Date/Time
Date(s) - 09/11/2023
3:00 pm - 4:00 pm

Location
Communicore, C1-15

• Adam Khalifa, Ph.D., Assistant Professor, Department of Electrical & Computer Engineering, University of Florida
  Validation of Next-Generation Stimulating Neural Interfaces
  In the last decade, the quest for a minimally invasive, distributed neural interface has accelerated. Current technologies, relying on intracortical or depth electrodes, either lack coverage or become too invasive. To address these issues, we have created wirelessly powered, injectable microdevices smaller than 0.01 mm³, offering a minimally invasive solution for chronic brain stimulation. These devices circumvent surgical complications tied to open craniotomy and are powered by a scalp-mounted transmitter coil. My talk will also cover next-generation neural interfaces that stimulate tissue magnetically.
• Abbas Babajani-Feremi, Ph.D., Associate Professor, Department of Neurology, University of Florida
  The Power of magnetoencephalography (MEG): Examining Localization, Functional Mapping, and Connectomics in Epilepsy and Beyond
  Magnetoencephalography (MEG) has emerged as a powerful tool for investigating brain activity and has revolutionized our understanding of neurological conditions, particularly epilepsy. This presentation aims to explore the immense potential of MEG in examining localization, functional mapping, and connectomics in epilepsy and beyond. By comparing MEG with other modalities, understanding its applications in localization and functional mapping, and evaluating its reliability in advanced analyses, participants will be equipped with valuable knowledge to harness the potential of MEG in their own research and clinical practice, ultimately contributing to advancements in the diagnosis and treatment of epilepsy and other neurological conditions.
• Diego Guarin, Ph.D., Assistant Professor, Department of Applied Physiology & Kinesiology, University of Florida
  Revolutionizing Parkinson’s Disease Diagnosis and Monitoring by Leveraging Video-Based Movement Measures and Machine Learning
  Parkinson’s disease (PD) is the second most common and fastest-growing neurodegenerative disorder. PD diagnosis often relies on cardinal motor symptoms and the response to pharmacological therapies assessed via standardized motor tasks. There is an urgent need to establish practical and accessible markers that facilitate detection and assessment in early PD. In this short seminar, we will explore how applying machine learning and computer vision algorithms to estimate movement from videos can broaden our comprehension of motor symptoms in Parkinson’s disease and potentially revolutionize our approach to monitoring the early progression of motor symptoms.