Faculty Bibliography

BACKGROUND:Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring. NEW METHOD/UNASSIGNED:In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake. RESULTS:WSN achieves an epoch by epoch mean accuracy of 0.86 and mean F1 score of 0.82 compared to manual scoring by a human expert. In mice experiencing mechanically induced sleep fragmentation, an overall epoch by epoch mean accuracy of 0.80 is achieved by WSN and classification of non-REM (NREM) sleep is not compromised, but the high level of sleep fragmentation results in WSN having greater difficulty differentiating REM from NREM sleep. We also find that WSN achieves similar levels of accuracy on an independent dataset of externally acquired EEG/EMG recordings with an overall epoch by epoch accuracy of 0.91. We also compared conventional summary sleep metrics in mice sleeping ad libitum. WSN systematically biases sleep fragmentation metrics of bout number and bout length leading to an overestimated degree of sleep fragmentation. COMPARISON WITH EXISTING METHODS/UNASSIGNED:In a cross-validation, WSN has a greater macro and stage-specific accuracy compared to a conventional random forest classifier. Examining the WSN, we find that it automatically learns spectral features consistent with manual scoring criteria that are used to define each class. CONCLUSION/CONCLUSIONS:These results suggest to us that WSN is capable of learning visually agreeable features and may be useful as a supplement to human manual scoring.

The presence of spiral ganglion cells (SGCs) is widely accepted to be a prerequisite for successful speech perception with a cochlear implant (CI), because SGCs provide the only known conduit between the implant electrode and the central auditory system. By extension, it has been hypothesized that the number of SGCs might be an important factor in CI outcomes. An impressive body of work has been published on findings from the laborious process of collecting temporal bones from CI users and counting the number of SGCs to correlate those numbers with speech perception scores, but the findings thus far have been conflicting. We performed a meta-analysis of all published studies with the hope that combining existing data may help us reach a more definitive conclusion about the relationship between SGC count and speech perception scores in adults.

Social behaviors, such as mating, fighting, and parenting, are fundamental for survival of any vertebrate species. All members of a species express social behaviors in a stereotypical and species-specific way without training because of developmentally hardwired neural circuits dedicated to these behaviors. Despite being innate, social behaviors are flexible. The readiness to interact with a social target or engage in specific social acts can vary widely based on reproductive state, social experience, and many other internal and external factors. Such high flexibility gives vertebrates the ability to release the relevant behavior at the right moment and toward the right target. This maximizes reproductive success while minimizing the cost and risk associated with behavioral expression. Decades of research have revealed the basic neural circuits underlying each innate social behavior. The neural mechanisms that support behavioral plasticity have also started to emerge. Here we provide an overview of these social behaviors and their underlying neural circuits and then discuss in detail recent findings regarding the neural processes that support the flexibility of innate social behaviors.

High-yield electrophysiological extracellular recording in freely moving rodents provides a unique window into the temporal dynamics of neural circuits. Recording from unrestrained animals is critical to investigate brain activity during natural behaviors. The use and implantation of high-channel-count silicon probes represent the largest cost and experimental complexity associated with such recordings making a recoverable and reusable system desirable. To address this, we have designed and tested a novel 3D printed head-gear system for freely moving mice and rats. The system consists of a recoverable microdrive printed in stainless steel and a plastic head cap system, allowing researchers to reuse the silicon probes with ease, decreasing the effective cost, and the experimental effort and complexity. The cap designs are modular and provide structural protection and electrical shielding to the implanted hardware and electronics. We provide detailed procedural instructions allowing researchers to adapt and flexibly modify the head-gear system.

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.

To examine the association of preoperative opioids and/or benzodiazepines on postoperative outcomes in total knee and hip arthroplasty, we retrospectively compared postoperative outcomes in those prescribed preoperative opioids and/or benzodiazepines versus those who were not who underwent elective total knee and hip arthroplasty at a single urban academic institution. Multivariable logistic regression was performed for readmission rate, respiratory failure, infection, and adverse cardiac events. Multivariable zero-truncated negative binomial regression was used for length of stay. After exclusions, there were 4307 adult patients in the study population, 2009 of whom underwent total knee arthroplasty and 2298 of whom underwent total hip arthroplasty. After adjusting for potential confounders, preoperative benzodiazepine use was associated with increased odds of readmission (p < 0.01). Preoperative benzodiazepines were not associated with increased odds of respiratory failure nor increased length of stay. Preoperative opioids were not associated with increased odds of the examined outcomes. There were insufficient numbers of infection and cardiac events for analysis. In this study population, preoperative benzodiazepines were associated with increased odds of readmission. Preoperative opioids were not associated with increased odds of the examined outcomes. Studies are needed to further examine risks associated with preoperative benzodiazepine use.

Current clinical MRI evaluation of musculature largely focuses on nonquantitative assessments (including T1-, T2- and PD-weighted images), which may vary greatly between imaging systems and readers. This work aims to determine the efficacy of a quantitative approach to study the microstructure of muscles at the cellular level with the random permeable barrier model (RPBM) applied to time-dependent diffusion tensor imaging (DTI) for varying diffusion time. Patients (N = 15, eight males and seven females) with atrophied calf muscles due to immobilization of one leg in a nonweight-bearing cast, were enrolled after providing informed consent. Their calf muscles were imaged with stimulated echo diffusion for DTI, T1-mapping and RPBM modeling. Specifically, After cast removal, both calf muscles (atrophied and contralateral control leg) were imaged with MRI for all patients, with follow-up scans to monitor recovery of the atrophied leg for six patients after 4 and 8 weeks. We compare RPBM-derived microstructural metrics: myofiber diameter, a, and sarcolemma permeability, κ, along with macroscopic anatomical parameters (muscle cross-sectional area, fiber orientation, <θ>, and T1 relaxation). ROC analysis was used to compare parameters between control and atrophied muscle, while the Friedman test was used to evaluate the atrophied muscle longitudinally. We found that the RPBM framework enables measurement of microstructural parameters from diffusion time-dependent DTI, of which the myofiber diameter is a stronger predictor of intramuscular morphological changes than either macroscopic (anatomical) measurements or empirical diffusion parameters. This work demonstrates the potential of RPBM to assess pathological changes in musculature that seem undetectable with standard diffusion and anatomical MRI.

Responsive neurostimulation is increasingly required to probe neural circuit function and treat neuropsychiatric disorders. We introduce a multiplex-then-amplify (MTA) scheme that, in contrast to current approaches (which necessitate an equal number of amplifiers as number of channels), only requires one amplifier per multiplexer, significantly reducing the number of components and the size of electronics in multichannel acquisition systems. It also enables simultaneous stimulation of arbitrary waveforms on multiple independent channels. We validated the function of MTA by developing a fully implantable, responsive embedded system that merges the ability to acquire individual neural action potentials using conformable conducting polymer-based electrodes with real-time onboard processing, low-latency arbitrary waveform stimulation, and local data storage within a miniaturized physical footprint. We verified established responsive neurostimulation protocols and developed a network intervention to suppress pathological coupling between the hippocampus and cortex during interictal epileptiform discharges. The MTA design enables effective, self-contained, chronic neural network manipulation with translational relevance to the treatment of neuropsychiatric disease.

Dopamine (DA) is a critical modulator of brain circuits that control voluntary movements, but our understanding of its influence on the activity of target neurons in vivo remains limited. Here, we use two-photon Ca²⁺ imaging to monitor the activity of direct and indirect-pathway spiny projection neurons (SPNs) simultaneously in the striatum of behaving mice during acute and prolonged manipulations of DA signaling. We find that increasing and decreasing DA biases striatal activity towards the direct and indirect pathways, respectively, by changing the overall number of SPNs recruited during behavior in a manner not predicted by existing models of DA function. This modulation is drastically altered in a model of Parkinson's disease. Our results reveal a previously unappreciated population-level influence of DA on striatal output and provide novel insights into the pathophysiology of Parkinson's disease.

A degraded, black-and-white image of an object, which appears meaningless on first presentation, is easily identified after a single exposure to the original, intact image. This striking example of perceptual learning reflects a rapid (one-trial) change in performance, but the kind of learning that is involved is not known. We asked whether this learning depends on conscious (hippocampus-dependent) memory for the images that have been presented or on an unconscious (hippocampus-independent) change in the perception of images, independently of the ability to remember them. We tested five memory-impaired patients with hippocampal lesions or larger medial temporal lobe (MTL) lesions. In comparison to volunteers, the patients were fully intact at perceptual learning, and their improvement persisted without decrement from 1 d to more than 5 mo. Yet, the patients were impaired at remembering the test format and, even after 1 d, were impaired at remembering the images themselves. To compare perceptual learning and remembering directly, at 7 d after seeing degraded images and their solutions, patients and volunteers took either a naming test or a recognition memory test with these images. The patients improved as much as the volunteers at identifying the degraded images but were severely impaired at remembering them. Notably, the patient with the most severe memory impairment and the largest MTL lesions performed worse than the other patients on the memory tests but was the best at perceptual learning. The findings show that one-trial, long-lasting perceptual learning relies on hippocampus-independent (nondeclarative) memory, independent of any requirement to consciously remember.