Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 × 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.
BackgroundEffective social problem-solving abilities can contribute to decreased risk of poor mental health. In addition, physical activity has a favorable effect on mental health. These previous studies suggest that physical activity and social problem-solving ability can interact by helping to sustain mental health. The present study aimed to determine the association between attitude and practice of physical activity and social problem-solving ability among university students.MethodsInformation on physical activity and social problem-solving was collected using a self-administered questionnaire. We analyzed data from 185 students who participated in the questionnaire surveys and psychological tests. Social problem-solving as measured by the Social Problem-Solving Inventory-Revised (SPSI-R) (median score 10.85) was the dependent variable. Multiple logistic regression analysis was employed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for higher SPSI-R according to physical activity categories.ResultsThe multiple logistic regression analysis indicated that the ORs (95% CI) in reference to participants who said they never considered exercising were 2.08 (0.69–6.93), 1.62 (0.55–5.26), 2.78 (0.86–9.77), and 6.23 (1.81–23.97) for participants who did not exercise but intended to start, tried to exercise but did not, exercised but not regularly, and exercised regularly, respectively. This finding suggested that positive linear association between physical activity and social problem-solving ability (p value for linear trend < 0.01).ConclusionsThe present findings suggest that regular physical activity or intention to start physical activity may be an effective strategy to improve social problem-solving ability.Electronic supplementary materialThe online version of this article (doi:10.1186/s12199-017-0625-8) contains supplementary material, which is available to authorized users.
The success of human life in modern society is highly dependent on occupation. Therefore, it is very important for people to identify and develop a career plan that best suits their aptitude. Traditional test batteries for vocational aptitudes are not oriented to measure developmental changes in job suitability because repeated measurements can introduce bias as the content of the test batteries is learned. In this study, we attempted to objectively assess vocational aptitudes by measuring functional brain networks and identified functional brain networks that intrinsically represented vocational aptitudes for 19 job divisions in a General Aptitude Test Battery. In addition, we derived classifiers based on these networks to predict the aptitudes of our test participants for each job division. Our results suggest that the measurement of brain function can indeed yield an objective evaluation of vocational aptitudes; this technique will enable a person to follow changes in one's job suitability with additional training or learning, paving a new way to advise people on career development.
We analyzed the correlations between the T 2 shift and integrated electromyographic (iEMG) values in the masseter and temporal muscles. Six healthy adults engaged in a clenching task over two durations at various bite forces. We evaluated the mean T 2 shift per voxel and assessed their correlations with iEMG using a linear mixed model. The regression coefficients were different for each muscle type, similar for the left and right sides, and decreased upon doubling duration.
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