 publications were included. Length of follow-up varied between 12 weeks and 12 months. Few studies had low risk of bias. All complete and subgroup analyses except 1 had I² ≥90%, with prevalence of persistent symptoms range of 0%-93% (pooled estimate [PE], 42.1%; 95% prediction interval [PI], 6.8% to 87.9%). Studies using routine healthcare records tended to report lower prevalence (PE, 13.6%; PI, 1.2% to 68%) of persistent symptoms/pathology than self-report (PE, 43.9%; PI, 8.2% to 87.2%). However, studies systematically investigating pathology in all participants at follow up tended to report the highest estimates of all 3 (PE, 51.7%; PI, 12.3% to 89.1%). Studies of hospitalized cases had generally higher estimates than community-based studies.

Conclusions

The way in which Long COVID is defined and measured affects prevalence estimation. Given the widespread nature of SARS-CoV-2 infection globally, the burden of chronic illness is likely to be substantial even using the most conservative estimates.

Functional gene delivery to and across brain vasculature of systemic AAVs with endothelial-specific tropism in rodents and broad tropism in primates

Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds, and in rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and in ex vivo human brain slices, although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial-specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. We apply this approach to Hevin knockout mice, where AAV-X1-mediated ectopic expression of the synaptogenic protein Sparcl1/Hevin in brain endothelial cells rescued synaptic deficits.

A review of cytokine-based pathophysiology of Long COVID symptoms

The Long COVID/Post Acute Sequelae of COVID-19 (PASC) group includes patients with initial mild-to-moderate symptoms during the acute phase of the illness, in whom recovery is prolonged, or new symptoms are developed over months. Here, we propose a description of the pathophysiology of the Long COVID presentation based on inflammatory cytokine cascades and the p38 MAP kinase signaling pathways that regulate cytokine production. In this model, the SARS-CoV-2 viral infection is hypothesized to trigger a dysregulated peripheral immune system activation with subsequent cytokine release. Chronic low-grade inflammation leads to dysregulated brain microglia with an exaggerated release of central cytokines, producing neuroinflammation. Immunothrombosis linked to chronic inflammation with microclot formation leads to decreased tissue perfusion and ischemia. Intermittent fatigue, Post Exertional Malaise (PEM), CNS symptoms with "brain fog," arthralgias, paresthesias, dysautonomia, and GI and ophthalmic problems can consequently arise as result of the elevated peripheral and central cytokines. There are abundant similarities between symptoms in Long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). DNA polymorphisms and viral-induced epigenetic changes to cytokine gene expression may lead to chronic inflammation in Long COVID patients, predisposing some to develop autoimmunity, which may be the gateway to ME/CFS.

Functional gene delivery to and across brain vasculature of systemic AAVs with endothelial-specific tropism in rodents and broad tropism in primates

Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds and rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and ex vivo human brain slices although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. Vasculature-secreted Hevin (a synaptogenic protein) rescued synaptic deficits in a mouse model.

A core outcome set for post-COVID-19 condition in adults for use in clinical practice and research: an international Delphi consensus study

Health consequences that persist beyond the acute infection phase of COVID-19, termed post-COVID-19 condition (also commonly known as long COVID), vary widely and represent a growing global health challenge. Research on post-COVID-19 condition is expanding but, at present, no agreement exists on the health outcomes that should be measured in people living with the condition. To address this gap, we conducted an international consensus study, which included a comprehensive literature review and classification of outcomes for post-COVID-19 condition that informed a two-round online modified Delphi process followed by an online consensus meeting to finalise the core outcome set (COS). 1535 participants from 71 countries were involved, with 1148 individuals participating in both Delphi rounds. Eleven outcomes achieved consensus for inclusion in the final COS: fatigue; pain; post-exertion symptoms; work or occupational and study changes; survival; and functioning, symptoms, and conditions for each of cardiovascular, respiratory, nervous system, cognitive, mental health, and physical outcomes. Recovery was included a priori because it was a relevant outcome that was part of a previously published COS on COVID-19. The next step in this COS development exercise will be to establish the instruments that are most appropriate to measure these core outcomes. This international consensus-based COS should provide a framework for standardised assessment of adults with post-COVID-19 condition, aimed at facilitating clinical care and research worldwide.

Studying the post-COVID-19 condition: research challenges, strategies, and importance of Core Outcome Set development

BACKGROUND

A substantial portion of people with COVID-19 subsequently experience lasting symptoms including fatigue, shortness of breath, and neurological complaints such as cognitive dysfunction many months after acute infection. Emerging evidence suggests that this condition, commonly referred to as long COVID but also known as post-acute sequelae of SARS-CoV-2 infection (PASC) or post-COVID-19 condition, could become a significant global health burden.

MAIN TEXT

While the number of studies investigating the post-COVID-19 condition is increasing, there is no agreement on how this new disease should be defined and diagnosed in clinical practice and what relevant outcomes to measure. There is an urgent need to optimise and standardise outcome measures for this important patient group both for clinical services and for research and to allow comparing and pooling of data.

CONCLUSIONS

A Core Outcome Set for post-COVID-19 condition should be developed in the shortest time frame possible, for improvement in data quality, harmonisation, and comparability between different geographical locations. We call for a global initiative, involving all relevant partners, including, but not limited to, healthcare professionals, researchers, methodologists, patients, and caregivers. We urge coordinated actions aiming to develop a Core Outcome Set (COS) for post-COVID-19 condition in both the adult and paediatric populations.

Characterizing long COVID in an international cohort: 7 months of symptoms and their impact

BACKGROUND

A significant number of patients with COVID-19 experience prolonged symptoms, known as Long COVID. Few systematic studies have investigated this population, particularly in outpatient settings. Hence, relatively little is known about symptom makeup and severity, expected clinical course, impact on daily functioning, and return to baseline health.

METHODS

We conducted an online survey of people with suspected and confirmed COVID-19, distributed via COVID-19 support groups (e.g. Body Politic, Long COVID Support Group, Long Haul COVID Fighters) and social media (e.g. Twitter, Facebook). Data were collected from September 6, 2020 to November 25, 2020. We analyzed responses from 3762 participants with confirmed (diagnostic/antibody positive; 1020) or suspected (diagnostic/antibody negative or untested; 2742) COVID-19, from 56 countries, with illness lasting over 28 days and onset prior to June 2020. We estimated the prevalence of 203 symptoms in 10 organ systems and traced 66 symptoms over seven months. We measured the impact on life, work, and return to baseline health.

FINDINGS

For the majority of respondents (>91%), the time to recovery exceeded 35 weeks. During their illness, participants experienced an average of 55.9+/- 25.5 (mean+/-STD) symptoms, across an average of 9.1 organ systems. The most frequent symptoms after month 6 were fatigue, post-exertional malaise, and cognitive dysfunction. Symptoms varied in their prevalence over time, and we identified three symptom clusters, each with a characteristic temporal profile. 85.9% of participants (95% CI, 84.8% to 87.0%) experienced relapses, primarily triggered by exercise, physical or mental activity, and stress. 86.7% (85.6% to 92.5%) of unrecovered respondents were experiencing fatigue at the time of survey, compared to 44.7% (38.5% to 50.5%) of recovered respondents. 1700 respondents (45.2%) required a reduced work schedule compared to pre-illness, and an additional 839 (22.3%) were not working at the time of survey due to illness. Cognitive dysfunction or memory issues were common across all age groups (~88%). Except for loss of smell and taste, the prevalence and trajectory of all symptoms were similar between groups with confirmed and suspected COVID-19.

INTERPRETATION

Patients with Long COVID report prolonged, multisystem involvement and significant disability. By seven months, many patients have not yet recovered (mainly from systemic and neurological/cognitive symptoms), have not returned to previous levels of work, and continue to experience significant symptom burden.

FUNDING

All authors contributed to this work in a voluntary capacity. The cost of survey hosting (on Qualtrics) and publication fee was covered by AA's research grant (Wellcome Trust/Gatsby Charity via Sainsbury Wellcome center, UCL).

Patient-Led Research Collaborative: embedding patients in the Long COVID narrative

A large subset of patients with coronavirus disease 2019 (COVID-19) are experiencing symptoms well beyond the claimed 2-week recovery period for mild cases. These long-term sequelae have come to be known as Long COVID. Originating out of a dedicated online support group, a team of patients formed the Patient-Led Research Collaborative and conducted the first research on Long COVID experience and symptoms. This article discusses the history and value of patient-centric and patient-led research; the formation of Patient-Led Research Collaborative as well as key findings to date; and calls for the following: the acknowledgement of Long COVID as an illness, an accurate estimate of the prevalence of Long COVID, publicly available basic symptom management, care, and research to not be limited to those with positive polymerase chain reaction and antibody tests, and aggressive research and investigation into the pathophysiology of symptoms.

Extracting the dynamics of behavior in sensory decision-making experiments

Decision-making strategies evolve during training and can continue to vary even in well-trained animals. However, studies of sensory decision-making tend to characterize behavior in terms of a fixed psychometric function that is fit only after training is complete. Here, we present PsyTrack, a flexible method for inferring the trajectory of sensory decision-making strategies from choice data. We apply PsyTrack to training data from mice, rats, and human subjects learning to perform auditory and visual decision-making tasks. We show that it successfully captures trial-to-trial fluctuations in the weighting of sensory stimuli, bias, and task-irrelevant covariates such as choice and stimulus history. This analysis reveals dramatic differences in learning across mice and rapid adaptation to changes in task statistics. PsyTrack scales easily to large datasets and offers a powerful tool for quantifying time-varying behavior in a wide variety of animals and tasks.

Lateral orbitofrontal cortex promotes trial-by-trial learning of risky, but not spatial, biases

Individual choices are not made in isolation but are embedded in a series of past experiences, decisions, and outcomes. The effects of past experiences on choices, often called sequential biases, are ubiquitous in perceptual and value-based decision-making, but their neural substrates are unclear. We trained rats to choose between cued guaranteed and probabilistic rewards in a task in which outcomes on each trial were independent. Behavioral variability often reflected sequential effects, including increased willingness to take risks following risky wins, and spatial 'win-stay/lose-shift' biases. Recordings from lateral orbitofrontal cortex (lOFC) revealed encoding of reward history and receipt, and optogenetic inhibition of lOFC eliminated rats' increased preference for risk following risky wins, but spared other sequential effects. Our data show that different sequential biases are neurally dissociable, and the lOFC's role in adaptive behavior promotes learning of more abstract biases (here, biases for the risky option), but not spatial ones.

Efficient inference for time-varying behavior during learning

The process of learning new behaviors over time is a problem of great interest in both neuroscience and artificial intelligence. However, most standard analyses of animal training data either treat behavior as fixed or track only coarse performance statistics (e.g., accuracy, bias), providing limited insight into the evolution of the policies governing behavior. To overcome these limitations, we propose a dynamic psychophysical model that efficiently tracks trial-to-trial changes in behavior over the course of training. Our model consists of a dynamic logistic regression model, parametrized by a set of time-varying weights that express dependence on sensory stimuli as well as task-irrelevant covariates, such as stimulus, choice, and answer history. Our implementation scales to large behavioral datasets, allowing us to infer 500K parameters (e.g., 10 weights over 50K trials) in minutes on a desktop computer. We optimize hyperparameters governing how rapidly each weight evolves over time using the decoupled Laplace approximation, an efficient method for maximizing marginal likelihood in non-conjugate models. To illustrate performance, we apply our method to psychophysical data from both rats and human subjects learning a delayed sensory discrimination task. The model successfully tracks the psychophysical weights of rats over the course of training, capturing day-to-day and trial-to-trial fluctuations that underlie changes in performance, choice bias, and dependencies on task history. Finally, we investigate why rats frequently make mistakes on easy trials, and suggest that apparent lapses can be explained by sub-optimal weighting of known task covariates.

Transformation of Perception from Sensory to Motor Cortex

To better understand how a stream of sensory data is transformed into a percept, we examined neuronal activity in vibrissal sensory cortex, vS1, together with vibrissal motor cortex, vM1 (a frontal cortex target of vS1), while rats compared the intensity of two vibrations separated by an interstimulus delay. Vibrations were "noisy," constructed by stringing together over time a sequence of velocity values sampled from a normal distribution; each vibration's mean speed was proportional to the width of the normal distribution. Durations of both stimulus 1 and stimulus 2 could vary from 100 to 600 ms. Psychometric curves reveal that rats overestimated the longer-duration stimulus-thus, perceived intensity of a vibration grew over the course of hundreds of milliseconds even while the sensory input remained, on average, stationary. Human subjects demonstrated the identical perceptual phenomenon, indicating that the underlying mechanisms of temporal integration generalize across species. The time dependence of the percept allowed us to ask to what extent neurons encoded the ongoing stimulus stream versus the animal's percept. We demonstrate that vS1 firing correlated with the local features of the vibration, whereas vM1 firing correlated with the percept: the final vM1 population state varied, as did the rat's behavior, according to both stimulus speed and stimulus duration. Moreover, vM1 populations appeared to participate in the trace of the percept of stimulus 1 as the rat awaited stimulus 2. In conclusion, the transformation of sensory data into the percept appears to involve the integration and storage of vS1 signals by vM1.

EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery

Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "Brain-Computer Interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.

The association between schoolchildren intelligence and refractive error

PURPOSE

The relationship between refractory errors and intelligence and the importance of genetic, regional and environmental factors in such associations, were investigated in a group of school children.

SUBJECTS AND METHODS

One hundred and thirty-seven students (34.3% boys and 65.7% girls) from two primary schools were enrolled in the study. Cycloplegic refraction was performed and a spherical equivalent (SE) > or = 0.5D were determined as hyperopia; <-0.5D myopia and <1 cyl D astigmatism. Demographic factors, parent's education level, teacher based assessment of school performance and average score were also evaluated.

RESULTS

Seventy-eight (56.9%) of subjects showed a form of refractory error; 27%, 3% and 2.9% were myope, hyperope or astigmat, respectively, whereas 12.4% of them had both myopia and astigmatism and 10.2% showed hyperopia and astigmatism; 43.1% were normal.

CONCLUSIONS

Although our data revealed no distinction of average score between normal group and myopia, hyperopia, astigmatism or hyperopia-astigmatism, there is a statistically significant difference between normal group and those who had both myopia and astigmatism in which the later had a lower mediocre. Our results is somehow in contrast with other parallel studies demonstrating that positive connection between school performance and myopia can be explained by the geographical or racial discrepancies as well as subjects involved in the study and divergent set of cut off limits.

Lateral thinking, from the Hopfield model to cortical dynamics

Self-organizing attractor networks may comprise the building blocks for cortical dynamics, providing the basic operations of categorization, including analog-to-digital conversion, association and auto-association, which are then expressed as components of distinct cognitive functions depending on the contents of the neural codes in each region. To assess the viability of this scenario, we first review how a local cortical patch may be modeled as an attractor network, in which memory representations are not artificially stored as prescribed binary patterns of activity as in the Hopfield model, but self-organize as continuously graded patterns induced by afferent input. Recordings in macaques indicate that such cortical attractor networks may express retrieval dynamics over cognitively plausible rapid time scales, shorter than those dominated by neuronal fatigue. A cortical network encompassing many local attractor networks, and incorporating a realistic description of adaptation dynamics, may be captured by a Potts model. This network model has the capacity to engage long-range associations into sustained iterative attractor dynamics at a cortical scale, in what may be regarded as a mathematical model of spontaneous lateral thought. This article is part of a Special Issue entitled: Neural Coding.

Impact of Ramadan fasting on intraocular pressure, visual acuity and refractive errors

INTRODUCTION

Fasting evidently influences a variety of physiological parameters that can impact the ocular system. Among these modifications are alterations in insulin secretion, sympathetic activity, free fatty acids, lipid profile, melatonin, cortisol, electrolytes and catecholamines. In this study, we investigated the possible alterations in intraocular pressure (IOP), visual acuity and refractive errors during Ramadan fasting.

METHODS

IOP, visual acuity and refractive errors of both eyes of volunteers were measured on the first and last days of Ramadan (once in the morning and evening). Body weight was measured so as to estimate the amount of dehydration. Data from the two examinations was analysed using one-way analysis of variance. A p-value of less than 0.05 was considered statistically significant.

RESULTS

58 healthy, fasting male volunteers with a mean age of 40.7 +/- 7.1 years participated in the study. Statistical analysis demonstrated no difference in IOP, visual acuity or refractive errors on the first and last days of Ramadan, or within a single day (from morning to evening).

CONCLUSION

Our results reveal that Islamic Ramadan fasting does not profoundly affect physiological IOP, refractive error or visual acuity values in healthy volunteers. However, more detailed investigations using animal models should be designed to evaluate whether fasting has a pivotal influence on pathological conditions.

Converging neuronal activity in inferior temporal cortex during the classification of morphed stimuli

How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak approximately 120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.