Faculty Bibliography

INTRODUCTION/UNASSIGNED:PET imaging. METHODS/UNASSIGNED:F]SFB in acetonitrile/0.1 M borate buffer solution (final pH ~ 8.5) with an incubation of 20 min at room temperature, followed by purification on a PD MiniTrap G-25 size exclusion gravity column. RESULTS/UNASSIGNED:F]SFB and bispecific antibodies showed a 65%-83% conversion efficiency with radiochemical purities of 95%-99% by radio-TLC. CONCLUSIONS/UNASSIGNED:PET imaging.

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.

Pain associated with breast cancer is a prevalent problem that negatively affects quality of life. Breast cancer pain is not limited to the disease course itself but is also induced by current therapeutic strategies. This, combined with the increasing number of patients living with breast cancer, make pain management for breast cancer patients an increasingly important area of research. This narrative review presents a summary of pain associated with breast cancer, including pain related to the cancer disease process itself and pain associated with current therapeutic modalities including radiation, chemotherapy, immunotherapy, and surgery. Current pain management techniques, their limitations, and novel analgesic strategies are also discussed.

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.

Connecting neuronal activity to perception requires tools that can probe neural codes at cellular and circuit levels, paired with sensitive behavioral measures. In this chapter, we present an overview of current methods for connecting neural codes to perception using precision optogenetics and psychophysical measurements of synthetically induced percepts. We also highlight new methodologies for validating precise control of optical and behavioral manipulations. Finally, we provide a perspective on upcoming developments that are poised to advance the field.
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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.

Speech that has been artificially accelerated through time compression produces a notable deficit in recall of the speech content. This is especially so for adults with cochlear implants (CI). At the perceptual level, this deficit may be due to the sharply degraded CI signal, combined with the reduced richness of compressed speech. At the cognitive level, the rapidity of time-compressed speech can deprive the listener of the ordinarily available processing time present when speech is delivered at a normal speech rate. Two experiments are reported. Experiment 1 was conducted with 27 normal-hearing young adults as a proof-of-concept demonstration that restoring lost processing time by inserting silent pauses at linguistically salient points within a time-compressed narrative ("time-restoration") returns recall accuracy to a level approximating that for a normal speech rate. Noise vocoder conditions with 10 and 6 channels reduced the effectiveness of time-restoration. Pupil dilation indicated that additional effort was expended by participants while attempting to process the time-compressed narratives, with the effortful demand on resources reduced with time restoration. In Experiment 2, 15 adult CI users tested with the same (unvocoded) materials showed a similar pattern of behavioral and pupillary responses, but with the notable exception that meaningful recovery of recall accuracy with time-restoration was limited to a subgroup of CI users identified by better working memory spans, and better word and sentence recognition scores. Results are discussed in terms of sensory-cognitive interactions in data-limited and resource-limited processes among adult users of cochlear implants.

BACKGROUND:Children with familial dysautonomia (FD) are smaller and grow more slowly than the general population. It is unknown whether this abnormal growth is due to comorbidities that patients with FD live with, or if it is a direct effect of the disease-causing homozygous ELP-1 mutations. Here, we created growth curves for weight, height, and body mass index (BMI) in male and female children with FD to monitor the nutritional status of patients with FD. METHODS:We used the New York University (NYU) FD Registry which includes data from 680 individuals with FD who were followed longitudinally since birth. We generated sex-specific FD growth charts for three age ranges (birth to 36 months, 2 to 20 years, and 2 to 40 years) and compared them to the general population. We generated Kaplan-Meier curves to test the hypothesis that FD patients with low BMI had shorter survival than the rest of the cohort. RESULTS:Growth charts generated from 591 individuals with FD show that these patients grow more slowly, reach less height, and gain less weight than the general population. The impact of FD on height was more pronounced in girls than in boys. However, both groups showed markedly low weights, which resulted in low BMI. Low weight, but not height, is already evident at birth. In a subpopulation of FD patients, we found that treatment with growth hormone or spinal fusion surgery helped patients achieve the expected growth characteristic of FD patients, but these treatments did not lead FD patients to achieve the growth pattern of the general population. Contrary to our hypothesis, low BMI had no impact on patient survival. CONCLUSIONS:Pediatric patients with FD have lower height, weight, and BMI compared to the general pediatric population, but this does not appear to affect survival. Growth curves specific to the FD population are an important tool to monitor growth and nutritional status in pediatric patients with FD when the general population growth curves are of limited use.