Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Aging (1)
- Anxiety (1)
- CSF expansion (1)
- Cerebrospinal fluid expansion (1)
- Cognition (1)
-
- DLB (1)
- FTD (1)
- Grey matter loss (1)
- Hallucinations (1)
- Hierarchical clustering analysis (1)
- Hippocampal volume loss (1)
- IL8 (1)
- MMP1 (1)
- Mild cognitive impairment (1)
- Psychosis (1)
- Structural brain changes (1)
- TDP-43 proteinopathy (1)
- Trajectory (1)
- VEGF (1)
- Vascular cognitive impairment and dementia (1)
Articles 1 - 3 of 3
Full-Text Articles in Biostatistics
Distinct Clinicopathologic Clusters Of Persons With Tdp-43 Proteinopathy, Yuriko Katsumata, Erin L. Abner, Shama Karanth, Merilee A. Teylan, Charles N. Mock, Matthew D. Cykowski, Edward B. Lee, Kevin L. Boehme, Shubhabrata Mukherjee, John S. K. Kauwe, Richard J. Kryscio, Frederick A. Schmitt, David W. Fardo, Peter T. Nelson
Distinct Clinicopathologic Clusters Of Persons With Tdp-43 Proteinopathy, Yuriko Katsumata, Erin L. Abner, Shama Karanth, Merilee A. Teylan, Charles N. Mock, Matthew D. Cykowski, Edward B. Lee, Kevin L. Boehme, Shubhabrata Mukherjee, John S. K. Kauwe, Richard J. Kryscio, Frederick A. Schmitt, David W. Fardo, Peter T. Nelson
Biostatistics Faculty Publications
To better understand clinical and neuropathological features of TDP-43 proteinopathies, data were analyzed from autopsied research volunteers who were followed in the National Alzheimer’s Coordinating Center (NACC) data set. All subjects (n = 495) had autopsy-proven TDP-43 proteinopathy as an inclusion criterion. Subjects underwent comprehensive longitudinal clinical evaluations yearly for 6.9 years before death on average. We tested whether an unsupervised clustering algorithm could detect coherent groups of TDP-43 immunopositive cases based on age at death and extensive neuropathologic data. Although many of the brains had mixed pathologies, four discernible clusters were identified. Key differentiating features were age at …
Brain Structure Changes Over Time In Normal And Mildly Impaired Aged Persons, Charles D. Smith, Linda J. Van Eldik, Gregory A. Jicha, Frederick A. Schmitt, Peter T. Nelson, Erin L. Abner, Richard J. Kryscio, Richard R. Murphy, Anders H. Andersen
Brain Structure Changes Over Time In Normal And Mildly Impaired Aged Persons, Charles D. Smith, Linda J. Van Eldik, Gregory A. Jicha, Frederick A. Schmitt, Peter T. Nelson, Erin L. Abner, Richard J. Kryscio, Richard R. Murphy, Anders H. Andersen
Neurology Faculty Publications
Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70-78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a …
Hierarchical Clustering Analyses Of Plasma Proteins In Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based On Differential Levels Of Angiogenic And Inflammatory Biomarkers, Zachary Winder, Tiffany L. Sudduth, David W. Fardo, Qiang Cheng, Larry B. Goldstein, Peter T. Nelson, Frederick A. Schmitt, Gregory A. Jicha, Donna M. Wilcock
Hierarchical Clustering Analyses Of Plasma Proteins In Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based On Differential Levels Of Angiogenic And Inflammatory Biomarkers, Zachary Winder, Tiffany L. Sudduth, David W. Fardo, Qiang Cheng, Larry B. Goldstein, Peter T. Nelson, Frederick A. Schmitt, Gregory A. Jicha, Donna M. Wilcock
Sanders-Brown Center on Aging Faculty Publications
Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to …