Faculty Bibliography

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

We aimed to describe the clinical features of patients with pure autonomic failure (PAF) preceding phenoconversion that could be useful as predictive markers for advancing α-synuclein-associated neurodegeneration of the brain. Patients diagnosed with PAF were evaluated at 8 Centers (7-US based and 1 European) and enrolled in a longitudinal observational cohort study (NCT01799915). Subjects underwent detailed assessments of motor, sleep, olfactory, cognitive, and autonomic function and were followed prospectively to determine whether they developed parkinsonism or dementia for up to 10 years. We identified incident cases of Parkinson disease (PD), dementia with Lewy bodies (DLB), or multiple system atrophy (MSA) and computed hazard ratios for phenoconversion as functions of clinical features. A total of 209 participants with PAF with a median disease duration of 6 years (IQR: 3-10) were enrolled. Of those, 149 provided follow-up information at an office or telemedicine visit. After a mean follow-up duration of 3 years, 48 (33%) participants phenoconverted (42% to PD, 35% to DLB, and 23% to MSA). Faster phenoconversion from study enrollment to any diagnosis was associated with urinary and sexual dysfunction [HR 5.9, 95%CI: 1.6-22, and HR: 3.6, 95%CI: 1.1-12] followed by subtle motor signs [HR: 2.7, 95%CI: 1.2-6], trouble swallowing [HR 2.5, 95%CI: 1.4-4.5], and changes in speech [HR:2.4, 95%CI:1.1-4.8] at enrollment. Subjects reporting deterioration of handwriting were more likely to phenoconvert to PD (HR: 2.6, 95%CI: 1.1-5.9, ) and those reporting difficulty handling utensils were more likely to phenoconvert to DLB (HR: 6.8, 95%CI: 1.2-38). Patients with a younger age of PAF onset [HR: 11, 95%CI: 2.6-46], preserved olfaction [HR: 8.7, 95%CI: 1.7-45], anhidrosis [HR: 1.8, 95%CI: 1-3.1, p=0.042], and severe urinary problems [HR 1.6, 95%CI: 1-2.5, p=0.033] were more likely to phenoconvert to MSA. The best autonomic predictor of PD was a blunted heart rate increase during the tilt-table test (HR: 6.1, 95%CI: 1.4-26). Patients with PAF have an estimated 12% (95% CI: 9%-15%) per year annual risk following study entry of phenoconverting to a manifest CNS synucleinopathy.

Multiple system atrophy (MSA) is an adult-onset, sporadic synucleinopathy characterized by parkinsonism, cerebellar ataxia, and dysautonomia. The genetic architecture of MSA is poorly understood, and treatments are limited to supportive measures. Here, we performed a comprehensive analysis of whole genome sequence data from 888 European-ancestry MSA cases and 7,128 controls to systematically investigate the genetic underpinnings of this understudied neurodegenerative disease. We identified four significantly associated risk loci using a genome-wide association study approach. Transcriptome-wide association analyses prioritized USP38-DT, KCTD7, and lnc-KCTD7-2 as novel susceptibility genes for MSA within these loci, and single-nucleus RNA sequence analysis found that the associated variants acted as cis-expression quantitative trait loci for multiple genes across neuronal and glial cell types. In conclusion, this study highlights the role of genetic determinants in the pathogenesis of MSA, and the publicly available data from this study represent a valuable resource for investigating synucleinopathies.

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

In addition to their many functions in the healthy central nervous system (CNS), astrocytes respond to CNS damage and disease through a process called "reactivity." Recent evidence reveals that astrocyte reactivity is a heterogeneous spectrum of potential changes that occur in a context-specific manner. These changes are determined by diverse signaling events and vary not only with the nature and severity of different CNS insults but also with location in the CNS, genetic predispositions, age, and potentially also with "molecular memory" of previous reactivity events. Astrocyte reactivity can be associated with both essential beneficial functions as well as with harmful effects. The available information is rapidly expanding and much has been learned about molecular diversity of astrocyte reactivity. Emerging functional associations point toward central roles for astrocyte reactivity in determining the outcome in CNS disorders.

Holistic and analytic thinking are two distinct modes of thinking used to interpret the world with relative preferences varying across cultures. While most research on these thinking styles has focused on behavioral and cognitive aspects, a few studies have utilized functional magnetic resonance imaging (fMRI) to explore the correlations between brain metrics and self-reported scale scores. Other fMRI studies used single holistic and analytic thinking tasks. As a single task may involve processing in spurious low-level regions, we used two different holistic and analytic thinking tasks, namely the frame-line task and the triad task, to seek convergent brain regions to distinguish holistic and analytic thinking using multivariate pattern analysis (MVPA). Results showed that brain regions fundamental to distinguish holistic and analytic thinking include the bilateral frontal lobes, bilateral parietal lobes, bilateral precentral and postcentral gyrus, bilateral supplementary motor areas, bilateral fusiform, bilateral insula, bilateral angular gyrus, left cuneus, and precuneus, left olfactory cortex, cingulate gyrus, right caudate and putamen. Our study maps brain regions that distinguish between holistic and analytic thinking and provides a new approach to explore the neural representation of cultural constructs. We provide initial evidence connecting culture-related brain regions with language function to explain the origins of cultural differences in cognitive styles.

OBJECTIVES/UNASSIGNED:This study aims to synthesize current knowledge and outcomes related to pediatric auditory brainstem implantation (ABI) in children with severe inner ear malformations (IEMs). It highlights the clinical management practices, challenges, and potential future directions for consensus development in this field. METHODS/UNASSIGNED:A systematic review of findings presented at the Third International Pediatric ABI Symposium organized by the Hacettepe Cochlear Implant team between 3 and 5 September 2020 was conducted, incorporating data from 41 departments across 19 countries. Relevant clinical outcomes, imaging techniques, surgical approaches, and rehabilitation strategies were analyzed to identify key trends and variability in practices. RESULTS/UNASSIGNED:The review indicates that children receiving ABIs exhibit diverse auditory outcomes influenced by individual anatomical variations and developmental factors. Early implantation, particularly before the age of three, positively correlates with better auditory and language development. Multicenter experiences underscore the necessity of tailored decision-making, which considers both surgical candidacy and comprehensive rehabilitation resources. DISCUSSION:/UNASSIGNED:The variability in outcomes emphasizes the need for improved consensus and guidelines regarding eligibility, surgical techniques, and multidisciplinary rehabilitation approaches. Notable complications and the necessity for thorough imaging assessments were also identified as critical components affecting clinical decisions. CONCLUSION/UNASSIGNED:A formal consensus statement is warranted to standardize best practices in ABI management. This will not only enhance patient outcomes but also guide future research efforts to address the remaining challenges in the treatment of children with severe IEMs. Enhanced collaboration among team members will be pivotal in achieving these objectives.

BACKGROUND:Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US with the morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in cardiac intensive care unit (ICU). METHODS:We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. We prepared a cardiac ICU dataset using MIMIC-III database by annotating with physician adjudicated outcomes. This dataset that consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database that was also annotated with physician adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. RESULTS:CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top ten predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, Blood urea nitrogen, Systolic blood pressure, Serum chloride, Serum sodium, and Arterial blood pH. CONCLUSIONS:The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for the millions of patients who suffer from myocardial infarction and heart failure.

Laboratory polysomnography provides gold-standard measures of sleep physiology, but multi-night investigations are resource intensive. We assessed the night-to-night stability via reproducibility metrics for sleep macrostructure and electroencephalography oscillations in a group of cognitively normal adults attending two consecutive polysomnographies. Electroencephalographies were analysed using an automatic algorithm for detection of slow-wave activity, spindle and K-complex densities. Average differences between nights for sleep macrostructure, electroencephalography oscillations and sleep apnea severity were assessed, and test-retest reliability was determined using two-way intraclass correlations. Agreement was calculated using the smallest real differences between nights for all measures. Night 2 polysomnographies showed significantly greater time in bed, total sleep time (6.3 hr versus 6.8 hr, p < 0.001) and percentage of rapid eye movement sleep (17.5 versus 19.7, p < 0.001). Intraclass correlations were low for total sleep time, percentage of rapid eye movement sleep and sleep efficiency, moderate for percentage of slow-wave sleep and percentage of non-rapid eye movement 2 sleep, good for slow-wave activity and K-complex densities, and excellent for spindles and apnea-hypopnea index with hypopneas defined according to 4% oxygen desaturation criteria only. The smallest real difference values were proportionally high for most sleep macrostructure measures, indicating moderate agreement, and proportionally lower for most electroencephalography microstructure variables. Slow waves, K-complexes, spindles and apnea severity indices are highly reproducible across two consecutive nights of polysomnography. In contrast, sleep macrostructure measures all demonstrated poor reproducibility as indicated by low intraclass correlation values and moderate agreement. Although there were average differences in percentage of rapid eye movement sleep and total sleep time, these were numerically small and perhaps functionally or clinically less significant. One night of in-laboratory polysomnography is enough to provide stable, reproducible estimates of an individual's sleep concerning measures of slow-wave activity, spindles, K-complex densities and apnea severity.

Distributed hypothalamic-midbrain neural circuits help orchestrate complex behavioral responses during social interactions. Given rapid advances in optical imaging, it is a fundamental question how population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. This paper aims to investigate the correspondence between MFP data and social behaviors. &#xD;Approach: We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include a continuous-state linear dynamical system (LDS) and a discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. &#xD;Main Results: Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states, and produce interpretable latent states. Our approach is also validated in computer simulations in the presence of known ground truth.&#xD;Significance: Overall, these analysis approaches provide a state-space framework to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks. &#xD;&#xD.