ABSTRACT
Objective@#To explore the neural processing differences in inhibitory control and cognitive flexibility associated with motor development levels in preschool children, so as to provide a basis for motor learning and cognitive development in preschool children.@*Methods@#From March 20 to 31 in 2023, a total of 84 preschool children aged 4-6 were recruited from two kindergartens in Xi an City. The MOBAK-KG Motor Development Assessment Scale was used to assess the children s motor development levels. The Go/no go task paradigm was employed to test inhibitory control ability, and the Dimensional Change Card Sort (DCCS) task paradigm was utilized to evaluate cognitive flexibility. Functional near infrared spectroscopy (fNIRS) was used to monitor the preschool children s prefrontal cortex oxygenation dynamics during inhibitory control and cognitive flexibility tasks. Malab software and Homer 2 plugins were used to calculate prefrontal oxygenated hemoglobin concentration of preschool children during the tasks.@*Results@#The high motor skills group exhibited significantly higher task accuracy during inhibitory control and cognitive flexibility tasks [0.95(0.92, 0.97),(0.54±0.12)] compared to the low motor skill group[0.93(0.85, 0.97),(0.45±0.13)] ( Z/t =-2.09, 3.14 , P <0.05). During the inhibitory control task, the high motor skill group [0.24(0.10,0.41), 0.34(0.16,0.62), 0.30(0.07, 0.52 ), 0.26(0.09, 0.53), 0.15(0.01, 0.43), 0.34(0.10, 0.67)mol/L] showed significantly higher oxygenated hemoglobin concentrations in the left and right dorsolateral prefrontal cortices (L-DLPFC, R-DLPFC), left and right pars triangular Broca s areas ( L- PTBA, R-PTBA), and left and right frontopolar areas (L-FPA, R-FPA) compared to the low motor skill group [0.04( -0.13 , 0.15), 0.00(-0.12, 0.11), -0.01(-0.17, 0.14), 0.04(-0.14, 0.16), -0.01(-0.16, 0.12), -0.03(-0.21, 0.15) mol/L ] ( Z=-4.83, -5.57, -4.77, -4.10, -3.45, -5.74, P ＜0.01). During the cognitive flexibility task, the high motor skill group[0.21(0.03, 0.36), 0.28(0.15, 0.45), 0.15(0.05, 0.30), 0.20(0.05, 0.37), 0.04(-0.17, 0.26), 0.14(-0.08, 0.40) mol/L ] exhibited significantly higher oxygenated hemoglobin concentrations in the L-DLPFC, R-DLPFC, L-PTBA, R-PTBA, L- FPA, R-FPA brain regions compared to the low motor skill group [0.02(-0.20, 0.23), 0.02(-0.12, 0.21), 0.00(-0.22, 0.16 ), 0.00(-0.16, 0.15), -0.05(-0.25, 0.06), 0.01(-0.23, 0.20)mol/L] ( Z=-3.63, -4.45, -3.58, -3.75, -2.18, -1.98 , P ＜0.05).@*Conclusions@#The motor development level in preschool children is closely related to inhibitory control and cognitive flexibility. It is crucial to emphasize motor learning in early childhood to further promote holistic development of both mind and body.
ABSTRACT
Objective:To identify the risk factors of cognitive dysfunction in patients with atrial fibrillation and to establish a risk prediction model.Methods:The convenience sampling method was used to evaluate 260 patients with atrial fibrillation who were hospitalized in the Department of Cardiology of the Affiliated Hospital of Jining Medical College from January to December 2020. The cognitive function of the patients was evaluated with the Montreal Cognitive Function Assessment Scale (MoCA). Univariate analysis was used to screen the independent variables that had influence on the occurrence of cognitive dysfunction, and the statistically significant variables were included in the multivariate Logistic regression model. According to the regression coefficients of statistically significant variables, a line map was drawn to construct the risk prediction model of cognitive dysfunction in patients with atrial fibrillation.Results:There were 209 cases with cognitive impairment and 51 cases without cognitive impairment. Univariate analysis showed that sex, age, smoking history, drinking history, education level, free thyroxine, hemoglobin, D-dimer and BMI ( χ2 values were 4.08-18.83, t values were -6.04-2.94, Z=-2.76) were significantly different between the patients with or without cognitive dysfunction. The results of multivariate Logistic regression analysis showed that age ( OR values were 1.13), education level ( OR=0.01-0.05), quit smoking history ( OR=0.36), drinking history ( OR=0.35) and free thyroxine( OR=1.14) had significantly statistical significance ( P<0.05). The area under ROC curve (AUC) = 0.878 and AUC>0.8, this model had good clinical prediction ability. Conclusions:The construction of cognitive dysfunction risk prediction model for patients with atrial fibrillation can prevent or intervene high risk factors in advance, facilitate clinical use, and provide data support for the improvement of cognitive function in patients with atrial fibrillation.