Cortical auditory evoked potentials in mild cognitive impairment: Evidence from a temporal‐spatial principal component analysis

2019 ◽  
Vol 56 (12) ◽  
Author(s):  
Ana Buján ◽  
Jennifer J. Lister ◽  
Jennifer L. O’Brien ◽  
Jerri D. Edwards
Keyword(s):  
Principal Component Analysis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Evoked Potentials ◽  
Auditory Evoked Potentials ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Principal Component Analysis ◽  
Cortical Auditory Evoked Potentials

scholarly journals Neuropsychological profile in the preclinical stages of dementia: principal component analysis approach

2021 ◽  
Vol 15 (2) ◽  
pp. 192-199
Author(s):  
Claudia Rivera-Fernández ◽  
Nilton Custodio ◽  
Marcio Soto-Añari
Keyword(s):  
Older Adults ◽  
Principal Component Analysis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Verbal Fluency ◽  
Principal Component ◽  
Component Analysis ◽  
Subjective Cognitive Decline ◽  
Neuropsychological Evaluation

ABSTRACT. The preclinical stages of dementia include subtle neurocognitive changes that are not easily detected in standard clinical evaluations. Neuropsychological evaluation is important for the classification and prediction of deterioration in all the phases of dementia. Objective: Compare the neuropsychological performance in healthy older adults with subjective cognitive decline (SCD) and with mild cognitive impairment (MCI) using principal components analysis. Methods: We evaluated 94 older adults with a clinical protocol which included general measures of mental, emotional and functional state. The neuropsychological protocol included tasks of memory, executive function, attention, verbal fluency and visuoconstructional abilities. We used principal component analysis (PCA) to reduce variables´ dimensionality on neuropsychological evaluation. Results: 33(35%) participants had a normal cognitive function, 35(37%) had subjective cognitive decline and 26(28%) had a mild cognitive impairment. The PCA showed seven factors: processing speed, memory, visuoconstruction, verbal fluency and executive components of cognitive flexibility, inhibitory control and working memory. ANOVA had shown significant differences between the groups in the memory (F=4.383, p=0.016, η2p=0.087) and visuoconstructional components (F=5.395, p=0.006, η2p=0.105). Post hoc analysis revealed lower memory scores in MCI than SCD participants and in visuospatial abilities between MCI and SCD and MCI and Normal participants. Conclusions: We observed differentiated cognitive profiles among the participants in memory and visuoconstruction components. The use of PCA in the neuropsychological evaluation could help to make a differentiation of cognitive abilities in preclinical stages of dementia.

scholarly journals Developmental Effects on Pattern Visual Evoked Potentials Characterized by Principal Component Analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 1
Author(s):  
Carlyn Patterson Gentile ◽  
Nabin R. Joshi ◽  
Kenneth J. Ciuffreda ◽  
Kristy B. Arbogast ◽  
Christina Master ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Principal Component ◽  
Component Analysis ◽  
Developmental Effects ◽  
Pattern Visual Evoked Potentials ◽  
Visual Evoked

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

Peak Detection of Somatosensory Evoked Potentials Using an Integrated Principal Component Analysis–Walsh Method

2012 ◽  
Vol 29 (2) ◽  
pp. 165-173 ◽  
Author(s):  
Krishnatej Vedala ◽  
Ilker Yaylali ◽  
Mercedes Cabrerizo ◽  
Mohammed Goryawala ◽  
Malek Adjouadi
Keyword(s):  
Principal Component Analysis ◽  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Principal Component ◽  
Component Analysis ◽  
Peak Detection
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