Harnessing the Power of Discovery

Harnessing the Power of Discovery

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Palm Health Foundation Donates $1M for New Program
By Bethany Augliere

Averaging just three pounds, the human brain is considered one of the most complex structures in the universe, controlling all functions of the body and interpreting information from the outside world. Analogies of the brain to supercomputers are common. Regardless of the similarities, neuroscientists are increasingly turning to mathematicians, statisticians, modelers and computer scientists to advance brain science.

“Since the creation of modern computers, researchers have tried to harness computational power to speed up scientific discovery,” said Ilyas Yildirim, Ph.D., an assistant professor in the department of chemistry and biochemistry, Charles E. Schmidt College of Science. That’s why FAU’s Stiles-Nicholson Brain Institute launched a new program in computational brain science and health, supported by the Palm Health Foundation (PHF) and its gift of $1 million. The gift will support the recruitment of an inaugural program director, support three graduate fellowships per year to advance training in computational neuroscience, and fund faculty pilot research projects that can lead to multi-year, external funding. Yildirim recently received the first pilot award funded by the PHF. As a theoretical and computational biophysical chemist, his new pilot project will create a novel computational method to predict the 3D structures of RNA. “RNA molecules are produced by the DNA of our genomes to allow for protein production throughout our body, including the brain. Predicting how these molecules fold into complex structures remains a challenge”, Yildirim said. Using advanced computational approaches, his team aims to reveal the folding properties of RNA molecules associated with brain diseases, such as those that impact risk for dementia, Alzheimer’s disease, and Parkinson’s disease, among others.

This research offers the possibility of developing novel RNAtargeted medications to treat brain disorders. “Developing computational models that can reliably predict the structure of RNA will have far-reaching and profound effects on our ability to rapidly and accurately develop targeted pharmacotherapeutics for millions of Americans,” Yildirim said.      

Ilyas Yildirim, Ph.D.

2022 Palm Health Foundation Fellowships

Four doctoral students recently received 2022 Palm Health Foundation Fellowships in Computational Brain Science and Health, awarded by the Stiles-Nicholson Brain Institute. These fellowships were supported by the Foundation’s $1 million gift to the institute to establish a program in computational brain science and health.

Hadi Esfandi
Hadi Esfandi, third-year graduate student

Mentor: Ramin Pashaie, Ph.D., associate professor, department of electrical engineering and computer science, College of Engineering and
Computer Science

Research: Alzheimer’s disease is associated with issues involving oxygen and nutrients reaching neurons, referred to as neurovascular coupling (NVC). Esfandi will design a computational model of disrupted NVC in the context of Alzheimer’s pathology. This work will help scientists better understand the course and impact of the disease and promote the possibility of using blood flow signals for early detection of Alzheimer’s.


Yosun Yoon
Yosun Yoon, fourth-year graduate student

Mentor: Sang Wook Hong, Ph.D., associate professor, department of psychology, Charles E. Schmidt College of Science

Research: Computational models have identified the synchronization of neural activity among complex brain networks as key to flexible behavior. Within the brain’s frontoparietal region exists the frontoparietal network, also known as the control network. This network is responsible for sustained attention, complex problem-solving and working memory. Yoon will use a non-invasive brain stimulation technique and computational analyses to examine the effects of externally induced neural synchronization of the brain’s frontoparietal network on cognitive flexibility. The long-term goal of the project is to build a foundation for developing drug-free treatments for people with deficits in cognitive flexibility, such as those with autism spectrum disorder, attention-deficit/hyperactivity disorder and major depressive disorder.

Jasmine Chan
Jasmine Chan, fourth-year graduate student

Mentors: Terrence Barnhardt, Ph.D., associate scientist, department of psychology, Charles E. Schmidt College of Science

Behnaz Ghoraani, Ph.D., associate professor, department of electrical engineering and computer science, College of Engineering and
Computer Science

Teresa Wilcox, Ph.D., professor, department of psychology and interim dean of the Charles E. Schmidt College of Science

Research: When people are presented with new situations, they often have to search their knowledge to form a new category of items that are appropriate to use for the given scenario. Interestingly, these ad hoc categories are not generated as effectively among older adults and patients with Alzheimer’s. Chan will use tensor decomposition, a powerful signal processing approach, along with machine learning to identify the patterns of brain activity that occur when individuals form ad hoc categories in novel situations. Findings from this project may yield diagnostic tools for the identification of cognitive deficits in older adults and patients with Alzheimer’s.


Joseph McKinley
Joseph McKinley, fourth-year graduate student

Mentors: Christopher Beetle, Ph.D., associate professor, department of physics, Charles E. Schmidt College of Science

Emmanuelle Tognoli, Ph.D., research professor, Center for Complex Systems and Brain Sciences, Charles E. Schmidt College of Science

Summary: Neurostimulation is a health intervention that disrupts pathological states of neuron activity, with broad applications in the treatment of many illnesses including Parkinson’s disease, chronic pain, and major depression. McKinley will develop computational models to provide a theoretical foundation for the dynamics of neurostimulation. Such a framework will help tailor treatment protocols to the unique needs of individual patients, improving efficacy and minimizing side effects.

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