Faculty Bibliography

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.

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.

INTRODUCTION/UNASSIGNED:In spite of its apparent ease, comprehension of spoken discourse represents a complex linguistic and cognitive operation. The difficulty of such an operation can increase when the speech is degraded, as is the case with cochlear implant users. However, the additional challenges imposed by degraded speech may be mitigated to some extent by the linguistic context and pace of presentation. METHODS/UNASSIGNED:An experiment is reported in which young adults with age-normal hearing recalled discourse passages heard with clear speech or with noise-band vocoding used to simulate the sound of speech produced by a cochlear implant. Passages were varied in inter-word predictability and presented either without interruption or in a self-pacing format that allowed the listener to control the rate at which the information was delivered. RESULTS/UNASSIGNED:Results showed that discourse heard with clear speech was better recalled than discourse heard with vocoded speech, discourse with a higher average inter-word predictability was better recalled than discourse with a lower average inter-word predictability, and self-paced passages were recalled better than those heard without interruption. Of special interest was the semantic hierarchy effect: the tendency for listeners to show better recall for main ideas than mid-level information or detail from a passage as an index of listeners' ability to understand the meaning of a passage. The data revealed a significant effect of inter-word predictability, in that passages with lower predictability had an attenuated semantic hierarchy effect relative to higher-predictability passages. DISCUSSION/UNASSIGNED:Results are discussed in terms of broadening cochlear implant outcome measures beyond current clinical measures that focus on single-word and sentence repetition.

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.

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:Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning. METHODS AND MATERIALS/UNASSIGNED:After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning. RESULTS/UNASSIGNED:The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71. CONCLUSIONS/UNASSIGNED:HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.

Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique that is being tested clinically for treatment of a variety of neural disorders. Animal studies investigating the underlying mechanisms of tACS are scarce, and nearly absent in the cerebellum. In the present study, we applied 10-400 Hz alternating currents (AC) to the cerebellar cortex in ketamine/xylazine anesthetized rats. The spiking activity of cerebellar nuclear (CN) cells was transsynaptically entrained to the frequency of AC stimulation in an intensity and frequency-dependent manner. Interestingly, there was a tuning curve for modulation where the frequencies in the midrange (100 and 150 Hz) were more effective, although the stimulation frequency for maximum modulation differed for each CN cell with slight dependence on the stimulation amplitude. CN spikes were entrained with latencies of a few milliseconds with respect to the AC stimulation cycle. These short latencies and that the transsynaptic modulation of the CN cells can occur at such high frequencies strongly suggests that PC simple spike synchrony at millisecond time scales is the underlying mechanism for CN cell entrainment. These results show that subthreshold AC stimulation can induce such PC spike synchrony without resorting to supra-threshold pulse stimulation for precise timing. Transsynaptic entrainment of deep CN cells via cortical stimulation could help keep stimulation currents within safety limits in tACS applications, allowing development of tACS as an alternative treatment to deep cerebellar stimulation. Our results also provide a possible explanation for human trials of cerebellar stimulation where the functional impacts of tACS were frequency dependent.

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.