Faculty Bibliography

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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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.

Ethanol exposure in neonatal mice induces acute neurodegeneration followed by long-lasting glial activation and GABAergic cell deficits along with behavioral abnormalities, providing a third trimester model of fetal alcohol spectrum disorders (FASD). Retinoic acid (RA), the active form of vitamin A, regulates transcription of RA-responsive genes and plays essential roles in the development of embryos and their CNS. Ethanol has been shown to disturb RA metabolism and signaling in the developing brain, which may be a cause of ethanol toxicity leading to FASD. Using an agonist and an antagonist specific to RA receptor α (RARα), we studied how RA/RARα signaling affects acute and long-lasting neurodegeneration and activation of phagocytic cells and astrocytes caused by ethanol administered to neonatal mice. We found that an RARα antagonist (BT382) administered 30 min before ethanol injection into postnatal day 7 (P7) mice partially blocked acute neurodegeneration as well as elevation of CD68-positive phagocytic cells in the same brain area. While an RARα agonist (BT75) did not affect acute neurodegeneration, BT75 given either before or after ethanol administration ameliorated long-lasting astrocyte activation and GABAergic cell deficits in certain brain regions. Our studies using Nkx2.1-Cre;Ai9 mice, in which major GABAergic neurons and their progenitors in the cortex and the hippocampus are labeled with constitutively expressed tdTomato fluorescent protein, indicate that the long-lasting GABAergic cell deficits are mainly caused by P7 ethanol-induced initial neurodegeneration. However, the partial reduction of prolonged GABAergic cell deficits and glial activation by post-ethanol BT75 treatment suggests that, in addition to the initial cell death, there may be delayed cell death or disturbed development of GABAergic cells, which is partially rescued by BT75. Since RARα agonists including BT75 have been shown to exert anti-inflammatory effects, BT75 may rescue GABAergic cell deficits by reducing glial activation/neuroinflammation.

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.

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.

IMPORTANCE/UNASSIGNED:Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. OBJECTIVES/UNASSIGNED:To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings. DESIGN SETTING AND PARTICIPANTS/UNASSIGNED: = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5-3 yr.; 38 autism and 75 controls) was used for additional validation. MAIN OUTCOMES AND MEASURES/UNASSIGNED:We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. RESULTS/UNASSIGNED:In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal's spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. CONCLUSION AND RELEVANCE/UNASSIGNED:Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

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.

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.

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.