Objective: Walking is one of the most complex human movements that can be affected by various sources of attention. Dual tasks reduce attention, increase information processing, and may alter control mechanisms such as synergy. However, the effect of dual tasks on muscle synergy remains unknown. Therefore, this study aimed to investigate the effect of cognitive and motor dual tasks on the synergy of lower limb muscles during walking.
Methods: Twenty-four participants were selected voluntarily. The activity of the eight lower limb muscles was recorded under three different conditions: normal walking without a dual task, walking with a cognitive dual task, and walking with a motor dual task. A nonnegative matrix factorization algorithm and the variance accounted for were used to extract muscle synergy. The repeated-measures analysis of variance test and Pearson's correlation coefficient were performed to analyze the data.
Results: In this study, five muscle synergies were extracted from electromyography data using the variance accounted for method under three different conditions. The pattern of muscle synergies showed moderate to strong correlations. Peaks of synergies changed, and a time shift in synergy peaks during walking was observed. However, the number of extracted synergies did not change.
Conclusion: The number of recruited muscle synergies remained consistent across different conditions. Dual tasks affect the higher levels of the motor control system, causing interference in information processing that leads to a shift in the tendency of synergy and weight coefficients of the muscles, ultimately resulting in a change in walking mechanics.
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http://dx.doi.org/10.1123/mc.2024-0080 | DOI Listing |
Brief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
View Article and Find Full Text PDFFront Psychol
December 2024
Laboratory of Functional Neurosciences (UR UPJV 4559), Jules Verne University of Picardie, Amiens, France.
Introduction: Numerous studies have explored the linguistic and executive processes underlying verbal fluency using association designs, which provide limited evidence. To assess the validity of our model, we aimed to refine the cognitive architecture of verbal fluency using an interference design.
Methods: A total of 487 healthy participants performed letter and semantic fluency tests under the single condition and dual conditions while concurrently performing a secondary task that interferes with speed, semantics, phonology, or flexibility.
Front Robot AI
December 2024
MSc SmartEdTech, Université Côte d'Azur, Nice, France.
Creative Problem Solving (CPS) is an important competency when using digital artifacts for educational purposes. Using a dual-process approach, this study examines the divergent thinking scores (fluidity, flexibility, and originality) and problem-solving speed in CPS of different age groups. Participants engaged in CreaCube CPS tasks with educational robotics for two consecutive instances, with performance analyzed to explore the influence of prior experience and creative intentions.
View Article and Find Full Text PDFJ Addict Dis
December 2024
Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
No FDA-approved medications for methamphetamine (MA) use disorder (MUD) are available. Suvorexant (SUVO), a dual orexin receptor antagonist that is FDA approved for insomnia treatment, reduces MA self-administration and MA-induced reinstatement responding in preclinical studies. SUVO may also reduce MA use by targeting substance use risk factors, including insomnia, stress, cue reactivity, and craving.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Life Sciences, Centre for Clinical and Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
Multitasking (MT)-performing more than one task at a time-has become ubiquitous in everyday life. Understanding of how MT is learned could enable optimizing learning regimes for tasks and occupations that necessitate frequent MT. Previous research has distinguished between MT learning regimes in which all tasks are learned in parallel, single-task (ST) learning regimes in which all tasks are learned individually, and mixed learning regimes (Mix) in which MT and ST regimes are mixed.
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