RESUMO
Objective:To study the relationship between cognitive ability and plasma monoamine neurotransmitters in children with acute lymphoblastic leukemia.Methods:The clinical data of 31 children with acute lymphoblastic leukemia from October 2018 to April 2019 in Tongji Hospital Affiliated to Tongji University were retrospectively analyzed. Among them, 20 cases were in consolidation or intensive treatment stage after complete remission (treatment stage group), and 11 cases were in complete remission and drug withdrawal after treatment (withdrawal group). The plasma levels of dopamine (DA), 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA) were measured, the cognitive function was measured by continuous performance test (CPT) and Stroop test, and the results were compared with those of 20 healthy children (healthy control group).Results:There were no statistical differences in plasma DA and 5-HIAA among 3 groups ( P>0.05). The 5-HT in treatment stage group was significantly lower than that in healthy control group: 1 769.69 (912.86, 2 812.56) ng/L vs. 3 085.29 (2 191.79, 5 310.13) ng/L, and there was statistical difference ( P<0.05). There was no statistical difference in 5-HT between withdrawal group and healthy control group ( P>0.05). CPT results showed that there was no statistical difference in the number of errors among 3 groups ( P>0.05); the correct number in treatment stage group and withdrawal group was significantly less than that in healthy control group: (28 ± 12) and (33 ± 11) pieces vs. (43 ± 10) pieces, the missed number was significantly higher than that in healthy control group: (53 ± 14) and (52 ± 13) pieces vs. (39 ± 14) pieces, and there were statistical differences ( P<0.05). Stroop test results showed that, there was no statistical difference in word-color consistency test among 3 groups ( P>0.05); in word-color contradiction test, the correct number in treatment stage group and withdrawal group was significantly less than that in healthy control group, the number of errors and missed were significantly more than those in healthy control group, and there were statistical differences ( P<0.05); in word color irrelevance test, there was no statistical difference in the number of errors among 3 groups ( P<0.05); the correct number in treatment stage group and withdrawal group was significantly less than that in healthy control group, the number of missed errors in withdrawal group was significantly more than that in healthy control group, and there were statistical differences ( P<0.05). The correlation analysis results showed that plasma DA was negatively correlated with the number of errors in Stroop word-color consistency test, and positively correlated with the number of errors in Stroop irrelevance test ( P<0.05); 5-HIAA was negatively correlated with the correct numbers in CPT test and error number in Stroop consistency test ( P<0.05 or<0.01); 5-HT was positively correlated with the correct number in the word-color contradiction test ( P<0.05) in children with acute lymphoblastic leukemia. Conclusions:Children with acute lymphoblastic leukemia could have continuous and selective attention injury, after being diagnosed with leukemia, the level of 5-HT in plasma monoamine neurotransmitter is lower than that in healthy children. The change of attention may be related to the change of monoamine neurotransmitter.
RESUMO
How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective ( ., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson's correlation and sparse representation) and the commonly used feature selection methods (two-sample -test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.