Faculty Bibliography

Developed by the Center for Robust Speech Systems at the University of Texas at Dallas, in collaboration with New York University and the University of Wisconsin-Madison, CCi-MOBILE addresses a critical challenge in optimizing cochlear implant (CI) fitting and enhancing sound coding strategies. Existing clinical CI processors and research tools often lack either the necessary computational power and flexibility or the portability required for real-world testing. CCi-MOBILE bridges this gap by enabling the implementation and evaluation of diverse real-time sound coding algorithms in both laboratory and real-world settings, including those requiring synchronized bilateral stimulation. Building upon previous publications, this paper provides new detailed discussion on parameter setting for stimulus generation with CCi-MOBILE and serves as a comprehensive resource for scientists and engineers developing novel real-time sound coding and signal processing strategies with this platform. As part of an ongoing development effort, future generations of CCi-MOBILE may offer additional functionalities beyond those described here.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

BACKGROUND:Despite evidence supporting the efficacy of culturally tailored interventions in reducing stigma, such approaches are lacking for women living with HIV/AIDS (WLWHAs) in China. We conducted this study to determine the efficacy of the culturally tailored Helping Overcome Perceived Stigma (HOPES) intervention in reducing perceived stigma among WLWHAs in China. METHODS:A single-blinded, two-arm parallel-group randomized clinical trial was conducted from 2023 to 2024 in South and Southwest China. WLWHAs from four hospitals were assigned using a WeChat-embedded randomization application to the control group (usual care) or the HOPES intervention. Data analysts remained blinded. Interventions were conducted virtually using Leave No One Behind (LNOB) platform for 3 months. The primary outcome, perceived stigma score, was assessed at baseline, immediately after the intervention and at 3 months post-intervention using 7 items from the HIV/AIDS Stigma Experience Questionnaire (HASEQ), with data analyzed through repeated measures analysis. RESULTS:Of 136 WLWHAs screened, we randomized 101 WLWHAs (50 HOPES; 51 controls). The HOPES group demonstrated a statistically significant reduction in perceived stigma scores immediately after the intervention (-3.86 points, 95 % CI: 5.34 to -2.38, P < .001) and at three months post-intervention (-5.83 points, 95 % CI: 7.20 to -4.47, P < .001) compared to the control group. CONCLUSION/CONCLUSIONS:The findings demonstrate HOPES' efficacy in reducing perceived stigma in WLWHA. However, the clinical significance of these changes needs further investigation. Future research should focus on defining meaningful patient-reported thresholds, assessing long-term impact, and optimizing delivery methods.

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

OBJECTIVES/OBJECTIVE:To provide a level-adjusted correction to the current standard relating anatomical cochlear place to characteristic frequency (CF) in humans, and to re-evaluate anatomical frequency mismatch in cochlear implant (CI recipients considering this correction. It is proposed that a level-adjusted place-frequency function may represent a more relevant tonotopic benchmark for CIs in comparison to the current standard. DESIGN/METHODS:The present analytical study compiled data from 15 previous animal studies that reported isointensity responses from cochlear structures at different stimulation levels. Extracted outcome measures were CFs and centroid-based best frequencies at 70 dB SPL input from 47 specimens spanning a broad range of cochlear locations. A simple relationship was used to transform these measures to human estimates of characteristic and best frequencies, and nonlinear regression was applied to these estimates to determine how the standard human place-frequency function should be adjusted to reflect best frequency rather than CF. The proposed level-adjusted correction was then compared with average place-frequency positions of commonly used CI devices when programmed with clinical settings. RESULTS:The present study showed that the best frequency at 70 dB SPL (BF70) tends to shift away from CF. The amount of shift was statistically significant (signed-rank test z = 5.143, p < 0.001), but the amount and direction of shift depended on cochlear location. At cochlear locations up to 600° from the base, BF70 shifted downward in frequency relative to CF by about 4 semitones on average. Beyond 600° from the base, BF70 shifted upward in frequency relative to CF by about 6 semitones on average. In terms of spread (90% prediction interval), the amount of shift between CF and BF70 varied from relatively no shift to nearly an octave of shift. With the new level-adjusted place-frequency function, the amount of anatomical frequency mismatch for devices programmed with standard-of-care settings is less extreme than originally thought and may be nonexistent for all but the most apical electrodes. CONCLUSIONS:The present study validates the current standard for relating cochlear place to CF, and introduces a level-adjusted correction for how best frequency shifts away from CF at moderately loud stimulation levels. This correction may represent a more relevant tonotopic reference for CIs. To the extent that it does, its implementation may potentially enhance perceptual accommodation and speech understanding in CI users, thereby improving CI outcomes and contributing to advancements in the programming and clinical management of CIs.

RATIONALE & OBJECTIVE/OBJECTIVE:Despite national efforts, the uptake of home dialysis (peritoneal dialysis or home hemodialysis) remains low. Characteristics of the built environment may differentially impact home dialysis use. STUDY DESIGN/METHODS:Retrospective cohort study (2010-2019). SETTING & PARTICIPANTS/METHODS:1,103,695 adults (aged≥18 years) initiating dialysis in the US Renal Data System. EXPOSURE/METHODS:We examined 3 built environment domains based on residential ZIP code: (1) medically underserved areas (MUAs), defined as neighborhoods with limited primary care access; (2) distance to the nearest dialysis facility; and (3) distribution of housing characteristics (structure and overcrowding). OUTCOME/RESULTS:Uptake of home dialysis modalities at dialysis initiation. ANALYTICAL APPROACH/METHODS:We quantified associations between built environment characteristics and home dialysis initiation using multilevel logistic regression stratified by urbanicity type (urban, suburban, small-town, and rural). RESULTS:Among adults initiating dialysis, 40.8% lived in MUAs. Across ZIP codes, the mean percentage of overcrowded housing was 4.2% (SD, 4.7%), and the percentage of detached housing was 61.1% (SD, 21.1%); mean distance to the nearest dialysis facility was 5.5km (SD, 9.1km). Living in MUAs was associated with reduced home dialysis use only in urban (OR, 0.94; 95% CI, 0.91-0.96) and suburban (OR, 0.92; 95% CI, 0.89-0.94) areas. Similarly, housing overcrowding was associated with decreased home dialysis use only in urban (OR, 0.88; 95% CI, 0.86-0.89) and suburban (OR, 0.91; 95% CI, 0.90-0.93) areas. Longer distance to a dialysis facility was the most salient neighborhood factor associated with increased home dialysis use in small towns (OR, 1.14; 95% CI, 1.12-1.16) and rural areas (OR, 1.17; 95% CI, 1.15-1.19). LIMITATIONS/CONCLUSIONS:Housing characteristics were measured at the ZIP code level. CONCLUSIONS:Built environment characteristics associated with home dialysis uptake vary by urbanicity. Policies should address built environment barriers that are specific to urbanicity level. For example, increasing the frequency of dialysate delivery schedules could address housing space constraints in urban and suburban areas, and promoting home dialysis might be more effective for patients living far from dialysis centers in small-town and rural areas.

Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.

OBJECTIVES/UNASSIGNED:To review the different analgesic modalities and benefits of non-opioid pain management options as well as their evidence-based, established superiority, compared to opioid medications. MATERIALS/UNASSIGNED:We review the updated literature about pain management of renal colic, a prevalent and painful urologic condition. Prescribers must know the efficacy, safety and possible ramifications of analgesic selections. RESULTS/UNASSIGNED:Commonly prescribed medications in the United States (US) include non-steroidal anti-inflammatory drugs (NSAIDs), acetaminophen, and opioids. In the context of the current epidemic of death from overdoses of opioids in the US, the frequency of opioid prescribing for renal colic is likely excessive, problematic and potentially remediable. We also present analgesic modalities revolving around interventions with peri-procedural pain management for ureteroscopy and percutaneous nephrolithotomy. After touching on the implications of misguided opioid use, especially in the context of kidney stone disease, and despite the evidence and consensus guidelines supporting NSAIDs in renal colic, current evidence has shown that many clinicians continue to prescribe opioids as first-line treatment. Finally, we highlight current efforts targeted at the reduction of opioid use and prescription in the setting of provider education and decision aids in curbing misguided opioid use in renal colic. CONCLUSIONS/UNASSIGNED:While the evidence against treating kidney stones with opioids is clear, more work is needed to shift current practices to reflect that renal colic is a non-opioid-requiring condition.

BackgroundRespiratory impairment in neuromuscular disorders (NMDs) is generally assessed using forced vital capacity (FVC). Any improvement in FVC trajectory will delay ventilatory support; however, the change required for patients to perceive a noticeable clinical benefit, the clinically meaningful threshold (CMT), has not been defined in NMDs.ObjectiveTo derive the within-person and between-group CMTs for FVC (% predicted) in patients with late-onset Pompe disease (LOPD).MethodsThis analysis leverages data from the Phase 3 COMET trial (NCT02782741, registered 25 May 2016), which assessed the efficacy of avalglucosidase alfa (AVA) versus alglucosidase alfa (ALG) on upright FVC (% predicted) in LOPD. Anchor- and distribution-based methods were used to estimate the within-person and between-group CMTs for FVC at Weeks 49 and 97.ResultsCOMET enrolled 99 participants aged ≥18 years (52% male; mean age 48.0 years). The within-person CMT for absolute change in FVC expressed as % predicted was estimated as 3.0% [95% confidence interval (CI) 2.3, 3.8]. The proportion of patients with a meaningful increase in FVC was higher in the AVA versus ALG group across the CI of the estimated CMT (odds ratios: 2.3-2.6; nominal p-values: 0.026-0.058). The between-group CMT, needed to evaluate differences between treatment groups, was estimated as 2.1% predicted [95% CI 1.1, 3.1].ConclusionsWe identified a narrow range of within-person and between-group CMTs for upright FVC (% predicted) in LOPD. Post hoc application of these thresholds to COMET showed that a greater proportion of patients in the AVA group had clinically meaningful improvement in FVC versus ALG. These findings may aid in interpretation of data from studies in other NMDs.