scholarly journals Principal Component Analysis Reveals Age-Related and Muscle-Type-Related Differences in Protein Carbonyl Profiles of Muscle Mitochondria

2008 ◽  
Vol 63 (12) ◽  
pp. 1277-1288 ◽  
Author(s):  
J. Feng ◽  
M. Navratil ◽  
L. V. Thompson ◽  
E. A. Arriaga
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Protein Carbonyl ◽  
Muscle Type ◽  
Muscle Mitochondria ◽  
Age Related

Principal Component Analysis of Working Memory Variables during Child and Adolescent Development

2016 ◽  
Vol 19 ◽  
Author(s):  
Catarina I. Barriga-Paulino ◽  
Elena I. Rodríguez-Martínez ◽  
María Ángeles Rojas-Benjumea ◽  
Carlos M. Gómez
Keyword(s):  
Principal Component Analysis ◽  
Working Memory ◽  
Memory Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Test Battery ◽  
Childhood And Adolescence ◽  
Behavioral Measures ◽  
Age Related ◽  
Speed Accuracy

AbstractCorrelation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6–18 years old. The correlation analysis (p < .05) results showed a common maturational trend in working memory performance between these two types of tasks. Applying PCA (Eigenvalues > 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

Age-Related Differences in Gait Kinematics, Kinetics, and Muscle Function: A Principal Component Analysis

2016 ◽  
Vol 45 (3) ◽  
pp. 695-710 ◽  
Author(s):  
Sarah A. Schloemer ◽  
Julie A. Thompson ◽  
Amy Silder ◽  
Darryl G. Thelen ◽  
Robert A. Siston
Keyword(s):  
Principal Component Analysis ◽  
Muscle Function ◽  
Principal Component ◽  
Component Analysis ◽  
Gait Kinematics ◽  
Age Related

scholarly journals Investigating the age-related “anterior shift” in the scalp distribution of the P3b component using principal component analysis

2014 ◽  
Vol 51 (7) ◽  
pp. 620-633 ◽  
Author(s):  
Brittany R. Alperin ◽  
Katherine K. Mott ◽  
Dorene M. Rentz ◽  
Phillip J. Holcomb ◽  
Kirk R. Daffner
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Scalp Distribution ◽  
Age Related

scholarly journals Developmental effects on pattern visual evoked potentials characterized by principal component analysis

2020 ◽  
Author(s):  
Carlyn Patterson Gentile ◽  
Nabin R Joshi ◽  
Kenneth Ciuffreda ◽  
Kristy Arbogast ◽  
Christina Master ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Course ◽  
Principal Component ◽  
Component Analysis ◽  
Training Dataset ◽  
Pattern Reversal ◽  
Age Related ◽  
Significant Difference ◽  
Age And Sex ◽  
Visual Evoked

Purpose: Peak amplitude and latency in the pattern reversal visual evoked potential (prVEP) vary with maturation. We considered that principal component analysis (PCA) may be used to describe age-related variation over the entire prVEP time course and provide a means of modeling and removing variation due to developmental age. Methods: prVEP was recorded from 155 healthy subjects ages 11-19 years during two sessions (spaced 0.7 to 17 months apart). We created a model of the prVEP by identifying principal components (PCs) that explained >95% of the variance in a training dataset of 40 subjects. We examined the ability of the PCs to explain variance in an age- and sex-matched test subject group (n=40) and calculated the intra-subject reliability of the PC coefficients between the two sessions. We then explored the effect of subject age and sex upon the PC coefficients. Results: Seven PCs accounted for 96.0% of the variability. The model was generalizable (training vs. test coefficient distributions p>0.36 for all PCs) with good within-subject reliability (R>0.7 for all PCs). The PCA model did not show a significant difference between males and females (F(7,147)=1.69, p=0.12), but showed a significant effect of subject age (F(7,147)=4.37, p=0.0002). Conclusions: PCA is a generalizable, reliable, and unbiased method of analyzing prVEP that can quantify and remove developmental variability present in the global temporal VEP signal. Translational relevance: We describe a novel application of PCA to characterize developmental changes of prVEP in youth that can be used to compare healthy and pathologic pediatric cohorts.

scholarly journals Age-Related Variation in Wing Shape Differs between Bird Orders: Implications for Interpretation of the Pointedness Index (C2 Axis) in a Size-Constrained Principal Component Analysis (SCCA)

Ardeola ◽  
2020 ◽  
Vol 67 (2) ◽  
pp. 341
Author(s):  
Xabier Cabodevilla ◽  
Javier Pérez-Tris ◽  
Lara Moreno-Zarate ◽  
Antón Pérez-Rodríguez ◽  
José Francisco Lima-Barbero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Wing Shape ◽  
Related Variation ◽  
Age Related

Analysis of association of risk factors for age-related macular degeneration

2021 ◽  
pp. 112067212199890
Author(s):  
Kajo Bucan ◽  
Marko Lukic ◽  
Damir Bosnar ◽  
Andrijana Kopic ◽  
Tomislav Jukic ◽  
...  
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
High Risk ◽  
Macular Degeneration ◽  
Principal Component ◽  
Component Analysis ◽  
T Test ◽  
Age Related Macular Degeneration ◽  
Risk Indicator ◽  
Age Related

Purpose: To evaluate the significance of risk factors and analyze their interrelationship in developing age-related macular degeneration (AMD). Materials and design: This is a multicenter, cross-sectional study conducted in eight ophthalmology centers in Europe. The STARS (Simplified Thea AMD Risk-Assessment Scale) questionnaire was used to assess 12 risk factors grouped in four major categories. We used Welch’s t-test/ F ratios to determine statistically significant changes. The principal component analysis was done to investigate the association between risk factors. Results: There were 3297 participants included in our data analysis. Nineteen percent of patients had a high risk of developing AMD, whilst 45.92% and 34.85% had moderate and small risk, respectively. Atherosclerosis appeared as the most relevant risk indicator for AMD development (Cohen’s d = 0.861). Tukey’s post hoc analysis of the smoking variable showed that ex-smokers ( p < 0.001) have a significantly high risk of developing AMD. The Welch’s t-test showed pseudophakic patients have a higher risk of developing AMD than phakic ones. Then, we conducted the principal component analysis, which revealed a significant connection between smoking and male gender and between smoking and atherosclerosis. Pseudophakic patients were generally older and had more often myocardial infarction as compared to phakic patients. We showed that higher BMI, history of arterial hypertension, hypercholesterolemia, and atherosclerosis tend to occur together as risk factors for AMD. Conclusion: Risk factors evaluated in our study should be considered for the development of AMD.

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