Faculty Bibliography

Although PVCs commonly lead to degraded cine cardiac MRI (CMR), patients with PVCs may have relatively sharp cine images of both normal and ectopic beats ("double beats") when the rhythm during CMR is ventricular bigeminy, and only one beat of the pair is detected for gating. MRI methods for directly imaging premature ventricular contractions (PVCs) are not yet widely available. Localization of PVC site of origin with images may be helpful in planning ablations. The contraction pattern of the PVCs in bigeminy provides a "natural experiment" for investigating the potential utility of PVC imaging for localization. The purpose of this study was to evaluate the correlation of the visually assessed site of the initial contraction of the ectopic beats with the site of origin found by electroanatomic mapping. Images from 7 of 86 consecutive patients who underwent CMR prior to PVC ablation were found to include clear cine images of bigeminy. The visually apparent site of origin of the ectopic contraction was determined by three experienced, blinded CMR readers and correlated with each other, and with PVC site of origin determined by 3D electroanatomic mapping during catheter ablation. Blinded ascertainment of visually apparent initial contraction pattern for PVC localization was within 2 wall segments of PVC origin by 3D electroanatomic mapping 76% of the time. Our data from patients with PVCs with clear images of the ectopic beats when in bigeminy provide proof-of-concept that CMR ectopic beat contraction patterns analysis may provide a novel method for localizing PVC origin prior to ablation procedures. Direct imaging of PVCs with use of newer cardiac imaging methods, even without the presence of bigeminy, may thus provide valuable data for procedural planning.

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

GABAergic inhibitory neurons are the principal source of inhibition in the brain. Traditionally, their role in maintaining the balance of excitation-inhibition has been emphasized. Beyond homeostatic functions, recent circuit mapping and functional manipulation studies have revealed a wide range of specific roles that GABAergic circuits play in dynamically tilting excitation-inhibition coupling across spatio-temporal scales. These span from gating of compartment- and input-specific signaling, gain modulation, shaping input-output functions and synaptic plasticity, to generating signal-to-noise contrast, defining temporal windows for integration and rate codes, as well as organizing neural assemblies, and coordinating inter-regional synchrony. GABAergic circuits are thus instrumental in controlling single-neuron computations and behaviorally-linked network activity. The activity dependent modulation of sensory and mnemonic information processing by GABAergic circuits is pivotal for the formation and maintenance of episodic memories in the hippocampus. Here, we present an overview of the local and long-range GABAergic circuits that modulate the dynamics of excitation-inhibition and disinhibition in the main output area of the hippocampus CA1, which is crucial for episodic memory. Specifically, we link recent findings pertaining to GABAergic neuron molecular markers, electrophysiological properties, and synaptic wiring with their function at the circuit level. Lastly, given that area CA1 is particularly impaired during early stages of Alzheimer's disease, we emphasize how these GABAergic circuits may contribute to and be involved in the pathophysiology.

INTRODUCTION/UNASSIGNED:conditions. METHODS/UNASSIGNED:Alternating current (AC) stimulation with frequencies from 0.5 to 20 Hz was applied to the surface of the cerebellum in anesthetized rats. Extracellular recordings were obtained from Purkinje cells (PC), cerebellar and vestibular nuclear neurons, and other cerebellar cortical neurons. RESULTS AND DISCUSSION/UNASSIGNED:AC stimulation modulated the activity of all classes of neurons. Cerebellar and vestibular nuclear neurons most often showed increased spike activity during the negative phase of the AC stimulation. Purkinje cell simple spike activity was also increased during the negative phase at most locations, except for the cortex directly below the stimulus electrode, where activity was most often increased during the positive phase of the AC cycle. Other cortical neurons showed a more mixed, generally weaker pattern of modulation. The patterns of Purkinje cell responses suggest that AC stimulation induces a complex electrical field with changes in amplitude and orientation between local regions that may reflect the folding of the cerebellar cortex. Direct measurements of the induced electric field show that it deviates significantly from the theoretically predicted radial field for an isotropic, homogeneous medium, in both its orientation and magnitude. These results have relevance for models of the electric field induced in the cerebellum by AC stimulation.

Developmental exposure to ethanol is a leading cause of cognitive, emotional and behavioral problems, with fetal alcohol spectrum disorder (FASD) affecting more than 1:100 children. Recently, comorbid sleep deficits have been highlighted in these disorders, with sleep repair a potential therapeutic target. Animal models of FASD have shown non-REM (NREM) sleep fragmentation and slow-wave oscillation impairments that predict cognitive performance. Here we use a mouse model of perinatal ethanol exposure to explore whether reduced sleep pressure may contribute to impaired NREM sleep, and compare the function of a brain network reported to be impacted by insomnia-the Salience network-in developmental ethanol-exposed mice with sleep-deprived, saline controls. Mice were exposed to ethanol or saline on postnatal day 7 (P7) and allowed to mature to adulthood for testing. At P90, telemetered cortical recordings were made for assessment of NREM sleep in home cage before and after 4 h of sleep deprivation to assess basal NREM sleep and homeostatic NREM sleep response. To assess Salience network functional connectivity, mice were exposed to the 4 h sleep deprivation period or left alone, then immediately sacrificed for immunohistochemical analysis of c-Fos expression. The results show that developmental ethanol severely impairs both normal rebound NREM sleep and sleep deprivation induced increases in slow-wave activity, consistent with reduced sleep pressure. Furthermore, the Salience network connectome in rested, ethanol-exposed mice was most similar to that of sleep-deprived, saline control mice, suggesting a sleep deprivation-like state of Salience network function after developmental ethanol even without sleep deprivation.

Classic experiments with peripheral sympathetic neurons established an absolute dependence upon NGF for survival. A forgotten problem is how these neurons become resistant to deprivation of trophic factors. The question is whether and how neurons can survive in the absence of trophic support. However, the mechanism is not understood how neurons switch their phenotype to lose their dependence on trophic factors, such as NGF and BDNF. Here, we approach the problem by considering the requirements for trophic support of peripheral sympathetic neurons and hippocampal neurons from the central nervous system. We developed cellular assays to assess trophic factor dependency for sympathetic and hippocampal neurons and identified factors that rescue neurons in the absence of trophic support. They include enhanced expression of a subunit of the NGF receptor (Neurotrophin Receptor Homolog, NRH) in sympathetic neurons and an increase of the expression of the glucocorticoid receptor in hippocampal neurons. The results are significant since levels and activity of trophic factors are responsible for many neuropsychiatric conditions. Resistance of neurons to trophic factor deprivation may be relevant to the underlying basis of longevity, as well as an important element in preventing neurodegeneration.

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer's disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach.

PURPOSE/UNASSIGNED:The COVID-19 pandemic led to a drastic expansion in utilizing telemedicine, circumventing some of the geographical barriers to accessing pain care. However, uncertainties around the impact of telemedicine across various sociodemographic groups still exist, prompting further exploration. Therefore, this study aimed to evaluate the impact of sociodemographic factors in telemedicine utilization during and after the COVID-19 pandemic. PATIENTS AND METHODS/UNASSIGNED:All outpatient non-procedural visits at the pain medicine division of a large academic institution in the epicenter of the pandemic (New York, USA), between March 2019 and October 2021, were retrospectively included. Sociodemographic data including gender, age, ethnicity/race, postal code, and type of health insurance, across three time periods associated with the COVID-19 pandemic - pre-lockdown (in-office visits only), lockdown (telemedicine visits only) and post-lockdown (offering both in-office and telemedicine visits) - were analyzed and compared. RESULTS/UNASSIGNED:In total, 12,615 unique patients - The majority being women (58%) - were seen during the whole study period. In the post-lockdown period, telemedicine was utilized by 42% of all patients. Follow-up visits, younger patients, white patients, patients residing further away from the hospital, and privately insured patients were more likely to utilize telemedicine post-lockdown (p <0.05). Older patients, minorities, Manhattan residents, and Medicare/Medicaid recipients, were more likely to use in-office visits post-lockdown (p <0.05). CONCLUSION/UNASSIGNED:We identified disparities in the utilization of telemedicine in Pain Medicine, which may be due to socioeconomic factors such as lack of access to reliable internet access, cost of devices, and technological know-how. This emphasizes the need for further studies to better understand the reasons for and barriers to telemedicine use. This could help inform policymaking to safeguard equitable access to telemedicine use for pain care.