Interrelation of indicators of efficiency of throws and individually typological properties of the higher nervous activity and sensorimotor functions of athletes in basketball

Li, YunFan; Cherkashin, I. A.; Cherkashina, Elena; Kopylova, Victoria

doi:10.1051/shsconf/20197009004

Cited by 3 publications

(3 citation statements)

References 5 publications

(3 reference statements)

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“…This allows psychophysiological compliance to be determined with some professional requirements. PTs have been validated as a cost effective and reliable tool to screen for professional maladjustment in sports and extreme professions (Li et al, 2019 ; Boichuk et al, 2020 ; Myroshnychenho et al, 2020 ). Unfortunately, clinical psychology does not meet the unconditioned cutoff criteria for major tests (Statsenko and Charykova, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Statsenko

Habuza

Charykova

et al. 2021

Front. Aging Neurosci.

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Background: Neuronal reactions and cognitive processes slow down during aging. The onset, rate, and extent of changes vary considerably from individual to individual. Assessing the changes throughout the lifespan is a challenging task. No existing test covers all domains, and batteries of tests are administered. The best strategy is to study each functional domain separately by applying different behavioral tasks whereby the tests reflect the conceptual structure of cognition. Such an approach has limitations that are described in the article.Objective: Our aim was to improve the diagnosis of early cognitive decline. We estimated the onset of cognitive decline in a healthy population, using behavioral tests, and predicted the age group of an individual. The comparison between the predicted (“cognitive”) and chronological age will contribute to the early diagnosis of accelerated aging.Materials and Methods: We used publicly available datasets (POBA, SSCT) and Pearson correlation coefficients to assess the relationship between age and tests results, Kruskal-Wallis test to compare distribution, clustering methods to find an onset of cognitive decline, feature selection to enhance performance of the clustering algorithms, and classification methods to predict an age group from cognitive tests results.Results: The major results of the psychophysiological tests followed a U-shape function across the lifespan, which reflected the known inverted function of white matter volume changes. Optimal values were observed in those aged over 35 years, with a period of stability and accelerated decline after 55–60 years of age. The shape of the age-related variance of the performance of major cognitive tests was linear, which followed the trend of lifespan gray matter volume changes starting from adolescence. There was no significant sex difference in lifelong dynamics of major tests estimates. The performance of the classification model for identifying subject age groups was high.Conclusions: ML models can be designed and utilized as computer-aided detectors of neurocognitive decline. Our study demonstrated great promise for the utility of classification models to predict age-related changes. These findings encourage further explorations combining several tests from the cognitive and psychophysiological test battery to derive the most reliable set of tests toward the development of a highly-accurate ML model.

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Section: Introductionmentioning

confidence: 99%

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Statsenko

Habuza

Charykova

et al. 2021

Front. Aging Neurosci.

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show abstract

“…The components of sensorimotor response are closely interrelated with the high-level mental functions (Mitchell et al, 2019 ) that underlie the efficiency of the activity and imply for the performance of the sensorimotor test. This makes it possible to use the tests for the functional assessment of the central nervous system in aging (Cassady et al, 2019 , 2020 ) and professional activities (Li et al, 2019 ; Boichuk et al, 2020 ; Myroshnychenho et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Statsenko

Habuza

Gorkom

et al. 2021

Front. Aging Neurosci.

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Background: Neuroscience lacks a reliable method of screening the early stages of dementia.Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains.Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes.Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age.Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

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“…The components of sensorimotor response are closely interrelated with the high-level mental functions (Mitchell et al, 2019) that underlie the efficiency of the activity and imply for the performance of the sensorimotor test. This makes it possible to use the tests for the functional assessment of the central nervous system in aging (Cassady et al, 2019(Cassady et al, , 2020 and professional activities (Li et al, 2019;Boichuk et al, 2020;Myroshnychenho et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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Interrelation of indicators of efficiency of throws and individually typological properties of the higher nervous activity and sensorimotor functions of athletes in basketball

Cited by 3 publications

References 5 publications

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Predicting Age From Behavioral Test Performance for Screening Early Onset of Cognitive Decline

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Data_Sheet_1.PDF

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