Objective:The study aims to explore the abnormal characteristics in cerebral cortex among children with different subtypes of attention deficit hyperactivity disorder (ADHD).Methods:Four hundred and twelve samples were obtained from the Healthy Brain Network project of American Child Mind Institute. There were 288 children with ADHD (all subjects: age,M=10.03,SD=3.11; 151 of ADHD-C, 20 of ADHD-H, and 117 of ADHD-I) and 124 healthy controls (age,M=9.98,SD=2.98). Using FreeSurfer software, we processed the brain structure images and obtained the cortical volume, cortical thickness and surface area for each subject. Analysis of Variance (ANOVA) and post hoc comparison analyses were conducted.Results:ANOVA analysis showed significant differences of the cortical volume located in the left superior parietal gyrus ( Z=5.94) and superior temporal gyrus ( Z=5.49) among the 3 subtypes of ADHD children and the healthy controls (Monte Carlo, P<0.05). Compared with the healthy controls, ADHD-H group exhibited an increased cortical volume in the left superior parietal gyrus ( Z=6.79), while the ADHD-I group had a decreased volume in the left superior temporal gyrus ( Z=-5.12) and lateral occipital cortex ( Z=-6.40). ADHD-C group also had a decreased volume in the left lateral occipital cortex ( Z=-3.37). Among 3 subtypes of ADHD patients, both ADHD-I and ADHD-C groups had a smaller volume in the left superior parietal gyrus than that of the ADHD-H group (ADHD-I: Z=-7.33,MNI coordinate:x=-26.8,y=-60.6,z=45.4; ADHD-C: Z=-7.14,MNI coordinate:x=-26.6,y=-60.2,z=45.4). Additionally, there was no statistical difference in cortical volume between the ADHD-I and ADHD-C group (Monte Carlo, P>0.05). Subsequent supplementary analyses showed that the sample size and age had no significant effect on the above results. Moreover, analysis of cortical thickness and the surface area showed that the abnormality of the cortical volume in different ADHD subtypes was mainly determined by the surface area of the cerebral cortex. Conclusion:Cortical measures in the superior parietal gyrus might be the crucial features that distinguishes the different subtypes of ADHD. These results enable us to further explore the neurodevelopmental mechanism of ADHD and guide the precise and specific clinical treatment.

Objective:The study aims to explore the abnormal characteristics in cerebral cortex among children with different subtypes of attention deficit hyperactivity disorder (ADHD).Methods:Four hundred and twelve samples were obtained from the Healthy Brain Network project of American Child Mind Institute. There were 288 children with ADHD (all subjects: age,M=10.03,SD=3.11; 151 of ADHD-C, 20 of ADHD-H, and 117 of ADHD-I) and 124 healthy controls (age,M=9.98,SD=2.98). Using FreeSurfer software, we processed the brain structure images and obtained the cortical volume, cortical thickness and surface area for each subject. Analysis of Variance (ANOVA) and post hoc comparison analyses were conducted.Results:ANOVA analysis showed significant differences of the cortical volume located in the left superior parietal gyrus ( Z=5.94) and superior temporal gyrus ( Z=5.49) among the 3 subtypes of ADHD children and the healthy controls (Monte Carlo, P<0.05). Compared with the healthy controls, ADHD-H group exhibited an increased cortical volume in the left superior parietal gyrus ( Z=6.79), while the ADHD-I group had a decreased volume in the left superior temporal gyrus ( Z=-5.12) and lateral occipital cortex ( Z=-6.40). ADHD-C group also had a decreased volume in the left lateral occipital cortex ( Z=-3.37). Among 3 subtypes of ADHD patients, both ADHD-I and ADHD-C groups had a smaller volume in the left superior parietal gyrus than that of the ADHD-H group (ADHD-I: Z=-7.33,MNI coordinate:x=-26.8,y=-60.6,z=45.4; ADHD-C: Z=-7.14,MNI coordinate:x=-26.6,y=-60.2,z=45.4). Additionally, there was no statistical difference in cortical volume between the ADHD-I and ADHD-C group (Monte Carlo, P>0.05). Subsequent supplementary analyses showed that the sample size and age had no significant effect on the above results. Moreover, analysis of cortical thickness and the surface area showed that the abnormality of the cortical volume in different ADHD subtypes was mainly determined by the surface area of the cerebral cortex. Conclusion:Cortical measures in the superior parietal gyrus might be the crucial features that distinguishes the different subtypes of ADHD. These results enable us to further explore the neurodevelopmental mechanism of ADHD and guide the precise and specific clinical treatment.

Objective: To understand the status quo of quality of work life and turnover intention of nurses, and to explore the correlation between them, so as to provide scientific basis for formulating nursing intervention measures to improve quality of life and reduce turnover intention. Methods: In December 2018, Random sampling method was used to select 400 nurses from a third-class hospital as the subjects of investigation. The quality of life of nurses was assessed by the quality of work life scale. The turnover intention of nurses was assessed by the turnover intention scale. Results: Nurses' quality of work life was average 2.97±0.59 points, 2.78±0.60 points for job security, 2.61±0.66 points for work welfare, 3.11±0.70 points for job change, 3.34±0.71 points for self-growth, 2.97±0.63 points for environmental support, 2.51±0.80 points for promotion system, 3.41±0.68 points for interpersonal interaction and 3.01±0.72 points for decision-making. Nurses' turnover intention was average 2.46 points. The eight dimensions of quality of work life were negatively correlated with turnover intention I (the possibility of resigning from the present job) and II (the motivation to seek other jobs) (P<0.05) , but not with turnover intention III (the possibility of obtaining external work) (P>0.05) . Multivariate regression analysis showed that age, nurse-job seniority, annual income and self-growth, job change, environmental support, job security and job welfare were negatively correlated with turnover intention I and II (P<0.05) , while night shift frequency was positively correlated with turnover intention I and turnover intention II (P<0.05) . Conclusion The quality of nursing staff's work life is low, which is closely related to their turnover intention. Managers should take targeted measures in some aspects such as environ mental support and promotion system to improve the quality of nursing staff's work life and reduce their turnover intention.