Research Overview

Electrical neuromodulation has transformed clinical management of diseases in which pharmaceuticals alone are less effective. However, even established, highly impactful therapies like deep brain stimulation (DBS) for Parkinson’s disease and spinal cord stimulation for chronic pain have average efficacy rates of ~60%, and outcomes are worse for emerging therapies such as DBS for treatment-resistant Depression. The result is that tens of thousands of implanted individuals remain with debilitating signs and symptoms. Personalization of neuromodulation therapies could substantially increase therapeutic response rates by directing electric current more selectively to reduce therapy-limiting side effects and by clarifying the relationship between neural targets and clinical outcomes.

My lab develops computational models that push the state-of-the-art to enable personalized neuromodulation therapies. In parallel, my lab leverages state-of-the-art modeling, electrophysiology, and data science methods to understand emerging methods for selective modulation of the nervous system. Given the growth in electrical neuromodulation for both autonomic and central nervous systems (e.g., excessive inflammation in rheumatoid arthritis, blood pressure in hypertension, and rehabilitative therapies post-stroke), my lab pursues outcomes that will have a widespread impact on clinical practice.

Lab website

Education

• Postdoctoral Associate, Biomedical Engineering, Duke University, 2023
• PhD, Biomedical Engineering, University of Minnesota, 2017
• BS, Biomedical Engineering, University of California, Irvine, 2013
• BS, Electrical Engineering, University of California, Irvine, 2013

Selected Publications

Peña E, Pelot NA, Grill WM. Spatiotemporal parameters for energy efficient kilohertz-frequency nerve block with low onset response. J NeuroEngineering Rehabil 2023;20:72. https://doi.org/10.1186/s12984-023-01195-8.

Peña E, Mohammad TM, Almohammed F, AlOtaibi T, Nahrir S, Khan S, et al. Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson’s Disease. Front Hum Neurosci 2021a;15:640591. https://doi.org/10.3389/fnhum.2021.640591.

Peña E, Pelot NA, Grill WM. Non-monotonic kilohertz frequency neural block thresholds arise from amplitude- and frequency-dependent charge imbalance. Sci Rep 2021b;11:5077. https://doi.org/10.1038/s41598-021-84503-3.

Peña E, Pelot NA, Grill WM. Quantitative comparisons of block thresholds and onset responses for charge-balanced kilohertz frequency waveforms. Journal of Neural Engineering 2020;17:046048. https://doi.org/10.1088/1741-2552/abadb5.

Slopsema JP, Peña E, Patriat R, Lehto LJ, Gröhn O, Mangia S, et al. Clinical deep brain stimulation strategies for orientation-selective pathway activation. J Neural Eng 2018;15:056029. https://doi.org/10.1088/1741-2552/aad978.

Peña E, Zhang S, Patriat R, Aman JE, Vitek JL, Harel N, et al. Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways. J Neural Eng 2018;15:066020. https://doi.org/10.1088/1741-2552/aae12f.

Peña E, Zhang S, Deyo S, Xiao Y, Johnson MD. Particle swarm optimization for programming deep brain stimulation arrays. Journal of Neural Engineering 2017;14:016014. https://doi.org/10.1088/1741-2552/aa52d1.

Rich TL, Menk JS, Rudser KD, Chen M, Meekins GD, Peña E, et al. Determining Electrode Placement for Transcranial Direct Current Stimulation: A Comparison of EEG- Versus TMS-Guided Methods. Clin EEG Neurosci 2017;48:367–75. https://doi.org/10.1177/1550059417709177.

Xiao Y, Peña E, Johnson MD. Theoretical Optimization of Stimulation Strategies for a Directionally Segmented Deep Brain Stimulation Electrode Array. IEEE Transactions on Biomedical Engineering 2016;63:359–71. https://doi.org/10.1109/TBME.2015.2457873.