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Biomedical Engineering and Bioengineering Commons™
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Full-Text Articles in Biomedical Engineering and Bioengineering
Pairwise Correlation Analysis Of The Alzheimer’S Disease Neuroimaging Initiative (Adni) Dataset Reveals Significant Feature Correlation, Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood, Devorah O. Stucki, Katrisa M. Ward, Mark T. W. Ebbert, John S. K. Kauwe, The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Metabolomics Consortium, Justin B. Miller
Pairwise Correlation Analysis Of The Alzheimer’S Disease Neuroimaging Initiative (Adni) Dataset Reveals Significant Feature Correlation, Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood, Devorah O. Stucki, Katrisa M. Ward, Mark T. W. Ebbert, John S. K. Kauwe, The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Metabolomics Consortium, Justin B. Miller
Sanders-Brown Center on Aging Faculty Publications
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the …
Broad Kinase Inhibition Mitigates Early Neuronal Dysfunction In Tauopathy, Shon A. Koren, Matthew J. Hamm, Ryan Cloyd, Sarah N. Fontaine, Emad Chishti, Chiara Lanzillotta, Jennifer Rodriguez-Rivera, Alexandria Ingram, Michelle Bell, Sara M. Galvis-Escobar, Nicholas Zulia, Fabio Di Domenico, Duc Duong, Nicholas T. Seyfried, David K. Powell, Moriel Vandsburger, Tal Frolinger, Anika M. S. Hartz, John Koren Iii, Jeffrey M. Axten, Nicholas J. Laping, Jose F. Abisambra
Broad Kinase Inhibition Mitigates Early Neuronal Dysfunction In Tauopathy, Shon A. Koren, Matthew J. Hamm, Ryan Cloyd, Sarah N. Fontaine, Emad Chishti, Chiara Lanzillotta, Jennifer Rodriguez-Rivera, Alexandria Ingram, Michelle Bell, Sara M. Galvis-Escobar, Nicholas Zulia, Fabio Di Domenico, Duc Duong, Nicholas T. Seyfried, David K. Powell, Moriel Vandsburger, Tal Frolinger, Anika M. S. Hartz, John Koren Iii, Jeffrey M. Axten, Nicholas J. Laping, Jose F. Abisambra
Sanders-Brown Center on Aging Faculty Publications
Tauopathies are a group of more than twenty known disorders that involve progressive neurodegeneration, cognitive decline and pathological tau accumulation. Current therapeutic strategies provide only limited, late-stage symptomatic treatment. This is partly due to lack of understanding of the molecular mechanisms linking tau and cellular dysfunction, especially during the early stages of disease progression. In this study, we treated early stage tau transgenic mice with a multi-target kinase inhibitor to identify novel substrates that contribute to cognitive impairment and exhibit therapeutic potential. Drug treatment significantly ameliorated brain atrophy and cognitive function as determined by behavioral testing and a sensitive imaging …
Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin
Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin
Sanders-Brown Center on Aging Faculty Publications
Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ …