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

Abstract

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.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

“…Consequently, they risk masking relevant variation in performance across the relevant domains. In contrast, approaches that explore individual cognitive domains have great potential to improve our understanding and prediction of cognitive health and pathology (Barch & Ceaser, 2012;Covey et al, 2011;Rivera-Fernández et al, 2021), and have been effectively applied to predict performance in various populations, including dementia (Hackett et al, 2018;Rivera-Fernández et al, 2021) stroke aphasia (Butler et al, 2014;Mirman et al, 2019) and healthy aging (Reddy et al, 2015). We therefore considered a wide range of behavioural measures from the HCP dataset, and PCA analysis revealed four components underpinning performance on these cognitive tasks: retention and retrieval, processing speed, self-regulation, and encoding processes.…”

“…In contrast, approaches that explore individual cognitive domains have great potential to improve our understanding and prediction of cognitive health and pathology (Barch & Ceaser, 2012;Covey et al, 2011;Rivera-Fernández et al, 2021), and have been effectively…”

“…applied to predict performance in various populations, including dementia (Hackett et al, 2018;Rivera-Fernández et al, 2021) stroke aphasia (Butler et al, 2014;Mirman et al, 2019) and healthy aging (Reddy et al, 2015). We therefore considered a wide range of behavioural measures from the HCP dataset, and PCA analysis revealed five components underpinning performance on these cognitive tasks: Executive Function, Self-regulation, Language, Encoding and Sequence Processing.…”

“…However, composite intelligence scores are theory-driven and do not necessarily reflect the underlying variance. This means that they serve as good summaries of cognitive performance but it may loose on important information about cognitive health (Barch & Ceaser, 2012; Covey et al, 2011; De Felice & Holland, 2018; Rivera-Fernández et al, 2021), Consequently, further investigations should explore how the relationship between structure and function impacts performance across individual cognitive domains.…”

Cognitive Component Structure of a Neuropsychological Battery Administered to Cognitively-Normal Adults in the SIU Longitudinal Cognitive Aging Study

“…Individual cognitive test measures are not typically sensitive enough to distinguish between normal age-related cognitive decline and pathological cognitive decline, especially in the preclinical phase of mild cognitive impairment (MCI) and Alzheimer’s dementia (AD) ( Collie et al, 1999 ; Hayat et al, 2021 ; Lu et al, 2019 ; Mansbach et al, 2014 ; Riordan, 2017 ; Rivera-Fernández et al, 2021 ; Wessels et al, 2015 ). In addition, when scores from multiple individual measures are used in analyses, the likelihood of a Type 1 error increases due to increasing multiplicity in the dataset ( Riordan, 2017 ).…”