scholarly journals Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

2019 ◽  
Vol 40 (18) ◽  
pp. 5315-5329 ◽  
Author(s):  
David A. Hoagey ◽  
Jenny R. Rieck ◽  
Karen M. Rodrigue ◽  
Kristen M. Kennedy
Keyword(s):  
White Matter ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Brain Aging ◽  
White Matter Microstructure ◽  
Cortical Morphometry

scholarly journals Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

2019 ◽  
Author(s):  
David A. Hoagey ◽  
Jenny R. Rieck ◽  
Karen M. Rodrigue ◽  
Kristen M. Kennedy
Keyword(s):  
White Matter ◽  
Least Squares ◽  
Surface Area ◽  
Partial Least Squares ◽  
Cortical Thickness ◽  
Brain Aging ◽  
Univariate Analysis ◽  
Tissue Type ◽  
Cortical Atrophy ◽  
Multivariate Techniques

AbstractCortical atrophy and degraded axonal health have been shown to coincide during normal aging; however, few studies have examined these measures together. To lend insight into both the regional specificity and the relative timecourse of structural degradation of these tissue compartments across the lifespan, we analyzed grey matter (GM) morphometry (cortical thickness, surface area, volume) and estimates of white matter (WM) microstructure (fractional anisotropy, mean diffusivity) using traditional univariate and more robust multivariate techniques to examine age associations in 186 healthy adults aged 20-94 years old. Univariate analysis of each tissue type revealed that negative age associations were largest in frontal grey and white matter tissue and weaker in temporal, cingulate, and occipital regions, representative of not only an anterior-to-posterior gradient, but also a medial-to-lateral gradient. Multivariate partial least squares correlation (PLSC) found the greatest covariance between GM and WM was driven by the relationship between WM metrics in the anterior corpus callosum and projections of the genu, anterior cingulum, and fornix; and with GM thickness in parietal and frontal regions. Surface area was far less susceptible to age effects and displayed less covariance with WM metrics, while regional volume covariance patterns largely mirrored those of cortical thickness. Results support a retrogenesis-like model of aging, revealing a coupled relationship between frontal and parietal GM and the underlying WM, which evidence the most protracted development and the most vulnerability during healthy aging.

Creativity as a Tool of Tourism Development

2020 ◽  
pp. 504-530
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

Other Tensor Analysis and Further Direction

2011 ◽  
pp. 226-252
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tensor Analysis ◽  
Tensor Methods

In this chapter, we describe tensor-based classifiers, tensor canonical correlation analysis and tensor partial least squares, which can be used in biometrics. Section 11.1 gives background and devolvement of these tensor methods. Section 11.2 introduces tensor-based classifiers. Section 11.3 gives tensor canonical correlation analysis and tensor partial least squares. We summarize this chapter in Section 11.4.

Creativity as a Tool of Tourism Development

2017 ◽  
pp. 67-93
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

scholarly journals Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Chen ◽  
Aiping Liu ◽  
Z. Jane Wang ◽  
Hu Peng
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Medical Knowledge ◽  
Data Sets ◽  
Data Set ◽  
Activity Modeling ◽  
Multiple Data Sets

Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposed method takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG and EMG data collected in a Parkinson’s disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

NeuroImage ◽  
2015 ◽  
Vol 107 ◽  
pp. 289-310 ◽  
Author(s):  
Claudia Grellmann ◽  
Sebastian Bitzer ◽  
Jane Neumann ◽  
Lars T. Westlye ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Genetic Data ◽  
Combined Analysis
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