Relationship between PPI and baseline startle response

Prepulse inhibition (PPI) of the startle response to a sudden noise is the reduction in startle observed when the noise is preceded shortly by a mild sensory event, which is often a tone. A part of the literature is based on the assumption that PPI is independent of the baseline startle. A simple model is presented and experimental validation provided. The model is based on the commonly accepted observation that the neuronal circuit of PPI differs from that of startle. But, by using a common output, the measures of both phenomena become linked to each other. But, how can we interpret the numerous experimental data showing PPI to be independent of the startle level? It is suggested that in a number of such cases the baseline startle would have been stabilized by a ceiling effect in the startle/PPI neuronal networks. Reducing the startle level, for example in a PPI evaluation procedure, may disclose properties of startle masked by this ceiling effect. Disclosure of habituation to the startle eliciting noise produced an increase of PPI along its initial measurements. Taken together, even if the neuronal process that sustains startle and PPI are distinct, separating them experimentally requires careful parametric methods and caution in the interpretation of the corresponding observations

Association of Race and Ethnicity With COVID‐19 Outcomes in Rheumatic Disease: Data From the COVID‐19 Global Rheumatology Alliance Physician Registry

OBJECTIVE: Racial/ethnic minorities experience more severe outcomes of COVID-19 in the general United States (US) population. The aim of this study was to examine the association between race/ethnicity and COVID-19 hospitalization, ventilation status, and mortality in people with rheumatic disease. METHODS: US patients with rheumatic disease and COVID-19 entered into the COVID-19 Global Rheumatology Alliance physician registry March 24 - August 26, 2020 were included. Race/ethnicity was defined as white, Black, Latinx, Asian and other/mixed race. Outcomes included hospitalization, requirement for ventilatory support, and death. Multivariable regression models were used to estimate odds ratios (OR) and 95% confidence intervals controlling for age, sex, smoking, rheumatic disease diagnosis, comorbidities, medications taken prior to infection, and rheumatic disease activity. RESULTS: A total of 1,324 patients were included, of whom 36% were hospitalized and 6% died; 26% of hospitalized patients required mechanical ventilation. In multivariable models, Black (OR=2.74, 95% CI 1.90, 3.95), Latinx (OR=1.71, 95% CI 1.18, 2.49), and Asian (OR=2.69, 95% CI 1.16, 6.24) patients had higher odds of being hospitalized compared to white patients. Latinx patients also had three-fold increased odds of requiring ventilatory support (OR=3.25, 95% CI 1.75, 6.05). No differences in mortality based on race/ethnicity were found, though power may have been limited to detect associations. CONCLUSION: Similar to findings in the general US population, racial/ethnic minorities with rheumatic disease and COVID-19 had increased odds of hospitalization and ventilatory support. These results illustrate significant health disparities related to COVID-19 in people with rheumatic diseases. The rheumatology community should proactively address the needs of patients currently experiencing inequitable health outcomes during the pandemic

Brief Report: Sensorimotor Gating in Idiopathic Autism and Autism Associated with Fragile X Syndrome

Prepulse inhibition (PPI) may useful for exploring the proposed shared neurobiology between idiopathic autism and autism caused by FXS. We compared PPI in four groups: typically developing controls (n = 18), FXS and autism (FXS+A; n = 15), FXS without autism spectrum disorder (FXS−A; n = 17), and idiopathic autism (IA; n = 15). Relative to controls, the FXS+A (p < 0.002) and FXS−A (p < 0.003) groups had impaired PPI. The FXS+A (p < 0.01) and FXS−A (p < 0.03) groups had lower PPI than the IA group. Prolonged startle latency was seen in the IA group. The differing PPI profiles seen in the FXS+A and IA indicates these groups may not share a common neurobiological abnormality of sensorimotor gating

Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry

OBJECTIVES: To determine factors associated with COVID-19-related death in people with rheumatic diseases. METHODS: Physician-reported registry of adults with rheumatic disease and confirmed or presumptive COVID-19 (from 24 March to 1 July 2020). The primary outcome was COVID-19-related death. Age, sex, smoking status, comorbidities, rheumatic disease diagnosis, disease activity and medications were included as covariates in multivariable logistic regression models. Analyses were further stratified according to rheumatic disease category. RESULTS: Of 3729 patients (mean age 57 years, 68% female), 390 (10.5%) died. Independent factors associated with COVID-19-related death were age (66-75 years: OR 3.00, 95% CI 2.13 to 4.22; >75 years: 6.18, 4.47 to 8.53; both vs ≤65 years), male sex (1.46, 1.11 to 1.91), hypertension combined with cardiovascular disease (1.89, 1.31 to 2.73), chronic lung disease (1.68, 1.26 to 2.25) and prednisolone-equivalent dosage >10 mg/day (1.69, 1.18 to 2.41; vs no glucocorticoid intake). Moderate/high disease activity (vs remission/low disease activity) was associated with higher odds of death (1.87, 1.27 to 2.77). Rituximab (4.04, 2.32 to 7.03), sulfasalazine (3.60, 1.66 to 7.78), immunosuppressants (azathioprine, cyclophosphamide, ciclosporin, mycophenolate or tacrolimus: 2.22, 1.43 to 3.46) and not receiving any disease-modifying anti-rheumatic drug (DMARD) (2.11, 1.48 to 3.01) were associated with higher odds of death, compared with methotrexate monotherapy. Other synthetic/biological DMARDs were not associated with COVID-19-related death. CONCLUSION: Among people with rheumatic disease, COVID-19-related death was associated with known general factors (older age, male sex and specific comorbidities) and disease-specific factors (disease activity and specific medications). The association with moderate/high disease activity highlights the importance of adequate disease control with DMARDs, preferably without increasing glucocorticoid dosages. Caution may be required with rituximab, sulfasalazine and some immunosuppressants

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression

The Role of Stimulus Salience and Attentional Capture Across the Neural Hierarchy in a Stop-Signal Task

Inhibitory motor control is a core function of cognitive control. Evidence from diverse experimental approaches has linked this function to a mostly right-lateralized network of cortical and subcortical areas, wherein a signal from the frontal cortex to the basal ganglia is believed to trigger motor-response cancellation. Recently, however, it has been recognized that in the context of typical motor-control paradigms those processes related to actual response inhibition and those related to the attentional processing of the relevant stimuli are highly interrelated and thus difficult to distinguish. Here, we used fMRI and a modified Stop-signal task to specifically examine the role of perceptual and attentional processes triggered by the different stimuli in such tasks, thus seeking to further distinguish other cognitive processes that may precede or otherwise accompany the implementation of response inhibition. In order to establish which brain areas respond to sensory stimulation differences by rare Stop-stimuli, as well as to the associated attentional capture that these may trigger irrespective of their task-relevance, we compared brain activity evoked by Stop-trials to that evoked by Go-trials in task blocks where Stop-stimuli were to be ignored. In addition, region-of-interest analyses comparing the responses to these task-irrelevant Stop-trials, with those to typical relevant Stop-trials, identified separable activity profiles as a function of the task-relevance of the Stop-signal. While occipital areas were mostly blind to the task-relevance of Stop-stimuli, activity in temporo-parietal areas dissociated between task-irrelevant and task-relevant ones. Activity profiles in frontal areas, in turn, were activated mainly by task-relevant Stop-trials, presumably reflecting a combination of triggered top-down attentional influences and inhibitory motor-control processes

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates

Computational Models of Classical Conditioning guest editors’ introduction

In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: (1) Models were to be tested against a list of previously agreed-upon phenomena; (2) the parameters were fixed across simulations; and (3) the simulations used to test the models had to be made available. These requirements resulted in three major products: (a) a list of fundamental classical-conditioning results for which there is a consensus about their reliability; (b) the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and (c) a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the 2012 state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development