BME students selected to represent UF at National Posters on the Hill Event

Congratulations to UF BME students Marion Hagstrom and Parker Kotlarz for being selected to represent UF at the National Posters on the Hill event sponsored by the National Council on Undergraduate Research (CUR). These students were selected in a very competitive process open to undergraduate researchers from across the country. 

This prestigious event celebrates the impressive work of the accepted students and supports the messages of the importance of undergraduate research at the federal level.

“Parker and I got to talk with Paul Bonicelli, the Senior Policy Advisor for Sen. Rick Scott. We spoke to him about supporting biomedical research for undergraduates and specifically research for Alzheimer’s disease. Afterward, we joined a zoom of about 120 people for the Posters on the Hill presentations. We split into breakout rooms and showcased our research to attendees,” said Marion.

Posters on the Hill is a critical element in CUR’s advocacy efforts. It is more important than ever that the voice of undergraduate researchers and their mentors is heard on Capitol Hill. 

“It was exciting to present our research on a national stage and highlight the continued need to support undergraduate research. Additionally, we were also recognized by IEEE-USA for their Excellence in Engineering Award which truly rewarded us for our work in finding a biomarker for Alzheimer’s disease,” said Parker.

Posters on the Hill 2022 took place on April 26-27, 2022. 

Marion Hagstrom

Marion Hagstrom:










Parker Kotlarz

Paker Kotlarz:

Title: Early Diagnosis of Alzheimer’s Disease Using Artificial Intelligence

Abstract: Alzheimer’s disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Artificial intelligence can provide a unique tool for early detection and diagnosis of AD to improve patient outcomes. In this work, functional magnetic resonance images (fMRI) of AD mice models were analyzed through mathematical approaches such as graph theory in combination with artificial intelligence techniques. Additionally, AD neurodegeneration was modeled using novel percolation theory by simulating attacks on brain networks and comparing them to the AD mouse model. Our findings identified an anterior-to-posterior disconnect in the AD brain which has been previously proposed as a possible model of AD neurodegeneration. Critical nodes, or brain regions, involved in this anterior-to-posterior disconnect were also identified and a preliminary control-to-disease progression model was demonstrated. Early changes in these brain regions may be able to be utilized as a potential early indicator, or biomarker, of AD or possibly even act as a target for treatment. Furthermore, the AD brain was shown to be more vulnerable to simulated attacks compared to the control brain. This difference in resilience may enable a snapshot comparison of vulnerability to determine if a patient is predisposed to developing AD in the future. Through our artificial intelligence fMRI brain analysis, we were able to identify several potential methods for the early diagnosis of Alzheimer’s disease which may eventually help in improving patient quality of life and delay the onset of the AD symptomatology.