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Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study
Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecolo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422551/ https://www.ncbi.nlm.nih.gov/pubmed/37571766 http://dx.doi.org/10.3390/s23156983 |
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author | Borghesi, Francesca Chirico, Alice Pedroli, Elisa Cipriani, Giuseppina Elena Canessa, Nicola Amanzio, Martina Cipresso, Pietro |
author_facet | Borghesi, Francesca Chirico, Alice Pedroli, Elisa Cipriani, Giuseppina Elena Canessa, Nicola Amanzio, Martina Cipresso, Pietro |
author_sort | Borghesi, Francesca |
collection | PubMed |
description | Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson’s, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used. |
format | Online Article Text |
id | pubmed-10422551 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104225512023-08-13 Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study Borghesi, Francesca Chirico, Alice Pedroli, Elisa Cipriani, Giuseppina Elena Canessa, Nicola Amanzio, Martina Cipresso, Pietro Sensors (Basel) Article Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson’s, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used. MDPI 2023-08-06 /pmc/articles/PMC10422551/ /pubmed/37571766 http://dx.doi.org/10.3390/s23156983 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Borghesi, Francesca Chirico, Alice Pedroli, Elisa Cipriani, Giuseppina Elena Canessa, Nicola Amanzio, Martina Cipresso, Pietro Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title | Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title_full | Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title_fullStr | Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title_full_unstemmed | Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title_short | Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study |
title_sort | exploring biomarkers of mental flexibility in healthy aging: a computational psychometric study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422551/ https://www.ncbi.nlm.nih.gov/pubmed/37571766 http://dx.doi.org/10.3390/s23156983 |
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