Akshita Rao

All Publications

• Simultaneous stomach-brain electrophysiology reveals dynamic coupling in human sleep. bioRxiv : the preprint server for biology Rao, A. A., Fredericks, M., Dresler, M., Rebollo, I., Zeitzer, J. M., Schoch, S. F., Coleman, T. P. 2025

  Abstract

  Sleep involves continuous communication between the brain and body, yet the dynamics of peripheral signals during human sleep remain poorly understood. Here we tested whether gastric electrophysiology exhibits infraslow structure and coordinated fluctuations with cortical rhythms indicative of sleep physiology. Simultaneous high-density electroencephalography (EEG) and electrogastrography (EGG) were recorded across sixty participants and three nights. Gastric power was consistently higher during NREM than REM sleep and declined across successive cycles, consistent with stage-dependent autonomic modulation of visceral activity. For the first time, we show that the gastric rhythm itself exhibits intrinsic infraslow amplitude modulations (∼0.007 Hz), which are selectively amplified during NREM sleep and temporally aligned with infraslow fluctuations in cortical sigma power, strongest during N3 sleep. Event-locked analyses further revealed transient increases in gastric amplitude following cortical slow wave oscillations, particularly when accompanied by sleep spindles. Across nights, variance in gastric infraslow amplitude predicted subjective sleep quality beyond standard polysomnographic and cardiac measures. Together, these findings position the human stomach as a peripheral oscillator whose infraslow dynamics track thalamocortical activity during sleep and predict subjective sleep quality, extending the interoceptive regulatory loop into the sleeping brain.

  View details for DOI 10.1101/2025.11.13.686572

  View details for PubMedID 41292824

  View details for PubMedCentralID PMC12642427

• Dynamic Facial Analysis for Predicting Facial Palsy Outcomes: Comparing Landmark Detection Models and Integrating Ordinal Regression. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Rao, A. A., Greene, J. J., Coleman, T. P. 2025; 2025: 1-7

  Abstract

  This study aims to enhance the prediction and video-based assessment of facial nerve (FN) recovery in facial palsy patients through incorporating modern landmark detection models and regression techniques. Our goal is to determine if these methods offer significant improvements to our previously reported predictive framework over conventional approaches.METHODS: We extend our previous methodology by comparing state-of-the-art facial landmark detection models, such as ones that use deep learning, with Dlib. These models are evaluated based on their accuracy, computational cost, and impact on clinical score predictions. Additionally, we replace our previous least-squares linear regression model with ordinal regression to predict House-Brackmann (HB) scores, leveraging Wasserstein and Mahalanobis distances to better capture the ordered nature of the HB grading system.RESULTS: Dlib offered the best balance of computational efficiency and clinical accuracy, while other higher-resolution models did not improve performance in predicting clinical scores. Ordinal regression significantly outperformed naive linear regression, demonstrating better interpretability, improved accuracy, and reduced mean absolute error by properly accounting for the ordinal structure of the HB scale.SIGNIFICANCE: This study extends our previous work by incorporating modern landmark detection techniques and a more clinically appropriate predictive model for FN assessment. By bridging the gap between computational models and real-world clinical applications, this framework enhances the precision of facial palsy monitoring, offering a more robust tool for surgical decision-making and longitudinal patient assessment.

  View details for DOI 10.1109/EMBC58623.2025.11254715

  View details for PubMedID 41337343

• Machine learning methods to track dynamic facial function in facial palsy. IEEE transactions on bio-medical engineering Rao, A. A., Greene, J. J., Coleman, T. P. 2025; PP

  Abstract

  For patients with facial palsy, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and eating from flaccid oral sphincter muscles, and psychological morbidity from the inability to smile or express emotions can be devastating. There are limited methods to assess ongoing facial nerve regeneration: clinicians rely on subjective descriptions, imprecise scales, and static photographs to evaluate facial functional recovery. We propose a more precise evaluation of dynamic facial function through video-based machine learning analysis to facilitate a better understanding of the sometimes subtle onset of facial nerve recovery and improve guidance for facial reanimation surgery.We present machine learning methods employing likelihood ratio tests, optimal transport theory, and Mahalanobis distances to: 1) assess the use of defined facial landmarks for binary classification of different facial palsy types; 2) identify regions of asymmetry and potential palsy during specific facial cues; and 3) quantify palsy severity and map it directly to widely used clinical scores, offering clinicians an objective way to assess facial nerve function.Our results demonstrate that video analysis provides a significantly more accurate and detailed assessment of facial movements than previously reported.Our work allows for precise classification of facial palsy types, identification of asymmetric regions, and assessment of palsy severity.This project enables clinicians to have more accurate and timely information to make decisions for facial reanimation surgery, which will have drastic consequences on the quality of life for affected patients.

  View details for DOI 10.1109/TBME.2025.3567984

  View details for PubMedID 40333095

• Dynamic facial analysis for predicting facial palsy outcomes: Comparing landmark detection models and integrating ordinal regression Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Rao, A. A., Greene, J. J., Coleman, T. P. 2025
• An Integrated Optogenetic and Bioelectronic Platform for Regulating Cardiomyocyte Function. Advanced science (Weinheim, Baden-Wurttemberg, Germany) Bolonduro, O. A., Chen, Z., Fucetola, C. P., Lai, Y., Cote, M., Kajola, R. O., Rao, A. A., Liu, H., Tzanakakis, E. S., Timko, B. P. 2024: e2402236

  Abstract

  Bioelectronic medicine is emerging as a powerful approach for restoring lost endogenous functions and addressing life-altering maladies such as cardiac disorders. Systems that incorporate both modulation of cellular function and recording capabilities can enhance the utility of these approaches and their customization to the needs of each patient. Here is report an integrated optogenetic and bioelectronic platform for stable and long-term stimulation and monitoring of cardiomyocyte function in vitro. Optical inputs are achieved through the expression of a photoactivatable adenylyl cyclase, that when irradiated with blue light causes a dose-dependent and time-limited increase in the secondary messenger cyclic adenosine monophosphate with subsequent rise in autonomous cardiomyocyte beating rate. Bioelectronic readouts are obtained through a multi-electrode array that measures real-time electrophysiological responses at 32 spatially-distinct locations. Irradiation at 27 W mm-2 results in a 14% elevation of the beating rate within 20-25 min, which remains stable for at least 2 h. The beating rate can be cycled through "on" and "off" light states, and its magnitude is a monotonic function of irradiation intensity. The integrated platform can be extended to stretchable and flexible substrates, and can open new avenues in bioelectronic medicine, including closed-loop systems for cardiac regulation and intervention, for example, in the context of arrythmias.

  View details for DOI 10.1002/advs.202402236

  View details for PubMedID 39054679

• Heart-on-a-Chip Model with Integrated Extra- and Intracellular Bioelectronics for Monitoring Cardiac Electrophysiology under Acute Hypoxia NANO LETTERS Liu, H., Bolonduro, O. A., Hu, N., Ju, J., Rao, A. A., Duffy, B. M., Huang, Z., Black, L. D., Timko, B. P. 2020; 20 (4): 2585-2593

  Abstract

  We demonstrated a bioelectronic heart-on-a-chip model for studying the effects of acute hypoxia on cardiac function. A microfluidic channel enabled rapid modulation of medium oxygenation, which mimicked the regimes induced by a temporary coronary occlusion and reversibly activated hypoxia-related transduction pathways in HL-1 cardiac model cells. Extracellular bioelectronics provided continuous readouts demonstrating that hypoxic cells experienced an initial period of tachycardia followed by a reduction in beat rate and eventually arrhythmia. Intracellular bioelectronics consisting of Pt nanopillars temporarily entered the cytosol following electroporation, yielding action potential (AP)-like readouts. We found that APs narrowed during hypoxia, consistent with proposed mechanisms by which oxygen deficits activate ATP-dependent K+ channels that promote membrane repolarization. Significantly, both extra- and intracellular devices could be multiplexed, enabling mapping capabilities unachievable by other electrophysiological tools. Our platform represents a significant advance toward understanding electrophysiological responses to hypoxia and could be applicable to disease modeling and drug development.

  View details for DOI 10.1021/acs.nanolett.0c00076

  View details for Web of Science ID 000526413400046

  View details for PubMedID 32092276

• From biomimicry to bioelectronics: Smart materials for cardiac tissue engineering NANO RESEARCH Bolonduro, O. A., Duffy, B. M., Rao, A. A., Black, L. D., Timko, B. P. 2020; 13 (5): 1253-1267

  View details for DOI 10.1007/s12274-020-2682-3

  View details for Web of Science ID 000515939700006