Researcher(s)
- Kaitlyn McKenna, Neuroscience, University of Delaware
Faculty Mentor(s)
- Roxana Burcui, KAAP, University of Delaware
Abstract
Kaitlyn McKenna 1 and Roxana G. Burciu, Ph.D. 2
1 Department of Psychological & Brain Sciences, University of Delaware, Newark, DE
2 Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that begins years
before motor symptoms emerge. By the time of diagnosis, over half of the brain’s
dopaminergic neurons are already lost. The prodromal phase is when individuals exhibit
subtle symptoms or risk factors without meeting diagnostic criteria – it offers a critical
window for early detection and intervention. However, the brain changes that
differentiate those who eventually convert to PD from those who remain at risk are
poorly understood. In this pilot study, we leveraged longitudinal data from the
Parkinson’s Progression Markers Initiative (PPMI), a large international study supported
by the Michael J. Fox Foundation, to investigate brain changes in individuals at risk for.
We analyzed resting-state fMRI data from two timepoints (baseline and 2-year
follow-up) in 13 prodromal participants. Six converted to PD shortly after year 2, while
seven did not. The two groups had similar sociodemographic characteristics at baseline.
We conducted seed-based voxelwise analyses of resting-state fMRI data using the left
and right putamen – key regions presenting functional changes in PD. At baseline, there
were no detectable differences in the local connections within the basal ganglia or
cortical motor regions. By year 2, converters exhibited reduced local connectivity within
the basal ganglia combined with increased connectivity between the putamen and
cortical regions, including motor regions. These changes, not seen in non-converters,
may reflect early network reorganization, preceding symptom onset. Converters showed
more prodromal symptoms and risk factors, including family history, with no mutations in
known PD genes in either group. These resting-state connectivity changes and risk
profiles may serve as early biomarkers of conversion but require validation in larger
cohorts. Our findings highlight the potential of multimodal approaches – combining
imaging, clinical, and genetic data – to detect Parkinson’s disease in its earliest stages.