Objective To explore the association between behavioral, emotional problems and life events among adolescents, and to determine which factors of life events correlate most highly with the behavioral, emotional problems. Method A total of 1 325 adolescents were investigated with Youth Self-Report (YSR) of Achenbach’s behavior checklist and Adolescent Self-Rating Life Events Checklist (ASLEC), and the data were analyzed with canonical correlation analysis. Results Canonical correlation was statistically significant. The correlation coefficients of the first pair of canonical variables in the male and female group were 0.631 3 and 0.621 1, respectively, and the cumulative proportion of the first two pairs of canonical variables was above 0.95. In the first pair of canonical variables, the loadings of anxious/depressed, interpersonal sensitivity and study pressure were higher, while in the second pair, withdrawal and punishment were the most important factors. Conclusions The effects of life events on emotional problems mainly contributed to interpersonal sensitivity and study pressure.
Working memory is an important foundation for advanced cognitive function. The paper combines the spatiotemporal advantages of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to explore the neurovascular coupling mechanism of working memory. In the data analysis, the convolution matrix of time series of different trials in EEG data and hemodynamic response function (HRF) and the blood oxygen change matrix of fNIRS are extracted as the coupling characteristics. Then, canonical correlation analysis (CCA) is used to calculate the cross correlation between the two modal features. The results show that CCA algorithm can extract the similar change trend of related components between trials, and fNIRS activation of frontal pole region and dorsolateral prefrontal lobe are correlated with the delta, theta, and alpha rhythms of EEG data. This study reveals the mechanism of neurovascular coupling of working memory, and provides a new method for fusion of EEG data and fNIRS data.