Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine.

Mild cognitive impairment (MCI) is now thought as the prodromal phase of Alzheimer's disease (AD), and the usual method for diagnosing the disease would be a battery of neuropsychological assessment. The present study proposes to integrate a feature selection scheme with support vector machine (SVM) to identify patients with MCI by using attention network test (ANT) and demographic data. Forty-two patients with MCI and forty-five normal individuals underwent ANT recording, and the reaction time and accuracy of ANT and demographics (age, gender, and educational level) were selected as original features.

A structural equation model of depression and the defense system factors: a survey among Chinese college students.

Questionnaires were administered to a large sample of subjects (1363 Chinese college students), to evaluate the mediating risk factors in the defense system of depression, including personality, coping skills, interpersonal context and family environment. Structural equation modeling (SEM) was used to analyze a total of 12 variables in order to understand how they interact with each other. Eysenck's Neuroticism, Extraversion, and Psychoticism personality types act as the essential parts of the model, both directly and indirectly impacting depression.