Home
Mission
The mission of the Center for Neurodegenerative Disease Research (CNDR) is to promote and conduct multidisciplinary clinical and basic research to increase the understanding of the causes and mechanisms leading to brain dysfunction and degeneration in neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Lewy body dementia (LBD), Frontotemporal degeneration (FTD), Amyotrophic lateral sclerosis (ALS), Primary lateral sclerosis (PLS), Motor neuron disease (MND), and related disorders that occur increasingly with advancing age. Implicit in the mission of the CNDR are two overarching goals: 1.) Find better ways to cure and treat these disorders, 2. Provide training to the next generation of scientists.
“My goal for CNDR is not only to collaborate with researchers at Penn and from institutions across the globe with the mutual goal of finding better ways to diagnose and treat neurodegenerative diseases, but also to inspire and encourage the next generation of scientists on the importance of investigating these disorders that occur more frequently with advancing age.” – Virginia M.-Y. Lee, PhD, Director, CNDR
John Q. Trojanowski, MD, PhD | 1946 - 2022
In loving memory of John Q. Trojanowski, MD, PhD
Latest Research
-
CSF α-Synuclein Seed Amplification Assays and Skin Immunofluorescence: Clinical Applications, Research Opportunities, and Knowledge Gaps
Tuesday, January 13, 2026
The development of biomarkers capable of reliably detecting pathologic forms of α-synuclein (aSyn) in vivo marks a significant step forward in the field of neurodegeneration. Over the recent years, CSF aSyn seed amplification assays (aSyn-SAA) and skin biopsy phospho-aSyn immunofluorescence (skin aSyn-IF) testing were developed that detect pathologic aSyn seeds and phosphorylated aSyn aggregates, respectively, in patients with synucleinopathies, which include Parkinson disease, dementia with...
-
Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review
Tuesday, January 13, 2026
The application of machine learning (ML) and artificial intelligence (AI) algorithms in medical imaging is an emerging area of interest, particularly in the context of clinical decision-making. Here, we report on the overall performance (i.e., sensitivity, specificity, and accuracy) of commonly used ML/AI techniques including convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and ensemble approaches on the clinically relevant task of distinguishing between...
-
Polyphenol-Enriched Fraction from Chestnut Shells as a Source of Bioactive Compounds for Friedreich Ataxia
Saturday, January 10, 2026
We explored the ability of the low molecular weight, polyphenol-rich fractions obtained from chestnut shells to inhibit ferroptosis in Friedreich Ataxia (FRDA), an inherited neuro- and cardio-degenerative disease. We prepared an aqueous extract by an eco-sustainable method and obtained a polyphenol-rich fraction (fraction D) of molecular weight less than 1.0 kDa after molecular size fractionation. The total phenols were 173.28 ± 4.97 μg gallic acid equivalents/mg fraction, and analysis by...