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The aim of this study was to predict mental stress levels of aged people at rest from two-channel near-infrared spectroscopy (NIRS) data from the prefrontal cortex (PFC). We used the State-Trait Anxiety Inventory (STAI) for the mental stress index.We previously constructed a machine learning algorithm to predict mental stress level using two-channel NIRS data from the PFC in 19 subjects aged 20-24 years at rest (Sato et al., Adv Exp Med Biol 765:251-256, 2013). In the present study, we attempted the same prediction for aged subjects aged 61-79 years (10 women; 7 men). The mental stress index was again STAI. After subjects answered the STAI questionnaire, the NIRS device measured oxy- and deoxy-hemoglobin concentration changes during a 3-min resting state. The algorithm was formulated within a Bayesian machine learning framework and implemented by Markov Chain Monte Carlo. Leave-one-subject-out cross-validation was performed.Average prediction error between the actual and predicted STAI values was 5.27. Prediction errors of 12 subjects were lower than 5.0. Since the STAI score ranged from 20 to 80, the algorithm appeared functional for aged subjects also.

The aim of this study was to predict mental stress levels of aged people at rest from two-channel near-infrared spectroscopy (NIRS) data from the prefrontal cortex (PFC). We used the State-Trait Anxiety Inventory (STAI) for the mental stress index.

The aim of this study was to develop a simple technique for objective assessment of mental stress levels by measuring hemoglobin concentration changes in the bilateral prefrontal cortex (PFC) at rest, employing two-channel near-infrared spectroscopy (NIRS). Each subject was instructed to think about nothing in particular for 3 min and then to complete the State-Trait Anxiety Inventory (STAI) test. Next, NIRS measurements were taken and the left/right asymmetry of PFC activity at rest was evaluated by calculating the proposed Laterality Index at Rest (LIR). There was a significant positive correlation between the LIR and STAI score in 39 subjects. The present method allowed evaluation of mental stress level from NIRS data in the PFC at rest.

The aim of this study was to develop a simple technique for objective assessment of mental stress levels by measuring hemoglobin concentration changes in the bilateral prefrontal cortex (PFC) at rest, employing two-channel near-infrared spectroscopy (NIRS). Each subject was instructed to think about nothing in particular for 3 min and then to complete the State-Trait Anxiety Inventory (STAI) test. Next, NIRS measurements were taken and the left/right asymmetry of PFC activity at rest was evaluated by calculating the proposed Laterality Index at Rest (LIR). There was a significant positive correlation between the LIR and STAI score in 39 subjects. The present method allowed evaluation of mental stress level from NIRS data in the PFC at rest.

Several distinctive activity patterns have been observed in the brain at rest. The aim of this study was to determine whether the STAI index can be predicted from changes in the oxy- and deoxy-hemoglobin (Hb) concentrations by using two-channel prefrontal cortex (PFC) NIRS data for the resting state. The study population comprised 19 subjects. Each subject performed four trials, each of which consisted of resting with no task for 3 min. Data were acquired using a portable NIRS device equipped with two channels. The prediction algorithm was derived within a Bayesian machine learning framework. The prediction errors for seven subjects were not greater than 5.0. Because the STAI index varied between 20 and 80, these predictions appeared reasonable. The present method allowed prediction of mental status based on the NIRS data at resting condition obtained in the PFC.

Several distinctive activity patterns have been observed in the brain at rest. The aim of this study was to determine whether the STAI index can be predicted from changes in the oxy- and deoxy-hemoglobin (Hb) concentrations by using two-channel prefrontal cortex (PFC) NIRS data for the resting state. The study population comprised 19 subjects. Each subject performed four trials, each of which consisted of resting with no task for 3 min. Data were acquired using a portable NIRS device equipped with two channels. The prediction algorithm was derived within a Bayesian machine learning framework. The prediction errors for seven subjects were not greater than 5.0. Because the STAI index varied between 20 and 80, these predictions appeared reasonable. The present method allowed prediction of mental status based on the NIRS data at resting condition obtained in the PFC.

According to the valence asymmetry hypothesis, the left/right asymmetry of prefrontal cortex (PFC) activity is correlated with specific emotional responses to mental stress and personality traits. Here, we evaluated the relation between emotional state and asymmetry in PFC activity at rest by using near-infrared spectroscopy (NIRS). We measured spontaneous oscillation of oxyhemoglobin (oxy-Hb) concentrations in the bilateral PFC at rest in normal adults employing two-channel NIRS. In order to analyze left/right asymmetry of PFC activity at rest, we calculated the laterality index at rest (LIR) (see text). We investigated the correlation between the LIR and anxiety levels evaluated by the State-Trait Anxiety Inventory (STAI) test. We found that the right PFC was more active at rest than the left PFC, corresponding to a higher anxiety level measured by the STAI; that is, subjects with right-dominant activity at rest showed higher STAI scores, while those with left-dominant oxy-Hb changes at rest showed lower STAI scores. Aging had no significant effect on the relation. The present results obtained by NIRS are consistent with the valence asymmetry hypothesis. We emphasize NIRS may be a useful tool for objective assessment of anxiety levels.