Neurophotonic Systems: From Flexible Polymer Implants to in situ Ultrasonically-drive Light Guides

Maysam Chamazar will give a webinar on June 18, 2019 at 1:00pm, as part of the IEEE (Institute of Electrical and Electronics Engineers) Brain Webinar series.

Understanding the neural basis of brain function and dysfunction may inform the design of effective therapeutic interventions for brain disorders and mental illnesses. Optical techniques have been recently developed for structural and functional imaging as well as targeted stimulation of neural circuits. One of the challenges of optical modality is light delivery deep into the brain tissue in a non-invasive or at least minimally invasive way.

Scattering and absorption prevents deep penetration of light in tissue and limits light-based methods to superficial layers of the tissue. To overcome this challenge, implantable photonic waveguides such as optical fibers or graded-index (GRIN) lenses have been used to deliver light into the tissue or collect photons for imaging. Existing large and rigid optical waveguides cause damage to the brain tissue and vasculature. In this talk, Dr. Maysam Chamanzar will discuss his research on developing next generation optical neural interfaces. First, Dr. Chamanzar will introduce a novel compact flexible photonic platform based on biocompatible polymers, Parylene C and PDMS, and GaN active light sources for optogenetic stimulation of neural circuits with high spatiotemporal resolution. This photonic platform can be monolithically integrated with implantable neural probes.

Then, Dr. Chamanzar will discuss his recent work on developing a novel complementary approach to guide and steer light in the brain using non-invasive ultrasound. Dr. Chamanzar will show that ultrasound waves can sculpt virtual graded-index (GRIN) waveguides in the tissue to define and steer the trajectory of light without physically implanting optical waveguides in the brain.

These novel neurophotonic techniques enable high-throughput bi-directional interfacing with the brain to understand the neural basis of brain function and design next generation neural prostheses.

You can register for free.

The University of Pittsburgh
Department of Epidemiology Presents

T32 Training Grant Retreat

Population Neuroscience of Age-related Dementias

Thursday, June 20, 2019
11:00 – 1:30 pm

Trainee Poster Presentations 11:00 – 12:30 pm
GSPH Commons – Luncheon provided

Keynote Speaker 12:30 – 1:30 pm
“Population neuroscience, what progress are we making?”
Emiliano Albanese, MD, PhD
Director of the WHO Collaborating Center for
Research and Training in Mental Health,
University of Geneva, Geneva
Head of the Division of Public Mental Health and Aging,
Institute of Global Health, Geneva
A115 Public Health

Contact Person: Becky Meehan meehanb@pitt.edu

Thesis Defense
Date: June 27, 2019
Time: 10:30pm (EDT)
Place: 6501 GHC
Speaker: Qiong Zhang
Neural Computation & Machine Learning PhD Program
Title: The When, Where and Why of Human Memory Retrieval
Abstract:
Memory retrieval is fundamental in our daily experiences, whether it is to recognize a friend, to decide what to order from a menu or to navigate on the street. The process of memory retrieval, however, is latent and embedded among other cognitive processes such as perceptual encoding, decision making, and motor response. To track precisely when memory retrieval takes place, my research isolates individual cognitive processes from observed neural signal, by modeling the psychological activity in subjects’ minds as a sequence of latent stages.
With precise timing of when memory retrieval occurs, I then examine where in the brain there is greater activity during the moment of memory retrieval. Developing a method that aligns neural recordings across subjects, and with better spatial resolution of an ECoG dataset, I provide a detailed mapping of the contributions of individual brain regions in a working memory task.
To further understand why memories are retrieved the way they are, I compare how well different cognitive mechanisms achieve the computational goal of a memory task. Principle of rationality posits that human cognition should adapt optimally to the task demands in the environment through learning and evolution. The more optimal cognitive mechanisms are more favorable to be used by human cognition. In a semantic fluency task, I demonstrate that an alternative memory search mechanism derived from reinforcement learning outperforms existing cognitive mechanisms both in their performance over simulations and in accounting for human behavioral data.
As a whole, my thesis work provides an integrated theory of human memory retrieval by uncovering its temporal dynamics, neural correlates, and underlying computational goal.
Thesis Committee:
John R. Anderson, (Co-chair)
Robert E. Kass, (Co-chair)
Leila Wehbe
Kenneth A. Norman, (Princeton Neuroscience Institute)
Link to thesis document: https://www.andrew.cmu.edu/user/qiongz/thesis_QZ.pdf