When
Monday, April 3, 2026, at 12:00 p.m.
Location: Keating 103 | Zoom link
Hosts: Swarna Ganesh and Kellen Chen
Nadia Abu Farha
PhD Candidate, Witte lab
"Optimization of Transcranial Acoustoelectric Brain Imaging of Neuronal Currents in a Human Head Model"
Abstract: Mapping brain function requires high spatial and temporal resolution. While scalp electroencephalography (EEG) and Functional magnetic resonance imaging (fMRI) have advanced neurological research, EEG has poor spatial resolution and fMRI lacks temporal sensitivity. These inherent limitations create a critical gap in non-invasive electrical brain mapping at the neuronal scale, limiting progress in diagnosing and treating disorders such as epilepsy, Alzheimer’s disease, Parkinson’s disease and depression.
Transcranial Acoustoelectric Brain Imaging (tABI) with neuronavigation is a new method for mapping EEG-derived currents through the human skull. In tABI, ultrasound (US) is focused and steered in the brain as surface electrodes record an acoustoelectric (AE) interaction signal. Space and time varying current maps are then generated at a resolution determined primarily by the US focal size. Evoked neuronal current densities in the human brain typically range from 0.01 to 0.5 mA/cm², increasing during seizure activity. Because these signals are relatively weak under normal conditions, optimizing tABI sensitivity is critical for detecting low-amplitude neuronal currents. Additionally, ictal activity associated with epileptic seizures often overlaps with background brain activity, highlighting the need for enhanced selectivity to accurately distinguish and localize different current sources and patterns at the voxel level.
The three main objectives of this work are to:
- Improve tABI sensitivity and selectivity under seizure activity using data-driven methods, including Singular Value Decomposition (SVD) and Wiener filtering (WF),
- Validate tABI in a human head model with a real skull for detecting and mapping weak, neuronal-like currents, and
- Develop real-time, continuous tABI scanning as a translational step toward human application.
The completion of these objectives enables efficient, safe and accurate localization of pseudo-neuronal currents in a human head model and justifies proof-of-concept studies in human subjects.
Sajeda Al-Hammouri
PhD Candidate, Hazeli lab
"Markerless Motion Capture System for Quantifying Gait Stability"
Abstract: Falls are a leading cause of injury and mobility loss, yet most clinical tools for assessing fall risk rely on observational scoring systems that do not capture the mechanical mechanisms underlying instability during walking. The Margin of Stability (MoS) provides a physics-based measure of dynamic balance based on the relationship between the center of mass and the base of support, but its use has been constrained to laboratory environments requiring expensive marker-based motion capture systems and force plates.
This work investigates approaches for translating biomechanical stability assessment into clinically accessible settings. Instability-related gait features were used to develop a framework for identifying movement patterns associated with reduced dynamic stability and increased fall risk during walking. In a second stage, a multi-camera markerless motion capture system based on synchronized RGB imaging and three-dimensional pose reconstruction was evaluated for estimating center-of-mass motion and MoS without reflective markers. So far, we have demonstrated excellent agreement between markerless and marker-based measurements, suggesting that a portable markerless motion capture is a viable approach for estimating physics-based and clinically relevant stability metrics. This work supports the development of accessible tools for quantifying gait stability outside specialized biomechanics laboratories and provides a foundation for extending mechanical models of stability toward real-world fall-risk assessment and rehabilitation monitoring.