Literature DB >> 34324958

Effects of cognitive workload on heart and locomotor rhythms coupling.

Daniela De Bartolo1, Chiara De Giorgi2, Luca Compagnucci2, Viviana Betti3, Gabriella Antonucci4, Giovanni Morone5, Stefano Paolucci5, Marco Iosa4.   

Abstract

Different physiological signals could be coupled under specific conditions, in some cases related to pathologies or reductions in system complexity. Cardiac-locomotor synchronization (CLS) has been one of the most investigating coupling. The influence of a cognitive task on walking was investigated in dual-task experiments, but how different cognitive tasks may influence CLS has poorly been investigated. Twenty healthy subjects performed a dual-task walking (coupled with verbal fluency vs calculation) on a treadmill at three different speeds (comfortable speed CS; fast-speed: CS + 2 km/h; slow-speed: CS-2 km/h) while cardiac and walking rhythms were recorded using surface electrodes and a triaxial accelerometer, respectively. According to previous studies, we found a cognitive-motor interference for which cognitive performance was affected by motor exercise, but not vice-versa. We found a CLS at the baseline condition, at fast speed in both cognitive tasks, while at comfortable speed only for the verbal fluency task. In conclusion, the cardiac and locomotor rhythms were not coupled at slow speed and at comfortable speed during subtraction task. Cognitive performances generally increased at faster speed, when cardiac locomotor coupling was stronger.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Dual task; Heart rhythms; Physiological coupling; Walking rhythms

Mesh:

Year:  2021        PMID: 34324958     DOI: 10.1016/j.neulet.2021.136140

Source DB:  PubMed          Journal:  Neurosci Lett        ISSN: 0304-3940            Impact factor:   3.046


  2 in total

1.  Editorial: Rhythmic Patterns in Neuroscience and Human Physiology.

Authors:  Nadia Dominici; Marco Iosa; Giuseppe Vannozzi; Daniela De Bartolo
Journal:  Front Hum Neurosci       Date:  2022-05-25       Impact factor: 3.473

2.  Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke.

Authors:  Marco Iosa; Maria Grazia Benedetti; Gabriella Antonucci; Stefano Paolucci; Giovanni Morone
Journal:  Sensors (Basel)       Date:  2022-02-11       Impact factor: 3.576

  2 in total

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